Abstract

Many studies have been conducted on the National Football League’s (NFL) strategies for expanding its brand internationally. However, the NFL has recently implemented and amplified many strategies that were not as prevalent beforehand. This Literature Review will examine these new strategies. It will also examine the reasons that previous strategies were ineffective and unsuccessful. The paper specifically examines the strategy of “Glocalization”, the act of using preexisting networks and influential people in foreign nations, to appeal to the widest possible audience.  The paper uses this context to investigate how the NFL uses the Global Markets Program, social media, fan clubs, and international games to attract new fans. The paper explores how these strategies can not only bring in new fans, but also cause deeper levels of fandom for already existing fans. These more dedicated fans can also bring the league more money through ticket and merchandise sales. The study concludes that glocalization is a successful strategy that combats the alienating feelings of past NFL international endeavors. The study also concludes that one area of growth for the NFL is the development of the playing of the sport internationally. The Literature Review addresses how the NFL needs to develop grass roots leagues, international equipment, and international facilities because fans who play the sport are also more likely to watch it.

            Keywords: National Football League, international expansion, glocalization

Introduction

The National Football League (NFL) was founded in 1920 with 14 teams (Jozsa, 2014). Of those 14 teams, only 2 exist today and neither from their original city (Ibid). The NFL added four teams in 1922(Ibid). The first NFL season looked very different to the current league: the average attendance was just over 4000, the playoffs did not exist, and the champion was decided through a vote (Crepeau, 2020).

The NFL needed to expand its viewership besides fans watching live, in person games. In 1956, CBS became the first network to televise games (Felser, 2008). By broadcasting games, the NFL could broadly appeal to fans across the country, not just fans attending games. Therefore, by 1960, television viewership became increasingly important. NFL commissioner Peter Rozelle came up with the idea of a unified television deal with revenue sharing (Ibid). In April 1961, he signed a 2 year 9.1 million dollar contract with CBS granting all broadcasting rights to all NFL regular season games. The American Football League (AFL), at the time a competitive league to the NFL, sold their broadcasting rights to NBC for 42 million dollars in 1964 (Crepeau).

            The natural next step forward for the NFL was to expand its market. In 1966 the NFL and AFL merged (Felser). The league reorganized to have 26 teams. This merger was a smart strategy for the NFL. The AFL had preexisting teams and fans that were brought into the NFL fanbase. They had stars to bring in new fans such as Joe Namath, the quarterback of the New York Jets. By creating this merger, the NFL was able to nearly double the number of locations of their teams. Their appeal across the country was even larger.

            The NFL implemented new strategies to mythologize the league. In the 1970s the NFL created Monday Night Football which brought in many viewers on a new day of the week (Crepeau). They marketed rivalries to deepen fandom by creating animosity for other teams. In 1989, the NFL created John Madden Football, a video game in which people could control their favorite team(Ibid). Sports bars started to open that catered towards specific fanbases.

            The Jacksonville Jaguars and the Carolina Panthers were the NFLs most recent expansion in 1996, bringing the league to its current 32 teams (Jozsa). The NFL continued to look at new places to add fans.

Literature Review

Global Markets Program

Firstly, to attract long lasting fandom in international markets, the NFL adopts the strategy of assigning teams to specific nations through the Global Markets Program (NFL, 2026). Within these nations, NFL teams can market, hold fan events, and have local sponsorships. This assignment forces teams to individualize specific strategies for nations and not have an ineffective broad strategy. One major obstacle in NFL globalization is that the NFL can feel like an alien spectacle. To combat this feeling, markets are oftentimes assigned to teams based upon preexisting connections between the cities and their target nation.

For example, the Saints are assigned to France because New Orleans was formerly under French control. Teams such as the Miami Dolphins are assigned to Spanish speaking countries due to their city’s large hispanic population. The Rooney family who owns the Steelers has Irish origins (Ibid). These assignments create an environment where distant fans are already connected to a team.

Next, these teams participate in glocalization and collaborate with popular, prerecognizable brands and people within their assigned nation. Many teams partner with soccer clubs and players to reach into a known fanbase. During the 2026 World Cup, teams were advertising towards tourists to increase international fandom.

Additionally, teams partner with local fashion brands to pack merchandise in stores. The Jets, for instance, collaborated with trapstar London, a British streetwear brand.

The specificity of this marketing would be impossible if each team was forced to market to all nations. The Global Markets Program allows teams to create a cohesive narrative for fans to follow. The NFL forms television deals with international markets so distant fans can follow their assigned teams season long. Teams have year long, not sporadic, advertising in their assigned nations to create long lasting relationships. Consequently, distant fans become permanent fans and do not just exist during international games. Overall, the Global Markets Program lets teams be ingrained in international society and create long lasting fandom.

International Games

            The NFL also builds international fandom by hosting games abroad. The NFL international series started in 2007 when the Giants defeated the Dolphins 13-10 in Wembley stadium in London with over 80,000 fans watching. International games garner large foreign support. International fans desire an experiential factor in their NFL experience. Going to games provides a different, better experience than just watching on television. At games, fans are surrounded by similarly minded fans who also want to support football, creating a fan on fan connection. Additionally, fans want to experience tailgates and halftime shows that are unique to the in person experience. A higher level of fandom is possible when fans can support teams in person and not just distantly because they gain a personal connection to the team.

In 2012, Shahid Khan became owner of the Jacksonville Jaguars and every year since then (besides 2020 due to the Covid-19 pandemic) decided to hold one home game in London. This decision was revolutionary because he opted to give up revenue from a home game to create international fandom. Consequently, the Jacksonville Jaguars have cultivated a large distant fan base in London despite being a small market team with a bottom five record since Khan’s takeover (Drew, 2024).

The Jaguars are increasingly relying on the London fanbase with two home games scheduled to be played in London for the 2026-2027 season. These games have grown increasingly popular overseas. The 2024-2025 season saw a 35% increase in ticket sales over the year before (Ibid). These games grant fans the opportunity to see the teams they support in person. Fans receive a positive memory from attending a game and are therefore more likely to support the team in the future.

These NFL games also participate in glocalization, attempting to adapt to already existing platforms for the games. The NFL utilizes pre-existing infrastructure for other sporting events. Games in the United Kingdom are played in Tottenham Hotspur and Wembley stadium, both premier league stadiums. In Melbourne, games are played on the Melbourne Cricket Ground (NFL, 2026). These venues provide both a convenient location for the NFL and a familiar spot for locals.

NFL merchandising is also an important part of NFL international games. Many international fans do not own NFL merchandise before games. NFL stores at international games are oftentimes larger than the stores at domestic games because new fans are anxious to buy new merchandise. They want to support a team and have memorabilia to remember their in game experience (Drew).

Social Media Marketing

The NFL also adopts glocalization when it comes to marketing through social media. Firstly, the NFL creates accounts for foreign countries. The NFL has created accounts for Brazil (1.1 million followers; NFL Brasil, 2026), Mexico (683,000 followers; NFL Mexico, 2026), Spain (147,000 followers, NFL España, 2026), and France (147,000 followers, NFL France, 2026) . These accounts post content in their nation’s home language, making it simpler for fans to follow along.

During the offseason these accounts post updates on players’ lives with captions in their native language. These posts are essential because they create a narrative outside of the game. This storytelling allows fans to build a deeper connection to the players. This strategy is especially important for international fans because they are able to see similarities between themselves and players who live in another country that may be unexpected.

During the regular season, highlights are posted on these accounts. This provides fans with an opportunity to watch plays and stay up to date on games they could have missed due to time zone differences. This strategy limits any differences due to time restraints.

Additionally, social media is an important space for fan on fan engagement. Many United States fans are able to experience these interactions during in person games and at bars. These opportunities are less common for worldwide fans. If an international fan wanted to watch a game at home they could see posts in their social media feed creating an energy and sense of community over events happening in the game. (Brattain) Furthermore, social media creates a space for online discourse. International fans are able to debate their opinions with each other online. International NFL accounts commonly create posts that encourage fans to debate in the comment section. For example, NFL Brasil posted power rankings with fans arguing in the comments (NFL Brasil, 2026). Fans are able to debate and argue online creating a greater fan-fan connection (Brattain). A study of Brazilian NFL fans found that Twitter, now known as X, provided fans with a virtual space for online fandom. This online connection deepened fandom(Moura).

            These accounts also participate in glocalization by posting specific localized content. They collaborate with local celebrities to endorse the game which touches on a preexisting market. These accounts post many futbol players to create a connection between the sports. For instance, NFL UK posted a video of Jaguars Kicker Cam Little exchanging jerseys with Fulham FC player Antonee Robinson (NFL UK, 2026). Accounts will also post the opposite by showing NFL players at international events assimilating to British culture. British comedian Troy Hawke was posted welcoming Jaguar players to London by teaching them British expressions (NFL UK, 2026). These posts make the NFL feel less alien because the assimilation is double sided.

The NFL also sponsors many local influencers to support their events. They bring many influencers to games in their international series. Additionally, Influencers are brought out to announce picks during the NFL Draft. This strategy utilizes the large preexisting reach of influencers in these international markets.

One way that the NFL recognizes these fans is through the NFL international fan of the year program (NFL). Each team chooses an international fan who is involved in creating a community in their home country. Many chosen fans are social media influencers who support the NFL.

The NFL will also host events with influencers. For example, the NFL hosted the Sao Paulo creator cookout before 2025’s football game in Brazil. They brought together popular Brazilian creators to cook while answering NFL trivia questions as promotional material for the International Series. (NFL, 2025)

International Supporter Groups

The NFL also utilizes supporter groups to build lasting fandom for teams. Supporter Clubs are an organized group of fans that watch and support a sports team together. Distant supporter groups organize watch parties at bars to watch games together.

            Supporter clubs are necessary to build interpersonal relationships between fans. As previously mentioned, it is more difficult for international, distant fans to be able to find a community to be a part of who supports a team. While social media can provide this community, fan clubs create a deeper, in person social interaction. Fan clubs create a stronger sense of team affiliation by facilitating a community around these games (Katz, 2020). One distant Steelers fan in the Midwest stated,

“You know I have always said, I can sit at home and watch the games in my pajamas and yell at the TV. But why not do it with 100+ other people who are feeling the same way you do. We all want that sense of belonging with fans like us” (Stensland, 2020).

            While not international, this fan club provided a fan with likeminded people to be surrounded by during games.

            Moreover, fans experience a stronger sense of affiliation by forming smaller communities within the larger community of the team (Katz). Supporter groups sometimes maintain smaller groups within the larger group to provide an even more interpersonal connection. Theoretically, if a person is developed farther into the fan group, they will have a deeper connection (Ibid).

            The most important fans in the fan groups are the most connected fans, not necessarily the most vocal. The most connected fans are those who are involved with the most people and subgroups within the fan club (Ibid). Many of these people are founders and leaders of international fan clubs. They are able to lead large groups because they are interconnected to everyone. It is important for the NFL to market towards these people because they pass down information to everyone else.

            The NFL also uses the International Fan of Year award to recognize leaders in these fan groups. Many are founders and leaders of fan clubs. Stephen O’Brien, for example, is the founder of the UK and Irish Packers Fan Club, where British Packers fans can connect and watch games together (NFL, 2025). The Packers honored him and he announced a draft pick at the NFL draft. The NFL understands that if they can market towards and support these leaders, they can also market toward the fans they influence.

NFL International Player Pathway

The NFL International Player Pathway is a pipeline for international players to join the NFL. International players train in Florida and perform a combine before the NFL draft. Since its inception in 2017 five players have been drafted from this program (NFL, 2026). Additionally, the NFL allows a seventeenth practice squad spot for international players from this program (Ibid). It allows premier athletes a chance at becoming an NFL player.

International fans have a built-in support of players from their home countries. They connect with players who grew up in a similar place and manner to them. Fans are likely to support a player and therefore also support the team they compete for. Many teams from the Global Markets Program are assigned to their country because they drafted a player from that country. For example, the Eagles are assigned to Australia because they drafted Pro Bowl Offensive Lineman Jordan Mailata who was born there.

Discussion

The NFL has had many previous attempts to reach international fans. Firstly, the NFL held the American Bowl, a preseason game, internationally from 1986 to 2005 . These preseason games utilized a similar strategy to the current international series. They attempted to gain international fandom by giving foreign nations international games. These games were very successful because they brought in large international crowds to NFL games. Specifically, the American Bowl in Mexico City had over 110,000 fans attend the game. This success led the NFL to realize that international games of a premier product were an important strategy to implement later on.

            The NFL also tried to start leagues internationally. They started the World League of American Football, (WLAF) in 1989. It originally included teams from both North America and Europe. At first, the league was successful in Europe at first, but the American teams were a failure. The NFL wanted it to be similar to a g league for the NFL. NFL fans in the United States did not want to support something that was not a premier product.

            The league was put onto hiatus for two years. It was rebranded to NFL Europe. There were six teams within the league. The league was very unprofitable for the NFL. It lost 30,000,000 dollars per year. Attendance for the league was very low towards the end. London, one of the most populated cities in the league, had attendance of under 10,000 people per game. It was rebranded to NFL Europa. Germany was the only country where the league was successful. At the end of the league, five of the six remaining teams were located in Germany.

            The league failed for many reasons. Firstly, the league failed to secure broadcasting rights in Europe. While the games were taking place in Europe, it was difficult to watch the game. This difficulty made a deeper fandom less likely. Additionally, the league felt alien and out of place. It appeared in these countries without taking initiative to assimilate into their culture. It was difficult to build fandom because people had no good reason to connect to a team.

            The most important strategy that the NFL is adopting is glocalisation. Past attempts at expanding the NFL into other countries felt forced and unnatural. The NFL adopted the strategy of globalization to combat this issue. They do not want their sport to feel alien to the countries they expand into.

The NFL partners with already popular local brands because they need to build into an already built in fanbase. Brands already have customers who support them. By partnering with these brands, the NFL feels natural and normal in these nations. They also partner with local influencers. They are able to tap into a preexisting network to spread their influence. They use important, connected figures to spread good word about the league. They similarly use highly connected individuals within fan clubs to build fandom. They work with the individuals who have the greatest influence within the fan club to connect to new fans of the NFL.

            The NFL needs to improve their overseas player development. While the International player pathway exists, it is not very successful. Only two players were selected from the international player pathway during the 2026 NFL draft: Seydou Traore and Uar Bernard. The lack of international talent in the NFL is especially apparent when compared to other American sports leagues. Only 3% of NFL players are international. Comparatively, 30% of the National Basketball Association (NBA) and 26% of the Major League Baseball (MLB) are foreign born. 0 NFL Most Valuable Players (MVPs) have been international, while the last 8 NBA MVPs are all foreign born .

If the NFL builds superstar talent abroad, international fans will have a more personal connection with the league. One important factor that the NFL misses out on is player development in foreign nations. It is necessary for the NFL to work on building foreign youth talent.

Firstly, the NFL needs to teach football in other nations. They somewhat already do this by sponsoring and supporting flag football leagues. They commonly feature and advertise this content with their games. However, teaching flag football is not enough to build talent. If the NFL wants to build real talent in other nations they need to support the teaching of actual football.

Also, there is an important correlation between playing and supporting a sport. Athletes who grow up playing a sport are much more likely to watch and follow the sport. They have a deeper emotional connection towards football meaning a likelier deeper level of fandom.

Another way the NFL can support the growth of playing football on an international level is by making football more accessible. Other sports such as soccer or basketball are much easier to play at any given notice. A person can bring a soccer ball to a field at a park or a basketball to an open basketball court and they would be able to improve their skills easily. It is much more difficult for a person to develop football skills, especially in a foreign nation. Firstly, there are much fewer facilities to practice at in these areas. Other sports built facilities to facilitate the growth of their sport. Fifa, for example, built soccer courts in less affluent areas, so lower income individuals would have an increased ability to play the game. They were given access to facilities that they wouldn’t normally have. Similarly, there are public basketball courts in many foreign nations. The NFL needs to build football facilities to grow the playing of the sport internationally.

Similarly, the NFL needs to provide better access to football equipment. One problem with football is that more equipment is necessary to fully play tackle football. Players need pads, helmets, and mouthguards, not just footballs to learn the game. This equipment can be expensive and hard to come across. The NFL needs to provide easier, cheaper access to this equipment for players to learn the full game and develop into better players.

Overall, the NFL needs to support the development of youth players and leagues to build a deeper emotional fandom and to develop players to compete in the league.

Conclusion

The NFL has developed and grown many successful international strategies within the past decade. Most importantly the NFL has understood glocalization and adapting to countries to make american football not feel like an alien product. The NFL has used this strategy within the Global Markets Program. They realized that they can appeal more towards international fans if they have a team to support. By assigning teams to countries the NFL allowed each team to specialize towards their assigned market and deepen the level of fandom because each team has a given fandom to support. The NFL has also broadened their reach by increasing the amount and locations of international games. These games deepen fandom because a greater connection is created when fans can watch their favorite teams live. The National Football League also utilizes social media by creating accounts for different nations and partnering with local influencers. These local partnerships help them reach the broadest possible audience. They also fan clubs, understanding that the most influential fans are the ones who are the most connected. The NFL needs to improve their development of the playing of football internationally to create a deeper fandom. Future studies in this field can examine how the NFL is attempting to grow the playing of the sport internationally. It can focus on the effectiveness of those strategies and what new more productive strategies can be put in use.

References

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About the author

Shane Handman

Shane Handman is currently a senior at Harvard Westlake in Studio City, California. He has an interest in economics, business, math and the world of sports. Besides academics, he is the captain of his cross country team at his school.

The post The National Football League’s Strategies for International Expansion appeared first on Exploratio Journal.

Introduction

In Poland, it is my babcia’s 80th birthday, but she did not know it herself because of how much her dementia had progressed. She sat at the head of the table with a confused pout on her face and her head down. Her head was down as she fumbled with a small napkin on her lap, its surface patterned with two colorful flowers. She would nervously fold the napkin and unfold it and fold it again with shaky hands as if she was trying to fix it. Then she would trace the flower’s shape with her finger as if it were the only thing that was real to her, the only thing she understood at a birthday table surrounded by her family.  She needed something to distract her as we spoke around her. Then, on the radio, an old 60’s Polish song starts playing, called “Mamo moja jada goscie,” a song she would sing all the time as a child growing up in the traditional part of Poland called Kurpie. She stops fiddling with the tiny paper and looks up and smiles, then rocking back and forth, she sings “la” along to the song. Her voice followed the song as if it never left her, and she tapped her hand to the beat. She finally seemed present, and the whole family joined alongside her. After being isolated the whole dinner, we found a way to show my babcia how much love surrounded her on her special day. By the end of the song, her sister goes to hug her, and she hugs back and kisses her on the cheek. She knew it was her.

These experiences with my grandmother inspired me to learn more about her impairment. Cognitive impairment can take away a skill and take away a person’s sense of who they are. However, such impairment should not have to mean being isolated or socially disappeared. Because music operates through subcortical circuits that remain functional, even when explicit language, motor, or memory processing is damaged or overridden, music can bypass that impairment and reach the parts of a person’s brain that are still intact, restoring not only function but also personhood. Understanding this function on paper was not enough for me — I needed to see it for myself.  This particular power led me to explore this possibility during my internship at Mount Sinai’s Louis Armstrong Center for Music & Medicine, as I worked alongside music therapists like Kristen and Dr. Rossetti, and researchers like Tammy.

The Power of Music, Broadly Defined

Music profoundly influences us. It has lived with and within us for as long as humans have existed. Music serves many different purposes, including emotional, physical, symbolic, and social ones, for a diverse range of groups of people.

The power of music is shown in how it can physically reshape your brain. When music interacts with the brain, all parts of the brain are active. Perception, movement, memory, and emotion are all being exercised in a way an isolated task never would. That whole-network engagement is what builds plasticity, and plasticity is what eventually allows music to do something no other stimulus reliably can: reach around damage instead of through it. This is monumental because the brain is connected to the body, and the body functions through the signals it sends. So ultimately, when exercising a part of the brain to improve it, the bodily function that part of the brain controls will also improve, which is exactly what opens the door to bypassing impairment in the first place. Hallam and Himonides state that “music has a multiplicity of functions which operate at several levels: that of the individual, the social group and society in general” (Hallam and Himonides 4), showing that music can not be reduced to a singular function and its power is plural, plasticity only being the first piece of it.

A person’s emotions are among the most complex concepts a person holds, and the hardest part of a person to access directly. You can be the most articulate person, but states like grief, love, longing, and fear can be hard to put into words fully. However, music does not have that limitation. It can carry the shape of an emotion in a way language cannot. That is why someone can hear a song and break into tears without knowing why. The emotional power of music lies in the fact that it is not just a sound to enjoy, but an access point. Music serves as “a vehicle for emotional expression, conveying ideas and emotions which might be difficult to communicate in ordinary verbal interchanges” (Hallam and Himonides 5), reaching feelings that language cannot form. This concept will be important later in this paper because I will address how it influences a person who loses language altogether.

As strong as music is for its effect on the individual listener, it also holds tremendous social power. When music shifts from an individual personal experience to a shared experience, it takes on new meaning. Making music together creates a synchronized experience that’s built in real time. This synchrony does not just feel like connection — it becomes connection, at the level of brain chemistry, because it “creates and strengthens social bonds amongst interacting group members through endorphins, which are released during synchronized exertive movements in singing or playing together” (Hallam and Himonides 6). Shared music-making requires something of people that listening alone does not. It forces people to be vulnerable with their voice, timing, and imperfect participation. For someone with a cognitive impairment or mental illness, shared music experiences can offer a comfortable way to be seen without judgment and to stop being isolated and engage in the present moment. This is why “music fosters prosocial behavior, a shared sense of success, physical coordination, shared attention, shared motivation and group identity” (Hallam and Himonides 6) because it gives people a reason to lower their guard together. When music is shared, it does not just shape an individual and their emotions; it also shapes who “we” are and forms a group’s identity. This is similar to what sociologist Émile Durkheim calls “collective effervescence,” the heightened, almost electric sense of unity that arises when a group moves and feels something together, dissolving the boundary between individual and collective in the process. Durkheim was describing religious ritual, but the mechanism he described is the same one at work when a room full of strangers finds the same beat.

Beyond its power to connect people, music does something even more fundamental. Music does not just go through one ear and out the other; it physically alters your brain. Hallam and Himonides state that making music “draws on many brain functions, including those related to perception, action, cognition, emotion, learning and memory” (Hallam and Himonides 16). In other words, it moves through the whole system at once and is not confined to a single narrow pathway, as most single tasks are. This engagement with music is not short-lived. Over time, “extensive active engagement with music induces cortical reorganization, producing functional changes in how the brain processes information” (Hallam and Himonides 18). The brain is altering itself in a way that lasts and not just responding to music. Some clinical research suggests that music’s neurological power does not stop at the level of conscious thought.

According to Stephen Porges, the neuroscientist behind Polyvagal Theory, music also communicates directly with the autonomic nervous system, the part of the nervous system that regulates safety and threat, and operates completely outside conscious control. Andrew Rossetti, a music therapist who directs oncology music therapy at Mount Sinai, draws directly on Porges’ work. He explains that music “supports connectedness and co-regulation” and “communicates cues of safety to our nervous system,” which “down-regulate defense and optimize homeostatic processes” (Rossetti, ‘Environmental Music Therapy’ 135). This is not just theoretical and actually holds up under clinical testing. In a randomized clinical trial testing this mechanism in a radiation oncology waiting room, patients who received live, therapeutic music showed a significant drop in anxiety. Their anxiety had a median decrease of 16.6 points on a standard anxiety scale, compared to an increase in the control group (Rossetti et al., 2023, ‘A Clinical Pilot Study’ 58). If Porges’ theory explains why music can calm a nervous system that it never consciously reaches, this trial proves that it actually does. Music has been shown to interact with the brain on a whole other, unconscious level.

This unconscious quality of music is not a new revelation either; it is something researchers have speculated about for over a century. The philosopher Friedrich Nietzsche, who devoted himself to music and philosophy, put it, “we listen to music with our muscles” — a line the neurologist Oliver Sacks returns to in his own writing on music and the brain (qtd. in Sacks 2528). Kristen, another music therapist I worked alongside during my internship at Mount Sinai, echoed the same idea, saying, “music moves the muscles.” With Porges’ theory and Rossetti’s data, we can see that this is not just speculation. Music actually engages the body and brain as a single system, which is what makes listening to music such a distinct experience. 

If music’s neurological power lies in its ability to engage the brain and body as a connected system, then its critical significance lies in what happens when part of that system is damaged. For example, language relies heavily on specific and fragile brain regions responsible for grammar, word retrieval, and speech production, concentrated mostly in the left hemisphere. Music, on the other hand, draws on a much wider, more redundant network involving rhythm, pitch, and melody, which is processed in both the right and left hemispheres, in areas associated with movement, memory, and emotion. The two systems intersect at points, but they are not built on the same foundation, which means an injury precise enough to knock out language can still leave music’s broader network largely intact. Hallam and Himonides second this, saying, “clinical studies of those with brain damage revealed separate deficits in music and language” (Hallam and Himonides 46). In other words, a person can lose the ability to comprehend speech or to speak while their capacity for music remains completely untouched, and vice versa. This is what allows music to reach certain pathways and reach a person when language cannot, or they are impaired.

The Bypass Mechanism

This is exactly what happened to my babcia at our own dinner table. She sat in a wordless state due to her dementia, taking almost all speech away– until the melody of  “Mamo moja jada goscie” reached her.   Something inside her answered back. Looking at this mechanism more closely, I see that this separation between music and language is not something I noticed by chance. It has a name and a direct clinical application: melodic intonation therapy. For example, rather than asking a patient to speak, therapists sing sentences to them in an exaggerated, musical version of normal speech until it resembles a song. As Hallam and Himonides explain, this technique, developed by “Sparks and colleagues (1974)”, works by using “sung intonation of propositional sentences in such a way that the intoned pattern is similar to the natural prosodic pattern of the sentence when it is spoken” (74). The patients Sparks studied were not easy fixes. They were “eight severely, but not globally, impaired right-handed aphasic subjects with left hemisphere damage,” each of whom “had shown no improvement in verbal expression for at least six months” of standard language therapy (Hallam and Himonides 74-75).

This is the idea behind bypassing cognitive impairment: the therapy works not by forcing language back in through the original damaged pathway, but by rerouting it through music instead. My babcia was never enrolled in a clinical trial, but she is living proof of the same mechanism at work. Why is music able to bypass so many different types of damage, such as those affecting language, hearing, or memory? Part of the explanation goes deeper than any single neural pathway, reaching into the brain’s most ancient reward systems. The pleasure we get from music stimulates what neuroscientist Stefan Koelsch describes as “a phylogenetically old reward network that functions to ensure the survival of the individual and the species” (Koelsch 170). In other words, music does not depend on the same evolutionarily more recent circuits that language relies on. Instead, it has its own much older route into the brain, one that can remain intact even when more recent systems break down.

Kristen shared something a woman with Alzheimer’s said to her during a session: “I feel like I have forgotten more than I have ever known.” This is what daily pain is like with Alzheimer’s. It is not one dramatic loss but a subtle, contrasting awareness of disappearing until it has all gone. That is what a disease like dementia takes away from someone: not just words, but the certainty of who you were before you lost them. Music therapy cannot bring back everything that is gone, but it can reach and bring out the person that is underneath all that forgetting.

This idea of neurological bypassing can apply to any compromised system, and the effectiveness of music therapy in accomplishing this is supported by Hallam and Himonides, who summarize a study by Good and colleagues (2017) on children with cochlear implants. The children who received music training showed improved discrimination of melodic contour and rhythm, incidental memory for melodies, and emotional speech prosody perception, whereas a matched group given painting lessons showed no such improvements (Hallam and Himonides 74). This means the benefit was specific to music, not simply structured attention or care.

Work like this is also implemented in the Newborn Intensive Care Unit for premature infants. During my volunteer work at Mount Sinai, I observed music therapy in the Level IV neonatal intensive care unit, the Jo Carol and Ronald S. Lauder NICU. My mentor, Tammy, was studying how entrainment — matching the tempo of live singing to a baby’s breathing and heartbeat — affects oxygen and respiratory stability in infants born with conditions like Respiratory Distress Syndrome (RDS). While we do not yet have research findings, I observed it in practice at the hospital. I observed a baby with a small adhesive sensor strip on his forehead, connected to a near-infrared spectroscopy (NIRS) machine that tracked his fractional tissue oxygen extraction — a measure of how well the brain is receiving oxygen. As Tammy sang a soft jazz tune, matching her tempo to his breathing, he drifted off to sleep, and his vital signs remained stable throughout. This mirrors what Virtala and Partanen found in older infants: that ‘music-making and parental singing prompted infants’ early language development and auditory neural processing’ (Hallam and Himonides 74). Here, “bypass” is not the right word because there is not yet a damaged language pathway for music to route around. Instead, music arrives first to stabilize the nervous system before language has even begun to form, so that when language does emerge, it has a steadier foundation to build on.

The bypass also extends to motor impairment. Earlier this year, I heard this principle explained directly by David Leventhal, founding teacher of Dance for PD, in a talk at a conference on music and medicine called “Mechanism of Pain and Music.” Leventhal described the patients who arrived rigid, frozen, sometimes barely able to initiate a single step, and the patients who left moving with fluidity and grace once music and rhythm entered the room — the same body transformed by an external beat. There is a real mechanism behind this happening. Parkinson’s disease damages the brain’s internal timing system, the one responsible for cueing smooth, sequenced movement. Researchers explain “PD is associated with a loss of internal cueing systems, affecting rhythmic motor tasks such as walking,” but that “music and dance encourage spontaneous rhythmic coupling between sensory and motor systems” (Krotinger and Loui). The mechanism, they argue, works because rhythmic training can strengthen “neural pathways involved in motor control, auditory-motor entrainment, action observation, and/or kinesthetic or proprioceptive feedback mechanisms that could then be utilized as an alternative to impaired motor areas” (Krotinger and Loui). This means that rhythmic training builds alternative neural pathways that can substitute for the internal timing system the disease has damaged.

Leventhal described people in his dance classes learning to forget that movements are commands sent from their brains to their bodies, and to use music and rhythm to shift what they do from a common action to something they create. I think of it like this: instead of thinking of walking as moving your feet in front of each other, one at a time, you use the rhythm of the music to make it an action of moving from stone to stone across a calm river. Then, soon after creating these new scenarios, people who could take a step learn to create these full dances. The body is not relearning how to move. It is borrowing a rhythm it can no longer generate on its own.

What struck me most, though, was not the mechanism – it was what Leventhal said about identity. Parkinson’s, he explained, “affects every aspect of a person’s sense of self.” But in his classes, patients are given “a new identity — that of dancer, instead of just patient” (“David Leventhal”). That distinction is not small. It is, in miniature, the entire argument of this paper: the bypass is not just of a damaged motor pathway, but of an erased identity.

So far, every population in this section has had a body or brain system that was physically compromised. PTSD complicates that picture — the injury is not structural, but the isolation it produces looks remarkably similar. Combat-related PTSD often produces ‘feelings of loneliness and isolation from society, intrusive memories, outbursts of anger and generalized feelings of helplessness’ (Bensimon, Amir, and Wolf 34). Traumatic memories resist the kind of verbal processing ordinary memories allow — they are stored as raw sensory fragments, leaving many survivors unable to translate what happened into words at all. This is often called “the unspeakable,” however, I would push back slightly on where the block actually sits. It may also be that people around them cannot bear to listen to it, and that unwillingness closes a door that survivors might otherwise have tried to open. Music offers a route around that block: because music and trauma are both “sensory mediated,” music therapy can function “as a means of sensory approach to traumatic memories as a detour of linguistic and logical mediation” (Bensimon, Amir, and Wolf 40)—a way to reach a survivor without requiring either side of that broken exchange to work first.

In a study of six soldiers diagnosed with combat- or terror-related PTSD, group drumming produced measurable “increased sense of openness, togetherness, belonging, sharing, closeness, connectedness and intimacy” (Bensimon, Amir, and Wolf 34). One participant described the effect directly: “Once you beat the drum, although you don’t know anyone, it gives a feeling of togetherness which makes it possible for you to share everything with the group. It’s as if you’ll go naked in front of them. . . If something facilitated intimacy above all the instruments, it was the drums” (Bensimon, Amir, and Wolf 38). These soldiers were already receiving individual psychotherapy alongside the drumming session, yet it was the drumming, specifically, that participants pointed to when describing this particular kind of openness. That distinction matters. It shows that rhythm reached something in these men that conversation, on its own, had not yet reached.  

Mania presents a different kind of impairment altogether– not a damaged pathway, but a mind in crisis. A recent randomized study of 102 inpatients hospitalized for manic episodes found that those who received group therapy alongside routine nursing care showed significantly lower mania severity scores and significantly higher emotional stability scores than those who received routine care alone. This shows that the intervention “significantly enhances emotional stability and satisfaction among hospitalized manic patients” (“Effect of Group Music Therapy” e46082). Here, the damage was to the mind’s instability in general and not to a single damaged circuit that needs to be routed around. And still, music reached something that routine clinical care alone did not.

As I explain later in this paper, I would witness this effect firsthand in a room full of patients diagnosed with schizophrenia, bipolar disorder, and psychosis.

Wholeness as the Goal, Not Just Function

Bypassing impairment at the level of the brain is only half the picture. For many patients, the deeper impairment is not neurological at all but the isolation that the illness produces, and reaching a person there requires more than rerouting a damaged circuit. It requires community. Music therapy and community music are not quite the same thing, and the distinction matters here. As MacGlone and colleagues explain, “community music interventions are distinct as they may have positive therapeutic outcomes, but these are secondary to the realization of an enjoyable, sociable experience for participants” (MacGlone et al. 2). In other words, community music does not set out to fix anything. It sets out to include someone, and healing comes as a byproduct of that inclusion rather than the goal. This reframes the word “treatment” from correcting something to restoring someone’s wholeness.

That reframing matters because isolation is not a side effect of illness — it is often part of the injury itself. “Isolation due to social exclusion is a key contributing factor to such mental health impacts” (MacGlone et al. 2), and the reverse is just as true: reducing isolation can itself be a form of treatment, not just a pleasant side benefit of one. Nowhere is this clearer than in the case of Jack, a young man with a disability who took part in a community music program studied by researchers at the University of Edinburgh. In the year before the program began, his mother had watched him steadily withdraw, losing interest in things that once brought him joy. A month of medical investigation turned up no clear cause, though doctors suggested depression. Then the music workshops started. According to a staff member, Jack “came back to life,” joining with enthusiasm he had not shown in over a year, cracking jokes again, playing with expectations during the group’s songs the way he once had. His mother described it: “It’s like seeing the old Jack… they’ve just seen bits of him that’s like they used to see years ago, coming back.” (MacGlone et al. 8). What is striking about Jack’s case is what did not happen. Nothing about his underlying diagnosis changed. No pathway was repaired, no circuit rerouted. And yet something unmistakable had come back. That gap is the clearest possible illustration of what “wholeness” means in this paper. It is not the absence of impairment, but the presence of the person who was there all along, however hidden the impairment had made them.

This idea has a name, specifically in dementia care. Psychologist Tom Kitwood defined personhood as “a standing or status that is bestowed upon one human being, by others, in the context of relationship and social being,” adding that it “implies recognition, respect and trust” (qtd. in Tieu and Matthews). I keep coming back to that word “bestowed.” Kitwood is not saying that personhood is something a person has, locked safely away, no matter what happens to them. He is saying it is something given continuously by the people around them, which means it can also be withheld. A caregiver, doctor, or distracted family member can fail to bestow it through impatience, talking over someone instead of listening, and by treating a diagnosis as the whole of who that person is. Doing this can erode a personhood that no brain scan would ever show as damaged. This reframes what happened to Jack, and what happens every time my babcia sings. Neither the community music workshop nor my babcia’s song repaired anything measurable. But both moments gave back something that had quietly gone missing. For Jack, that was a sense of self his withdrawal had taken from him, and for my babcia, that was a place in her own family’s conversation, which her dementia had left her locked out of. What came back in both cases was not a restored function but recognition finally being offered again.

One of the researchers behind Jack’s program, Kirsty Brown, who has a disability herself and worked as a lay researcher on the study, gave this phenomenon the name “voice”. She defined voice as a form of self- expression that does not have to be verbal at all — participants could “sing, speak, play instruments,” but just as often, their voice showed up as “increased eye contact with others, bodily movement or gesture” (MacGlone et al. 6). Voice, in other words, is not the same thing as speech. It is whatever a person still has left to offer those around them, in whatever form it takes.

My babcia’s voice, that night at the dinner table, was a single syllable repeated in place of words she could no longer find: “la, la, la,” pitched exactly where the lyrics should have been. It was enough to bring her back.

None of this is merely an observation, and it does not apply only to dementia and disabilities. A ten-year follow-up study of people living with first-episode psychosis found that rates of “personal recovery,” “life satisfaction,” and “pleasure” were all higher than rates of “clinical recovery” — the disappearance of symptoms (Simonsen et al. 2112). In fact, “personal recovery overlapped more with emotional wellbeing than with clinical recovery” (Simonsen et al. 2118). Sixty-eight percent of participants had achieved at least one positive outcome after ten years, leading the researchers to conclude that a “good life with psychosis” was possible (Simonsen et al. 2112). This is the finding, stated in the language of psychiatric research rather than music therapy, that this entire paper has been circling. Wholeness was never the same as the absence of impairment and does not require a cure; it requires only what Kitwood said it does: constant recognition, respect, and trust. Sometimes that comes through a clinical trial. Sometimes it comes through a drum circle, a dance class, or a single syllable, sung at a dinner table, by a woman who lost the words but not the voice. Comprehending all of this can be done through reading, but watching it happen in front of your eyes in a room is so much more than an article can convey.

“It Brings Me Life”

I entered the “inpatient adult and geriatric psychiatric service unit” on the hospital’s fourth floor. The space is intimidating because people roam freely, and this section of the hospital is where patients with serious mental illness reside. . . It is my first time working with patients in these conditions, so I don’t know how to act. Not because I don’t want to be there, but because I can be standing two feet away from someone and have no clue what is going through their mind or what’s real and what’s not for them.

Patients carry their droopy faces as they drift, pace, and prowl around freely without reason. One person approached and stood closely behind me. In the common area, sets of eyes stare blankly at a TV playing. The eyes are not even watching the screen; they are just fixed there, not blinking or moving away.

The therapy room is different from the hallways. It’s clean and simple with a little bookshelf in the corner for patients to use. The chairs are arranged in a circle, with tables in the center, creating a sense of both distance and connection. Patients trickle in slowly, and I watch them carefully as each of their eyes darts around the room, trying to find the chair in the circle they feel the safest in. A guy on my left aggressively scratches his head and fixates on things around the room while the girls across from him look down rapidly tapping their feet. Then there is an older man at the end, and he is sitting back in his chair with his hands under the table, folded in his lap, his attention on the instruments placed down the middle of the circle. Kristen told me he rarely stays for the full sessions, trying to remain in his comfort zone. The feeling in the room feels uncertain until Kristen, the facilitator, asks a simple question. “What do you like about music?” People around the circle answer one at a time, and the old man at the end says, “It brings me life.” Kristen slowly starts playing her guitar and singing “Waiting on the World to Change” softly, almost under her breath. The soft strums allow everyone to drift into the shared experience comfortably. The instruments laid out are simple, nothing too sharp-sounding or jarring, like maracas, shakers, and small hand drums. Most people start reaching for instruments to play, whether it’s just to have something to hold or to add to the sound. The room, once filled with silence, drifts into a mix of different sounds, so softly played together, and the instruments disappear to the beat of the song Kristen is now singing.

Kristen stops singing at some point, and more questions about change and pushing people out of their comfort zones follow. I have previously learned some of their diagnoses, like schizophrenia, psychosis, and bipolar disorder, but as they play together and answer Kristen’s questions, their diagnoses disappear, and they turn into more complex humans. The older man finally grabs a small drum and a stick and plays so softly under all the other music that you can barely make it out. But as the bridge comes, he smiles and starts to sing along with Kristen. He stays for the full session.

Why This Matters: The Cost of Calling Music Optional

Nothing in this article is new information. The evidence for music therapy’s power to bypass impairment and restore personhood is not new, not thin, and not controversial within the field. And yet music therapy remains, in practice, an afterthought. Music therapy is treated as something offered when there is room in the budget, rather than built into the structure of care itself. Part of the reason is that music therapy does not fit neatly into a healthcare system built around pharmaceuticals and procedures. These things can be billed, coded, and reimbursed on a predictable schedule. A pill has a price, and a scan has a code. A conversation between a music therapist and a patient, no matter how transforming, often does not have that role in the patient’s treatment plan. As one recent review of music therapy in healthcare puts it plainly, “in many healthcare facilities and community settings, music therapy remains inconsistently funded, often relying on charitable support or short-term grants” (Lan et al.). Charity and short-term grants are not what you build a system of care on. They are what you build a program on and can be cut when the budget needs to be tightened.

This is not a problem of ignorance. The same researchers note that even mainstream medicine has been forced to take non-pharmacological approaches seriously, precisely because of “the limited efficacy and certain adverse drug reactions (ADRs)” of the drugs doctors have relied on instead (Lan et al.). In other words, the medical system already knows the current model has real costs, measurable harm, and real limits. It has turned to music therapy anyway due to necessity and not out of conviction. Funding has not kept pace with the growing evidence.

The disparity is not evenly distributed, either. Music therapy “has been formally recognized as a professional field in numerous Western countries, such as the United States, Canada, the United Kingdom, and Australia; yet, in many low- and middle-income countries. . . There is a lack of standardized recognition and proper reimbursement mechanisms” (Lan et al.). Access to a person’s own personhood being restored should not have to depend on where that person happens to live and how much money goes into the healthcare system they are born into.

This concept is the most visible in long-term and nursing home care — the exact setting where Kitwood’s model of personhood was first developed, because it was in those settings that he saw personhood most routinely eroded. A resident in a facility built around cost containment is, structurally, a line item before they are a person — a bed to fill, a set of medications to administer on schedule, a body to keep alive as efficiently as possible. Bestowing personhood, Kitwood’s word (constant recognition, respect, trust), takes time, attention, and staff who are not stretched so thin they cannot give it. None of that shows up on a balance sheet as a cost saved. It shows up, if it shows up at all, as a resident who is easier to manage, calmer, and less likely to need intervention. These are all outcomes a spreadsheet can register, even when it cannot register the reason behind them.

The researchers behind this same review are unambiguous about what needs to change: “government, clinicians, insurers, and healthcare payers must recognize music therapy as a reimbursable health service. . . establishing reimbursement pathways or national funding frameworks would legitimize music therapy as a part of the formal, standard practice” (Lan et al.). This is not a radical claim, nor is it strictly about music. It is a claim about what a healthcare system chooses to count as a real treatment, and what it allows itself to treat as an option or just a nice, extra form of care. That extra form of care has reached people like no pill can.

My babcia is fortunate because she has family who can give her the music that she cares about. Not every patient with dementia, not every survivor of trauma, not every person losing themselves has that. Whether they do should not rely on what a hospital’s budget can absorb this year. It should depend on what we, as a system that claims to care for people and not just their symptoms, are willing to call essential.

Conclusion

I wanted to write a paper like this as soon as I saw my babcia reawakened from a 60-year-old song that found an opening into her being. I needed to know more about what I had witnessed. I found that what reached her was not a miracle, but a mechanism. Music and language are not the same communication modality, no matter how intertwined they are, and if one is damaged, the other is not. The same rerouting that allowed her to sing was the reason a man rigid with Parkinson’s moved again after hearing a rhythm his own brain could no longer generate, and why deaf children were able to learn to hear speech through rhythm. This bypass is more than strengthening an area of the brain to restore a daily function because it is actually able to reach people with issues that do not impact a function but take over their entire mental stability: people with trauma so severe they cannot put it into words have used music to overcome it. This is possible because music’s influence on the brain is older than language itself and foundational enough to remain after other parts of the brain have been compromised. However, on closer inspection, it is apparent that none of this is actually the point. A bypassed pathway is not the goal, but restoring the person is. What is actually happening when my babcia starts singing a song she once knew, when a shy man opens up to a group of strangers, every time Jack “comes back to life”, is closer to what Kitwood called bestowal. The quiet, repeated act of recognizing someone as a person, respecting them as one, and trusting that they are still there to be reached.

The larger argument of this paper is that music can help heal and that neurological impairment does not have to mean disappearance. Music is one of the most powerful ways we have found to meet a person where they are. It should not be a luxury or a grant-funded extra but a requirement. We are born with one heart and one body, so if there is a way to prevent impairment from taking who we are away from the one life we have, there should be no discussion.

My babcia still goes through every day with dementia, and there is no cure. However, knowing that we can achieve those little moments where my babcia and others are not lost forever restores hope — not that the disease like this one will stop taking, but that what they take can still, sometimes, be given back.

References

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“David Leventhal.” Dance for PD, danceforparkinsons.org/about-us/staff-and-nyc-faculty/david-leventhal/.

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Hallam, Susan, and Evangelos Himonides. The Power of Music: An Exploration of the Evidence. Open Book Publishers, 2022, https://doi.org/10.11647/OBP.0292.

Koelsch, Stefan. “Brain Correlates of Music-Evoked Emotions.” Nature Reviews Neuroscience, vol. 15, no. 3, 2014, pp. 170–180, https://doi.org/10.1038/nrn3666.

Krotinger, Anna, and Psyche Loui. “Rhythm and Groove as Cognitive Mechanisms of Dance Intervention in Parkinson’s Disease.” PLOS ONE, vol. 16, no. 5, 2021, e0249933, https://doi.org/10.1371/journal.pone.0249933.

Lan, Yung-Yi, et al. “Music Therapy in Health Care Practice: Promise, Pitfalls, and Policy Implications.” Frontiers in Human Neuroscience, vol. 20, 2026, article 1768102, https://doi.org/10.3389/fnhum.2026.1768102.

MacGlone, Una M., et al. “Understanding the Wellbeing Effects of a Community Music Program for People With Disabilities: A Mixed Methods, Person-Centered Study.” Frontiers in Psychology, vol. 11, 2020, article 588734, https://doi.org/10.3389/fpsyg.2020.588734.

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About the author

Maya Nakahara

Maya Nakahara is a student in the Instrumental Music program at LaGuardia High School, where she plays piano and trumpet and has recently started learning guitar. Her academic interests sit at the intersection of music, neuroscience, and therapeutic application — an interest she has pursued through volunteer work at Mount Sinai’s Louis Armstrong Center for Music & Medicine. Outside the classroom, she plays volleyball for her school’s varsity team and for Brooklyn Elite Volleyball, a club team, and serves as the Event Manager for her school’s Mental Health Initiative.

The post How Can Music Bypass Impairment to Reach The Wholeness of a Person? appeared first on Exploratio Journal.

Abstract

Previous studies have established a relationship between foreign direct investment and environmental quality, but have stopped short of proving a consistent positive or negative impact on human health. The following study examines the relationship between foreign direct investment, PM2.5, and infant mortality across countries from 1990-2020, with specific attention to income groups. Using weighted averages and regression analysis, this study concludes no statistically significant relationship globally with infant mortality. However, when countries are separated by income groups, distinct patterns emerge. Upper middle income countries reveal a strong and positive relationship between FDI and infant mortality, while low income countries have a marginally significant relationship. Finally no significant relationship is found within lower middle income and high income countries. In particular, the significant relationship within upper middle income countries suggests the possibility that environmental consequences that come with foreign investments may influence infant health. Further research in urbanization, PM2.5, and GDP per capita further demonstrate the results of dependence on each country’s stage of development. Overall, these results demonstrate the relationship is not universal between FDI and infant mortality which emphasizes the importance of delving deeper within specific levels of economic development instead of relying on a single global relationship.

1. Introduction

Foreign direct investment is seen as a stimulant that creates jobs and economic growth promoting a country’s well being, but it may also be the silent killer in the case of air pollution. The most intense and dangerous particle is PM2.5, fine airborne particles smaller than 2.5 micrometers that can bypass the human body’s natural filters (World Health Organization, n.d.). Children and infants are particularly susceptible due to their still developing lungs and immune systems (Institute for Health Metrics and Evaluation, 2025). Furthermore, exposure to air pollution during pregnancy has shown to leave newborns with low birth weight, as well as an increase in preterm birth and infant mortality (Health Effects Institute, 2025). 

This study tests which theory holds across 186 countries by examining the relationship of foreign direct investment, air pollution, and infant mortality, while taking into account urbanization, GDP, and income groups. Using a weighted cross-country sample of 186 nations from 1990 to 2020, this study finds that the effect of foreign direct investment on infant mortality is not universal but varies systematically by income level. In upper-middle-income countries, higher FDI inflows are strongly and significantly associated with higher infant mortality rates (p = 0.002), consistent with a pollution-mediated mechanism. A marginally significant positive effect is also observed in low-income countries (p = 0.062). No significant effect is detectable globally or in high-income countries, where institutional quality and healthcare systems appear to absorb the environmental costs of foreign investment.

2. Background/Data/Methods

Researchers continue to debate the impact of FDI on environment quality. The pollution halo hypothesis argues that multinational corporations can introduce cleaner technologies, while the pollution haven hypothesis suggests that rigorous pollution industries are simply relocated to countries with weaker economic regulations (Cole et al., 2017). Singhania and Saini (2021) examined 21 countries from 1990-2016 directly supporting the pollution haven hypothesis and found that FDI can contribute to environmental deterioration. Contrastingly Heft-Neal et al. (2018) found a strong relationship between PM2.5 and infant mortality through analyzing one million births across sub Saharan Africa. Together these studies suggest a potential connection between FDI, pollution, and infant health that has often been studied separately. 

The data for this study comes from the World Bank’s World Development Indicators and includes 186 countries while varying income levels. The primary variables are foreign direct investment (FDI) as a percentage of GDP, PM2.5 air pollution, and infant mortality. Along with GDP per capita and urbanization. Countries are also classified into low, lower middle, upper middle, and high income as classified by the World Bank Group using Gross National Income per capita. 26 countries qualify for low income with under $1,175 income per capita, 53 lower middle countries ($1,176- $4,635), 53 upper middle income countries with $4,636-$14,375, and lastly 54 high income countries categorized under high income for over $14,376. Weighted averages were used to summarize each country’s data across the 1990-2020 period into a representative value, allowing long term patterns to be compared across countries rather than focusing on short term yearly fluctuations and recent periods having disproportionate influence. Then using weighted ordinary least squares (OLS) regression, the statistical analysis reflected the weighting of the data. A global regression was conducted using the full sample, but resulted in no strong relationship so the countries were split into income groups to examine hidden relationships.

3. Results and Discussion

From 1990-2020 FDI fluctuated greatly, but overall increased for much of the period while PM2.5 generally decreased substantially which suggests no simple aggregate relationship  between greater foreign investment and greater pollution.  

However the histograms show a wide range for countries.FDI is relatively low for most countries with a few very high outliers while PM2.5 levels are more distributed revealing that countries can have various combinations of FDI and pollution.

This variation across countries suggests other factors have an impact on the environmental consequences of FDI, instead of FDI automatically contributing to higher pollution. The global model verifies this pattern with an R-squared value of 0.53. The near zero coefficient of 0.002 indicates for every one percentage point increase in FDI, infant mortality is associated with an increase of 0.002 deaths per 1,000 births, effectively zero. Additionally the p-value of 0.99 suggests there is almost no statistical evidence that FDI has a direct impact on infant mortality. 

Separate regressions by income group reveal that FDI has a significant positive relationship with infant mortality in upper middle countries and a marginally significant relationship in low income countries, while no significant relationship is seen in lower middle and high income countries. Average FDI increases in high income countries, while average PM2.5 decreases from 32.6 to 21.5 mcg/m3.

 The FDI time series graph additionally displays these differences by showing the volatility where high income groups tend to have many extreme spikes and lower income groups remain relatively stable. 

Scatterplots between FDI vs PM2.5 and FDI vs infant mortality continue to show separation with low and lower middle income groups with higher levels of PM2.5 and infant mortality and higher income countries more centered with lower levels, despite a trend of receiving higher FDI. 

The regression results lead to a clearer picture. With a coefficient of 1.5052 and a p value of 0.0023,

Upper middle income countries have the strongest relationship between infant mortality and FDI, while low income countries follow with a coefficient of 2.2574 and a p value of 0.0621 suggesting that poorer countries may be more susceptible to negative foreign investment effects. However there is no statistically significant relationship in lower middle income countries (coefficient of -1.5753, p = 0.2051) and high income countries (coefficient of -0.0215, p = 0.5926). Lower middle income countries show that general economic development may be more relevant towards infant mortality with a p value of 0.001 with GDP where broad economic development drives infant health improvement. Higher income countries, which are associated with stronger institutions and health systems, can absorb the negative impacts of FDI therefore resulting in a weaker relationship.

Important limitations of this study include that the results prove correlation, not causation, meaning the regression cannot prove that FDI directly causes changes in infant mortality. In addition, other factors may influence both FDI and infant mortality such as government policies, healthcare access, or infrastructure. This data also does not identify the sector receiving FDI. A manufacturing plant can have very different environmental consequences than a call center. The type of foreign investment may aid in explaining differences found across income groups.

 There are also limitations within the data itself, including a low sample group of low income countries potentially making that data more sensitive to individual countries more than larger income groups. Lastly PM2.5 is measured with varying quality throughout countries, with some countries having more advanced pollution monitoring systems than others. 

4. Conclusion

Ultimately, the relationship between infant mortality and foreign direct investment is more complex than a single global trend. These differences within income groups show that the effects of 

foreign direct investment is up to the environmental and economic conditions of the country receiving it. Future research can strengthen this study by using panel data to track changes within individual countries instead of relying on averages from 1990-2020. Additionally FDI could be separated by economic sectors, such as manufacturing, to help determine whether the effects of FDI on infant health change depending on which industry receives the investment. 

Lastly, future research can investigate why the relationship differs with income groups. Specifically, researchers can examine whether strong environmental regulations weaken the relationship found in upper middle countries. Or if carbon emissions or water pollution, other measures of pollution, can be studied to see if the same pattern is continued with PM2.5. These questions can help identify the specific conditions that determine whether health outcomes are benefited or harmed by foreign investment.

Works Cited

World Bank. (2026). World Development Indicators [Data set]. World Bank DataBank. https://databank.worldbank.org/source/world-development-indicators

Cole, M. A., Elliott, R. J. R., & Zhang, L. (2017). Foreign direct investment and the environment. Annual Review of Environment and Resources, 42, 465–487. https://doi.org/10.1146/annurev-environ-102016-060916

Health Effects Institute. (2025). State of global air 2025. State of Global Air. https://www.stateofglobalair.org/

Heft-Neal, S., Burney, J., Bendavid, E., & Burke, M. (2018). Robust relationship between air quality and infant mortality in Africa. Nature, 559, 254–258. https://doi.org/10.1038/s41586-018-0263-3

Institute for Health Metrics and Evaluation. (2025). Global Burden of Disease Study 2023.

Singhania, M., & Saini, N. (2021). Demystifying pollution haven hypothesis: Role of FDI. Journal of Business Research, 123, 516–528. https://doi.org/10.1016/j.jbusres.2020.10.007

World Health Organization. (n.d.). Air pollution. https://www.who.int/health-topics/air-pollution

About the author

Avina Christie

Avina is a high school student interested in economics, environmental science, and understanding how global economic activity affects human well being. She enjoys using data to uncover patters across countries and real world issues. Outside of academics Avina loves to volunteer, play soccer, dance ballet, and spend time with family, friends, and her two dogs.

The post The Human Cost of Investment: FDI and Infant Mortality appeared first on Exploratio Journal.

Abstract

The South Bronx is one of New York City’s most diverse and densely populated neighborhoods and hosts a range of industrial facilities and highways that serve the city. These facilities affect the health of residents in the South Bronx, who face immense environmental burdens, including air pollution, water contamination, and loss of green space. Addressing these issues requires examining their historical roots, identifying current patterns and causes, and highlighting the community-led efforts already underway to fight environmental injustices in the area. This analysis reveals that while the South Bronx continues to contend with ecological harms, community-driven initiatives and resilience play a crucial role in addressing local inequities. It also identifies the need for equitable urban planning and policy interventions to advance environmental justice in the South Bronx and similar communities.

Introduction to The South Bronx

The South Bronx is a densely populated urban area in New York City bordering Bronx Park and the Bronx River. This region serves as a critical intersection between urban development and the health of natural ecosystems, reflecting broader challenges faced by communities throughout the South Bronx. Accordingly, land use has been a source of controversy throughout NYC’s rapid rise to one of the world’s most notable cities for its economic and cultural riches. Use of the land as a hub for industrial infrastructure, at the expense of expanded greenspace for community residents, is evidenced by the construction of the Cross Bronx Expressway and the Sheridan Expressway (now Sheridan Boulevard) from the 1950s to the 1980s, which tore through neighborhoods to provide benefits to communities and companies that reside elsewhere. These major roads have affected environmental conditions in communities adjacent to or within the South Bronx, with the most prominent examples being consistently poor air quality, increased heat exposure, and greater vulnerability to climate-related impacts. Demographically, the locality is made up of predominantly low-income communities of color,
populations that have long faced socio-economic disruptions and challenges. Because of this unique combination of dense urban development and environmental challenges, the South Bronx neighborhood provides a valuable case study for examining environmental justice and community-led climate resilience in New York City.

The History of Environmental Racism

The disproportionate exposure to ecological harms that the South Bronx faces is not accidental. For decades, urban planning officials have purposefully sited toxic or hazardous facilities in communities of color, with one of the most frequent places being the South Bronx. This process is often referred to as environmental racism, and it has been a growing issue.

According to South Bronx Unite, a community-led organization that advocates for social, environmental, and economic justice in neighborhoods of the South Bronx, the majority of The Waterfront Place, located in the Port Morris neighborhood of the South Bronx, sits on a 96-acre plot of public waterfront land leased in 1991 to Harlem River Yards. The plot is currently subleased to a range of industries, most notably FreshDirect, a 5,000-ton-per-day waste transfer station, and several fossil-fuel peaker power plants. This issue, in an area that has accumulated many hazardous facilities emitting pollutants into nearby residential areas, has been a longstanding one throughout the Bronx (“The Mott Haven-Port Morris Waterfront Plan”).

Figure 1: Freeways intersecting South Bronx neighborhoods. (“Reimagine the Cross Bronx Expressway Final Vision”)​

Figure 1 above, included in a recent report by Segregation by Design, visualizes the rapid development in the South Bronx in the 1900s and further shows how prior city planning has foregrounded the environmental racism that groups in the Bronx now face. The report explains how the construction of expressways and urban projects in the 1950s and 1960s physically divided the South Bronx, displaced more than 60,000 residents, and hindered the progress of hundreds of homes and businesses, with the latter also bearing economic consequences. The Cross Bronx Expressway was constructed from 1948 to 1972 under Robert Moses, a U.S. state and municipal official who oversaw much of New York’s landscape transformation through public works planning. The expressway (the broad horizontal yellow highway, in Figure 1) split densely populated neighborhoods and demolished thousands of homes and businesses, harming much of the area’s economic potential and making it a key factor behind the low income rates and stifled economic growth the area now faces (“Reimagine the Cross Bronx Expressway Final Vision”). Despite backlash from tenant activism and community protests, the expressway was built, permanently altering the socioeconomic status of the South Bronx.

Asthma Alley and Its Economic Consequences

Unfortunately, the South Bronx’s designation as “Asthma Alley” is not an overstatement; the area has recorded asthma hospitalization rates up to five times the national average (“Asthma Alley”).

Figure 2: Distribution of Youth Asthma-Related Emergency Room Visits in NYC (Monegro)

Figure 2 illustrates the growing impact of air pollution in the South Bronx (highlighted within the black circle) by showing the stark distribution of Child Asthma-Related Emergency Room (CARER) visits across NYC neighborhoods. Highways release large amounts of nitrogen dioxide (NO₂) and fine particulate matter (PM₂.₅), mainly from tire wear, vehicle exhaust, and brake dust. Children in close proximity to these roadways repeatedly inhale these pollutants, which can clog airways and trigger inflammation, damage lung tissue, and increase the frequency and severity of asthma attacks (“Health – Respiratory Diseases.”). Hence, the density of major roads and highways in the South Bronx area correlates directly with asthma rates among youth. As seen in Figure 2, wealthier and predominantly whiter neighborhoods, such as Lower Manhattan, Williamsburg, or Long Island City, experience little to no CARER visits. Emergency room visits in lower-income areas, often in the Bronx, can be especially costly, as some families either cannot afford health insurance or are underinsured, resulting in high out-of-pocket costs. Repeated emergency room visits, medications, and follow-up care associated with childhood asthma can create significant financial strain. Out-of-pocket healthcare costs reduce the money families can spend on improving their standard of living and their children’s education. This financial burden associated with asthma treatment, coupled with the fact that caregivers often lose wages when caring for sick children, creates a cycle of poor health and financial hardship, which reinforces itself.

Why the South Bronx is a “Sacrifice Zone”

“Sacrifice zones” are often defined as populated areas with high levels of pollution and environmental hazards due to nearby toxic or polluting industrial facilities, because the health and safety of people in these communities are being sacrificed for the economic gains and prosperity of others (“Sacrifice Zones”). Historical racism is why many label the South Bronx as a sacrifice zone, highlighting the unequal consequences of industrial development for its residents.

Figure 3: Map showing industrial facilities in Harlem and the Bronx. (Monegro)

​The map above (Figure 3) shows the uneven distribution of industrial facilities in East Harlem and the Bronx (the area inside the black oval). The proposed benefit of these facilities, according to the NYC industrial plan, is that the overall concentration of facilities in the area supports 16,900 industrial jobs, accounting for 33% of all private-sector employment. However, these facilities have severe environmental impacts on nearby residents. In a 2018 interview, Joyce Hogi, then-president of the Bronx Council for Environmental Quality, discusses the implications of the facilities surrounding the 145th Street Bridge. She states that the neighborhood off the interstate bridge is among “the most polluted in America,” as it also maintains a recycling center that creates lines of garbage trucks up every street. Hogi explains that the community never wanted this development; instead, they advocated for a community park. She also frankly states that she feels her community is being “pissed on” and “not considered” when it comes to choices regarding development (Bronx Council for Environmental Quality), underlining how other forms of development that would enhance their quality of life, such as investment in green spaces, have been ignored by city officials.

Highlighting Key Trends Using Tree Equity Score

There is a clear relationship between proximity to industrial development and environmental and public health harms. Industrial facilities also benefit other parts of NYC, such as the Upper East Side, Brooklyn Heights, and Tribeca, which reputedly have some of the best waste management, food distribution, sanitation, and electricity services, all of which are made possible by the location of polluting facilities in the South Bronx. This leads to vast differences in many key variables between these affluent neighborhoods and areas throughout the South Bronx.

A valuable tool for measuring these discrepancies is the Tree Equity Score, an index created by American Forests, the oldest national nonprofit conservation organization in the United States. The score reflects whether a neighborhood has sufficient tree canopy, considering both existing tree cover and community characteristics such as income, race, health burdens, heat exposure, age, and unemployment.​

Figure 4: Tree Equity Scores in affluent NYC neighborhoods. (“Tree Equity Score”)                           

Figure 5: Tree Equity Scores in South Bronx neighborhoods. (“Tree Equity Score”)​

Figures 4 and 5 compare the Tree Equity Scores of wealthier NYC neighborhoods with neighborhoods in the South Bronx. Each score is taken from a specific segment of city blocks within each neighborhood; however, these scores are indicative of the broader circumstances of each respective area. Despite discrepancies between variables in the two types of neighborhoods, some trends are apparent.

First is the correlation between race and poverty. The South Bronx neighborhoods, which have 82–98% people of color and 53–86% poverty rates, receive low Tree Equity Scores (51–72) and are designated as high- or highest-priority for canopy coverage. By contrast, affluent neighborhoods with 6–19% people of color and 0–4% poverty rates score 92–98 and are considered low priority.

​Furthermore, communities with the greatest need have the least canopy coverage. South Bronx neighborhoods have only 8–9% tree canopy coverage, compared with a recommended 40–50%, whereas upscale areas have around 20–30% canopy coverage and already have higher overall equity scores. Neighborhoods most vulnerable to environmental and health risks are also the most underserved in terms of urban greenery.​

Tree Equity Scores corroborate multiple forms of disadvantage, not just a lack of trees or canopy coverage. The South Bronx scores were consistently worse, along with unemployment, poverty rates, health burden indices, and heat disparities. This indicates that low tree equity is part of broader socio-environmental inequality, rather than an isolated environmental issue. 

How Resilient is the South Bronx?

Resilience in any society or environment is important, as it ensures that a community or system can maintain stability in the face of disruptions. Resilience, in this case, can be assessed through the ecological, social, and infrastructural components of the South Bronx. Ecological resilience is perhaps the weakest of the three.

Ecological Resilience

As evidenced by tree equity data, the South Bronx lacks sufficient tree canopy coverage to remedy environmental harms. Higher canopy cover coincides with improved air quality, as increased tree canopy helps filter airborne pollutants and reduce particulate matter. A lack of tree cover and vegetation also impacts stormwater management. Combined sewer overflows (CSOs) are one of the South Bronx’s most persistent environmental challenges (Bronx River Alliance, “Green Infrastructure”). During heavy rainfall, the city’s combined sewer system becomes overwhelmed, causing untreated sewage mixed with stormwater to be discharged directly into the Bronx River, reducing water quality and damaging aquatic habitats. This leads to lower biodiversity. With fewer marine species and less vegetation, the ecosystem’s resilience to environmental damage is hindered. Because of CSOs, habitat fragmentation is extensive. A recent study of the Bronx River found benthic macro-invertebrate (aquatic) diversity to be extremely low, with only 2–7 species recorded at study sites and almost no pollution-sensitive species (Mahmud et al.).

Figure 6: Graph: Development vs. Ecological Diversity (Mahmud et al.).

As shown in Figure 6, the team conducted a study on how development affects Simpson’s index of the Bronx River. The Simpson index ranges from 0 to 1 (0 indicating one species dominates the ecosystem, and 1 indicating an even distribution of many species throughout the ecosystem). Clearly, there is a negative correlation between development and ecological diversity. This highlights how limited tree coverage, CSO protection, and biodiversity constrain the ecological resilience of the South Bronx.

Social Resilience

The South Bronx’s social resilience is strengthened by grassroots organizations that have come together to advocate for a more equitable present and future for their community. The Green Space Program, for example, fights to reclaim the land the South Bronx lost to buildings in the 1960s and 1970s. Included in the initiative is the Maria Sola Community Garden. The Garden is part of New York State’s “Adopt-a-Highway” program, which began in the 1990s. In 2018, when many founding members of South Bronx Unite found it underused, they stepped in to help steward it. The Garden has created a space for initiatives such as pollinator planting, stormwater management, compost, and year-round neighborhood events (The Green Space Program — South Bronx Unite). Mothers on the Move (MOM), founded in 1982, works with Latina women in the South Bronx toward a sustainable future for younger generations. MOM and the Natural Resources Defense Council (NRDC) filed a public nuisance lawsuit against New York City and the New York Organic Fertilizer Company (NYOFCo) over years of sewage odors affecting Hunts Point residents. The resulting 2010 statement in a press release by the New York City Department of Environmental Protection ended the City’s shipments of sewage sludge to the NYOFCo facility, required stronger odor-control standards for future sludge-processing facilities, funded the restoration of 1.2 acres of waterfront green space adjacent to Barretto Point Park, and mandated an independent study of odor emissions from the Hunts Point wastewater treatment plant. The outcome of this case remains one of the most significant victories in the fight for environmental justice, demonstrating the value of community organizing and resistance (New York City Department of Environmental Protection).​

Resilience of Current Infrastructure

Infrastructure resilience and the South Bronx’s productive capacity are limited. Bronx Park, Starlight, and Concrete Plant Park are different types of green infrastructure, each strengthening the resilience of the South Bronx in its own way. Bronx Park has a natural floodplain and restored river habitat, which stabilize riverbanks and help reduce intense urban heat during hotter periods. Starlight Park has rain gardens and infiltration basins, both of which reduce erosion and filter runoff during heavy rainfall. Concrete Plant Park is home to both native wetland vegetation and permeable green space, allowing residents to reconnect with the river and buffer flooding. While these South Bronx parks are effective at mitigating natural or seasonal harms, the effects of global warming have become increasingly severe, reducing their ability to meet future challenges. Additionally, the concentration of environmentally harmful facilities tends to outweigh the benefits of environmentally friendly or green infrastructure in the South Bronx, resulting in overall poor infrastructure resilience.

Case Studies of Solutions

The issues seen in the South Bronx, while unique, are also more broadly evident in other case studies in Northeast America, with two comparable examples in Philadelphia and Providence.​

In Philadelphia, as in the South Bronx, there is a risk of billions of gallons of stormwater and diluted sewage flowing into local waterways each year. To address this, the Philadelphia Water Department has devised the “Green City, Clean Waters” initiative. Implemented in 2010, with all goals set to be met by 2036, the initiative primarily focuses on developing and introducing “Green Tools” (Sell). Green Tools help absorb sewage-laden runoff after periods of intense rainfall, proving crucial for a city like Philadelphia, situated between two major rivers.

Figure 7: Diagram of green infrastructure types in Philadelphia. (“Green Infrastructure: BREC”)

Figure 7 visualizes the process of green tools and how they effectively sequester and minimize pollution in rivers. The most common types of green tools include rain gardens, bioswales, permeable pavements, green roofs, and urban tree canopies (“Green Infrastructure: BREC”).

The initiative brings greater benefits to the city as a whole. Environmentally, the increase in citywide greenery offsets much of the air pollution from car use, provides a natural means of cooling, with shade reducing the energy needed for widespread air conditioning, and improves ecosystem services such as pollination through an increase in native plants and flowers. Economically, the project creates local jobs, allowing Philadelphia youth to become more involved in the process; additionally, green spaces near residential areas can increase property values, and clean rivers encourage recreational tourism, boosting the local economy. Socially, “Green City, Clean Waters” increases access to green space, fostering community growth through more pleasant outdoor areas and collaboration in maintaining parks and neighborhood streets. Overall, it addresses all three pillars of sustainability—social, environmental, and economic (Purvis et al.)—for Philadelphia’s future.  ​

The Providence Climate Justice Action Plan foregrounds a more social approach to addressing such environmental impacts. These consequences, extreme heat, flooding, poor air quality, and limited green space, are disproportionately experienced by low-income communities and communities of color (“Providence’s Climate Justice Plan.”), underscoring a parallel to the circumstances in the South Bronx. Examples include the inclusion of environmental justice organizations, youth leaders, and local advocacy groups, who worked alongside city officials from the start of the planning process. The city gave frontline communities a meaningful role that greatly influenced final policy decisions. An example of this was the creation of Green Justice Zones, in which residents helped identify priority neighborhoods and directed public funding toward necessary projects (“Providence”).

Proposed Plan for the South Bronx

Drawing on the successes in Providence and Philadelphia, a solution to many of the South Bronx issues could be to expand green infrastructure through community-led planning that prioritizes neighborhoods with the lowest Tree Equity Scores, while minimizing automobile traffic and the further development of major roadways.

EPA Environmental and Climate Justice Grants awarded around $1 million to the Bronx River Alliance in 2024 to improve heat mitigation and air quality through tree planting and rain gardens (Crawford). While improvements have been made, they have not been enough to combat the overall environmental conditions that have consistently worsened, with summers in particular becoming dangerously hot due to the combination of concentrated energy-intensive facilities and a lack of canopy cover. However, city planners have consistently rejected proposals to locate these types of facilities in other, wealthier areas. These facilities need to run continuously, as most of New York City relies on industries based in the South Bronx. It is therefore unlikely for these facilities to be shut down or relocated from the South Bronx.

There is an opportunity for further social advocacy and grants for the South Bronx community. Groups such as the U.S. Forest Service, the Kresge Foundation, the Surdna Foundation, and the JPB Foundation fund community-based environmental justice and green infrastructure projects. There are several tangible examples. In 2023, the U.S. Forest Service received $1.5 billion under the Inflation Reduction Act, which was subsequently converted into $1 billion in grants to community-based and tribal organizations across all 50 states (“Urban Forests”). More recently, in 2025, the Kresge Foundation reported giving out $160 million in grants, of which $38.4 million was designated for environmental causes (“Grants & Social Investments”). Lastly, in 2022, the Sundra Foundation approved one of its largest grants, $2.1 million to the Right to the City Alliance (“Grants Database), with the aim of funding sustainable and affordable homes (“Home – Right to the City”)  

As demonstrated by the histories of both the Bronx River Alliance and South Bronx Unite, continuous advocacy against environmental racism and for climate justice yields tangible results. Hence, through sustained campaigning, and specific grant proposals to the previously listed organizations, these organizations can gather the required funding for street tree planting, bioswales, rain gardens, and the expansion of green roofs, ensuring that future community efforts for environmental justice in New York City prioritize the communities that currently and historically bear a disproportionate share of New York City’s environmental burdens, with perhaps the most important being the South Bronx.

References

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The Guardian, Guardian News and Media, 4 Apr. 2019, www.theguardian.com/us-news/2019/apr/04/new-york-south-bronx-minorities-pollution-inequity?scrlybrkr=66c948bf.

Bronx Council for Environmental Quality. “Gateway to the South Bronx Pollution.” Bronx Council for Environmental Quality, 3 May 2018, https://bceq.org/2018/05/03/gateway-to-the-south-bronx-pollution/.

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“Climate Justice in Providence.” One Square World, www.onesquareworld.org/climate-justice-in-providence. Accessed 10 Aug. 2026.

Crawford, Iris M. “EPA Announces Nearly $1 Million to Support Involving South Bronx Communities in Climate Resiliency Planning.” EPA, Environmental Protection Agency, 25 July 2024, www.epa.gov/newsreleases/epa-announces-nearly-1-million-support-involving-south-bronx-communities-climate?

“Grants Database.” Surdna Foundation, 11 May 2020, surdna.org/grants-database/?program_id=11&sub_program_id=all&grantsYear=all&sort_column=amount_recommended&grantsSearch=.

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Mahmud, Meleha, et al. “The Impact of Land Use and Human Population Density on Benthic Macroinvertebrate Diversity in a Highly Urbanized River.” CATE – Cities and the Environment, Digital Commons, May 2024, digitalcommons.lmu.edu/cate/vol17/iss1/5/.

Monegro, Taína. “Asthma Alley: The Health Plight of Being BIPOC in Urban Spaces.” Medium, 2025, https://medium.com/@tm4733/asthma-ally-the-health-plight-of-being-bipoc-in-urban-spaces-4a623c0d495b. Accessed 17 July 2026.

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About the author

Zubin Narula-Green

Zubin Narula-Green is a senior at the United Nations International School, where he is pursuing the International Baccalaureate (IB) diploma. In recent years, Zubin has taken a strong interest in humanitarian and environmental issues and has become a passionate advocate for the rights of marginalized communities, including those highlighted in his work on the South Bronx. In the future, Zubin hopes to study Environmental Economics with a focus on Sustainable Development.

The post Beyond the Burden: Environmental Challenges and Community Resilience appeared first on Exploratio Journal.

Abstract

Solar-powered unmanned aerial vehicles (UAVs) carry no fuel, meaning their endurance is governed by a balancing act crafted between the energy harvested from sunlight and the energy consumed throughout flight, rather than conventional fuel capacity constraints. Over multi-day missions, this balance becomes hard to predict, as solar harvesting depends on the Sun’s position relative to the UAV whilst energy consumption depends on the aerodynamic forces required to sustain flight. Both of these are also affected by flight conditions and cruise altitudes, meaning a complex optimisation problem emerges. Many simplified models treat the Sun as stationary, an assumption only valid for short missions over small timeframes and distances. In this paper, solar harvesting will be modelled through a fixed panel and gimballed panel approach, where a gimballed panel traces the Sun’s position relative to the UAV and points the panel at the optimal orientation for maximum irradiance.

Overall, this paper develops a model that investigates the energy balance aforementioned, using the example of a transatlantic crossing from London to New York. There are three overarching MATLAB models that are integrated into this energy balance: great-circle navigation, aerodynamic power consumption, and solar energy harvesting. The flight parameters have been derived from steady-level flight mechanics, standardised NACA tables, photovoltaic conversion data and solar geometry. 

Having completed the simulations, it can be seen that an idealised gimballed solar panel increases total harvested energy substantially, by 62%, but much of the additional energy that it harvests cannot be stored, since the battery charge is already full during daylight hours, where there is peak harvesting. This is shown as the minimum state-of-charge only increases by 5% for a gimballed panel, starkly lower than the 62% rise in energy harvesting aforementioned. As a result, it can be said that endurance in this context cannot be governed solely by aerodynamic efficiency or solar collection gains, but by the capacity of the battery to carry energy across extended periods of insufficient insolation.

Future work on this issue should therefore treat battery capacity as a design variable to be considered alongside altitude and cruise conditions, whilst also relaxing assumptions discussed throughout. This concept has potential real-world applications in wildfire monitoring, surveillance/law enforcement, shipping, and ocean monitoring.

1. Introduction

1.1 Motivation

Solar-powered unmanned aerial vehicles (UAVs) offer the ability of extremely long endurance without the constraints provided by conventional fuel. Their endurance is governed simply by the balance and optimisation between the electrical energy harvested by the sun, and the energy consumption by the UAV in flight. As a result, solar flight is attractive for a multitude of applications that require persistent operation, over large and remote areas. Such examples include maritime search and rescue, wildfire detection and atmospheric surveying in remote areas. This concept has been demonstrated experimentally before, with AtlantikSolar achieving an 81-hour continuous flight.(Oettershagen, et al., A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight, 2015)(Oettershagen, et al., Design of small hand-launched solar-powered UAVs: From concept study to a multi-day world endurance record flight, 2017) The concept explored in this paper also explores the idea of a moveable (on the pitch and roll axis) solar panel on the UAV, allowing it to maximise the electrical energy it harvests from the sun, a concept yet to be quantified by existing studies. The primary reason this has not been integrated into mainstream UAV technology is due to the aerodynamic and mass penalties of such a gimbal system, which are noted as limitations throughout.

In general, the primary difficulty with a solar-powered UAV tends to be that the energy balance of the UAV is directly proportional to the aircraft’s flight conditions and environmental. Solar collection also depends on the position of the sun relative to the aircraft (with a non-gimballed system), which varies with latitude, time of day, and season. Furthermore, aircraft energy consumption varies on the aerodynamic forces required to maintain steady-level flight in a cruise state. These forces vary significantly with changes in airspeed, air density, and lift coefficient, with air density itself varying with altitude. Consequently, due to the balancing act of these two factors, one cannot simply consider aerodynamic or solar conditions alone, creating a complex problem.

Multiple attempts have been made to simplify or solve this problem, with some choosing to limit mission distance and time as being short relative to the sun’s position, allowing it to be treated as stationary. (Klesh & Kabamba, 2009)Others provide validated airframes and power-collection models but lack investigation into cruise-altitude or even route optimisation for a multi-day mission. (Oettershagen, et al., A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight, 2015)The trade-off between altitude, flight conditions, and battery margin over a multi-day mission therefore remains unestablished, and this paper attempts to address this gap.

2. Preliminaries

2.1 Steady Level Flight

The aircraft is modelled in steady, level cruise. This is primarily due to time constraints and aerodynamical complexities involved in take-off, climb, descent and landing phases, which could further complicate the solar harvesting model mentioned later on. The diagram below contains four forces acting in the aircraft’s x-z plane: lift perpendicular to the flight path, drag opposing the direction of motion, thrust along the flight path, and weight acting vertically downwards.

Figure 1 – Forces Acting on Aircraft in Steady-Level Flight

Steady level flight involves no acceleration, so these forces balance in pairs:

Together, these two conditions establish the relationship between airspeed, lift coefficient and altitude: once aircraft parameters are established, the air density at a given altitude details the airspeed required at a chosen lift coefficient. (Fidowski, Atkins, & Powell, 2019)(Anderson, 1999)

The equations for lift and drag forces are given by Equation [2] below:

Here,  V is true airspeed, ρ air density,  S is the wing reference area over which airflow occurs, and C_D   and C_L  are drag and lift coefficients. The quantity 1/2 ρV²  therefore describes the dynamic pressure. (Fidowski, Atkins, & Powell, 2019)

If we combine Equation [2] with the lift-weight balance condition in Equation [1], we get the following, Equation [3], the airspeed required to sustain a specified lift coefficient at a particular altitude:

The aerodynamic coefficients are derived from two-dimensional NACA 4415 section data. (Abbott & von Doenhoff, 1959)(Airfoil Tools, n.d.) NACA 4415 was selected due to its combination of lift-to-drag performance and sufficient thickness to accommodate both solar-cell installation and endurance structural requirements. We can calculate the Reynold’s Number of the aerofoil using Equation [4] below:

At this Reynolds number the section lift curve is markedly non-linear: the local slope falls from approximately 0.21 per degree near zero incidence to 0.06 per degree at eight degrees, a consequence of a laminar separation bubble whose extent varies with incidence. A single linear fit is therefore not representative, and the tabulated section data are used directly rather than through a lift-curve slope approximation. The drag polar is obtained by fitting the drag coefficient against the square of the lift coefficient over the same data, which does not require the lift curve to be linear. (Airfoil Tools, n.d.)(Drela, 1989)                                     

The two-dimensional data are then corrected for a finite wing. A real wing sheds tip vortices, which induce a downwash and generate induced drag proportional to the square of the lift coefficient. The downwash also reduces the effective angle of attack seen by the section, so the geometric angle of attack required to achieve a given lift coefficient exceeds the two-dimensional value by the induced angle. (Anderson, 1999)

InFor this model, there are two relevant aerodynamic operating points. The first is maximum lift-to-drag ratio, which can be calculated by differentiating   with respect to   and setting the derivative equal to zero. (Fidowski, Atkins, & Powell, 2019) This can be seen below in Eq. [7]. 

This condition occurs when parasitic drag and induced drag are equal. This is at  C_L = 0.593,  kC²_L = 0.00861, matching C_D0  exactly. The condition also occurs at minimum power, given by Eq. [8]:

Here, at the minimum power condition we can see that induced drag is three times the parasitic drag. This is because power is the product of the drag and airspeed, so the additional velocity dependence causes the optimum condition to be shifted to a higher lift coefficient.

The difference between these operating conditions matters for a long-duration solar flight, as maximum L/D ratio would minimise drag and therefore the energy required over a fixed distance. Since that energy is the product of the drag and the distance flown, it corresponds to a maximum range condition. On the other hand, minimum power minimises the electrical demand at each instant and therefore corresponds to a maximum endurance condition. 

 Figure 2 demonstrates the comparison between maximum L/D ratio and minimum power utilising the flight parameters of our aircraft that can be found in the Appendix.

2.2 Standard Atmospheric Conditions

Air density will vary significantly across the altitude range considered. A function of air density as altitude varies can be obtained, combining the hydrostatic relationships and ideal gas laws with a linear temperature profile in the troposphere. This is given in Eq. [9] and [10] below:

The constants in the equations above are:

(International Organization for Standardization, 1975)

This model is valid up to 11km altitude. This function is utilised to calculate the airspeed required to generate lift at different altitudes, as when density falls, the aircraft must fly faster to generate the same lift at a given lift coefficient, changing the power required.

2.3 Propulsion

For a propellor-driven aircraft, electrical power is the most useful measure to determine energy consumption. This is because the battery energy balance depends on the electrical consumption of the propulsion system. Eq. [11] gives the propulsion model, combining aerodynamic power requirements with the propulsion efficiency n_prop  and the constant avionics load P_av . (Fidowski, Atkins, & Powell, 2019)

2.4 Solar Position

The solar power available to harvest by the aircraft is set by the irradiance incidence on the illuminated panel area, and the conversion efficiency of the photovoltaic system. Irradiance will vary continuously with geographic position, time of day, season, and altitude, so solar harvesting must be evaluated along the trajectory of its transatlantic route, rather than being treated as a constant power source.

The Sun’s position relative to the aircraft follows from the solar declination angle, the aircraft latitude, and the solar hour angle. (Duffie & Beckman, 2013)(Cooper, 1969) For a day of year  , the declination can be approximated through the following equation:

The hour angle follows from local solar time, with H = 0  at solar noon:

The solar elevation above the local horizontal can therefore be given through the spherical law of cosines, applied to the triangle formed by the pole, aircraft zenith, and the Sun:

Here,   is the aircraft latitude. Eq. [14] is the same as computing the great-circle central angle between the aircraft and the subsolar point, since the hour angle is the longitude difference between them.

2.5 Atmospheric Attenuation

The irradiance reaching the aircraft is also reduced by absorption and scattering across the atmospheric path, and as the Sun sits lower throughout the day, the longer that atmospheric path becomes. The relative optical air mass can be approximated using the Kasten-Young formulation, which accounts for atmospheric curvature, and remains finite at the horizon. (Kasten & Young, 1989)

Flying at altitude also reduces the mass of atmosphere above the aircraft, which can be accounted for by scaling the air mass by the local pressure ratio from the standard atmosphere model described in Section 2.2. The direct normal irradiance then can be placed in an empirical clear-sky attenuation model:

Here, I₀ = 1361 Wm^-2  is the solar constant, and the coefficient 0.7 corresponds to the fraction transmitted at unit air mass. (Meinel & Meinel, 1976) The exponent 0.678 is an empirical fit accounting for non-uniformity in the distinct atmospheric layers. This model captures both the reduction in available energy as the Sun approaches the horizon and the increase in irradiance at higher cruise altitudes. Diffused and ground-reflected components of irradiance have been assumed to be negligible as part of this investigation.

  Figure 3 demonstrates the effects of the differing insolation models.

2.6 Photovoltaic Conversion

The electrical power generated from an illuminated panel is:

Where A_PV is the photovoltaic cell area, η_PV  the module conversion efficiency,  η_PMMT the efficiency of the maximum-power-point tracking electronics, and   the angle between the solar direction and the panel normal. For the aircraft considered in this paper, A_PV = 1.7223 m² , as it has been taken as equal to the wing planform area. Furthermore, η_PV = .2 and η_PMMT = .95. The conversion process is entirely independent of flight conditions, so variation in harvested power arises entirely from solar geometry and atmospheric attenuation. (Duffie & Beckman, 2013)(Oettershagen, et al., A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight, 2015)

3. Methods

The following sections detail the mathematics behind MATLAB models utilised in this study. Access to these models can be found through the GitHub repository linked here:https://github.com/rohaannarula/solar-uav-energy-model

3.1 Panel Orientation & Solar Collection

The incidence angle,  ɸ introduced in Eq. [17] follows from the relative orientation of the Sun and the solar panel. Both directions are represented as unit vectors in a local North-East-ηPV (NED) frame. (Fidowski, Atkins, & Powell, 2019) The solar direction is:

Here, a is the solar azimuth, measured clockwise from North, and θs is the solar elevation. The negative third component is due to the downwards axis being positive, so the Sun above the horizon has a negative vertical component. For a panel with roll angle ɸp and pitch angle θp , with yaw fixed at zero, the outward normal is:

Because both of these vectors have unit length, their dot product gives the cosine of the incidence angle directly. The result is fixed above zero, since a negative value implies the Sun lies behind the panel, and no energy is collected:

Two configurations will be evaluated. The first is a flat panel, representing the roughly horizontal photovoltaic surface of an aircraft in steady-level cruise. At a maximum lift-to-drag condition, the required lift coefficient of 0.593 is established using an average cruise angle of 1.11°. This is close to the horizontal, making a flat panel assumption reasonable. Setting both orientation angles to zero reduces Eq. [20] to exactly the sine of the solar elevation:

The second configuration is an idealised gimballed panel; tracing the Sun’s position. The required orientation is obtained by equating the panel normal to the solar direction, which yields three separate component equations into two unknowns. The East component contains the roll angle and yields the pitch by dividing the North component by the Down component, which eliminates the roll term.

Two angles are enough for the three equations because the solar direction is a unit vector, so only two of its components are independent of each other. The yaw angle does not appear, as rotating the panel about its own axis does not alter the theoretical solar panel surface presented to the Sun.

In both configurations, the harvesting of solar energy is reduced significantly when solar elevation is below 5º , at which point the atmospheric path length is long enough that available irradiance becomes negligible. The gimballed panel can be treated as an idealistic case, rather than a realisable configuration, due to multiple assumptions required, such as the requirement for near vertical pitch near sunrise and sunset.

3.2 Great-Circle Route

The aircraft must follow the shortest possible path between London Heathrow and John F. Kennedy International Aiport on a spherical Earth, which is a great-circle arc. For endpoint co-ordinates with longitude difference Δƛ , the central angle subtended at the Earth’s centre is as follows:

(Fidowski, Atkins, & Powell, 2019)

As a result, the great circle distance and initial heading can be calculated, where   is the radius of the Earth:

A four-quadrant arctan2 function can be used throughout, in place of a simple arctan one, since dividing the sine and cosine components would discard their individual signs and therefore lose the directional element of the heading.

As great-circle routes do not maintain constant heading, the aircraft heading will change continuously throughout crossing. Intermediate vectors can be generated through spherical linear interpolation, where   is the fractional distance along the route, and   and   are unit position vectors:

 The result can then be converted back into latitude and longitude for the solar and atmospheric calculations. The route is decomposed into 2000 equally spaced points and is plotted below in Figure 4.

3.3 Cruise State & Energy Balance

For each cruise altitude and flight condition, the energy balance model is calculated independently. Utilising parameters obtained from Section 2; we can calculate the required electrical power and flight time:

Where P_elec is the required electrical power for a given altitude and flight condition.

We can calculate solar power required using the trapezium rule, as solar power harvested varies with both time and position, and is continuously varying:

The battery can be represented as an energy reservoir with limited capacity, updated according to the net electrical power balance:

Where E_{B max} = 850 Wh = 3.06MJ  and the battery is initially fully charged. (Oettershagen, et al., A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight, 2015) We can therefore calculate the state of charge, and the aircraft’s minimum state of charge over the flight:

The minimum state of charge is utilised as the primary measure of mission feasibility, as it is more informative than comparing net energy expenditure. This is because a positive net balance will not guarantee that sufficient energy is available throughout the flight.

The simulation of state of charge will be repeated for each altitude, with both cruise conditions and panel configurations. I will consider a minimum SOC requirement of 30% to consider feasibility, as assumptions discussed later diminish true viability of such a large margin.

4. Results

4.1 Solar Collection 

The great-circle route from London Heathrow to JFK New York spans 5540km. If the aircraft departs at 12.00 UTC on day 172 of the year, close to June solstice, the crossing at an altitude of 3000m encompasses 5 whole day-night cycles.

Figure 5 compares solar power generated by the flat and idealised gimballed configurations. Peak values appear similar, approximately 329W when gimballed against 291W, but the gimballed curve is broader, unlike the narrow daily peak of the flat configuration. Integrated across the crossing, at maximum L/D ratio, the two configurations collect 77.37MJ and 48.17MJ respectively, so continuous gimballed tracking yields 62% more energy harvested. The differences arise at low solar elevation, where a flat panel projects only 50% of the direct beam at 30 degrees and 17% at 10 degrees, compounding the atmospheric attenuation already present. This is represented by Figure 5.

4.2 Battery State-of-Charge

  Figure 6 below shows the battery state of charge over the same crossing. The battery reaches full capacity for several hours each day, meaning surplus energy harvested cannot be stored given current battery size constraints. Approximately 26.1 MJ is discarded across the maximum-L/D mission, exceeding the 22.02 MJ required to complete the crossing. Storage rather than collection is therefore the binding constraint, and the additional energy gathered by the gimballed panel does not translate proportionally into endurance. The minimum state of charge is a more useful measure of feasibility than net energy balance, as explained in section 3.3.

4.3 Panel Orientation 

   According to simulation, the ideal orientation requires panel roll to sweep between approximately ±60° each day and pitch to reach nearly −80° near sunrise and sunset, a range no practical mechanism could achieve, reinforcing the treatment of the gimballed case as an upper bound. The photovoltaic panel parameters can be found in the Appendix. 

4.3 Effect of Cruise Altitude and Flight Condition

Table 1 above summarises the design variables. The energy required falls only 4.3% across the entire altitude range, because drag at fixed   is independent of air density. On the other hand, battery margin falls much more sharply, from 0.437 to 0.102 under maximum L/D ratio, since greater altitudes raises power required from 43.3 to 71.4 W while night duration is unchanged. The 30% SOC requirement is only met to approximately 5 km. In terms of flight condition chosen; maximum L/D ratio consumes 15 percent less energy at every altitude, while minimum power leaves a larger reserve because it draws less overnight. This disparity grows with altitude, and at 6000m maximum L/D fails the margin requirement at 0.275 while minimum power satisfies it at 0.374. However, maximum L/D has immense benefits over minimum power in terms of practicality. The flight time of a maximum L/D condition cruise at 3000m is 39hr longer, impractical for real-world applications. Furthermore, it can be seen that a gimballed solar panel provides 62% more energy harvested at 3000m (maximum L/D), yet its minimum SOC is only 5% greater. This proves the constraint in this model is battery capacity, as most of the extra energy harvested has been discarded before it can be used, as there is simply not the storage available.

5. Assumptions & Evaluation

The performance of a multi-day crossing is governed by the interactions taking place between aerodynamics, solar geometry and finite storage rather than a specific singular parameter. The distinction between collected and usable energy is the most indicative finding. As 26.1 MJ of excess is unused against 22.02 MJ consumed, increasing collection alone offers diminishing returns unless storage is increased or surplus energy is otherwise employed. Changes in altitude selection is consequently better treated as a storage problem than an aerodynamic or harvesting one.

Several assumptions limit the quantitative accuracy of the study. The aircraft maintains steady level cruise in still air at constant mass, with launch, climb and descent neglected. The aerodynamic model uses a NACA 4415 wing polar rather than a complete aircraft polar, omitting fuselage and tail contributions; the resulting maximum L/D of 34.4 is approximately 13% more optimistic than the value inferred from published AtlantikSolar flight data. (Oettershagen, et al., A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight, 2015) (Selig, Guglielmo, Broeren, & Giguere, 1995) Moreover, the solar model assumes clear-sky direct radiation, neglecting diffuse and reflected components, and neglects the equation of time (the ‘mean sun’ versus true sun position throughout elliptical orbit). Furthermore, the use of an idealised gimballed panel should be read as an upper bound, since actuator mass, angular limits and airframe shadowing are not represented.(Cooper, 1969)Finally, it is important to note that wind, cloud and other weather variations are not represented, meaning that the calculated 30% battery state-of-charge margin is likely to diminish in real-world applications.

6. Conclusion

Endurance solar flight is limited by the energy balance detailed throughout the paper, and this study’s purpose was to determine the feasibility of a such a concept of near-perpetual flight, and in what conditions it would be possible. The paper’s approach has been to utilise pre-established components where possible, in order to reduce possible error from assumptions and to provide realistic aerodynamic and photovoltaic parameters. It also means that a further exploration of this issue could lead to design models of a bespoke aircraft design, optimised for a solar UAV.

Maximum Lift/Drag ratio is the cruise condition that allows for the lowest energy requirements whilst still fulfilling the 30% SOC requirement, with 22.02 MJ over 123.4hrs at a 3000m altitude. This is measured as the optimum conditions for such a crossing. Increasing altitude reduces the flight time, and atmospheric attenuation too, yet battery margin also decreases due to an increase in power requirements.

The use of an idealised gimballed solar panel increases solar harvesting by 62%, yet most of this amount is not usable due to the battery being full at these daytime hours. We can therefore label storage capacity as a limiting constraint, yet an increase in battery capacity could come with additional complications such as mass changes etc. For applications that require persistent operation over inaccessible areas, such as maritime rescue, marine life monitoring, or wildlife monitoring, this concept could be deployed to improve real-world applications’ efficiency.

7. Bibliography

Abbott, I. H., & von Doenhoff, A. E. (1959). Theory of wing sections: Including a summary of airfoil data. Dover Publications.

Airfoil Tools. (n.d.). NACA 4415 (naca4415-il) Xfoil prediction polar at RE=200,000, Ncrit=9. Retrieved from http://airfoiltools.com/polar/details?polar=xf-naca4415-il-200000

Anderson, J. D. (1999). Aircraft performance and design. McGraw-Hill.

Cooper, P. I. (1969). The absorption of radiation in solar stills. Solar Energy, 12(3), 333-346. doi:10.1016/0038-092X(69)90047-4

Drela, M. (1989). XFOIL: An analysis and design system for low Reynolds number airfoils. In T. J. Mueller (Ed.), Low Reynolds number aerodynamics (pp. 1-12). Springer. doi:10.1007/978-3-642-84010-4_1

Duffie, J. A., & Beckman, W. A. (2013). Solar engineering of thermal processes (4th ed.). John Wiley and Sons.

Fidowski, K., Atkins, E., & Powell, K. (2019). Introduction to aerospace engineering: Aero 201 course notes, Fall 2019. Ann Arbor, MI: University of Michigan.

International Organization for Standardization. (1975). Standard atmosphere (ISO 2533:1975). Geneva: ISO.

Kasten, F., & Young, A. T. (1989). Revised optical air mass tables and approximation formula. Applied Optics, 28(22), 4735-4738. doi:10.1364/AO.28.004735

Klesh, A. T., & Kabamba, P. T. (2009). Solar-powered aircraft: Energy-optimal path planning and perpetual endurance. Journal of Guidance, Control, and Dynamics, 32(4), 1320-1329. doi:10.2514/1.40139

Meinel, A. B., & Meinel, M. P. (1976). Applied solar energy: An introduction. Addison-Wesley.

Oettershagen, P., Melzer, A., Mantel, T., Rudin, K., Lotz, R., Siebenmann, D., . . . Siegwart, R. (2015). A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight. 2015 IEEE International Conference on Robotics and Automation (ICRA), (pp. 3986-3993). doi:10.1109/ICRA.2015.7139756

Oettershagen, P., Melzer, A., Mantel, T., Rudin, K., Stastny, T., Wawrzacz, B., . . . Siegwart, R. (2017). Design of small hand-launched solar-powered UAVs: From concept study to a multi-day world endurance record flight. Journal of Field Robotics, 34(7), 1352-1377. doi:10.1002/rob.21717

Selig, M. S., Guglielmo, J. J., Broeren, A. P., & Giguere, P. (1995). Summary of low-speed airfoil data, Volume 1. SoarTech Publications.

8. Appendix

8.1 Model Parameters

All model parameters are sourced from the following sources:

(Airfoil Tools, n.d.)(Drela, 1989)(Fidowski, Atkins, & Powell, 2019)(Oettershagen, et al., A solar-powered hand-launchable UAV for low-altitude multi-day continuous flight, 2015)(International Organization for Standardization, 1975)(Selig, Guglielmo, Broeren, & Giguere, 1995)(Oettershagen, et al., Design of small hand-launched solar-powered UAVs: From concept study to a multi-day world endurance record flight, 2017)(Kasten & Young, 1989)

8.1.1 Airframe and Propulsion

8.1.2 Aerodynamic Coefficients, NACA 4415 at Re = 200,000

8.1.3 Solar & Electrical Power

8.1.4 Atmosphere & Mission

About the author

Rohaan Narula

Rohaan is a student at King’s College School, Wimbledon in the United Kingdom, with a strong passion for engineering, specifically its applications to environmental concerns. Interweaving aeronautical, mechanical and environmental engineering throughout his research serves as a method of balancing both ethical and efficiency concerns whilst not losing sight of the endless pursuit of optimisation in these fields. Outside of the academic world, he is a keen cricketer and guitarist.

The post Modelling Energy Balance for a Solar-Powered Transatlantic UAV Route appeared first on Exploratio Journal.

1. Introduction

Heating, Ventilation, and Air Conditioning (HVAC) systems are essential across residential, commercial, healthcare, industrial, laboratory, and data-center environments, where maintaining thermal comfort, indoor air quality, and controlled operating conditions is critical (Lee & Chen, 2022). They regulate temperature, humidity, ventilation, and air quality, improving occupant comfort and well-being. As urbanization and living standards continue to rise, the demand for heating and cooling has increased significantly. Consequently, HVAC systems have become one of the largest consumers of energy in modern buildings (Lu et al., 2025). Buildings account for a major share of global energy consumption and carbon emissions. For this reason, improving HVAC efficiency has become an important research area, especially in regions experiencing rapid growth in cooling demand (Adelekan et al., 2022).

HVAC systems have largely relied on fixed schedules, thermostats, rule-based controls, and proportional-integral-derivative (PID) controllers. Although these methods are reliable, they may not respond effectively to change in conditions like room occupancy, outdoor weather and decline in equipment performance (Gharbi et al., 2025). As a result, these systems often continue operating for longer than required, resulting in excess energy consumption.

Recent advances in artificial intelligence (AI), machine learning (ML), the internet of things (IoT), cloud computing, and big data analytics are transforming HVAC operation. Instead of only relying on fixed settings, AI-based systems continuously analyse sensor data, control cooling and heating demands, and automatically adjust operation in real time  (Lee & Tsai, 2020). Studies show that these systems can reduce energy consumption while maintaining occupant comfort (Kannan et al., 2020). AI applications have expanded from traditional ML models to advanced techniques such as deep learning (DL), reinforcement learning (RL), digital twins (DT), and cloud-based optimization, pushing HVAC management toward autonomous operation(Lu et al., 2025). This change has made AI-enabled HVAC systems an important area of research, especially for smart buildings, sustainable development, and achieving carbon neutrality. In addition, AI-based HVAC systems support many United Nations Sustainable Development Goals (SDGs), namely SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action) (Hauashdh et al., 2024). Figure 1 shows how AI-enabled HVAC systems contribute to these goals. 

Figure 1: Contribution of AI-enabled HVAC systems to the United Nations Sustainable Development Goals

This review explains the basic working of HVAC systems, the main factors affecting their energy use, and how AI is making them more intelligent, adaptive, and energy-efficient.

2. HVAC Principles and Energy Use

2.1 Fundamentals and Working Principles

The main purpose of an HVAC system is to maintain comfortable indoor conditions by controlling temperature, humidity, airflow, and air quality. It works based on principles of thermodynamics like heat transfer, refrigeration, and automatic control. A typical HVAC system consists of four main parts: heating, cooling, ventilation, and a control system that manages their operation.

The cooling process usually uses the vapor-compression refrigeration cycle, which includes a compressor, condenser, expansion valve, and evaporator. These components work together to remove heat from indoor air and release it outside. Heating can be provided by boilers, furnaces, electric heaters, or heat pumps. Ventilation supplies fresh outdoor air while removing stale air, pollutants, and excess moisture to maintain a healthy indoor environment. 

In commercial buildings, HVAC systems are more sophisticated and include equipment such as chillers, cooling towers, air handling units, variable air volume systems, pumps, duct-network, and a building management system (BMS) (Lu et al., 2025). These parts respond to shifts in thermal loads from occupants, equipment, lighting, and the outdoor environment. Since all these parts interact with each other in a complex way, operating the system efficiently is a challenging task (Nashruddin et al., 2025).

2.2 Fundamentals of HVAC Energy Consumption

HVAC systems are one of the largest energy-consuming systems in buildings, this energy is used for cooling, heating, ventilation, pumping, and other supporting controls (Lu et al., 2025). Among these, cooling consumes the highest amount of energy, especially in tropical and subtropical regions where air conditioners operate for most of the year (Chua et al., 2013). 

The energy consumption of an HVAC system depends on several factors, namely:

• Outdoor temperature and weather patterns
• Building orientation and insulation
• Occupancy and schedules
• Internal heat gains from equipment and lighting
• Ventilation requirements
• Equipment efficiency and maintenance condition

Many buildings still use fixed temperature settings and pre-set schedules for operation, which cannot adjust to change in conditions in real time. As a result, problems such as over cooling and ventilation, and inefficient operation are common. Legacy buildings have an increased effect due to outdated equipment, poor insulation, and uneven load distribution (Nashruddin et al., 2025).

Efficiency of HVAC systems is commonly evaluated using performance indicators like the energy efficiency ratio (EER), seasonal energy efficiency ratio, coefficient of performance, and cooling seasonal performance factor (CSPF) are used across the literature. AI-based controls improve the same metrics through predictive scheduling, adaptive control, and demand forecasting (Raza & Khosravi, 2015).

3. AI Application and Enabling Technologies in HVAC Systems

3.1 Functions of AI-Enabled HVAC Systems

AI-based approaches can provide predictive and adaptive capabilities that are limited in traditional non-AI-based systems. Unlike traditional controllers, which work using fixed rules and preset values, AI-based controllers continuously analyze system data and improve their performance over time. Accordingly, depending on the system design applications may include:

• Real-time optimization
• Predictive energy management
• Occupancy-aware control
• Thermal comfort optimization
• Fault detection and diagnosis (FDD)
• Predictive maintenance
• Demand response management
• Self-learning and adaptation

These features allow HVAC systems to respond to changes in building conditions, instead of following a fixed schedule, helping reduce energy consumption while maintaining occupant comfort (Lu et al., 2025). According to Lee & Chen (2022), AI-based energy-saving methods can be grouped into six main categories: model predictive control (coupled with AI) (MPC), thermal comfort control, model-free predictive control, control optimization, multi-agent control (MAC), and knowledge-based systems (KBS). These techniques have been reported to achieve energy-savings ranging from 8.8% to 25.5%, although the actual savings depend on the building type, operating conditions, and control strategy employed (Lee & Chen, 2022).

3.2 Hardware–Software Integration and Model Development

AI-based HVAC systems rely on both hardware and software to collect, process, and analyze data for intelligent control. These components work together to monitor building conditions and optimize HVAC operations.

Hardware Infrastructure

AI-based HVAC system hardware typically includes temperature, humidity,  airflow and air-quality sensors, occupancy sensors, smart thermostats, power meters, edge controllers, and IoT gateways, which continuously collect environmental and operational data and send it to the control system for analysis (Lu et al., 2025). 

Software Architecture

The software design may include the BMS itself, cloud computing platforms, data acquisition modules, ML frameworks, visualization dashboards, DT environments, and optimization engines. Cloud-based systems allow control algorithms to be updated remotely without replacing physical equipment, making upgrades more efficient and potentially reducing maintenance costs (Lee & Tsai, 2020). However, this  may also introduce subscription costs and requires reliable internet connectivity and cybersecurity measures for secure operation. 

Model Development Workflow

AI models are generally developed through data collection, preprocessing, feature extraction, model training, validation, testing, deployment, and continuous optimization. Performance of prediction models depend largely on data quality, feature normalisation, and the model chosen in the first place (Nashruddin et al., 2025).

3.3 Current AI Approaches and Technologies

Different AI techniques are used in HVAC systems depending on the intended application. Applications such as prediction, optimization, system control, fault detection, and energy management require different AI methods. Traditional ML technologies are still widely used because of their simplicity and interpretability. However, as HVAC systems have become more complex and IoT devices generate larger amounts of data, techniques like DL and RL have gained prominence. The table below summarizes the main AI techniques reported in the literature and their common applications in HVAC systems.

Table 1: Major AI approaches reported in the HVAC literature, by application. (Lu et al., 2025; Manmatharasan et al., 2025; Kannan et al., 2020; Lee & Chen, 2022; Nashruddin et al., 2025; Lee & Tsai, 2020)

3.4 Data Requirements and Method Selection

The performance of an AI-based HVAC system depends on both the algorithm, and the quality, quantity and variety of the data it receives. Data collected from IoT sensors, smart meters, weather services, occupancy sensors, and BMS provide the information needed for intelligent decision-making and system optimization. This data typically covers:

• Indoor temperature
• Indoor humidity
• Outdoor weather conditions
• Occupancy information
• Energy consumption records
• Equipment operating status
• Indoor air quality measurements
• User preference and comfort settings

Some AI models are trained using very large datasets. For example, one cloud-based study developed energy-saving recommendations using operational data collected from 37,748 AC units (Kannan et al., 2020).

The choice of an AI technique depends on the type and amount of available data. Traditional ML methods such as SVM, decision trees (DeT), and random forests (RF) are commonly used for energy prediction, load forecasting, fault detection, and system optimization (Nashruddin et al., 2025; Lu et al., 2025). DL methods, LSTM networks specifically, need large time-series datasets to learn changes in weather, occupancy, and energy demand over time. RL learns a control policy through interactions with an environment. This process may require substantial training data or simulation and can create safety concerns when learning directly in occupied buildings (Lu et al., 2025). MPC combines prediction and optimization to plan future control actions. In current HVAC systems, MPC is often coupled with AI techniques like ML and DL to improve prediction accuracy and system adaptability, this makes it one of the most effective control methods (Lee & Tsai, 2020). Hybrid approaches like combining ML with fuzzy logic, genetic algorithms (GA), or Particle Swarm Optimization can further improve system performance (Lu et al., 2025). 

Overall, the most suitable AI method depends on data availability, computing resources, and the application requirements. Among the available methods, AI-based MPC is currently among the most practical solutions (Lee & Chen, 2022). Hybrid frameworks hold promising results as they improve prediction accuracy and control performance, although their effectiveness depends on the application and available computational resources. While DL, RL, transfer learning (TL), federated learning (FL), edge AI, and occupant-centric control hold the potential, hence drive future research (Lu et al., 2025).

4. Key Findings and Inference

This section compares several studies to understand how AI techniques employed in HVAC systems have developed. It examines the most widely used control strategies, applications and reported results. Comparing these findings of different review papers also helps identify the AI techniques and research trends that appear consistently across studies, providing a better understanding of how the field has evolved. 

4.1 Comprehensive Comparison of AI-Based HVAC Studies

Table 2 presents a comparative analysis of the selected review papers, shedding light on their research focus, AI techniques, results, and reported performance improvements. This comparison provides an overview of current advancements in AI-driven HVAC systems and identifies the strengths and contributions of each study. 

Table 2: Comparative summary of key review studies on AI applications in HVAC systems

4.2 AI Techniques: Energy-Saving Potential vs Legacy Building Implementation Difficulty 

Figure 2 provides a comparative overview of the primary AI techniques discussed in the selected studies by relating their reported maximum energy-saving potential to the inferred estimated difficulty of implementation in legacy buildings. The energy-saving values are based on either experimentally reported results or representative ranges summarized in the respective review papers, while the implementation difficulty is qualitatively assessed from factors such as retrofit requirements, sensor infrastructure, computational complexity, and integration with existing HVAC systems. The figure also highlights that simpler AI methods are easier to implement in existing buildings but usually provide moderate energy savings. In contrast, advanced techniques such as predictive control, DL, and RL can achieve much higher energy savings, although they require more complex implementation and greater computational resources. 

Figure 2: Plot of energy-savings and implementation difficulty of AI techniques used in the reviewed studies.

5. Challenges, Limitations, and Future Directions

The selected literature indicates that AI-based approaches have potential to improve HVAC energy management and operational performance. The reviewed studies consistently show that AI-based control performs better than conventional rule-based and PID controllers in terms of energy efficiency, thermal comfort, fault detection, and system reliability. However, several challenges still limit its large-scale implementation. 

The major challenge is data availability and quality. AI models depend on accurate data collected from sensors, BMS, and IoT devices. Many legacy buildings lack this infrastructure, making it difficult to collect reliable data for training AI models (Nashruddin et al., 2025). Retrofitting such buildings with modern sensors, communication networks, and automation systems is often expensive and technically challenging. In addition, differences between building automation platforms create interoperability issues, while concerns over data privacy and cybersecurity further slow down AI adoption (Lu et al., 2025). 

Generalization is another recurring gap. Most models are trained on one building or one climate and often perform poorly when applied to different buildings or environmental conditions (Lu et al., 2025). For this reason, TL and domain adaptation have become important research areas, as they allow knowledge gained from one building to be adapted to another with less retraining.

Improving energy efficiency should not compromise occupant comfort. Several studies considered comfort using indoor temperature stability, humidity control, Predicted Mean Vote, and Predicted Percentage Dissatisfied(Homod et al., 2012). AI techniques such as AI-based MPC, RL, fuzzy logic, and occupant-aware optimization consistently maintained or improved comfort while reducing energy use. Quantitative data supports this claim: Lee & Tsai (2020) reported a 9.12% gain in EER and 7.37% in CSPF without sacrificing indoor stability; Kannan et al. (2020) reached a 57.38% median energy saving by incorporating occupant preferences; and Lee & Chen (2022) found AI-based strategies typically cutting energy use by 8.8% to 25.5% while comfort held steady or improved.

Future research should focus on improving the practical adoption of AI in HVAC and air-conditioning systems through real-world validation, especially in legacy buildings. Greater emphasis is needed on integrating AI with IoT sensors, DT, Building Information Modeling , and 5G-enabled smart infrastructure for real-time monitoring and adaptive control. Advanced techniques such as TL can adapt knowledge from one building to another with less retraining, while FL can train models across multiple buildings without directly sharing sensitive data. Explainable AI can make AI decisions easier to understand and increase user trust, while novel neural network architectures can improve prediction and control for complex HVAC conditions. Surrogate models can provide faster approximations of complex building or HVAC simulations, supporting quicker optimization. However, these approaches still require further research to address limited historical data, differences between buildings, privacy concerns, model complexity, and real-world validation. Researchers should also develop multi-objective optimization frameworks that simultaneously consider energy efficiency, thermal comfort, indoor air quality, and carbon emissions. Occupant-aware control using richer behavioural data, computer vision, and personalized comfort models can further enhance system performance. Finally, more studies should evaluate AI solutions across diverse building types and climates, while addressing security, uncertainty, fault diagnosis, commercialization, and seamless integration with existing HVAC infrastructure.

6. Conclusion

This review shows that AI-based methods  have the potential to improve the adaptability and energy performance of HVAC systems. Unlike most of the traditional controllers that rely on fixed schedules and predefined rules, AI-based systems learn from real-time data and continuously adjust their operation to meet changing building conditions. The reviewed studies demonstrate the successful use of AI-based methods for energy management, thermal comfort optimization, occupancy prediction, fault detection, and predictive maintenance (Lu et al., 2025).

Several studies reported noticeable improvements in HVAC performance, with higher EER, improved energy, and efficiency metrics, while maintaining or improving occupant comfort (Lee & Tsai, 2020). The integration of cloud computing, IoT, and DT technologies has further increased the practicality and effectiveness of AI-driven HVAC systems(Lu et al., 2025). 

Despite these benefits, several challenges remain. Data quality, limited sensing infrastructure, interoperability between different platforms, cybersecurity concerns, and poor model generalization continue to limit wider adoption. In addition, implementing AI in legacy buildings is difficult because many lack the sensors, communication networks, and BMS required for intelligent control, making retrofitting expensive and technically challenging (Akbulut et al., 2025).

Future research should focus on developing more transparent, secure, and adaptable AI systems through the integration of DT, IoT, Building Information Modeling, renewable energy, and smart grids. Technologies such as Explainable AI, FL, TL, and multi-agent RL can further improve system reliability and scalability. As these technologies continue to develop, AI-driven HVAC systems are expected to play an important role in reducing building energy consumption, lowering carbon emissions, and supporting sustainable buildings(Lu et al., 2025). 

7. References

Adelekan, D. S., Ohunakin, O. S., & Paul, B. S. (2022). Artificial intelligence models for refrigeration, air conditioning and heat pump systems. Energy Reports, 8, 8451–8466. https://doi.org/10.1016/j.egyr.2022.06.062 

Akbulut, L., Taşdelen, K., Atılgan, A., Malinowski, M., Coşgun, A., Şenol, R., Akbulut, A., & Petryk, A. (2025). A Systematic Review of Building Energy Management Systems (BEMSs): Sensors, IoT, and AI Integration. Energies, 18(24), 6522. https://doi.org/10.3390/en18246522 

Chua, K. J., Chou, S. K., Yang, W. M., & Yan, J. (2013). Achieving better energy-efficient air conditioning – A review of technologies and strategies. Applied Energy, 104, 87–104. https://doi.org/10.1016/j.apenergy.2012.10.037 

Gharbi, A., Ayari, M., Albalawi, N., Touati, Y. E., & Klai, Z. (2025). Intelligent HVAC Control: Comparative Simulation of Reinforcement Learning and PID Strategies for Energy Efficiency and Comfort Optimization. Mathematics, 13(14), 2311. https://doi.org/10.3390/math13142311 

Hauashdh, A., Nagapan, S., Jailani, J., & Gamil, Y. (2024). An integrated framework for sustainable and efficient building maintenance operations aligning with climate change, SDGs, and emerging technology. Results in Engineering, 21, 101822. https://doi.org/10.1016/j.rineng.2024.101822 

Homod, R. Z., Mohamed Sahari, K. S., Almurib, H. A. F., & Nagi, F. H. (2012). RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD. Building and Environment, 49, 141–153. https://doi.org/10.1016/j.buildenv.2011.09.012 

Kannan, R., Roy, M. S., & Pathuri, S. H. (2020). Artificial Intelligence Based Air Conditioner Energy Saving Using a Novel Preference Map. IEEE Access, 8, 206622–206637. https://doi.org/10.1109/ACCESS.2020.3037970 

Lee, D., & Chen, L. (2022). Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects. Sustainability, 14(12), 7514. https://doi.org/10.3390/su14127514 

Lee, D., & Tsai, F.-P. (2020). Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner. Energies, 13(8), 2001. https://doi.org/10.3390/en13082001 

Lu, S., Zhou, S., Ding, Y., Kim, M. K., Yang, B., Tian, Z., & Liu, J. (2025). Exploring the comprehensive integration of artificial intelligence in optimizing HVAC system operations: A review and future outlook. Results in Engineering, 25, 103765. https://doi.org/10.1016/j.rineng.2024.103765 

Manmatharasan, P., Bitsuamlak, G., & Grolinger, K. (2025). AI-driven design optimization for sustainable buildings: A systematic review. Energy and Buildings, 332, 115440. https://doi.org/10.1016/j.enbuild.2025.115440 

Nashruddin, S. N. A. M., Salleh, F. H. M., Sulaiman, R., Zaiddy, M. I. B. M., Ramasamy, A., & Yahya, A. A. (2025). AI-driven optimization of air-conditioning systems in legacy Buildings: Evaluating machine learning models for enhanced energy efficiency. Journal of Building Engineering, 108, 112839. https://doi.org/10.1016/j.jobe.2025.112839 

Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352–1372. https://doi.org/10.1016/j.rser.2015.04.065 

About the author

Kethan Sai N

Kethan is a Grade 12 student majoring in Physics, Mathematics, and Chemistry, with Physics as his favorite subject. His curiosity for how the world works has led him to explore robotics, machine learning, and artificial intelligence, alongside a growing passion for sustainability. His research focuses on how AI and ML can be leveraged to build smarter, more sustainable systems, an interest reflected in his current paper on AI-driven approaches to energy-efficient HVAC systems.

Outside academics, he enjoys coding and losing himself in documentaries and videos exploring scientific mysteries and unknowns, a habit that fuels his constant search for new ideas and questions worth solving. He is equally active on the field and court, playing football, badminton, and basketball, believing that sharp thinking is best paired with an active body.

Combining a scientist’s curiosity with an engineer’s drive to build, he is passionate about using emerging technologies not just to innovate, but to create a more sustainable future for everyone. Looking ahead, he aims to build a career in robotics, AI, and machine learning, driven by a desire to turn these technologies into real, sustainable solutions for the world’s biggest challenges.

The post How AI is Transforming HVAC Systems: A Review of Smart Energy-Saving Techniques appeared first on Exploratio Journal.

Abstract

Separating market-wide movements from stock-specific returns is central to factor modeling, while it remains unclear whether more flexible factor models produce residuals that are more useful as trading signals in the Korean stock market. This study compares three models with increasing levels of flexibility which are principal component analysis (PCA), instrumented component analysis (IPCA), and conditional autoencoder (CAE). Using daily data of 156 KOSPI stocks, each model is trained on 70% of the valid sample and applied to the remaining 30% to produce out-of-sample residuals. The residuals are then tested through statistical arbitrage and idiosyncratic momentum strategies, ultimately evaluating which factor models’ residuals produce better or worse trading strategy performances. CAE produced the most tradable residuals within both statistical arbitrage and idiosyncratic momentum strategy. IPCA’s residuals performed better than PCA’s in the statistical arbitrage strategy, but the PCA’s residuals outperformed IPCA’s in idiosyncratic momentum strategy. Another result was that across all three factor models, idiosyncratic momentum strategy substantially outperformed the statistical arbitrage strategy. The finding from these results was that the most flexible factor model that is nonlinear and has characteristic dependent loadings produce the best residuals for trading signals in South Korea. However, it doesn’t imply that the more flexible factor models are, the more tradable the residuals are, since the result of the PCA and IPCA are different. Also, since the OU process didn’t work, it could be suggested as the reason why statistical arbitrage strategy underperformed compared to momentum strategies, suggesting how KOSPI residuals contain stronger information about persistent trends of stocks than short term mean reversion, as can be concluded from the comparison of idiosyncratic momentum strategy and statistical arbitrage strategy. However, there are several limitations to be considered in this study such as the size of the stock data, quality of the data, disregardments of small implementation details such as transaction costs, etc. and survivorship bias.

I. Introduction

Stock returns are affected by many variables, but they could be said as being mainly driven by two components: market-wide factor movements which affect all the stocks in the market, and stock-specific components which only affects a specific stock. The decomposition of the systematic risk, which is the market-wide force, and the idiosyncratic risk, the stock-specific effect, is what a factor model does. If a factor model removes the systematic risk accurately, the remaining idiosyncratic residual could be used to optimize asset pricing and reveal meaningful information such as temporary mispricing or stock specific trading signals.

Because factor decomposition is an important part in quantitative investing and portfolio optimization, factor models have evolved continuously throughout financial modeling history. Principal component analysis (PCA), which has been used as early from Chamberlain & Rothschild (1983) and Connor & Korajczyk (1986) and has continuously been popular among many researchers, is a linear factor model in which returns are represented as weighted sums of latent factors. Evolved from this is the Instrumented principal component analysis (IPCA) introduced by Kelly et al. (2019) [2], which is still linear but has its betas vary as linear functions of stock characteristics. Inspired by the IPCA model, Conditional Autoencoder was proposed by Gu et al (. using a nonlinear neural-network structure with changing betas by characteristics. [3,4] 

These three factor models have been tested and shown to be successful in U.S. stock data [5], but their effectiveness in the recent Korean stock market is less clear. Although there are prior studies that have applied factor models to Korean markets, very few are applied on recent data, including 2025 and beyond. Furthermore, there are only a few studies comparing the effectiveness of different factor models in the Korean stock market.

Therefore the primary objective of this study is to determine which factor model, leveled by its degree of flexibility, is most effective in the recent Korean stock market, measured by the trading performance from its residuals. The factor models of varying flexibility are PCA, the linear with fixed betas, IPCA, linear with linear-varying betas, and CAE, the nonlinear with nonlinear-varying betas. The evaluation of the factor model this study uses is based by  comparing them through the residuals they produce, which one produces the most effectful residuals that are most effectful for two financial methods, statistical arbitrage and idiosyncratic momentum strategy. The factor models are evaluated by comparing the residuals they generate, with the comparison focusing on which model produces the most tradable residuals that yield the strongest performance in two residual-based strategy.

II. Methodology

Data and Factor Model

In this study, I use daily data of KOSPI stocks from July 4, 2016, to July 1, 2026, from Yahoo. The sample initially consisted of the top 200 KOSPI stocks by market capitalization. Preferred shares were excluded, and approximately ten years of daily closing prices were collected. From the initial 200 stocks, only stocks with valid closing prices on at least 98% of the trading days within the sample period were retained, changing the sample size to 156 stocks. This filtering step was necessary because the factor models, residual estimates, OU parameters, and out-of-sample parameters all require aligned return observations. After collecting the closing prices, they were converted into daily returns. Excess returns were calculated by subtracting a 2.5% annual risk-free rate, converted to a daily rate by dividing by 252.

The first factor model used in this study is principal component analysis (PCA). It is a linear factor model with fixed factor loadings, beta. I first computed the covariance matrix of daily excess returns, performed eigendecomposition, and sorted the eigenvectors by eigenvalues to get the top k=5 principal components. Each stock’s fitted common return is reconstrcted by projecting excess returns onto the retained principal components.

The second model is instrumented principal component analysis (IPCA). IPCA is a linear factor model with its betas changing over time as linear functions of stock characteristics. While Kelly at al. (2019) analyzes 36 firm characteristics, due to small data set and limitations, this study uses 4 firm characteristics that are shown in the table below and an intercept. IPCA extends on the PCA and estimates the gamma matrix and factors by alternating least squares.

This table lists the firm characteristics and their definitions in this study  

The thrid model is the conditional autoencoder (CAE). CAE is a nonlinear model with a neural network mapping stock characteristics into betas, therefore a nonlinear specification of beta [1]. The model encodes stock returns, extracts the underlying factors, and reconstructs each stock’s return using the learned factor loadings and latent factors. The CAE uses the same characteristics inputs as IPCA.

For both IPCA and CAE, the hyperparameters are tuned using optuna, an optimization software that optimizes hyperparameters through using efficient sampling and pruning algorithms to search parameter spaces dynamically. [7] Hyperparameters of the IPCA are k and the ridge penalty gamma, hyperparameters of the CAE are hidden layer size, k, learning rate, and number of training epochs.

After each model is fitted, the residuals are obtained through the excess returns minus the model’s fitted common return component. The residuals are used as the main component in comparing factor models throughout the study. 

OU Parameters and Strategy Design

For each model and stock, I take the residuals and model it as the Ornstein-Uhlenbeck process (OU process), a model explaining a value that idiosyncratically drifts but means revert to an overall industry level. [6]

dXi(t) = κi (mi− Xi(t)) dt + σidWi(t), κi > 0.                 (1)

I fit an AR(1) model to each stock’s residual series, which is to regress each day’s residual on the previous day’s residual. The AR(1) coefficients are then converted into three OU parameters: κ as the speed of the mean-reversion [6], σ as the residual volatility, and m as the long-run residual mean. The estimated values of  ,  , and   for the three factor models are presented in the table below. The AR(1) coefficient b measures the day-to-day persistence of the idiosyncratic component. The R² measures how much of each stock’s residual variation is explained by its own value.

This table shows the average  (annual), average b (AR coefficient), and average R² of the residuals from PCA, IPCA, and CAE from the OU process.

These estimated parameters reveal significant limitations in modeling the daily KOSPI residuals as a continuous OU process. By dividing the average annual    by 252 trading days, a daily reversion speed of approximately 3.6 across all three models can be observed. The mean reversion time, calculated as the reciprocal of the daily   , is therefore about 0.27 trading days. Such rapid reversion makes it practically impossible to exploit using daily closing prices and strongly suggests that the models are fitting to high frequency noise. Furthermore, the AR(1) coefficients are negative for all three models, indicating that the residuals are oscillating rather than continuously mean reverting, which fundamentally violates the assumptions of an OU process.   

Despite these violations, I test statistical arbitrage, a strategy highly reliant on OU dynamics, as our first trading strategy. As the primary objective of this study is to compare factor models through the performance of residual based strategies, including statistical arbitrage serves as an essential baseline. Retaining this strategy also allows the demonstration of its unexpected underperformance under broken model assumptions and contrast it against other trading strategies.

 Statistical arbitrage is a strategy to exploit from short-term deviations of prices from a long-term equilibrium. [8] In this study the deviation is not of the raw stock returns’ deviation but one of the residuals. From the estimated OU parameters, a trading signal can be generated for statistical arbitrage strategy. This signal measures how far a stock’s residual is from its estimated equilibrium. If the signal is strongly positive, the strategy opens a short position expecting residual to decline; if the signal is strongly negative, the strategy opens a long position, expecting residual to rise. Positions are closed when the signal is short. [8] The following below is the threshold I use for this study.

Open short position (s_i,t> 0.5)  close short position (s < 0.25)

Open long position (s_i,t<-0.5)  close long position (s>-0.25) [8]

In this study, stocks with AR(1) R² < 0.02 are excluded and signals are shifted by one day before applying returns to avoid look ahead bias. Following this strategy it creates three statistical arbitrage portfolios: Statistical Arbitrage-PCA(StatArb-PCA), StatArb-IPCA, and StatArb-CAE.

The second trading strategy is the idiosyncratic momentum, also called as residual momentum. Conventional momentum strategies such as raw momentum strategy rank stocks by past performance and buy the stocks with the strongest past returns. I use the idiosyncratic momentum strategy, which ranks stocks on residuals instead of total returns. [9] It allows signals to be based on stock-specific performances after removing common factor movements, which improves the result. For each factor model, the strategy calculates a 12-month minus 1-month residual momentum score. It skips the most recent month to avoid short-term reversal effect. The strategy ranks stocks by this score each day, buys the top five stocks, and then weights them equally. This creates three idiosyncratic momentum portfolios: Idiosyncratic Momentum-PCA (IdioMom-PCA), IdioMom-IPCA, IdioMom-CAE. Also, a raw momentum benchmark is retained and tested too as a trading strategy for comparison.

Ultimately, the factor models and its residuals are evaluated by comparing the trading performance of strategies based on testing them through the out-of-sample testing method. The valid sample, from August 7, 2019 to July 1, 2026, is divided into an approximately 70% training period- August 7, 2019 to May 28, 2024; 1180 days- and a 30% test period-May 29,2024 to July 1, 2026; 507 days. Although the price data spans from July 4, 2016, the valid sample begins on August 7, 2019 because the 756day rolling market beta requires approximately three years of warmup before producing valid observations, leaving 1678 usable trading days. First, PCA, IPCA, and CAE are fitted to the in-sample data, producing in-sample residuals. OU parameters and idiosyncratic momentum lookback history are estimated from these in-sample residuals. The fitted models are applied to the test period to produce out-of-sample residuals. Finally, the statistical arbitrage and idiosyncratic momentum strategies are evaluated on the out-of-sample residuals, with their trading performance compared across models via Sharpe ratio and more.

III. Results

In total, 7 strategies are compared through the Out-of-sample testing, which are StatArb-PCA, StatArb-IPCA, StatArb-CAE, Idiomom-PCA, Idiomom-IPCA, Idiomom-CAE, and RawMom. Their performance is evaluated by annual returns, annual volatility, sharp ratio, max drawdown, and hit rate. KOSPI is also added as a benchmark. The results are summarized in Table 1, and the sharp ratio is visualized as a bar graph in Figure 1, as it serves as the primary measure of strategy performance in this study. 

This is a table of annual returns, annual volatility, sharpe ratio, maximum draw downs, and hit rate of the 7 strategies: statistical arbitrage PCA, statistical arbitrage IPCA, statistical arbitrage CAE, idiosyncratic momentum PCA, idiosyncratic momentum IPCA, idiosyncratic momentum CAE, and KOSPI’s Out-of-Sample performance. The test window is from May 29, 2024 to July 1, 2026.

From the summary table and figure 1, I can first compare the factor models within each strategy by their performance, as well as compare the performance across strategy type. Within the statistical arbitrage strategy, and from looking at the sharpe ratio of them, CAE residuals perform much better than IPCA and PCA, and the IPCA residuals performs better than the PCA residuals. This indicates that the higher flexibility a factor model has, the improved tradability residuals have when applied to statistical arbitrage strategy. 

Within the idiosyncratic momentum strategy, CAE residuals still outperform the other two factor models, but a difference from the statistical arbitrage is that PCA performs better than IPCA. Although it varies a little, this still shows how the highest flexibility factor model out of the three, CAE’s residuals perform the best within the strategy with the highest sharpe ratio. 

However, the biggest and clearest overall result is the difference between the two strategy families. The momentum strategies produced much higher annual returns, higher sharpe ratios, and overall better results. From all trading strategies, raw momentum was the most effective strategy in our sample too. 

IV. Discussion

The results indicate that, among the three factor models, the CAE produced the most effective residuals for both the statistical arbitrage and idiosyncratic momentum strategies. This supports the idea that more flexible factor models can generate more tradable residuals. PCA is transparent and stable, but its factor loadings are fixed over time, making it relatively restrictive. IPCA improves on this by allowing factor loadings to vary linearly with stock characteristics. While both PCA and IPCA remain linear models, CAE further allows nonlinear relationships between stock characteristics and factor loadings. The stronger performance of CAE residuals across both trading strategies suggests that this additional flexibility enables the model to better isolate stock-specific return components.

However, the results also show that the choice of trading strategy is just as important as the flexibility of the factor model. Although CAE produced the best-performing residuals among the factor models, the raw momentum strategy outperformed all residual-based strategies, suggesting that simple momentum signals were more effective than residual-based alternatives in the KOSPI market during the sample period. Furthermore, after raw momentum, all three idiosyncratic momentum strategies outperformed the statistical arbitrage strategies.

This drastic difference in performance between momentum strateigies and statistical arbitrage strategies could be explained by the estimated OU parameters. The parameter estimates from the methodology section has revealed that the daily residuals do not follow an OU process. Therefore, the statistical arbitrage strategy, relying heavily on mean reversion and OU derived signals, underperformed relative to momentum strategies. Furthermore, the breakdown of the OU process and the superior performance of momentum strategies suggest that  daily KOSPI residuals in this study’s sample are more effective for capturing persistent stock-specific trends than for exploiting short-term mean reversion. Therefore, while increasing factor model flexibility could improve the quality of the residuals, the profitability of residual-based trading also depends on whether the trading strategy is consistent with the underlying behavior of those residuals.

However, there are several limitations to be considered importantly in this study. First, the stock universe of the data is small and the sample period is relatively short compared with larger asset-pricing studies. This makes the results sensitive to the particular market period and to the stocks in the analysis, which could not make the same result when performed to a larger data set. Second, the data is from Yahoo Finance, which may contain missing values and data quality problems. Third, the back tests in this study do not fully include transaction costs, bid-ask spreads, liquidity constraints, and more. Especially in trading performance studies where small implementation details can strongly affect returns and the results, this must go through consideration. Lastly, Survivorship bias is another important limitation. The sample in this study is built from current large KOSPI stocks that have enough data during the study window, meaning that firms that have disappeared and merged are not fully represented. This can make performance appear better than it would be in reality since only the surviving firms remain in the dataset. In order to eliminate survivorship bias, a stronger design of data to include firms that were in the KOSPI universe at each point in time even if they later disappeared could be used.

Acknowledgment

I would like to express my gratitude to Prof. Panos Parpas at the Department of Computing at Imperial College London for his mentorship and guidance on financial modeling and mathematics studies.

References

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About the author

Joheun Lee

Joheun Lee is currently a sophomore in The Webb Schools in Claremont, California. She has a deep interest in math along with finance, AI, and data science, and enjoys connecting theories with real-life problems. Beyond academics, she is passionate in playing volleyball in her school.

The post Comparing Linear and Nonlinear Factor Model Residuals through Statistical Arbitrage and Idiosyncratic Momentum in Korean Stock Market appeared first on Exploratio Journal.

1. Introduction

Nowadays, artificial intelligence has become increasingly important in many areas, both academically and in real life. Many tasks that were once completed by humans can now be done by AI. In finance, AI is very important because it can analyze large amounts of data in a short period of time and help investors make decisions. See Blackrock (2023).

The stock market is one of the most important parts of the economy because it connects directly to the companies that need money with investors who want to raise their money. This paper will mainly focus on the American stock market. When people buy stocks, they buy small ownership shares in companies. If a company performs well, the stock price will rise, and investors can earn money through capital gains or dividends. On the other hand, the stock market also contains risks, prices can change quickly due to company news, interest rates, investor emotions, and global events. See Investors Business Daily(2024). Because of this, many investors try to find ways to ‘beat the market,’ meaning they want to earn higher returns than the majority of people.

At present, more investors and companies are now using artificial intelligence to analyze data and make investment decisions. AI can deal with large amounts of information much faster than humans, including financial statements, stock prices, and news. This could help investors find patterns, predict price movements, and make better trading decisions. See Blackrock (2023). However, the main concerns are whether AI can provide reliable information to investors and whether AI can provide efficient output in a highly competitive stock market.

AI can improve investment research and decision-making because it allows investors to analyze large amounts of market information more quickly and efficiently. AI can help identify patterns, predict possible price movements, and support better trading decisions. However, since financial markets are competitive, any kind of strategy can quickly become less effective once many investors realize it and start using it. Therefore, this paper will examine how AI can help investors understand the market and improve their decisions, while others realized using AI as a tool to invest. Also considering consistently beating the market, especially for investors without strong data, technology, and risk management. See Blackrock (2023).

This paper will examine whether artificial intelligence can help investors beat the stock market. First, it will explain the basic nature of stocks, how the stock market operates, and the factors that influence stock prices. See Investor’s Business Daily (2024). Second, it will discuss normal and abnormal market returns and explain how benchmarks such as the S&P 500 can be used to determine whether an investor has outperformed the overall market. See Maverick (2015). Third, it will examine the meaning of ‘beating the market’ through Efficient Market Theory and explain why consistently earning abnormal returns is difficult. See Investopedia (n.d.-d). The paper will then discuss several possible ways investors may gain an advantage, including fundamental analysis, investor behavior, technology, and artificial intelligence. It will examine how AI is used in financial markets through algorithmic and high-frequency trading and use DeepSeek as an illustration of AI’s ability to perform financial analysis. See BlackRock (2023), See U.S. Securities and Exchange Commission (2020), See Lu et al. (2025). Finally, the paper will consider the limitations of AI, including its dependence on data, changing market conditions, competition, and unequal access to technology, as well as the policy implications created by the increasing use of AI in financial markets. These sections will provide the basis for evaluating whether AI can create an advantage that allows investors to beat the market and whether that advantage can continue over the long term.

The main question of this paper is whether artificial intelligence can help investors beat the stock market. To answer this question, several related questions will also be considered. What does it actually mean to ‘beat the market,’ and how can investors determine whether their returns are truly above normal market returns? If stock prices already reflect available information under Efficient Market Theory, where could AI find an advantage? The paper will also examine whether AI’s ability to process information faster, identify patterns, and automate trading can create abnormal returns. Finally, it will consider whether these advantages can remain effective over time after accounting for risk, competition, investment costs, and the increasing use of similar AI technology by other investors.

2. Nature of Stock Market

Before understanding whether artificial intelligence can help investors ‘beat the market,’ it is important to understand the fundamental nature of the stock market and what stocks actually are. A stock represents a small ownership share in a company. When investors buy stocks, they become partial owners of that company and are entitled to a portion of its value. Companies offer stocks mainly to raise money for growth, research, and expansion. Investors hope that the company will increase in value over time, which raises the stock price. Investors can also earn money through dividends, which are payments made by companies to shareholders from their profits. See Investor’s Business Daily (2024).

The stock market is the system where stocks are bought and sold. It acts as a market that connects investors who want to buy stocks and those who want to sell them. Prices in the stock market are determined by supply and demand. If more people want to buy a stock than sell it, the price usually goes up. If more people want to sell, the price usually goes down. This change in price is what makes the stock market both profitable and risky. See Investor’s Business Daily (2024).

In the United States, there are two major stock exchanges: the New York Stock Exchange (NYSE) and the NASDAQ. The NYSE is one of the oldest and largest stock exchanges in the world and is known for listing many large, established companies. The NASDAQ is a fully electronic exchange and is more heavily focused on technology companies such as Apple, Microsoft, and Amazon. The NYSE usually has more stable companies, while the NASDAQ has more fast-growing technology companies that often change prices more quickly. Both exchanges function in similar ways, but they have different structures and the types of companies. These exchanges allow stocks to be traded efficiently and in real time, which is essential for modern financial markets. See Investopedia (n.d-b).

Another important concept in understanding the stock market is the idea of stock indices. The stock index is a measurement that tracks the performance of a group of stocks. Instead of looking at one company, an index shows the overall direction of a market or sector. One of the most commonly used indices is the S&P 500, which tracks 500 of the largest companies in the United States. Because it represents a wide range of industries, it is often used as a benchmark to measure the performance of investors and funds. Another major index is the Dow Jones Industrial Average (Dow Jones), which tracks 30 large and well-known U.S. companies and is often used to represent the overall stock market. For example, when people say they are trying to ‘beat the market,’ they usually mean they are trying to outperform the S&P 500. See Maverick (2015), See Investopedia (n.d.-a).

Stock prices are influenced by many different factors. Company performance is one of the most important factors, including revenue, profit, and future growth potential. Economic conditions such as interest rates, inflation, and unemployment also affect stock prices. Fear and excitement can cause prices to rise or fall even when there is no major change in a company’s fundamentals. Global events such as wars, pandemics, or political changes can also impact the entire market. See Investor’s Business Daily (2024).

Because of these factors, the stock market is often difficult to predict. Even though data and analysis can help investors make better decisions, there is always risk. This uncertainty is one of the main reasons why many investors and financial professionals attempt to use advanced tools, including artificial intelligence, to improve decision accuracy and efficiency.

Overall, the stock market is a complex system where companies try to raise the money and investors attempt to earn money. It is influenced by economic data, company performance, global events, and human behavior. Understanding these basic structures before analyzing whether AI can provide an advantage is essential.

3. Normal vs. ‘Abnormal’ Market Returns

It is also important to understand what normal market returns are and how investment performance is measured before determining whether investors can ‘beat the market.’ In this section, the discussion focuses on the American stock market, especially U.S. market indices such as the S&P 500 and the Dow Jones Industrial Average. Since individual investments can have very different performances, investors need a standard to compare their results. By comparing investment returns with historical market performance, investors can determine whether their returns are above the overall market. See The Investopedia Team (2025).

3.1 Normal Market Returns

Normal market returns represent the typical returns investors can expect from investing in the overall stock market over a long period of time. Historically, the stock market has produced average annual returns of approximately 9–10% over periods of 10 years. See Maverick (2015). But this does not mean investors can earn exactly 9–10% every year. Because this average is calculated from many years of market growth and decline. See O’Shea (2026).

Annual market returns can change significantly depending on economic conditions, company performance and global events. During economic growth, companies may gain higher profits, causing stock prices to increase. However, during economic downturns or financial crises, stock prices may decline.

Because yearly returns can be unpredictable, investors usually focus on long-term performance instead of short-term results. A strong investment strategy is not measured by earning a high revenue in one year but consistently produce high revenue over many years. For example, an investor who earns around 10% annually over several decades may be considered successful because they have achieved returns similar to the overall market. See O’Shea (2026).

Understanding normal market returns is important because it provides a scale for evaluating investment performance. If an investor earns returns close to the market average over a long period, they are performing similarly to the overall market. Many investors choose index funds that track major market indices because achieving market-level returns is often more realistic than consistently earning higher returns. See Downey (2025).

3.2 Abnormal Market Returns

Abnormal returns refer to returns that are significantly higher or lower than the expected return. Abnormal returns usually refer to earning returns above a market standard. For example, if the overall market produces an average annual return of 10%, an investor who consistently earns 15% has abnormal returns. See The Investopedia Team (2026).

However, consistently achieving abnormal returns is difficult because financial markets are highly competitive. When new information becomes available, many investors quickly analyze and respond to it. As more investors discover and use similar strategies, the advantage of that strategy often becomes smaller. This makes it challenging for any investor to maintain above-average returns over a long period of time. See Downey (2025).

A temporary high return does not necessarily prove that the investor can consistently beat the market. Short-term success may occur by market conditions, taking higher risks, or totally by accident. To demonstrate the ability to consistently beat the market, the investor must achieve higher returns consistently over many years while considering all the factors such as risk, costs, and changes in market. See The Investopedia Team (2026), See Downey (2025).

This difference between normal and abnormal returns is essentially while analyzing whether artificial intelligence can help investors beat the market. AI may allow investors to deal with enormous amounts of information faster, identify the patterns, and make decisions more efficiently. However, the crucial point is whether AI can create a long-term advantage that produces consistent abnormal returns in nowadays society. See BlackRock(2023), See Downey (2025).

3.3 Figure 1.Dow Jones diagram Over the Past 50 Years

See Macrotrends (n.d.).

This graph shows the long-term performance of the Dow Jones Industrial Average over the past 50 years. Even though the market experienced several periods of decline, such as the dot-com bubble, the 2008 financial crisis, and the COVID-19 market crash, the overall trend has increased over time. This shows that stock market returns can change significantly from year to year, investors who hold stocks in the long term have historically benefited from staying invested. The graph also explains why investors focus on average returns over many years instead of only looking at one year of performance. Some years may have significantly higher or lower returns, but the stock market has produced an average annual return of around 9–10% over a long period of time. See Thune, K (2023).

3.4 Standard Deviation and Investment Risk

Standard deviation can also help explain the risk shown in the graph. Standard deviation measures the distance between yearly returns and average return. If the standard deviation is high, it means the returns are more spread out, so the market has more ups and downs. If the standard deviation is low, it means the yearly returns are closer to the average and more stable. In the Dow Jones graph over past 50 years, the market does not increase smoothly every year. There are many sharp increases and decreases during different periods. This shows that even though the long-term average return may be around 9–10%, the actual return in a single year can be much higher or much lower. Therefore, standard deviation is important because it shows that investors must consider risk, not only average return, when evaluating stock market performance. See Investopedia (n.d.-c).

4. The Meaning of ‘Beating the Market’

Although investors may achieve returns above the market average, consistently producing abnormal returns is extremely difficult. This leads to an important question in finance: if many investors are trying to find advantages, why is it still difficult to consistently beat the market?

4.1 Efficient Market Theory (EMT)

Efficient Market Theory, also known as the Efficient Market Hypothesis (EMH), explains that stock prices reflect available information, including company performance, economic conditions, and public news. When new information appears, investors react quickly, causing prices to adjust. Because many investors compete using the same information, it is extremely difficult to consistently earn returns above the overall market. The implications of Efficient Market Theory are important for investors. If markets are efficient, investors cannot consistently beat the market by only using public information. Therefore, many investors choose passive investing strategies, such as index funds that follow major market indices.(See Investopedia, n.d.-d).

4.2 Three Forms of EMT

EMT has three forms. Weak-form efficiency states that stock prices already reflect past prices and trading patterns. Semi-strong efficiency states that prices reflect all publicly available information, including financial reports and company announcements. Strong-form efficiency argues that prices reflect both public and private information, however this form is controversial because investors cannot legally use private information to gain benefits. This paper will focus on Strong-form efficiency. (See Investopedia, n.d.-d, See U.S. Securities and Exchange Commission, 2023).

4.3 Assumptions for EMT to Hold

Efficient Market Theory helps explain why ‘beating the market’ is such a challenging goal. When information set public and already reflected in stock prices, investors cannot gain an advantage by using the same information available to everyone else. If a strategy creates an advantage, other investors may quickly discover and use the same strategy, reducing its effectiveness. For example, if an investor identifies a stock that appears undervalued, other investors may notice the same opportunity and purchase the stock. Their actions will cause the stock price to rise, reducing the≠ advantage. As more investors compete for the same opportunities, it becomes more difficult to maintain abnormal returns over a long period of time. Therefore, short-term success does not always prove that an investor has a reliable strategy, as high returns may result from favorable market conditions, higher risks, or chance (See Investopedia, n.d.-d).

5. Five Ways Investors May ‘Beat’ the Stock Market: A critical Assessment

Efficient Market Theory explains that consistently beating the market is difficult because available information is quickly reflected in stock prices. However, investors may achieve returns above the market through different strategies. Here are five possible ways include using non-public information, fundamental analysis, investor behavior, advanced technology, and taking greater risks.

5.1. Using Non-Public Information

One possible way to gain an advantage is by having important private information before it becomes public, which is also known as insider trading. Since the information has not yet been reflected in the stock price, an investor may be able to act before the rest of the market. This challenges the strong form of EMT, which states that stock prices reflect both public and private information.

 It also violates the EMT assumption that all relevant information is public because some investors have information that others do not. This advantage is not sustainable because it disappears once the information becomes public, and trading based on non-public information is illegal. See U.S. Securities and Exchange Commission (2023).

5.2. Finding Undervalued Stocks Through Fundamental Analysis

Investors can use fundamental analysis to find stocks they believe are undervalued. They can analyze a company’s financial statements, revenue, profits, debt, management, and future growth. If an investor finds a strong company whose stock price is lower than its actual value, the investor may earn abnormal returns when the price increases. 

This can violate the EMT assumption that the ability to extract information from a given signal is uniformly distributed because some investors may be better at analyzing the same financial information than others. This advantage may be sustainable for skilled investors, but it can decrease when other investors recognize the same opportunity.

5.3. Taking Advantage of Investor Behavior

Investor behavior can also create opportunities to beat the market. Investors may be influenced by fear, excitement, overconfidence, and herd behavior. For example, negative news may cause investors to be afraid and no willingness to hold the stock, causing its price to fall significantly. An investor who recognizes that the market has overreacted may buy the stock and benefit if the price later recovers. 

This violates the EMT assumption that people form their expectations in a rational manner because emotions and biases can affect their decisions. This advantage may continue because investor biases still exist, but predicting this correctly is difficult.

5.4. Taking Greater Risks

Investors may also achieve higher returns by taking greater risks. For example, an investor may invest heavily in a small number of stocks or choose faster-growing companies. If these investments perform well, the investor may earn significantly higher returns than the overall market. Higher returns should be considered with the amount of risk. For example, if the S&P 500 earns 10% and an investor earns 15% by taking significantly greater risks, the additional return will be related to the additional risk. Therefore, risk and return should both be considered when determining whether an investor has successfully beaten the market. 

This method does not directly violate an EMT assumption because efficient markets can still provide higher expected returns for investors who accept greater risk. This strategy may produce higher returns over time, but those returns do not necessarily represent an advantage over the market because they come with greater risk.

5.5. Using Hyper Technology, Data, and Artificial Intelligence

In this section, l  will examine more deeply how technology and artificial intelligence may give investors an advantage in the stock market. Technology and AI may give investors another advantage. AI can process enormous amounts of information, including financial reports, stock prices, economic data, and news, much faster than humans. It can also identify patterns that may be difficult for individual investors to discover. If AI identifies useful information or patterns before other humans, it may help investors make better decisions quicker and earn abnormal returns. See BlackRock (2023). 

This violates the EMT assumption that the ability to extract information from a given signal is uniformly distributed because investors with better AI technology may analyze information faster and more effectively than others. This advantage may become less sustainable as AI becomes more widely used and more investors discover the same patterns and opportunities.

These five methods show separate ways investors are able to gain returns above the market, including using different information, fundamental analysis, investor behavior, advanced technology such as AI, or taking greater risks. Each method involves different opportunities and risks, and its effectiveness can vary depending on market conditions and how it is used.

6. Nature and Impact of High-Frequency and Algorithmic Trading-A deeper look

One technology in investing is algorithmic trading. Algorithmic trading uses computer programs to automatically make trading decisions based on certain rules, market information, or mathematical models. Instead of having an investor watch the market, the computer can continuously monitor stock prices, trading volume, price changes, and other information. Once certain conditions are reached, the system can automatically execute a trade. This allows investment decisions and trades to be completed much faster than humans. See U.S. Securities and Exchange Commission (2020).

6.1 Nature of Algorithmic Trading and High Frequency.

6.1.1 Nature of Algorithmic Trading

These algorithms can be designed for different purposes. For example, an algorithm may compare the current price of a stock with its historical prices, monitor changes in trading volume, or compare prices between related securities. When the system identifies a condition that matches its strategy, it can immediately decide whether to buy or sell. This is especially useful because financial markets constantly produce new information, making it difficult for investors to monitor every change at the same time. Algorithms can also follow the market continuously without being affected by tiredness or emotions such as fear and excitement. See U.S. Securities and Exchange Commission (2020).

Before an algorithm is used in the real market, investors can also test it using historical market data. This process, commonly known as back testing, allows investors to examine how a trading strategy would have performed under previous market conditions. A strategy that performs successfully in historical testing may then be further tested and adjusted before real money is invested. Past performance does not guarantee that the same strategy will continue to work because future market conditions can be different. See U.S. Securities and Exchange Commission (2020).

6.1.2 Nature of High Frequency

High-frequency trading, also known as HFT, is a more advanced type of algorithmic trading that focuses heavily on speed. High-frequency traders use powerful computers and fast connections to analyze market information and complete a large number of trades within a very short period. The profit from an individual trade may be very small, but making a large number of trades make these small profits become larger. Speed is especially important because a trading opportunity may only exist for a very short period before other investors recognize it and stock prices adjust. See U.S. Securities and Exchange Commission (2020).

High-frequency trading operates on extremely short time periods. HFT systems continuously receive information about prices, buy and sell orders, and trading volume from financial markets. The computer analyzes this information and automatically submit, change, or cancel orders when it identifies a trading opportunity. Because these decisions are automated, the entire process can occur much faster than a human investor could analyze the information and manually place a trade. See U.S. Securities and Exchange Commission (2020).

One important factor in HFT is latency, which refers to the delay between receiving market information and responding to it. Even an exceedingly small delay can matter because many HFT firms may be competing for the same opportunity. For this reason, firms invest heavily in powerful computers, high-speed networks, and technology that allows them to receive market data and execute orders as quickly as possible. Some firms also place their computer systems close to the exchanges’ systems, a practice known as co-location, to reduce communication delays. See U.S. Securities and Exchange Commission (2020).

For example, a stock may temporarily have slightly different prices across markets. A high-frequency trading system may recognize this small difference and quickly buy at the lower price and sell at the higher price. The profit from one trade may be extremely small, but the strategy can become meaningful when it is repeated many times. As other trading systems recognize the same price difference, their buying and selling causes the prices to move closer together and the opportunity disappears. This explains why HFT companies invest heavily in computing power and fast connections. Even an exceedingly small difference in the time needed to receive information and execute a trade can affect whether the opportunity still exists. See U.S. Securities and Exchange Commission (2020).

6.2 Artificial Intelligence in Algorithmic Trading

Artificial intelligence can improve algorithmic trading by allowing computer systems to analyze larger and more complicated information. Traditional algorithms usually follow rules created by humans, such as buying or selling when a stock reaches a certain price or certain condition. AI models can analyze relationships between different types of data and identify patterns that may be difficult for humans to recognize. This can help trading systems react more efficiently to changing market conditions.See BlackRock (2023).

Figure 2. Process of AI-Supported Algorithmic Trading

See Coin Bureau (n.d.).

Figure 2 shows how an AI-supported trading system can move from receiving market information to executing a trade. Market information such as stock prices, trading volume, financial news, and economic data can first be collected and analyzed by an AI model. The system may then identify a pattern or trading opportunity and generate a buy or sell signal. If the conditions of the trading strategy are satisfied, the algorithm can automatically execute the trade. This process can occur continuously and much faster than a human investor could complete the same steps.

Machine learning can make this process more advanced because the system can learn relationships from historical data instead of depending only on fixed rules created by humans. For example, an AI model may consider price movements, trading volume, volatility, and other market information together to estimate the probability that a stock price will increase or decrease. The model can then use this prediction as one factor when deciding whether to trade. This does not mean that AI knows what the stock price will be in the future, but it can use patterns in data to estimate possible outcomes. See BlackRock (2023).

AI can also analyze information that is more difficult to represent with simple trading rules. For example, financial news and company announcements contain large amounts of written information. AI systems can analyze this information and identify whether it may have a positive or negative effect on a company. Combining this information with stock prices and trading data allows an algorithm to consider several types of information at the same time. This creates a major difference between a simple rule-based algorithm and a more advanced AI-supported trading system. See BlackRock (2023).

6.3 Impact of Algorithmic and High-Frequency Trading on the Market

Algorithmic and high-frequency trading can also affect the overall market. Research reviewed by the U.S. Securities and Exchange Commission found that some forms of high-frequency trading can improve market liquidity and price efficiency during normal market conditions. For example, automated trading can increase competition between buyers and sellers and help stock prices respond quickly to latest information. See U.S. Securities and Exchange Commission (2020).

Liquidity is important because it represents how easily investors can buy or sell an investment without causing a large change in its price. When automated traders continuously participate in buying and selling, there may be more opportunities for other investors to complete their trades. Algorithmic trading can also contribute to price discovery because new information can be analyzed and reflected in stock prices very quickly. See U.S. Securities and Exchange Commission (2020).

On the other hand, these systems contain risks. A computer can execute many trades extremely quickly, which means that an error in an algorithm may also affect the market very quickly. During unusual periods of market stress, some algorithmic trading activities may increase costs for investors. An algorithm that works successfully during normal conditions may also react differently when the market experiences an unusual event that was not considered when the system was developed. This shows that speed and automation can improve the trading process, but they also require careful control and risk management. See U.S. Securities and Exchange Commission (2020).

Faster price discovery means that when essential information becomes available, trading activity can help move a stock toward a price that better represents that information. Therefore, algorithmic trading does not only affect investors using the technology; it can also influence how quickly the overall market reacts to new information.See U.S. Securities and Exchange Commission (2020).

The development of algorithmic trading also changes the competition between investors. Investors with faster computers, better algorithms, more data, and stronger AI models may be able to analyze and respond to information before other investors. As more financial institutions develop similar technology, these differences can become smaller and trading opportunities may disappear more quickly. See BlackRock (2023).

This competition can also make the market itself more efficient. When an algorithm discovers a price difference and trades on it, its actions help move the price toward a level that reflects the available information. Therefore, algorithmic trading can create an advantage for investors who recognize opportunities quickly, while at the same time causing those opportunities to disappear more quickly. The success of algorithmic trading is not only based on finding useful patterns, but also on how quickly and accurately investors can act on those patterns before the rest of the market responds. See U.S. Securities and Exchange Commission (2020), See Investopedia (n.d.-d).

6.4 DeepSeek as an Illustration of AI in Financial Analysis

DeepSeek is a Chinese artificial intelligence company founded by Liang Wenfeng. Before establishing DeepSeek, Liang had a strong background in mathematics, artificial intelligence, and quantitative investing. He studied electronic information engineering at Zhejiang University and later earned a master’s degree in information and communication engineering. His early interest in using mathematics and computer technology to analyze financial markets eventually led him into quantitative investing. See Reuters (2025).

Liang later co-founded High-Flyer, a Chinese quantitative hedge fund. Unlike traditional investors who may depend heavily on personal experience and individual judgment, quantitative investment firms use mathematical models, large amounts of financial data, and computer algorithms to identify investment opportunities and make trading decisions. High-Flyer increasingly used artificial intelligence and computing technology as part of this process. This experience gave Liang and his team a strong connection between AI, data analysis, and financial markets. See Reuters (2025).

In 2023, Liang expanded his focus beyond using AI mainly for quantitative investing and established DeepSeek to focus on developing more advanced artificial intelligence models. DeepSeek began researching large language models that could perform tasks such as reasoning, coding, mathematics, and information analysis. The company developed several AI models and released DeepSeek-V3 in December 2024, followed by the reasoning-focused DeepSeek-R1 in January 2025. DeepSeek-R1 received significant international attention because of its reasoning performance and the company’s open approach to releasing its models.See Reuters (2025), See DeepSeek (2024).

The company has a connection to quantitative investing through High-Flyer. Before developing general-purpose AI models, Liang and his team already had experience using algorithms, large datasets, and computing power to analyze financial markets. DeepSeek therefore provides an interesting example of how technology originally developed and applied in a quantitative investment environment can contribute to the development of more advanced AI systems capable of analyzing many different types of information. Liang remains closely connected to High-Flyer today, while also leading DeepSeek, showing that his work in artificial intelligence continues to have a strong connection with his background in quantitative investing. See Reuters (2025).

DeepSeek can be used as an example of how AI can support financial analysis. Investors often need to read financial statements, compare companies, analyze economic information, and perform calculations before making investment decisions. These tasks can require investors to examine enormous amounts of information from different sources. AI can make this process more efficient by organizing and analyzing information much faster than a human investor.

For example, investors can use AI to analyze a company’s revenue, profits, expenses, and debt. AI can also summarize financial documents, compare different companies, and explain complicated financial information. An investor could provide financial information from several companies and ask an AI model to compare their revenue growth, profit margins, debt levels, and other financial measurements. Instead of manually searching through every part of a financial report, the investor could use AI to identify essential information and then examine it more carefully. This allows investors to process larger amounts of information in less time.

DeepSeek can also perform reasoning and numerical tasks that are important in financial analysis. Financial decisions often require more than simply finding information. Investors may need to calculate financial ratios, recognize relationships between different numbers, and determine what changes in financial performance could mean for a company. An AI model that combines information extraction with numerical reasoning can therefore help investors move from collecting information to analyzing it.

Research also shows DeepSeek’s ability to perform financial tasks. BizFinBench tested 25 AI models using 6,781 real-world financial questions involving calculation, reasoning, information extraction, and financial knowledge. DeepSeek-R1 performed especially well in numerical calculation and information extraction. See Lu et al. (2025).

These abilities could be useful when investors need to analyze many companies at the same time. A human investor has limited time to read financial reports from hundreds of companies, while an AI system can process information much more quickly. AI could therefore be used as an initial screening tool to identify companies or financial information that may deserve further analysis. This does not mean that DeepSeek can automatically identify which stocks will increase in price, but it can reduce the amount of time required to complete some parts of investment research.

At the same time, DeepSeek has limitations. The research found that no single AI model performed best in every financial task. Powerful performance in calculation or information extraction also does not necessarily mean that the model can accurately predict future stock prices. A company may have strong financial results while its stock price falls because investors already expected even better results. Stock prices can also be affected by interest rates, economic conditions, investor behavior, and unexpected events that are difficult for an AI model to predict. Therefore, AI can make financial analysis faster and more efficient, but investors still need to evaluate its results carefully instead of completely depending on AI when making investment decisions. See Lu et al. (2025).

7. Limitations

Artificial intelligence can help investors analyze financial information and make investment decisions, but it also has several important limitations. Financial markets are affected by many different factors, and AI cannot perfectly understand or predict every change. Its effectiveness depends on the quality of its data, market conditions, and how investors use its results.

One limitation is AI’s dependence on data. AI models use enormous amounts of historical information, including stock prices, financial statements, economic data, and news, to identify patterns and make predictions. If the data is inaccurate, incomplete, or biased, the results may also be inaccurate. Historical patterns also do not always represent what will happen in the future.

Unexpected events, Wars, pandemics, political changes, natural disasters, and sudden company problems can quickly affect stock prices. These situations may be very different from the historical information used by an AI model. As a result, a model that performs successfully during normal market conditions may perform poorly when the market suddenly changes.

AI models can also make mistakes when analyzing financial information. They may misunderstand information, make calculation errors, or provide conclusions that appear reasonable but are incorrect. The DeepSeek example discussed previously demonstrates this limitation. DeepSeek-R1 performed strongly in several financial tasks but did not perform best in every area. Investors cannot assume that one AI model will always provide accurate financial analysis. See Lu et al. (2025).

Competition can also reduce the advantage provided by AI. If an AI model discovers a useful market pattern, other investors have the same opportunity using similar technology. As more investors find out, AI will reduce the original advantage. This connects with Efficient Market Theory because competition causes new information to become reflected in market prices more quickly.

Another limitation is unequal access to technology. Large financial institutions may have better data, stronger computers, professional programmers, and more advanced AI models than individual investors. Investors may also become too dependent on AI and follow its recommendations without carefully checking the results. AI can support investment decisions, but human judgment and risk management remain important.

Overall, problems with data, unexpected events, AI errors, competition, and unequal access to technology can all affect the performance of AI in financial markets. These are the limitations when AI is used for investment analysis.

8. Policy Implications

As artificial intelligence becomes widely used in financial markets, regulators need to consider its potential risks. AI can improve financial analysis and trading, but incorrect use may affect investors and market stability. Policies should allow technological development but protect investors.

One important issue is transparency. Some AI models are difficult to understand, so financial institutions should remain responsible for decisions made using their systems. Human supervision is also important because AI can make mistakes or respond incorrectly to unusual market conditions.

Regulators also need to prevent misleading claims about AI. Some companies may exaggerate how they use AI or what their technology can achieve. The U.S. Securities and Exchange Commission has described misleading claims about AI as ‘AI washing’ and has taken enforcement actions against investment advisers for making false or misleading statements about their use of AI. See U.S. Securities and Exchange Commission (2024).

Market stability and data security are also important. Algorithmic trading systems can execute many trades very quickly, meaning errors can spread rapidly. AI systems may also use large amounts of sensitive financial information, which needs to be properly protected.

Financial institutions should also be required to test and monitor AI-based trading systems before and during their use. Because automated systems can execute many trades within a very short period, an error can spread quickly through the market. Firms should have risk controls that can limit or stop trading when unusual behavior is detected. See U.S. Securities and Exchange Commission (2020).

Overall, financial policies should balance the development of AI with investor protection, transparency, market stability, and data security.

9. Conclusion

AI can help investors beat the market in some situations because it can process large amounts of information quickly, identify patterns, and react faster than humans. Investors with better data, stronger models, and faster technology may gain an advantage before other investors recognize the same opportunity. See BlackRock (2023), See U.S. Securities and Exchange Commission (2020).

The main issue is whether this advantage can continue over a long period. If more investors use similar AI systems, they may discover the same opportunities, causing stock prices to adjust and reducing the original advantage. This is connected with Efficient Market Theory because competition causes information to become reflected in prices more quickly. See Investopedia (n.d.-d).

AI also has limitations. It depends on historical data, can make mistakes, and may perform poorly when market conditions suddenly change. The DeepSeek example shows that AI can perform well in some financial tasks, but no model performs best in every area. See Lu et al. (2025).

Therefore, AI can beat the market during certain periods, but there is not enough evidence to show that it can consistently beat the market over the long term after considering risk, competition, costs, and changing market conditions. AI is better understood as a powerful tool that can improve investment decisions rather than a guaranteed way to beat the market.

References:

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Downey, L. (2025, November 15). Efficient market hypothesis (EMH): Definition and critique. Investopedia.
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Investopedia. (n.d.-a). Dow Jones Industrial Average vs. S&P 500: What’s the difference? Investopedia.
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Investopedia. (n.d-d). What are the differences between weak, strong, and semi-strong versions of the efficient market hypothesis? Investopedia.
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Investor’s Business Daily. (2024, May 22). What is the stock market? Moneyworks from Investor’s Business Daily.
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Lu, G., Guo, X., Zhang, R., Zhu, W., & Liu, J. (2025). BizFinBench: A business-driven real-world financial benchmark for evaluating LLMs. arXiv.
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Macrotrends. (n.d.). Dow Jones – 100 year historical chart. Macrotrends.
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Maverick, J. B. (2015). S&P 500 average return and historical performance. Investopedia.
https://www.investopedia.com/ask/answers/042415/what-average-annual-return-sp-500.asp

O’Shea, A. (2026, March 4). What is the average stock market return? NerdWallet.
https://www.nerdwallet.com/investing/learn/average-stock-market-return

Reuters. (2025, January 29). High-Flyer: The AI quant fund behind China’s DeepSeek. Reuters.
https://www.reuters.com/technology/artificial-intelligence/high-flyer-ai-quant-fund-behind-chinas-deepseek-2025-01-29/

The Investopedia Team. (2025, December 31). What does it mean to ‘beat the market’? Investopedia.
https://www.investopedia.com/ask/answers/03/100903.asp

The Investopedia Team. (2026, May 22). Understanding abnormal returns: Causes and impact on investments. Investopedia.
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Thune, K. (2023, January 2). What is the average return of the stock market? Seeking Alpha.
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U.S. Securities and Exchange Commission. (2020, August 5). Staff report on algorithmic trading in U.S. capital markets. U.S. Securities and Exchange Commission.
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U.S. Securities and Exchange Commission. (2023). Insider trading. Investor.gov.
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U.S. Securities and Exchange Commission. (2024, March 18). SEC charges two investment advisers with making false and misleading statements about their use of artificial intelligence. U.S. Securities and Exchange Commission.
https://www.sec.gov/newsroom/press-releases/2024-36

About the author

Yiqi Xu

Yiqi is a Grade 12 student in Ontario, Canada, interested in mathematics, finance, artificial intelligence, and computer science. He is especially interested in understanding financial markets and the growing role of technology in investing. He enjoys exploring new ideas in math, coding, and finance.

The post Can you use AI to ‘beat’ the market? appeared first on Exploratio Journal.

Abstract

This paper examines three keys to successful performance in competitive distance running: maximal oxygen uptake (VO₂MAX), lactate threshold, and running economy. Although these are each influenced by genetics, certain controllable factors affect the extent of each input that a runner can use in a race. This review looked at research on the following seven such factors: hydration, nutrition, sleep and recovery, mental strategy, warm-up and mobility, supplementation and strength training. The findings were then organized into recommendations for: (a) before, (b) during and (c) after a race. First, before a race, a strong body of evidence supports the effectiveness of carbohydrate loading, extra sleep during the taper, a dynamic warm-up and dietary nitrate. Next, during a race, sodium-containing carbohydrate drinks help limit dehydration and maintain blood glucose, previous strength training improves running economy and motivational self-talk reduces perceived exertion. Finally, after a race, carbohydrate and protein intake and adequate sleep support recovery. This paper also identified two limitations: beetroot juice seems to benefit highly trained runners less than the average results in the literature suggest, and sweat-rate testing, while effective, is difficult for most runners to do without laboratory access. Therefore, recommendations should take into account a runner’s training level and the resources available to them.

Introduction

Competitive distance runners perform very close to their metabolic limits. At the highest levels of the sport, a runner’s performance is very much dependent on three physiological factors: maximal oxygen uptake (VO₂MAX), lactate threshold and running economy (Midgley et al., 2007). While genetics play a role in all three, how much of his or her potential a runner actually uses on race day depends to a meaningful extent on factors he or she can control.

Two of the most widely studied of these factors are fluid and fuel intake. We know that when dehydration exceeds about 2% of body mass, plasma volume falls, heart rate rises, and thermoregulation is impaired, all of which have the effect of slowing down the runner (von Duvillard et al., 2004; Casa et al., 2010). In the case of fuel, loading carbohydrate 36 to 48 hours before a marathon increases muscle glycogen stores (Burke, 2019), and runners who take in enough carbohydrate during the race are more likely to maintain their pace (Jiménez-Alfageme et al., 2025).

Strength training also makes a difference. Heavy lifting or plyometrics two to three times a week increases tendon stiffness and improves neuromuscular signaling, which can make runners more efficient without adding much muscle mass (Blagrove et al., 2018). On race day, a proper warm-up prepares these muscles without fatiguing them, and a small number of universally permitted supplements, like beetroot juice and beta-alanine, may provide an additional benefit.

Recovery becomes the final piece. Sleep, along with practices such as compression, easy active recovery, and heat or cold exposure, allows the body to adapt to training. Chronically obtaining poor amounts of sleep reduces the body’s ability to store carbohydrates, raises cortisol, and has been linked to higher injury risk (Fullagar et al., 2015). Getting extra sleep before a long event may even help. In a survey of more than 1,100 mountain ultramarathon finishers, runners who slept more in the week before the race reported fewer falls caused by sleepiness, although it should be noted they did not finish any faster (Kishi et al., 2024).

The research question for this paper is therefore: what are the main performance inputs for competitive distance running, and how does each one contribute before, during, and after a race? To answer it, this paper reviews a range of evidence on hydration, diet, sleep and recovery, mental strategy, warm-up and mobility, supplements, and strength training, and then the paper combines the findings into a practical framework covering the full race cycle.

Literature Review

Hydration Strategies and Fluid Balance

It is known that runners reduce their body temperature through sweating, and that during long races quite a significant amount of fluid is lost this way. One study pointed out that once fluid losses exceed about 2% of body mass, aerobic capacity starts to decline (von Duvillard et al., 2004). The reason for this was due to a drop in plasma volume, which results in lower stroke volume and causes the heart rate to rise to compensate, while less blood is available to carry heat to the skin.

Casa et al. (2010) noticed these effects in a study of runners on a very hot trail course, where dehydrated runners showed faster increases in core temperature and greater physiological strain. The actual beverage consumed during a race must be looked at. In events lasting several hours, simple water does not replace the electrolytes lost in sweat or provide energy, and in fact drinking large amounts of water can actually lead to terrible stomach discomfort or exercise-associated hyponatremia.

Drinks containing 4–8% carbohydrate and some sodium are more effective than plain water, because sodium speeds up fluid absorption in the gut and helps maintain plasma volume, while the carbohydrate works to ensure that blood glucose is kept up (von Duvillard et al., 2004). But, many runners do not follow these guidelines. O’Neal et al. (2011) surveyed half- and full-marathon runners and found that many did not correctly judge how much they needed to drink, followed arbitrary schedules, or waited until they were thirsty. Sweat-rate testing and drinking plans based on race pace could really help correct this.

Nutritional Interventions and Carbohydrate Optimization

Because distance running requires large amounts of energy, people should plan fueling needs before, during and after races. At easy, not-too-intense paces, fat provides much of this energy, but at or above lactate threshold carbohydrate becomes the main fuel. From a biological perspective, because the liver and muscles can store only a limited amount of glycogen, the extent of this storage largely determines when a runner “hits the wall.”

Burke (2019) recommends that fueling strategies be customized directly to race distance and conditions, rather than following a single approach. For shorter races, normal pre-race meals and a carbohydrate mouth rinse, which appears to stimulate the central nervous system, are generally sufficient. In contrast, for the marathon, a carbohydrate-loading phase over the 36 to 48 hours before the race is needed to fully saturate muscle glycogen (Burke, 2019).

Data from actual races support this. In a study of 160 runners in the Seville Marathon, Jiménez-Alfageme et al. (2025) found that runners who consumed the recommended 60–90 g of carbohydrate per hour during the race were more likely to finish in under three hours, although interestingly intake before and after the race was not related to finish time. We should not overlook day-to-day diet as well. Craddock et al. (2025) analyzed food diaries from Australian ultramarathon runners and found that many ate too little fruit, vegetables, and whole grains and too much discretionary food, resulting in only moderate diet quality scores. 

Rest, Recovery, and Sleep Architecture

The recovery phase is when adaptations to training mainly take place. High mileage causes muscle micro-damage, central fatigue, and low-grade inflammation, and a significant amount of the repair process occurs during sleep. In a review of sleep research on marathon and ultramarathon runners, Nikolaidis et al. (2023) reported that good sleep was associated with fewer injuries and infections and that sleep becomes more important to performance in proportion to actual race distance. Short or fragmented sleep has the effect of slowing this recovery.

Fullagar et al. (2015) reviewed research on sleep loss in athletes, and several of their findings apply to distance runners. Sleep restriction impairs glycogen storage, which can reduce the benefit of a carbohydrate load. It also increases cortisol, a hormone that promotes muscle breakdown, and slows reaction time, and in fact both of these have been linked to a higher risk of injury (Fullagar et al., 2015).

Mental Game, Cognitive Pacing, and Strategy

Psychological strategies also can impact endurance performance. McCormick et al. (2015) concluded that motivational self-talk, goal setting and imagery consistently brought down runners’ rating of perceived exertion (RPE). Since the same effort felt easier, it turned out that runners lasted longer before reaching their exhaustion limits and they performed time trials even more effectively, despite no changes to cardiovascular measures (McCormick et al., 2015). Brick et al. (2014) explained how successful endurance athletes are able to switch between monitoring their own bodies and focusing externally on the race while actually in performance mode.

Warm-up Modalities and Mobility Protocols

Ideally, a warm-up should really prepare the body for race pace without using up energy needed for the race itself. A warm-up’s main goals are known to be to raise muscle temperature, increase blood flow to the muscles and improve neuromuscular response while not using up much glycogen at all. Historically speaking. static non-dynamic stretching was used to warm up, but we now know that holding a stretch for more than about 60 seconds per muscle group can actually temporarily reduce muscle-tendon stiffness and force output (Behm & Chaouachi, 2011).

Therefore, now warm-ups are dynamic and include mobility drills and race-specific activation. Exercises like leg swings, high knees, walking lunges and progressive strides involve more motor units and prepare the nervous system, and they also loosen muscles and tendons, which all together has the effect of improving range of motion and stride mechanics. Blazevich et al. (2018) found that when a full dynamic warm-up was used, neither using nor not using short static stretches made a measurable difference in performance.

A warm-up also helps prepare the cardiovascular system. In a study of highly trained middle-distance runners, ending the warm-up with a sustained 200 meter run at race pace improved 800 meter time-trial performance by about one second compared with just using strides alone (Ingham et al., 2013). The most likely explanation is that starting the race with oxygen uptake already raised reduces the oxygen deficit early in the race. 

Ergogenic Aids and Physiological Supplementation

While training volume is the main determinant of aerobic capacity, some supplements can provide a small additional benefit. The IOC consensus statement concludes that only a few supplements have strong evidence of improving performance (Maughan et al., 2018). Of these, dietary nitrate, usually taken as beetroot juice, and beta-alanine, are in fact the most relevant to distance runners. Also, magnesium may be useful for athletes with low magnesium levels.

Beetroot juice is a concentrated source of nitrate, and the body converts it into nitric oxide. What happens is that nitric oxide widens blood vessels, increasing blood flow to working muscles, and reduces the oxygen usage of the body while it works (Jones, 2014). Then, this improves running economy and is most useful in middle- and long-distance events, where oxygen delivery is a main limitation on performance.

Beta-alanine functions through a different mechanism. It is the precursor to carnosine, a buffer inside muscle cells and regular supplementation actually causes an increase of muscle carnosine levels. This helps neutralize hydrogen ions during hard efforts and delays the acidosis that limits a runner’s finishing speed (Saunders et al., 2017). Magnesium, on the other hand, supports muscle relaxation, nerve conduction, and energy production, and supplementation appears to help mainly in athletes who are deficient (Volpe, 2015).

Neuromuscular Strength Training and Biomechanical Efficiency

The conventional wisdom has been that distance runners should avoid strength training because of concerns that extra muscle mass would slow them down. But, more recent research does not support this concern, since strength training combined with high-volume running produces neuromuscular improvements without significant muscle growth.

As noted earlier, endurance performance depends on VO₂MAX, the percentage of it a runner can sustain (lactate threshold), and running economy (Midgley et al., 2007). In a systematic review of strength training studies in middle- and long-distance runners, Blagrove et al. (2018) concluded that heavy, explosive, or plyometric training only two to three times per week actually improved running economy. Because VO₂MAX and blood lactate profiles stayed pretty much the same, the conclusion was that the improvement came from neural and structural changes (Blagrove et al., 2018).

Heavy and explosive training improves signaling between the brain and muscles, and increases motor unit synchronization and makes tendons stiffer. Biology tells us that a stiffer tendon works more like a spring, storing elastic energy when the foot lands and then releasing it during push-off. 

Discussion

To apply these findings, it is useful to divide race preparation into phases, because performance depends on decisions made in three different phases, consisting of before, during and after race day.

In the weeks and more importantly the days before a race, the main priorities must be fueling, sleep and preparation. A carbohydrate load 36 to 48 hours before a race works to fill liver and muscle glycogen stores (Burke, 2019). Long amounts of sleep during the last seven to ten days of the taper period helps offset the poor sleep many runners experience the night before a race (Fullagar et al., 2015), and in ultramarathon runners it was associated with fewer sleep-related falls (Kishi et al., 2024). Right before a race, runners should really avoid long static stretches and instead use dynamic mobility exercises and strides, which raise muscle temperature, involve motor units, and reduce the early oxygen deficit (Behm & Chaouachi, 2011; Blazevich et al., 2018; Ingham et al., 2013). Consuming beetroot juice (for its nitric oxide) two to three hours before the start can also lower the oxygen amount used to maintain race pace (Jones, 2014).

During the race, the focus then shifts to maintaining fluid balance and energy supply and keeping mental focus. Runners should drink a carbohydrate-electrolyte solution containing sodium at planned times instead of relying solely on water (von Duvillard et al., 2004). This beverage strategy helps keep fluid losses below 2% of body mass and maintains the supply of glucose to the muscles (Casa et al., 2010; Jiménez-Alfageme et al., 2025). Strength training completed in the months before the race also helps here, because stiff muscle-tendon units lower the energy cost of each stride (due to the spring mechanism referred to above) and conserve glycogen (Blagrove et al., 2018). Mentally, during the race, runners should make the attempt to switch between monitoring their bodies and focusing on the race around them which has proven to be an effective strategy (Brick et al., 2014), and motivational self-talk can lower perceived exertion, which reduces early pacing errors and the mental fatigue associated with hitting the proverbial wall (McCormick et al., 2015; Meijen et al., 2023).

After the race, recovery becomes a huge priority. Eating high glycemic carbohydrate with lean protein within about 30 to 60 minutes post-race speeds glycogen replacement and begins tissue repair (Burke, 2019). Because a marathon can disrupt a racer’s autonomic regulation and sleep for several nights after a race, getting quite a lot of sleep during this post-race period is vital (Nikolaidis et al., 2023). Easy active recovery, compression and light mobility work increase the flow of blood and help prepare the body for the next training cycle.

Although the research provides fairly clear guidance for each phase, not all runners benefit the same way. Two areas in particular show this limitation.

The first is dietary nitrate. Much of the improvement in running economy attributed to beetroot juice comes from studies of recreational and moderately trained runners, whose lower baseline nitric oxide efficiency leaves more room for improvement (Jones, 2014). In contrast, highly trained runners, whose vascular and mitochondrial systems are already hyper-efficient, tend to show smaller and less consistent responses, which is consistent with the individual variation in supplement response linked to training status, genetics and diet (Maughan et al., 2018). As a result, a plan based on the average effect reported in the literature would probably not yield the same benefit for the well-trained runners this paper focuses on, and coaches should rely on individual trials instead.

The second area of limitation is hydration. Individual sweat-rate testing is the most effective way to close the gap between hydration guidelines and what runners actually do, but it requires detailed body mass measurements before and after exercise and, ideally, sweat sodium analysis (O’Neal et al., 2011), which often requires specialized tools. This is realistic for a professional marathoner with access to sports science staff, but not for most “regular” amateur runners who train on their own. Simpler methods, such as standing on a bathroom scale before and after runs and paying attention to thirst over several weeks of training, are less precise but much more practical.

References

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Blagrove, R. C., Howatson, G., & Hayes, P. R. (2018). Effects of strength training on the physiological determinants of middle- and long-distance running performance: A systematic review. Sports Medicine, 48(5), 1117–1149. https://doi.org/10.1007/s40279-017-0835-7

Blazevich, A. J., et al. (2018). No effect of muscle stretching within a full, dynamic warm-up on athletic performance. Medicine & Science in Sports & Exercise, 50(6), 1258–1266.

Brick, N., MacIntyre, T., & Campbell, M. (2014). Attentional focus in endurance activity: New paradigms and future directions. International Review of Sport and Exercise Psychology, 7(1), 106–134.

Burke, L. M. (2019). Contemporary nutrition strategies to optimize performance in distance runners and race walkers. International Journal of Sport Nutrition and Exercise Metabolism, 29(2), 117–129.

Casa, D. J., Stearns, R. L., Lopez, R. M., Ganio, M. S., McDermott, B. P., Yeargin, S. W., Yamamoto, L. M., Mazerolle, S. M., Roti, M. W., Armstrong, L. E., & Maresh, C. M. (2010). Influence of hydration on physiological function and performance during trail running in the heat. Journal of Athletic Training, 45(2), 147–156. https://doi.org/10.4085/1062-6050-45.2.147

Craddock, J. C., Walker, G., Chapman, M., Lambert, K., & Peoples, G. E. (2025). The diet quality of ultramarathon runners taking part in an Australian event: A cross-sectional explorative study. Nutrients, 17(3), Article 485.

Fullagar, H. H. K., Skorski, S., Duffield, R., Hammes, D., Coutts, A. J., & Meyer, T. (2015). Sleep and athletic performance: The effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Medicine, 45(2), 161–186. https://doi.org/10.1007/s40279-014-0260-0

Ingham, S. A., Fudge, B. W., Pringle, J. S., & Jones, A. M. (2013). Improvement of 800-m running performance with prior high-intensity exercise. International Journal of Sports Physiology and Performance, 8(1), 77–83.

Jiménez-Alfageme, R., Pino Garrone, F., Rodriguez-Sanchez, N., Romero-García, D., Sospedra, I., Giménez-Monzó, D., Ayala-Guzmán, C. I., & Martínez-Sanz, J. M. (2025). Nutritional intake and timing of marathon runners: Influence of athlete’s characteristics and fueling practices on finishing time. Sports Medicine – Open, 11, Article 26.

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Kishi, A., Millet, G. Y., Desplan, M., Lemarchand, B., & Bouscaren, N. (2024). Sleep and ultramarathon: Exploring patterns, strategies, and repercussions of 1,154 mountain ultramarathons finishers. Sports Medicine – Open, 10, Article 34.

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About the author

Jasper Fine

Jasper is a high school senior focused on the intersection of quantitative tools and exercise science, with a particular interest on the effects of nutrition on performance.

The post Optimizing Competitive Long-Distance Running Performance: An Interdisciplinary Analysis of Physiological, Behavioral, and Nutritional Inputs appeared first on Exploratio Journal.

Abstract

Invasive brain-computer interfaces (BCIs) have the ability to restore deprived functions in people with severe paralysis such as communication and movement. The longevity of said devices hinge on whether implanted electrodes can integrate into the brain’s system and record neural signals over time. However, the brain often walls off these rigid, high-density architectures, which records the best, causing chronic neuroinflammation, degrading signal recordings over time.

In this review, we categorize different neural interfaces into four distinct stages, each with their benefits and drawbacks: Intruder, Neighbor, Partner, and Symbiotic Organ. The Intruder stage offers the highest quality recordings, trading off disturbing tissues locally in the brain due to the rigidity of electrode arrays. The Neighbor stage utilizes flexible and mesh electronics that are more compatible with the softness of brain tissue. The Partner stage employs new materials: graphene, carbon nanotubes, and liquid metals, which constitutes functions beyond basic electrical recording. In the Symbiotic Organ stage, an interface would integrate itself  into the neural tissue and has the ability of self reparation, allowing it to function as a part of the nervous system instead of a separate implant.

This paradigm is not a strict categorization of different device, it offers a way to label different systems to their strengths and limitations. Some devices are often hybridized combinations of different stages, and newer designs are not automatically superior to predecessors. Issues with longevity are not limited to biological reactions such as inflammation or scarring; it can also derive from abiotic failures, such as connector failure, insulation damage, and material degradation. On the other hand, rigid arrays holds some of the strongest long-term human records in signal quality. Overall, optimal systems will come from a combination of co-designing materials as well as adaptive computation together. The goal is not only to record stronger signals, or more electrode channel counts, but also to build interfaces that can remain useful inside the brain for many years.

Fig. 1. The four-stage framework for the evolution of invasive neural interfaces: Intruder, Neighbor, Partner, and Symbiotic Organ. Image generated by the author using ChatGPT (OpenAI).

1. Introduction

Over the past decade, invasive BCIs have become physical experiments. They have restored basic functions, such as control and communication for individuals inflicted with severe paralysis, including cursor operations through neural control (Hochberg et al., 2006; Simeral et al., 2011), high-performance clinical neural prostheses (Gilja et al., 2015), reach-and-grasp with a robotic arm (Hochberg et al., 2012), brain-controlled functional electrical stimulation for reanimating the limb (Ajiboye et al., 2017), and high-bandwidth brain-to-text communication (Willett et al., 2021).

How much of this bottleneck is physical rather than computational remains contested. One line of work treats signal degradation as a hard ceiling: once units are lost to chronic tissue response or device failure, no decoder can recover information absent from the recording (Barrese et al., 2013; Chestek et al., 2011; Salatino et al., 2017). A second line demonstrates that a great deal of apparent degradation is nonstationarity rather than information loss and that closed-loop adaptation (Orsborn et al., 2014) and variability-robust decoder architectures (Sussillo et al., 2016) absorb substantially more drift than the first framing implies. We take the acquisition layer as the primary determinant of longevity while noting that its boundary with computation is precisely what remains unsettled, a point we return to in section 3.3. In the end, how well the systems architecture integrates into the brain is the most important factor: signal quality, longevity, and clinical usefulness all suffer if the connection degrades.

This constraint limits the modality of designs of Brain Computer Interfaces. Non-invasive systems such as electroencephalography (EEG) are safer and more accessible, but volume conduction and the skull’s spatial filtering cap their spatial resolution and signal-to-noise ratio, limiting the neural detail recoverable at the scalp (Srinivasan et al., 1998). Invasive interfaces instead capture local field potentials and spiking activity with millisecond precision and cellular-scale specificity, which is what makes high-performance applications possible (Hochberg et al., 2006; Wolpaw et al., 2002). Rigid intracortical arrays such as the Utah Array (Maynard et al., 1997) and high-density silicon probes of the Neuropixels class (Jun et al., 2017; Steinmetz et al., 2021) are the pinnacle of highest-fidelity approach.

Increased signal fidelity will often sacrifice clinical usefulness after an extended period of time. Many high-density systems that rely on rigid, silicon-based construction, whose elastic modulus are several times bigger in magnitude in comparison to brain tissue. The mechanical mismatch amplifies micromotion-induced strain and chronic injury (Minev et al., 2015; Salatino et al., 2017), which in turn drive persistent inflammation, glial scarring (gliosis), neuronal loss near the implant, and progressive signal decline (Minev et al., 2015; Salatino et al., 2017). This is the paradox at the core of BCI hardware: the structures best suited to high-precision electrophysiology are often the ones most prone to causing long-term biological disruption (Minev et al., 2015; Salatino et al., 2017). The field has therefore started a shift opted in more compatible materials and designs which are less physically disruptive to the brain. As a result, these systems counteract mechanical mismatch and immune response without sacrificing the signal quality needed for useful recording.

This framework is not strictly in a chronological timeline. Some devices hybridize between different stages: Neuralink combines high channel count with flexible materials such as threads and robotic insertion, while mesh electronics may behave as Neighbor or Partner systems depending on whether the emphasis is mechanical integration or multifunctional sensing. The categories are therefore not fixed rules, rather flexible, useful labels.

To organize this response, we introduce a four-stage framework for the evolution of invasive neural interfaces: Intruder, Neighbor, Partner, and Symbiotic Organ (see Fig. 1). The sequence describes a progressive and linear shift from rigid foreign implants toward biologically integrated, adaptive bioelectronic systems, and it gives us a consistent way to compare how each generation navigates the paradox between signal fidelity and biological integration.

2. Evolution of Invasive Neural Interface Technologies

Invasive neural interfaces have developed through distinct paradigms, each a different attempt to reconcile high-fidelity recording with long-term biological compatibility.

2.1 Rigid High-Density Electrode Arrays: The “Intruder”

The Intruder paradigm is often categorized by a pursuit of high-fidelity recording, utilizing rigid, silicon-based microelectrode arrays to receive signals. Devices such as the Utah Array and, more recently, Neuropixels probes are the pinnacle of  spatial resolution, channel counts, and temporal precision, properties that can easily detect single-neuron activity and dynamics in population(Jun et al., 2017; Maynard et al., 1997; Steinmetz et al., 2021). Other devices such as Neuralink have scaled this approach along, further raising the ceiling of channel counts and automating implantation while maintaining signal quality transfusion. (Musk & Neuralink, 2019). However, it must be said that Chronic instability is not an inherent property of rigid devices. Neuropixels 2.0 was designed explicitly around stable long-term recording, pairing a miniaturized probe with methods for tracking the same units across multiple sessions, for an extended period of time in various conditions (Steinmetz et al., 2021), a rigid device pursuing the stability as their objective is usually credited to compliant ones. The paradigms are connecting shared goals from opposite starting points, which is why they are better labeled as design logic instead of chronology. Mechanical rigidity is a key focus: it gives positional stability during implantation and precise targeting between cortical layers, making these arrays the gold standard for short- to medium-term performance in both experimental and clinical settings. The Utah Array and Neuropixels have high signal fidelity, but they often sacrifice biocompatibility.

Additionally, the same rigidity of the paradigm’s design focus is their defining limitation. Because the elastic modulus of silicon-based probes are magnitudes bigger than the brain cells, the implant sits in permanent state of conflict with the soft, viscoelastic tissue around it. Over time this provokes a chronic foreign-body immune response, persistent inflammation, glial scarring, neuronal loss at the electrode surface, and micromotion-induced strain (Minev et al., 2015; Salatino et al., 2017), which steadily lowers the quality of recordings over a long period of time (Chestek et al., 2011; Simeral et al., 2011).

This causal story is, however, is an incomplete summary of attempts at correcting these flaws. Barrese et al.’s (2013) failure-mode analysis of silicon arrays in vivo attributed a large fraction of failures not to the foreign-body response but to abiotic modes, including connector and percutaneous pedestal failure, insulation cracking and delamination, and progressive material degradation. Their retrospective analysis of seventy-eight silicon arrays implanted in twenty-seven macaques found that most complete recording failures were not attributable to the foreign-body response at all. Acute mechanical failure was the single largest class at 48 percent, and 83 percent of those traced to the percutaneous connector rather than to anything occurring at the electrode-tissue boundary; material failures, including broken tips, insulation leakage, and parylene cracking and delamination, accounted for most of the remainder. Grossly observable biological failures made up 24 percent, the most common single mechanism being progressive meningeal encapsulation (14.5%). Mechanical and material modes together thus account for roughly three-fifths of failures (Barrese et al., 2013). A large share of array loss therefore occurs in packaging, wiring, and connector hardware, components that compliance-based redesign of the electrode does not touch.

One qualification protects this claim from over-reading. Barrese et al. (2013) define failure as the absence of extractable action potentials across all channels of an array, so their categories register catastrophic device death rather than the gradual attrition of usable units. Progressive, gliosis-driven decline in unit yield, the degradation most consequential for BCI performance and precisely what the Neighbor paradigm targets, would largely not count as a failure under this definition. The 24 percent figure therefore bounds the biological contribution to total array loss, not to signal degradation. This is the sharpest available illustration of why sources in this literature so often appear to disagree: Barrese et al. (2013) measure when a device dies, whereas chronic-yield studies (Chestek et al., 2011; Simeral et al., 2011) measure how well it performs while alive, and conclusions drawn from one cannot be transferred to the other without argument.

The Intruder paradigm’s limitation is not simply that it is too stiff: it fails along at least two partly independent axes, and the more frequent one is not biological. The Intruder paradigm does not resolve the paradox; it accepts biological instability as the price of maximal instantaneous fidelity.

Fig. 2. Five high-density planar silicon probes ordered by shank width (a 32-channel NeuroNexus polytrode, 128- and 255-channel NeuroSeeker probes, and two Neuropixels variants), with edge recording sites shown in green and center sites in red. Reproduced from Fiáth et al. (2021), Figure 1 (CC BY 4.0).

Fig. 3. Scanning electron micrograph of the Utah Intracortical Electrode Array, a 10 × 10 grid of 1.2-mm silicon needles on a 4.2-mm-square base. Reproduced from Maynard et al. (1997), Figure 1.

2.2 Flexible and Mesh Electronics: Transitioning to a “Neighbor”

The Neighbor paradigm softens the interface to better integrate to the needs of neighbouring brain tissues. Work on syringe-injectable electronics and macroporous mesh architectures showed that ultraflexible, polymer-based electrode networks integrate more seamlessly into tissue, attenuating the chronic immune response and enabling stable recording over extended timescales in animal models (Liu et al., 2015; Xie et al., 2015). By lowering bending stiffness to approach the mechanical compliance of brain tissue, these systems reduce the micromotion-induced damage that defines the Intruder problem (Jeong et al., 2015; Zhang et al., 2020).

Bio-integration is just part of the upside. The porous architecture of mesh electronics allows tissue interpenetration and vascularization, so the implant will not clash and compete(Xie et al., 2015). Neighbor designs therefore prioritize long-term integration and stability over recording density, and this comes at a considerable cost. Flexible architecture, however, raises the issue of not hitting the necessary threshold of electrode counts, and because they cannot penetrate tissue unaided, delivery depends on syringe injection (Liu et al., 2015) or temporary stiffening shuttles, approaches that will inevitable complicate the surgical procedure and whose procedures to the human cortex remains untested. Even so, the flexible and mesh electronics architecture serves as proof of principle that long-term neural implants are viable  without defects such as tissue disruption. This decouples one side of the paradox entirely. Recent reviews furthers this idea, arguing that longevity and soft, flexible structures go hand in hand, rather than opting for rigid recording elements alone (E. Song et al., 2020).

Fig. 4. Syringe-injectable mesh electronics: the mesh design and single unit cell, injection into the mouse brain, and a comparison of the open mesh with a conventional flexible thin-film probe. Reproduced from Zhou et al. (2017), Figure 1.

2.3 Novel Material-Based Electrodes and Hybrid Designs: The Functional “Partner”

The Partner paradigm does not further increase along the biocompatibility pathway, instead it diverges. Where mesh electronics optimize a single figure in its metric, longevity of passive recording systems(Xie et al., 2015), transparent graphene arrays, an example of a partner paradigm, are built to increase a different metric altogether, combining electrophysiology with simultaneous optical access to the same circuit (Kuzum et al., 2014). The two paradigms, Partner and Neighbour, are not contrasting solutions; instead, they sought to answer differently about what an interface is for, and who they are designed for. Resulting in the direct performance comparison between them as largely meaningless, arbitrary numbers. This stage is defined by materials such as graphene, carbon nanotubes, conductive polymers, and liquid metals that make up the interface. Strictly chosen for a combination of purposes: conductivity, optical transparency, stretchability, and chemical sensitivity (Chapman et al., 2018; Hong & Lieber, 2019; E. Song et al., 2020). Partner interfaces excel through its multimodal and biomimetic interactions rather than passive electrical functions represented by previous two paradigms.

Materials illustrate this shift. Compounds such as transparent graphene electrodes permit electrical recording and optical access to the same tissue at once, permitting an alternative pathway to “view” the brain. Whilst, also enabling simultaneous electrophysiology and two-photon calcium imaging of a single circuit, a pairing that opaque metal electrodes from previous paradigms physically obstruct (Kuzum et al., 2014). Additionally, carbon nanotube coatings enlarge effective surface area whilst lowering impedance, improving electrode-tissue coupling and recording quality (Keefer et al., 2008). Liquid metal conductors add extreme deformability while maintaining electrical continuity, so the interface can track tissue motion (Wen et al., 2019). Together, these materials support researchers in a multimodal approach to record the brain’s activity. By measuring electrical activity and neurochemical signals at once, recent reviews stress that interface performance now hinges on micro and nanoscale engineering, not just electrode composition alone (Chapman et al., 2018; Hong & Lieber, 2019).

This expansion in multimodal approach introduces its own functional costs. Fabrication is often intricate and less feasible, long-term material stability under chronic in vivo conditions remains under investigation, and integration with conventional electronics presents abnormal challenges. However, it must be said that Partner systems expand what the interface can measure, but at the cost of fabrication, packaging, long-term reliability, and clinical translation.

Fig. 5. A fully flexible Parylene C neural probe whose PEDOT/silica-nanoparticle-coated chemical sites electrically release glutamate (Glu) or GABA while neighboring microelectrodes record the resulting increase or decrease in spike rate in rat cortex. Reproduced from Malekoshoaraie et al. (2024) (CC BY 4.0).

2.4 Emerging Injectable and Self-Adaptive Interfaces: Toward a “Symbiotic Organ”

The current frontier reframes the interface as an adaptive extension of the nervous system rather than a static implant. The Symbiotic Organ paradigm proposes a structure of self-healing bioelectronic materials, injectable or self-deploying architectures, and closed-loop systems that respond to changes in neural and tissue state. This vision inherits the Partner stage’s costs and compounds them. The fabrication complexity and uncertain chronic material stability that constrain graphene, nanotube, and liquid-metal interfaces (Keefer et al., 2008; Kuzum et al., 2014; Wen et al., 2019) do not disappear when those materials are asked to additionally self-heal, self-deploy, and operate under closed-loop control; they multiply, and they are joined by power and telemetry demands that passive recording never faced. Self-healing interfaces, for instance, restore electrical and mechanical integrity after mechanical damage, a route to durability that does not depend on avoiding damage in the first place (K.-I. Song et al., 2020).

Dynamic adaptation is what distinguishes this stage. Where earlier paradigms rely on fixed geometries and static material properties, these systems continuously renegotiate the balance between signal quality and biological stability; coupling adaptive decoders with closed-loop control lets the interface respond to neural variability and drift in real time (Orsborn et al., 2014; Sussillo et al., 2016). In principle, an interface that evolves alongside the tissue it inhabits dissolves the paradox rather than trading around it, and bioinspired neuron-like designs push toward that goal by minimizing the structural distinction between device and neural tissue (Yang et al., 2019).

For now, this paradigm is still only a proof of concept, with unresolved obstacles in power delivery, wireless telemetry, computational load, long-term reliability, and safety. They define a long-term direction for the field rather than a deployable technology, and the gap between experimental demonstration and clinical translation remains wide.

Fig. 6. Conceptual vision of a symbiotic neural interface combining adaptive materials, tissue integration, intelligent on-chip electronics, and a wireless ecosystem. Image generated by the author using ChatGPT (OpenAI).

3. Discussion and Future Directions

The move from rigid intracortical arrays toward adaptive, tissue-integrated systems reflects a shift in the field’s underlying design philosophy. Early BCI work was driven by maximal signal fidelity and spatial resolution, predicated on the assumption that the highest-quality neural data would translate into robust decoding and clinical utility. Decades of chronic-implant experience overturned that assumption: performance depends not on instantaneous signal quality but on the long-term stability of the interface as a coupled biological-technical system. That realization reoriented the field’s priorities from absolute electrophysiological performance toward sustained harmony among tissue, materials, and computation, the transition the Intruder-to-Symbiotic-Organ progression describes.

3.1 Reconciling the Engineering Paradox: A Multi-Dimensional Optimization Problem

The paradox, that high-precision electrophysiology favors rigid, dense structures while long-term biocompatibility demands soft, compliant, adaptive ones has never been solved outright. Each stage handles this trade-off differently.

The Intruder paradigm buys fidelity by absorbing the biological cost: Utah arrays and Neuropixels probes set the benchmark for recording density and temporal resolution, and they accept chronic tissue disruption as the consequence of achieving it. But the accounting is less clean than the paradox implies. If abiotic failure modes contribute as heavily to array loss as Barrese et al. (2013) report, part of what the Intruder paradigm “pays” is not a biocompatibility cost at all, and no amount of movement along the compliance axis will refund it. The Neighbor paradigm moves the balance the opposite way, matching interface mechanics to tissue to lower inflammation and extend recording lifetimes, at the expense of electrode density, targeting precision, and spatial stability over time. The Partner paradigm widens the problem rather than picking a side: by adding optical transparency, neurochemical sensing, or stretchable multimodality, it redefines “performance” beyond electrical metrics, and in doing so exchanges a binary rigidity-versus-compliance choice for a harder question of fabrication and integration. The Symbiotic Organ paradigm attempts to remove the trade-off entirely, letting self-healing materials, self-deploying architectures, and closed-loop control adapt the interface to changing demands, an approach that, for now, exchanges the old paradox for unresolved problems of power, reliability, and safety. Read together, the four stages show the paradox migrating from a fixed constraint into a multi-dimensional optimization problem, not disappearing.

3.2 Insights from Cross-Technology Comparison and Application-Driven Design

No single paradigm dominates across every performance dimension. Rigid arrays lead on fidelity and spatial resolution, flexible and mesh electronics on chronic biocompatibility, material-based hybrids on functional diversity, and symbiotic concepts on adaptability and autonomy. The absence of a universally superior interface indicates that the field is bounded by fundamental trade-offs, not merely by incremental engineering inefficiencies.

Two implications follow. First, progress will come from context-specific optimization rather than a single “best” interface: applications demanding fine motor control or high-bandwidth speech decoding will likely continue to require high-density invasive arrays (Musk & Neuralink, 2019; Pandarinath et al., 2017; Willett et al., 2023), while long-term neuromodulation or monitoring may favor softer, lower-density systems built for chronic stability. Second, this makes application-driven design central: interface architecture should be matched to the physiological, temporal, and functional requirements of the specific use case rather than optimized in the abstract.

3.3 Persistent Field-Wide Challenges and the Need for Co-Design

Several challenges cut across all four paradigms. Long-term biostability is the central one, spanning not only immune response and tissue remodeling but also material degradation, delamination, and electrochemical drift over multi-year timescales. Manufacturing scalability and reproducibility form a parallel obstacle, especially for complex material systems that perform well in the laboratory but resist high yield consistent industrial fabrication. Power delivery and data telemetry impose further limits, particularly for wireless or minimally invasive implants, where energy efficiency and thermal safety become hard constraints.

Computation faces its own version of these problems. Representational drift, non-stationary signal statistics, and user-specific variability complicate decoder design and limit cross-session generalization. Where the ceiling on algorithmic mitigation actually sits is unresolved: results on closed-loop decoder adaptation (Orsborn et al., 2014) and variability-robust architectures (Sussillo et al., 2016) show that computation absorbs more instability than hardware-centric accounts assume, while chronic-yield data (Chestek et al., 2011; Simeral et al., 2011) show it cannot manufacture units that are no longer recorded. The disagreement is not merely academic, because the two positions imply different investments, one in materials, the other in algorithms, and neither literature can settle it alone. This is the strongest argument for co-design: the allocation question is unanswerable unless hardware and computation are developed and evaluated jointly. This interdependence is the argument for co-design, integrating hardware, materials science, and computation from the earliest stages of development rather than optimizing each in isolation. Recent reviews of electronic and flexible neural interfaces reach the same conclusion: future progress depends on tighter integration across materials, device engineering, and system-level signal processing (Hong & Lieber, 2019; E. Song et al., 2020; Zhang et al., 2020).

3.4 Global Perspectives and Collaborative Trajectories

Regional programs bring complementary strengths. In the United States, academic and industrial work often prioritizes high-performance invasive systems and advanced decoding, driven by clinical-translation initiatives and defense funding; Neuralink exemplifies the emphasis on channel count and surgical automation, while others pursue hybrids that balance performance against longevity. In China, research increasingly emphasizes materials science, flexible electronics, and minimally invasive platforms, drawing on strengths in large-scale manufacturing and material synthesis. European groups tend to lead in soft bioelectronics and neuromodulation, prioritizing safety, regulatory compatibility, and long-term implantation. The complementarity suggests that major breakthroughs are more likely to come from cross-border collaboration than from any single region’s dominance.

3.5 Near-Term Breakthroughs: Hybrid Systems at the Interface of Paradigms

A more cautious near-term expectation is that clinically relevant progress will come from partial hybrids rather than fully symbiotic systems. These platforms aim to combine signal quality sufficient for meaningful decoding with improved chronic stability and manageable fabrication complexity. Fully symbiotic systems, though the ultimate goal, still face barriers in power management, reliability, and regulatory approval that make near-term deployment unlikely. The next wave of translational impact is therefore more likely to come from incremental but durable gains in interface longevity, paired with adaptive decoding and closed-loop control.

4. Conclusion

High-performance BCIs could change neuroscience, medicine, and human-machine interaction, but they still face a basic problem: the clearest signals often come from devices that are difficult for brain tissue to tolerate over time. This review traced the field’s response through four stages: rigid Intruder systems, flexible Neighbors, multifunctional Partners, and emerging Symbiotic Organ concepts, each addressing the paradox through a different set of trade-offs. Chronic stability has improved, functional modalities have expanded, and adaptive behaviors have entered the design space, yet no existing technology optimizes every critical dimension at once. Reaching genuinely symbiotic interfaces will require sustained, interdisciplinary work across materials science, system integration, computational neuroscience, and ethical considerations. Four limitations in particular remain unresolved, and each cuts across all stages of the framework. First, an abiotic failure ceiling: because connector, insulation, and material degradation account for a substantial share of chronic array loss (Barrese et al., 2013), improvements in biocompatibility cannot by themselves extend device lifetime. Second, a translation gap: the chronic-stability case for compliant interfaces rests largely on rodent work (Liu et al., 2015; Xie et al., 2015), while the longest human recordings remain those of rigid arrays (Simeral et al., 2011). Third, power delivery and telemetry impose hard energy and thermal constraints on precisely the wireless, minimally invasive systems the Symbiotic Organ concept requires. Fourth, the division of labor between interface and algorithm is still contested (Chestek et al., 2011; Orsborn et al., 2014; Sussillo et al., 2016), leaving open how much instability future systems must engineer away rather than decode around. The clear direction of travel, however, is toward tissue-aware, adaptive design, interfaces that stop behaving as foreign bodies and begin functioning as durable, integrated collaborators within the nervous system.

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About the author

Anthony Qian

Anthony is a senior at Stevenson School with a strong interest in neuroscience, artificial intelligence, and brain-computer interfaces. His academic interests lie at the intersection of biology, computer science, and machine learning, particularly in understanding brain function and developing technologies that interpret neural activity and human behavior.

Through independent research and hands-on computational projects, Anthony has explored machine learning, behavioral data analysis, and neural signal processing. He is passionate about interdisciplinary research, scientific problem-solving, and applying emerging technologies to advance our understanding of the human brain.

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