Abstract

Texas maintains the largest state bridge inventory in the United States, creating a need for transparent tools that can support condition screening across a large network. We evaluate the 2025 Texas National Bridge Inventory (NBI), which contains 56,951 bridges labeled Good, Fair, or Poor. We aim to predict the bridge condition using 26 features including inventory, traffic, geometric, service, material, and deck-design while excluding component ratings and other fields that would leak the target. Cumulative-threshold ordinal logistic regression is evaluated with a five-fold stratified cross-validation procedure. We compare three operating approaches: standard ordinal logistic regression, fully class-weighted ordinal logistic regression, and a directional safety-cost decision rule applied to the standard model’s probabilities. The ordinary model provides the highest overall accuracy (72.45%) but identifies only 14 of 680 Poor bridges (2.05%). The class-weighted model identifies 580 Poor bridges (85.29% recall), and achieves 58.32% accuracy. The directional cost rule reduces the most severe Poor-to-Good error to 3 cases (lowest among all models) but identifies only 291 Poor bridges exactly and mislabels 386 as Fair. We therefore select the class-weighted model for a safety-first screening objective that prioritizes exact recognition of actually Poor bridges. Its 10,338 conservative Poor alerts create additional inspection workload, but they are not treated as the same safety failure as overlooking an actual Poor bridge. The study contributes a Texas-only, statewide 2025 Good/Fair/Poor evaluation with leakage control, reproducible validation, class-sensitive training, and explicit directional-error analysis.

Introduction

Bridge inspection data support decisions about maintenance, rehabilitation, replacement, and inspection scheduling. The National Bridge Inventory provides a consistent federal record of bridge attributes and condition information, making it a major source for data-driven infrastructure research. Texas is especially important because the 2025 NBI contains 56,951 Texas bridges, more than any other state. A network of this scale makes manual review of every record difficult and increases the value of transparent screening models that can organize attention without replacing engineering inspection.

Machine learning can relate observed condition labels to age, traffic, geometry, material, location, and other inventory characteristics. However, the apparent performance of a model depends on the target definition, the prediction horizon, the state or national population, and the treatment of class imbalance. In bridge data, Poor-condition examples are rare. A model can therefore achieve respectable overall accuracy by fitting the large Good and Fair groups while failing to recognize the class of greatest safety interest. This makes the confusion matrix, per-class recall and precision, balanced accuracy, and macro-F1 essential complements to accuracy.

The direction and distance of an error also matter because Good, Fair, and Poor are ordered. Any actual Poor bridge assigned Fair or Good is underpredicted; assigning it Good is the more severe two-level error because the label is especially reassuring. Errors in the opposite direction, such as assigning an actual Good or Fair bridge to Poor, are conservative over-alerts that may increase inspection workload but do not hide deterioration. We therefore make exact Poor recall and total Poor underprediction the primary safety measures, while still reporting accuracy, error direction, and false-alert burden for transparency.

We aim to (1) establish what prior research has and has not done with Texas NBI data; (2) distinguish training loss, class weighting, and post-training decision costs; (3) build a leakage-controlled ordinal classifier with 26 predictors; and (4) identify which transparent operating approach most effectively recognizes actually Poor bridges. The primary selection objective is to maximize exact Poor recall and minimize Poor bridges assigned to Fair or Good; Poor-to-Good remains the most severe type of underprediction.

Background

Machine learning for bridge condition prediction using NBI dataset

Research using state and national NBI subsets has examined current condition classification, next-inspection prediction, and long-term deterioration trajectories. The targets are not interchangeable. Some studies predict deck, superstructure, or substructure ratings; others predict structural evaluation or an overall rating derived from the minimum component condition. Studies also differ in whether the original 0-9 NBI scale is retained, ratings are grouped into binary classes, or conditions are grouped as Good, Fair, and Poor. Reported accuracy values should therefore be interpreted inside each study’s target and validation design rather than ranked as if they measured the same task.

State-specific work demonstrates the usefulness of geographically focused modeling. Assaad and El-Adaway (2020) compared computational approaches for Missouri deck deterioration. Rashidi Nasab and Elzarka (2023) evaluated multiple algorithms and feature selection methods for Ohio concrete bridge decks. Mia and Kameshwar (2023) predicted future component-condition classes from Louisiana histories. Liu and El-Gohary (2020, 2022) used Washington bridge data and deep-learning methods for deterioration prediction. These studies show that state-specific models are established, but their component targets, history requirements, and imbalance methods differ from a single-year statewide overall-condition classifier.

National studies provide larger and more varied training populations. Fard and Fard (2024) used more than one million historical U.S. records to predict deck ratings with random forest, XGBoost, and artificial neural networks. Chand and Choe (2026) evaluated 142,265 U.S. reinforced-concrete bridges and reported state-level performance for Texas and other subgroups. Such work includes Texas data but does not constitute Texas-only training and testing. The distinction is important because a national model can learn interstate patterns that may not match a Texas-exclusive population.

What prior research has done specifically in Texas

The 2019 TxDOT report requires careful classification. Its title and abstract identify its main purpose as a synthesis of bridge service-life prediction methods and practices for Texas. It is not primarily a machine-learning bridge-condition paper. Nevertheless, Chapter 4 contains an embedded case study using 1994-2016 Texas NBI records. The case study used 289,529 bridge-year observations, designated NBI Item 67 structural evaluation as the response, compared Naive Bayes, logistic regression, and decision-tree classifiers, and used a 75/25 split. Table 7 reports 0.95 accuracy, precision, and recall for the decision tree. The source is therefore adjacent at the report level but direct within its embedded ML subsection (Gao et al., 2019, PDF pp. 58-60).

Fang et al. (2023) is a direct bridge-condition prediction article. Its introduction states that feature engineering was performed on Texas bridge inventory data. The authors built four binary models for deck, superstructure, substructure, and structural evaluation, grouping ratings 0-6 and 7-9. The model-development sample counts range from 34,764 to 55,476, and the target-specific held-out accuracies range from 77.62% to 88.57%. The implementation case is different: Section 5 applies the structural-evaluation model to South Carolina bridges. Fang et al. is therefore Texas-specific in model development but not Texas-exclusive from data preparation through practical application.

Zhang et al. (2024) is the closest verified Texas-only overall-condition forecasting precedent. The study uses Texas NBI histories from 1992 to 2022, selects features with ReliefF, trains an Elman neural network, and uses a Markov chain to describe future deterioration. The accessible article reports 5,600 eligible bridge histories and a 70/20/10 training, validation, and testing division. Its overall-condition target and Texas-only history are directly relevant, but its longitudinal prediction problem differs from the present cross-sectional 2025 Good/Fair/Poor classification.

Two 2025 studies further limit broad originality claims while clarifying the present gap. Bayat, Kharel, and Li (2025) is an ASCE Technical Paper in the Journal of Structural Design and Construction Practice. It predicts Texas bridge deck condition from TxDOT historical data using autoencoder representations with random forest, XGBoost, and neural networks. Bayat and Kharel (2025) is an open-access research article focused on 1,443 Texas off-system bridge samples, including only eight observations in Condition 4. It combines focal-loss GAN augmentation, Tomek Links, and random-forest classification to improve minority-condition recognition. Both are Texas-only condition studies, but both focus on component or off-system subsets rather than the complete 2025 statewide overall Good/Fair/Poor population.

Loss functions, imbalance, and ordinal safety

Cross-entropy, or log loss, is the standard objective for probabilistic multiclass classification. Class weighting modifies this objective so that errors from rare classes have greater influence during fitting. Focal loss modifies cross-entropy by reducing the influence of easy examples and emphasizing difficult examples. Liu (2019) compared multiclass focal loss with cross-entropy for bridge deterioration, and later studies by Liu and El-Gohary (2020, 2022) extended focal-loss bridge analytics. Bayat and Kharel (2025) provides a direct Texas precedent for focal-loss-based imbalance treatment. Focal loss is therefore not itself a new Texas bridge contribution.

Sampling procedures must be discussed separately. SMOTE and ADASYN synthesize minority observations, undersampling removes majority observations, Tomek Links cleans overlapping class boundaries, and GANs generate additional data. These methods change the training sample rather than the loss. If used, they must be fitted only inside training folds to avoid contamination of validation or test data. The present first experiment uses class weighting because it is easy to audit and does not generate synthetic combinations of coded inventory attributes.

Class weighting and focal loss change how strongly training emphasizes difficult or rare examples. We use cumulative-threshold logistic regression to respect the order Poor < Fair < Good and compare ordinary training with full inverse-frequency class weighting. We also apply a directional cost matrix to the ordinary model’s probabilities as a sensitivity analysis. The cost matrix is a post-training decision rule, not a separately trained third model. Because this study’s primary safety objective is exact recognition of actually Poor bridges, the final approach is selected by Poor recall and total Poor underprediction rather than average cost alone.

Positioning of the present study

The verified literature does not support a claim that machine learning has never been used to model Texas bridge condition. It does support a narrower contribution. None of the verified sources matches the complete combination of the official 2025 Texas population, an overall ordered Good/Fair/Poor target, 26 leakage-controlled predictors, pooled five-fold stratified validation, and an explicit comparison of severe underprediction with false-alert burden. We therefore describe the contribution as a distinct statewide modeling and evaluation design rather than the first use of machine learning on Texas bridges.

Methodology

Dataset, Preprocessing, and Feature Engineering

We use the Federal Highway Administration’s (FHWA) 2025 National Bridge Inventory dataset for Texas. Each row represents one bridge record, and the structure identifier is unique in the analysis file. The final population contains 56,951 bridges. The target is the FHWA bridge-condition category: Good (G), Fair (F), or Poor (P). The distribution is 29,423 Good bridges (51.66%), 26,848 Fair bridges (47.14%), and 680 Poor bridges (1.19%). The severe imbalance of the Poor class motivates class-sensitive evaluation.

Twenty-Six Features and Leakage Control

We use 26 predictors. Fourteen are numeric:
Age_027,
Age_Reconstructed_106,
TRAFFIC_LANES_ON_028A,
TRAFFIC_LANES_UND_028B,
ADT_029,
DEGREES_SKEW_034,
MAIN_UNIT_SPANS_045,
APPR_SPANS_046,
MAX_SPAN_LEN_MT_048,
STRUCTURE_LEN_MT_049,
ROADWAY_WIDTH_MT_051,
APPR_WIDTH_MT_032,
DECK_WIDTH_MT_052,
PERCENT_ADT_TRUCK_109.
Twelve coded fields are categorical:
SERVICE_LEVEL_005C,
BASE_HWY_NETWORK_012,
FUNCTIONAL_CLASS_026,
DESIGN_LOAD_031,
SERVICE_ON_042A,
SERVICE_UND_042B,
STRUCTURE_KIND_043A,
STRUCTURE_TYPE_043B,
DECK_STRUCTURE_TYPE_107,
SURFACE_TYPE_108A,
MEMBRANE_TYPE_108B,
DECK_PROTECTION_108C.
Age_Reconstructed_106 uses the reconstruction year when it is positive and otherwise falls back to the original build year.

Deck, superstructure, substructure, culvert, structural-evaluation, and lowest-rating fields are excluded. The bridge-condition target is derived from component condition information, so using those fields as predictors would allow the model to recover the label directly and inflate performance. The structure identifier is retained only for uniqueness and audit checks, not as a predictor. All preprocessing is contained within the model pipeline so that imputation, scaling, and category encoding are learned separately from each training fold rather than from held-out records.

Model Development and Validation

We use five-fold stratified cross-validation as the primary validation design. Records are shuffled with random seed 42 and divided into five approximately equal folds. Each bridge is evaluated once by a model that was not trained on that bridge, producing one pooled out-of-fold prediction for every record. Imputation, scaling, category encoding, and any class weights are learned only from each training fold. Pooling the five validation folds produces a confusion matrix over all 56,951 bridges while preserving out-of-sample evaluation for every record. We fit cumulative-threshold ordinal logistic regression. One binary logistic equation estimates whether condition is above Poor, and a second estimates whether condition is above Fair; the two outputs are combined into valid Good/Fair/Poor probabilities. We evaluate three operating approaches on identical folds: (1) ordinary ordinal logistic regression with the highest-probability prediction, (2) the same ordinal structure with full inverse-frequency class weighting during training, and (3) a directional safety-cost sensitivity rule applied to the ordinary model’s probabilities. The third approach changes the final decision, not the fitted coefficients. The class-weighted model is the selected safety-first model because it yields the highest exact Poor recall and the fewest total Poor underpredictions.

How to read Table 2: a cost is a decision weight, not a number of bridges. For an actual Poor bridge, predicting Good receives the largest penalty (20), while predicting Fair receives a smaller penalty (10). This ordering reflects the study’s assumption that a Good label is the most falsely reassuring outcome. For each bridge, the selected prediction is the class with the lowest expected cost after the model probabilities are multiplied by the matrix. A zero on the diagonal represents a correct prediction.

Why Logistic Regression?

We use logistic regression because it is a standard baseline for classification, its coefficients and predicted probabilities are inspectable, and the calculation is simple enough to reproduce in a spreadsheet after preprocessing. These properties make the model easier to audit than a more complex black-box method. The ordinal cumulative-threshold construction also uses the known order Poor < Fair < Good rather than treating the labels as unrelated categories.

Evaluation Protocol and Metrics

We compute every metric from the pooled five-fold out-of-fold confusion matrix. For each class k, TP is the number correctly predicted as k, FP is the number incorrectly predicted as k, and FN is the number of actual k bridges assigned another label. Accuracy measures all exact classifications. Precision asks how often a predicted class is correct; recall asks how much of an actual class is found; and F1 balances precision and recall. Balanced accuracy and macro-F1 give each class equal influence. Because Poor is rare and underprediction is the main safety concern, we also report exact Poor recall, the Poor-underprediction rate, Poor-to-Good and Poor-to-Fair counts, and false Poor alerts. The model-selection hierarchy is: first maximize exact Poor recall, then examine the severity of remaining underpredictions and overall accuracy. Conservative false Poor alerts are reported as workload rather than treated as the same safety failure.

Accuracy = (TP_G + TP_F + TP_P) / N

Precision_k = TP_k / (TP_k + FP_k)

Recall_k = TP_k / (TP_k + FN_k)

F1_k = 2(Precision_k x Recall_k) / (Precision_k + Recall_k)

Balanced accuracy = (Recall_G + Recall_F + Recall_P) / 3

Macro-F1 = (F1_G + F1_F + F1_P) / 3

Poor underprediction rate = (Poor-to-Good + Poor-to-Fair) / Actual Poor

Results & Discussion

Five-fold cross-validated performance

The ordinary ordinal model has the highest accuracy, 72.45%, because it performs well on the dominant Good and Fair classes. It correctly identifies only 14 of 680 Poor bridges, however, for 2.06% Poor recall. Twenty-nine actual Poor bridges are labeled Good and 637 are labeled Fair. Overall accuracy alone therefore does not provide adequate evidence for rare-condition screening.

Full class weighting is the selected safety-first model. It identifies 580 of 680 actually Poor bridges as Poor, giving 85.29% Poor recall, and underpredicts only 100 Poor bridges: 63 as Fair and 37 as Good. This is the smallest total Poor underprediction among the three approaches. Its overall accuracy is 58.32%. It also produces 10,338 predicted-Poor alerts for actual Fair or Good bridges, including 1,850 Good-to-Poor conservative two-level errors. Under the study’s stated objective, these are accepted as additional inspection workload because they do not hide an actual Poor bridge.

The directional cost rule is retained as a sensitivity analysis. It reduces actual-Poor-to-predicted-Good errors to 3 of 680, but 386 Poor bridges are labeled Fair and only 291 are labeled Poor. Exact Poor recall is therefore 42.79%, and the total Poor-underprediction rate is 57.21%. Although the rule has the lowest average directional cost under Table 2, that average also rewards avoiding thousands of conservative false-Poor alerts. It does not match the present safety-first preference, which accepts those alerts in exchange for recognizing more actually Poor bridges exactly.

Implications for loss functions and decision costs

The three approaches answer different operational questions, but the paper makes one primary recommendation. If statewide exact accuracy is the only objective, the ordinary model is strongest. For the study’s safety-first objective – assigning as many actually Poor bridges as possible to Poor and accepting conservative alerts – the class-weighted ordinal model is strongest. The directional cost rule is preferable only under a different objective that places greater weight on reducing false-Poor workload and specifically minimizing Poor-to-Good errors, even when many Poor bridges remain labeled Fair.

Reproducibility

The polished analysis notebook downloads the official FHWA TX25.txt file, records its SHA-256 hash, constructs the 26 predictors, fixes randomized operations at seed 42, fits preprocessing only within training folds, and regenerates every table and figure. It also preserves a bridge-level interactive map. The processed dataset and code are available upon request.

Limitations

This analysis is cross-sectional. It classifies 2025 condition labels from 2025 inventory attributes and does not yet demonstrate prediction of a future inspection rating. A future forecasting study should construct bridge histories, use earlier years as predictors, and evaluate a later year or multi-year horizon without allowing future information into training. Five-fold random validation measures within-year generalization; geographic, district-held-out, and temporal validation may be more demanding.

The directional cost matrix is an explicit research assumption rather than a calibrated agency cost model. Its values should be reviewed with bridge engineers and asset managers. Poor precision in the selected class-weighted model is limited because only 1.19% of the inventory is Poor, so conservative screening creates many additional inspections. That workload is a real operational limitation even though it is not the same safety error as overlooking deterioration. The cumulative-threshold logistic model is transparent and respects class order but may not capture nonlinear relationships. The literature review is focused rather than registered and systematic, so the contribution claim remains limited to the verified sources. Future work should add temporal and district-held-out validation and should calibrate the class weights and decision costs with agency inspection capacity.

Conclusion

We conclude that statewide Texas bridge-condition modeling should be evaluated against the decision it must support, especially the consequence of underpredicting an actually Poor bridge. The ordinary ordinal model reaches 72.45% accuracy but identifies only 14 of 680 Poor bridges. The selected class-weighted ordinal model identifies 580 Poor bridges exactly, achieving 85.29% Poor recall and reducing total Poor underpredictions to 100. The directional cost rule reduces Poor-to-Good errors to 3 but identifies only 291 Poor bridges exactly and leaves 386 in Fair. Therefore, class weighting provides the best match to the study’s safety-first objective.

Our recommendation is qualified. The selected model’s 58.32% accuracy is lower than the ordinary baseline, its Poor precision is 5.31%, and it generates 10,338 conservative Poor alerts for actually Fair or Good bridges. It should therefore be used as a screening model whose Poor predictions trigger review, not as an autonomous condition assignment or a replacement for inspection. The ordinary model remains stronger when raw accuracy is the sole objective, while the directional cost rule remains useful when minimizing Poor-to-Good errors and inspection workload is more important than maximizing exact Poor recall.

The literature establishes that Texas bridge-condition machine learning is not entirely new. Texas-only precedents include Zhang et al. (2024) and recent deck-focused studies, while Fang et al. (2023) uses Texas data for model development and a South Carolina implementation case. Our work remains distinct in its combined use of the complete 2025 Texas inventory, an overall ordered Good/Fair/Poor target, 26 leakage-controlled predictors, pooled five-fold out-of-fold evaluation, class-weighted ordinal modeling, and a directional cost sensitivity analysis. Future work should test future-year forecasting, district-held-out validation, nonlinear ordinal models, and engineer-reviewed class-weight and cost calibration.

References

Assaad, R., & El-Adaway, I. H. (2020). Bridge infrastructure asset management system: Comparative computational machine learning approach for evaluating and predicting deck deterioration conditions. Journal of Infrastructure Systems, 26(3). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000572

Bayat, M., & Kharel, S. (2025). Leveraging artificial intelligence for predictive maintenance and condition rating of off-system bridges. Applied Sciences, 15(21), 11301. https://doi.org/10.3390/app152111301

Bayat, M., Kharel, S., & Li, J. (2025). An autoencoder-based machine- and deep-learning approach for predicting bridge deck conditions in Texas. Journal of Structural Design and Construction Practice, 30(4), 04025080. https://doi.org/10.1061/JSDCCC.SCENG-1799

Chand, P., & Choe, D.-E. (2026). Bridging the gap: A comprehensive analytical study of traditional, hybrid, and explainable AI for bridge condition prediction. Results in Engineering, 29, 109791. https://doi.org/10.1016/j.rineng.2026.109791

Fang, J., Hu, J., Elzarka, H., Zhao, H., & Gao, C. (2023). An improved inspection process and machine-learning-assisted bridge condition prediction model. Buildings, 13(10), 2459. https://doi.org/10.3390/buildings13102459

Fard, F., & Fard, F. S. N. (2024). Development and utilization of bridge data of the United States for predicting deck condition rating using RF, XGBoost, and ANN. Remote Sensing, 16(2), 367. https://doi.org/10.3390/rs16020367

Federal Highway Administration. (2025). National Bridge Inventory: Texas delimited data file (TX25.txt). https://www.fhwa.dot.gov/bridge/nbi/2025/delimited/TX25.txt

Federal Highway Administration. (n.d.). Specifications for the National Bridge Inventory. https://www.fhwa.dot.gov/bridge/snbi.cfm

Gao, L., Mo, Y.-L., Dhonde, S., Saldarriaga, D., Song, L., & Senouci, A. (2019). Synthesis of service life prediction for bridges in Texas: Final report (FHWA/TX-19/0-6938-1). Texas Department of Transportation. https://rosap.ntl.bts.gov/view/dot/63293/dot_63293_DS1.pdf

Liu, K. (2019). Machine learning-based analytics of structured and unstructured bridge data for data-driven bridge deterioration prediction [Doctoral dissertation, University of Illinois Urbana-Champaign]. https://www.ideals.illinois.edu/items/113975

Liu, K., & El-Gohary, N. (2020). A smart bridge data analytics framework for enhanced bridge deterioration prediction. Construction Research Congress 2020. https://par.nsf.gov/servlets/purl/10295991

Liu, K., & El-Gohary, N. (2022). Deep learning-based analytics of multisource heterogeneous bridge data for enhanced data-driven bridge deterioration prediction. Journal of Computing in Civil Engineering, 36(3). https://doi.org/10.1061/(ASCE)CP.1943-5487.0001018

Mia, M. M., & Kameshwar, S. (2023). Machine learning approach for predicting bridge components’ condition ratings. Frontiers in Built Environment, 9, 1254269. https://doi.org/10.3389/fbuil.2023.1254269

Rashidi Nasab, A., & Elzarka, H. (2023). Optimizing machine learning algorithms for improving prediction of bridge deck deterioration: A case study of Ohio bridges. Buildings, 13(6), 1517. https://doi.org/10.3390/buildings13061517

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About the author

Temitope Ogunyomi

Temitope (Tope) O. Ogunyomi is a 12th-grade student at Foster High School in Richmond, Texas. Tope is a 3x Junior Olympian and Junior Olympic bronze medalist in track and field. Beyond athletics, he is interested in engineering, technology, machine learning, and using research to address real-world problems. Tope hopes to continue pursuing academic research and athletics in college while developing solutions that positively affect communities.

The post Safety-Aware Ordinal Logistic Regression for Bridge-Condition Classification Using the 2025 Texas National Bridge Inventory appeared first on Exploratio Journal.

1. Introduction

Environmental, social and governance (ESG) ratings are a framework to score companies based on three broad criteria. Firstly, the environmental dimension considers a firm’s impact on the natural ecosystem. Secondly, the social dimension evaluates the firm’s relationship with its stakeholders: customers, suppliers and communities. Lastly, governance focuses or the board structure and diversity.

Rating the ethics of a company isn’t a new practice. Before ESG, there was corporate social responsibility (CSR) in the 1990s and even prior to that there was socially responsible investing (SRI) in the 1970s ((Krantz, 2024). But neither CSR nor SRI ever gained the recognition ESG has in the past decade. This can be accounted for due to a key difference in their foundations. Measures like CSR and SRI believed and conveyed that businesses should behave ethically because it is morally the correct thing to do. In contrast, ESG ratings aim to score a company’s sustainability and vulnerability to external factors – variables that link directly to monetary returns. As a result, the rigorous set of variables that ESG evaluates makes it an excellent metric to assess a company’s performance through factors that don’t appear on the traditional financial balance sheets. Hence, more and more investors are starting to incorporate these ratings, along with the financial data, to determine a company’s financial return on investment (ROI).

However, the author’s evaluation of rating methodology of Crisil ESG Ratings & Analytics Ltd, a central ESG provider in India, reveals a crucial flaw in current ESG calculation that significantly hinders ESG’s ability to indicate financial performance. Currently a uniform ESG pillar weightage framework of 40% governance 35% environment and 25% social is used across all sectors. While this may be apt for sustainability indication it hinders financial performance indication as the uniform weightage scheme doesn’t account for sector-specific materiality differences. For example, the environment pillar may be far more important for an energy company then for an information technology, for whom governance may be more important. To address this limitation, this section empirically investigates whether sector-specific ESG pillar weighting systems improves the strength of the relationship between ESG scores, and return on capital Employed (ROCE) and aims to find the most optimum ESG weighting systems for 12 sectors. Thus, the research paper aims to answer the question:

What is the optimal sector-specific weighting system of environmental, social, and governance pillars for explaining variation in ROI, and do these optimized models outperform equal-weight ESG frameworks in the Indian market?

The research paper is structured as follows. It begins with a literature review on the importance of considering a company’s qualitative aspects while investing and studies regarding correlation between ESG and ROI. Followed by this is the development of a sector specific ESG weightage scheme through correlations between ESG and return on capital employed (ROCE). The paper concludes by outlining limitations and identifying avenues for future research.

2. Literature Review

2.1 The Efficiency of Traditional Financial Metrics

Investors have relied on and further developed financial analysis for nearly a century. As a result, most indicators of future growth have already been discovered and are heavily used by investors. The Efficient Market Hypothesis, as developed by Fama (1970) states that in a semi-strong efficient market publicly available financial information is rapidly priced in. Consistent with this, Green, Hand, and Zhang (2013) show that most well-documented return predictors lose statistical significance once widely known, as arbitrage activity erodes whatever informational edge they carried.  

These findings collectively suggest that due to most forms of analysis of traditional financial metrics being well known company stocks tend to be priced accordingly. Due to this, it has become increasingly difficult to generate excess returns through these forms of financial analysis and investors must look for other sources of information.

2.2 Unexplained Volatility and the Case for Qualitative Metrics

Traditional financial variables explain less of stock price behavior than is commonly assumed. Roll (1988) documented that financial and industry factors only accounted for a small part of individual stock variation; the remainder being determined by firm specific qualitative factors. This is because financial analysis can’t predict regulatory exposure, reputational events, stakeholder dynamics, and governance failures.

More recent empirical work further proves this trend. Edmans (2011) finds that firms with strong employee satisfaction earn returns of roughly two to three percent annually above market benchmarks and these results persist over time. Similarly, Hartzmark and Sussman (2019) indicate that investor invest more money in response to sustainability signals even in underlying financial variables remain constant. This indicates qualitative firm characteristics influence share prices through channels that conventional financial metrics miss. 

This pattern along with the previous section show that quantitative financial data is priced quickly and efficiently, while qualitative signals are under processed. That gap is where investors can earn excess returns. ESG frameworks are an excellent cumulation of these qualitative indicators. The question is whether it reports this information in a way that is useful for predicting financial performance or does its goal of reporting sustainability cause it to stray from being an indicator of future financial performance.

2.3 Correlation Between ESG and Financial Performance

There is extensive literature on ESG and financial performance. The broad findings can be summarized by Friede, Busch, and Bassen (2015) review of over 2,000 empirical studies, finding that 90% reported a non-negative relationship between ESG scores and financial performance, with the majority showing a positive one. While this isn’t perfect it shows potential.

The study by Khan, Serafeim, and Yoon (2016) distinguish between ESG factor that are financially material to a given industry and those that are not. Firms outperform their competitors when they score well on the material ESG factors while the immaterial ones show no correlation at all.

These two studies, taken together show that currently ESG scores have not reached their financial performance indication potential, primarily due to composite scores not accounting for differing ESG factor materiality across different firms. This is a result of the uniform ESG weightage scheme across all sectors. Hence, this literature review shows that developing a new sector- specific ESG weightage system, so that the final ESG scores give more importance to the more material factors based on the sector, can make ESG an indicator of future financial performance. Furthermore, this would allow investors to identify companies not valued for their qualitative factors allowing them to earn excess returns.  

3. Proposed Sector-Specific Optimized ESG Weighting scheme and Correlation with ROCE

3.1 Methodology

3.11 Variables

Table 3.11  Variables Note. Author made

3.12 Data Sourcing and Sector Classification 

The study uses ESG scores published by Crisil for the financial years 2023 and 2024. The sample comprises 748 listed Indian companies spanning 12 sectors. The 12 sectors were decided to ensure simplicity as well as homogeneity in regard to main pillar importance in each sector.

The following sector classification has been used:

Table 3.12  Sector classification Note. Author made

Firm-level ROCE data was collected from two independent, reliable financial databases: “Screener” and “Equitymaster”. This ensured consistency and cross-verification of financial performance data. The ESG data for composite scores and main pillar scores was obtained from Crisil’s archives. Companies with incomplete ESG pillar scores or missing ROCE data were excluded.

3.13 Framework of Quantitative Analysis

The strength of the relationship between ESG scores and ROCE was measured using Spearman’s rank correlation coefficient ( . This is beneficial as outliers are extremely common in sectoral ROCE data, making it important to reduce their impact of empirical results as done by this metric. All correlations were computed on a sector-by-sector basis to account for structural heterogeneity across industries and correlations were reported to an accuracy of two decimal places to enforce accuracy.

A python program was developed to generate sector specific main pillar weightage systems that were more optimized ROCE indicators based on Crisil’s individual main pillar scores, environmental, social and governance. For each firm i in sector s, the composite ESG score was defined as:

subject to:

Two sets of weightage systems were generated:

1. Unconstrained Model:

The objective of this model was to generate weightage systems that maximized ESG’s correlation with ROCE. No lower bound for a main pillars weightage was set, which means the model could deliver a weightage scheme in which the optimum weightage for a main pillar for a sector is 0%.

1. Constrained Model:

The objective of this model remains the same as the unconstrained model but a lower bound for a main pillars optimum weight was set at 15%. By setting this minimum at 15% the conceptual integrity of ESG evaluation is preserved.

For each sector, three correlations were calculated to draw comparisons:

1. Crisil’s composite ESG score and ROCE
2. Unconstrained optimized ESG score and ROCE
3. Constrained optimized ESG score and ROCE

3.2 Baseline: Correlation Under Crisil’s Fixed Weightage Scheme

Crisil’s ESG framework calculates composite scores through a uniform weighting structure across sectors. Governance with the highest weightage (40%), followed by environmental (35%), and lastly social (25%). This allows comparability but assumes that environmental, social, and governance risks affect all industries equally. To test this assumption, sector-wise correlations between ESG scores (under Crisil’s weighting scheme) and ROCE were computed.

Table 3.2 Sector-Wise Correlation Between ESG and ROCE (Crisil Weightage Scheme) Note. Author’s calculations based on raw data from souces:18, 19, 20 and 21

The results demonstrate substantial heterogeneity not only in the magnitude but also in the economic interpretation of the ESG–ROCE relationship across sectors. 

Industries such as communication services, informational technology demonstrated strong positive correlations due to the structural importance of governance quality and regulatory compliance (governance and environment have high weightages in Crisil’s weightage scheme). In contrast, financials consumer discretionary – auto (-0.02) show near-zero correlations. This can be accounted to the fact that in financials, ROCE may be influenced more heavily by macroeconomic cycles driven through context rather than ESG scoring differentials. As for auto manufacturing, majority of the companies aren’t automobile brands themselves but brand that produce automobile parts. Due to the massive information asymmetries in India and more lenient production rules these companies tend to a low correlation with ESG in present. Additionally, cyclical demand fluctuations, technological transitions (e.g., EV shifts), commodity price volatility, and high fixed-cost structures materially shape ROCE. For this very reason, it was separated from the consumer discretionary sector to not obscure the correlation of the rest of the companies in that sector.

Relatively moderate positive correlations in energy, industrials, healthcare, consumer staples, consumer discretionary – durables and services, materials, real estate and utilities) suggest partial alignment but also potential miss weighting of pillars. For instance, environmental factors may be economically dominant in energy, but Crisil’s model still gives the environmental pillar the lowest weightage.

Collectively, these patterns indicate Crisil’s weightage scheme is able to predict financial performance more strongly for companies that whose ESG factor materiality aligns with Crisil’s weightage scheme. The findings therefore provide substantive economic reasoning for investigating sector-optimized weighting models rather than relying on a single standardized ESG structure.

3.3 Optimized ESG Weightage Scheme 

To test whether sectoral customization improves explanatory power, ESG weights were optimized to maximize correlation with ROCE. A python program was used to come up with unconstrained and constrained weightage schemes to maximize ESG’s correlation with ROCE.

Table 3.31  Sector-Wise Unconstrained Optimization System’s Correlation with ROCE Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics

As the results indicate, the unconstrained weighting system results in theory significant jumps in ESG’s correlation with ROCE. However, in practice these could vary significantly based on the data set used and since a lot of weightages for environmental, social or governance pillars are near zero in some sectors this weighting scheme pulls away from the very concept of ESG. Thus, it is of more relevance to determine and the optimized weighting system that is constrained at a minimum of 15% weightage for each main pillar as this keeps the core, multidimensional concept of ESG intact while allowing for a generalization as the most appropriate sector-specific weightage scheme in the Indian Market.

Table 3.32  Sector-Wise Constrained Optimization System (Minimum 15%) Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics

Under the constrained optimization system, communication services record the strongest correlation under the constrained framework, reflecting a very tight alignment between ESG performance and capital efficiency. Information technology and energy also demonstrate relatively strong positive associations due to the government-dominant allocations (G ≈ 0.68) that influence oversight, capital discipline, and regulatory management. Moderate correlations are observed in consumer staples, real estate, industrials, and utilities as while the optimization of weightages accounts for sector materiality ESG factors there are sector specific aspect in each that are not accounted by ESG factors.

However, financials and consumer discretionary – auto continue to exhibit weak correlations despite governance-heavy weightings (0.70 in both sectors). This suggests that even when pillar emphasis is adjusted to reflect sector logic, ESG scores may not translate strongly into ROCE variation due to reasons explained in detail in 3.2 which ESG factors simply can’t account for. ESG performance may influence long-term strategic positioning or risk mitigation in these sectors.

These findings indicate that while sector-specific optimization improves statistical alignment in several industries with most demonstration a strong or moderate correlation it cannot uniformly overcome structural characteristics that weaken the ESG–ROCE transmission mechanism. The constrained framework proposed therefore demonstrates the strengths and limits of ESG factor’s ability to indicate financial performance: its predictive power is considerable when firms are affected by the qualitative factors ESG reports but gets weaker when financial outcomes are driven primarily by macroeconomic or capital-cycle forces.

3.4 Comparative Performance: Crisil vs Proposed Sector-Specific Weighting Scheme

Table 3.4 Comparative Performance: Crisi’s Weightage scheme vs Author’s Proposed Weightage Scheme  Note. Author calculated based on raw data from Equitymaster, Screener and Crisil Ratings and Analytics

As indicated by the data, applying sector specific weightages drastically improves the correlation between ESG and ROCE across most sectors. The most significant improvement is seen in real estate (consumer discretionary – auto and financials have high percentage improvement but correlation is still near-zero) as the proposed weightage scheme gives allows for governance to drive the composite score as in India it is the most material to financial performance. Energy Consumer Staples and Utilities have also shown strong improvements due to improved sector-specific materiality considerations by the proposed weightage scheme. Moderate improvements can be seen in Consumer Discretionary – Durables & Services, Information Technology, Healthcare, Materials. Only two sectors Communication Services and Industrials showed small improvements as for both Crisil’s weightage scheme fairly rated these companies through their weightage system leaving little room for improvement.  

Taken together the table demonstrates the need for sector-specific weightage schemes for ESG calculation. The proposed weightage scheme gives importance to ESG pillars based on their sector specific materiality to financial performance allowing for ESG to become a substantial indicator of financial performance.

3.5 How to Calculate ESG Scores for Multi-Sectorial Companies.

The weightage schemes this research paper has presented makes it clear as to the method used to calculate the most reliable ESG ratings for most companies. However, there are certain companies who don’t entirely fit into one sector. An example of this is reliance limited.

For a multi-sector company like Reliance Industries Limited, the ESG score should be a weighted sum of the scores calculated using each sector’s optimal weightage.

The General Formula:

Where:

• ​W_sector is the percentage of revenue from that sector
• ESG_sector is the ESG score calculated using the optimal weights for that specific sector.

4. Strengths and Limitations of the Quantitative Analysis

This study has several methodological strengths. Data used is from reputable sources: Screener, Equitymaster and Crisil Ratings and Analytics. Furthermore, the study only uses data available to everyone publicly which makes this highly relevant for retail investors and smaller firms, not just larger corporations. The clear segregation into 12 sectors makes using the proposed weightage scheme straightforward and the system also accounts for multi-sectoral companies. Additionally, the use of ROCE as the dependent variable strengthens the analysis by focusing on operational capital efficiency instead of market-based performance. This aligns ESG evaluation with fundamental financial productivity. Statistically, the Spearman’s correlation was used to reduce weightage of outliers as when data is limited outliers could significantly harm reliability of the conclusion. Lastly, the implementation of both unconstrained and constrained optimization frameworks allows for differentiation between purely statistical maximization and theoretically grounded calibration. 

However, certain limitations must be acknowledged. The study is conducted over a limited period, due to lack of publicly available ESG data in India. This potentially restricts insights into the stability of optimized weightages overtime and across economic cycles. Secondly, ESG scores are published annually as an aggregate which doesn’t account for intra-year variation in sustainability performance that could have affected ROCE. Sizes of sector-level datasets may have also affected overall results. Most importantly, the dataset is exclusively composed of Indian companies. As a results, the proposed weightage scheme is only directly applicable to India and potentially other economies at similar stages of development. This is because the sector-specific weightage system gives importance to the pillars based on their materiality due to the Indian context such as local information asymmetries and the tendency of Indian government to impose regulation.  For example, it is likely that a highly developed country has low information asymmetry regarding business practices (between businesses and consumers), whereas in an underdeveloped or develop this asymmetry may be higher. Therefore, the optimized weightage schemes identified in this study are most appropriate for India or countries with comparable institutional and developmental characteristics. Due to this same concept, these ESG weightage systems are also only relevant in India while it remains in its current broad development stage.

5. Concluding Remarks

This research has examined the evolving role of ESG as an indicator of corporate financial performance within the Indian context. While frameworks like those provided by Crisil have significantly improved the accessibility of sustainability data, this study identifies a critical gap in the application of uniform, non-sector-specific weighting systems. Such models fail to capture the unique operational parameters and types of risks inherent to different industries, hence diluting the correlation between ESG performance and financial returns.

Through empirical analysis and quantitative modeling, this paper produces a sector-specific weighting scheme, tailored to the environmental, social, and governance priorities of specific industries, that serves as a far more robust indicator of ROCE. By improving the framework of existing ESG practices as financial performance indicators this paper aims for ESG ratings to be more widely used alongside financial metrics while predicting the future share performance of a firm. 

The proposed framework also carries broader implications for markets and society. By providing a stronger and more consistent correlation between ESG performance and financial returns, this framework increases the likelihood that sustainability considerations will be integrated into investment decision-making. This could lead to qualitative factors regarding companies’ sustainability and ethics also being priced in (as financial metrics are now) in the long run. Once this has happened more companies are likely to make most decisions keeping consequences regarding sustainability and ethics in mind with the profit-maximizing goal of improving their stock price. This will result in huge positive externalities as more firms demonstrate responsible environmental stewardship, ethical governance structures, and socially sustainable practices. In the long run, such alignment between financial incentives and sustainability outcomes has the potential to reinforce ethical corporate conduct and hence have considerable benefits to society from an economic and social perspective. 

6. Future Scope for Research

The author intends to pursue further research and publication in the areas outlined below, building upon the analytical framework developed in this study.

1) Cross-Country Optimal Weightage Comparative Studies: Applying this optimization framework across multiple countries at varying stages of development would determine whether sectoral ESG relevance is development dependent. It would provide insights into how differing governments’ regulation tendencies and information asymmetries across the development spectrum affect the ideal ESG weightages for financial performance indication. Moreover, conclusions from such a study could be significant as it could lead to a significant alteration in the methodology of major multinational ESG providers like MSCI transforming the ESG landscape completely and leading to much higher correlations between ESG and ROI globally. 

2) Multi-Year Longitudinal Analysis Across Economic Cycles: Extending this study over a larger time frame once there is Indian ESG data available over more years would make this system far more reliable and would also allow for insights into how the optimum ESG weightages change across sectors through economic cycles. 

3) High-Frequency or Quarterly ESG Data: If quarterly ESG metrics become available in India, studies on how ESG shocks influence ROCE within shorter intervals. This would be useful to determine the speed of ESG-financial transmission mechanisms which would be useful for investors as it will indicate the average time they have to act on information reported by ESG.

4) ESG in Stress Periods: Since ESG is primarily an indicator of sustainability, such a study would indicate whether firms with higher ESG ratings are able to outperform competitors during stress periods.  

References

“Crisil ESG Ratings & Analytics Limited.” CRISIL, 2021, www.crisilesg.com/en/home.html.

“Crisil ESG Ratings & Analytics Ltd Gets SEBI Nod to Offer ESG Ratings.” CRISIL, 2024, www.crisilratings.com/en/home/newsroom/press-releases/2024/04/crisil-esg-ratings-and-analytics-limited-gets-sebi-nod-to-offer-esg-ratings.html.

Edmans, Alex. “Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices.” Journal of Financial Economics, vol. 101, no. 3, 2011, pp. 621-640.

Equitymaster – Get Unbiased Equity Research on Indian Stocks and Share Market Updates for Value Investing in India. Equitymaster, 2025, www.equitymaster.com.

“ESG Ratings 2023.” CRISIL, 2023, www.crisilesg.com/en/home/esg-ratings/historical-ratings/esg-ratings-2023.html.

“ESG Ratings Criteria and Methodology.” CRISIL, 2025, www.crisilratings.com/content/dam/crisilesg/criteria-and-methodology-for-crisil-esg-ratings/archives/criteria-and-methodology-for-esg-ratings-feb-2025.pdf.

“ESG Ratings List 2024.” CRISIL, 2024, www.crisilesg.com/en/home/esg-ratings/historical-ratings/esg-ratings-2024.html.

Fama, Eugene F. “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance, vol. 25, no. 2, 1970, pp. 383-417.

Friede, Gunnar, et al. “ESG and Financial Performance: Aggregated Evidence from More than 2000 Empirical Studies.” Journal of Sustainable Finance & Investment, vol. 5, no. 4, 2015, pp. 210-233.

Green, Jeremiah, et al. “The Supraview of Return Predictive Signals.” Review of Accounting Studies, vol. 18, no. 3, 2013, pp. 692-730.

Hartzmark, Samuel M., and Abigail B. Sussman. “Do Investors Value Sustainability? A Natural Experiment Examining Ranking and Fund Flows.” The Journal of Finance, vol. 74, no. 6, 2019, pp. 2789-2837.

Khan, Mozaffar, et al. “Corporate Sustainability: First Evidence on Materiality.” The Accounting Review, vol. 91, no. 6, 2016, pp. 1697-1724.

Krantz, Tim. “The History of Environmental Social and Governance (ESG).” IBM, 8 Feb. 2024, www.ibm.com/think/topics/environmental-social-and-governance-history.

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“Stock Screener and Fundamental Analysis Tool for Indian Stocks – Screener.” Screener, www.screener.in.

About the author

Nimay Shah

Nimay is an IB Diploma student with a keen interest in finance, economics, and quantitative research. His work focuses on applying mathematical and data-driven methods to improve investment analysis, particularly in sustainable finance and ESG.

Through this research, Nimay aims to bridge the gap between academic theory and practical investing by developing more effective approaches to evaluating corporate performance. He hopes to pursue business, finance, or economics at university and continue exploring the intersection of data, markets, and sustainability.

The post ESG–ROI Linkages in India: Developing Sector-Specific Weightage Frameworks for Optimized Financial Performance Indication appeared first on Exploratio Journal.

Abstract

Cancer is a collection of diseases characterized by uncontrolled cellular proliferation driven by genetic and epigenetic alterations that disrupt DNA repair, cell cycle regulation, and apoptosis. These changes allow cancer cells to grow and divide without immune detection, making cancer one of the leading causes of mortality worldwide. Although treatments such as chemotherapy and radiation therapy can eliminate malignant cells, they often cause significant damage to healthy tissues, particularly the bone marrow, and fail to restore normal biological function after treatment. Hematopoietic stem cell transplantation addresses this limitation by restoring blood and immune cell production following high-dose cancer therapy. The focus is on how this approach compensates for treatment-induced damage while supporting long-term disease control. The discussion includes the biological role of hematopoietic stem cells, the different types of transplantation, and the process of immune system reconstitution after therapy. It also examines how transplanted stem cells migrate to the bone marrow, reestablish hematopoiesis, and restore essential physiological functions. Key points include the immune-mediated anticancer effect observed in allogeneic transplantation, the extended time required for immune recovery, and the procedure’s associated risks, including graft-versus-host disease. Stem cell transplantation is unique because it not only eliminates cancer but also restores the biological systems necessary for long-term survival, making it a critical strategy for treating malignancies that affect the hematopoietic and immune systems.

Cancer and its Impact on the Body

Cancer is a collection of diseases characterized by uncontrolled cellular proliferation driven by genetic and epigenetic alterations that disrupt DNA repair, cell cycle regulation, and apoptosis. These changes let cancer cells grow and divide without being detected by the immune system, which is why cancer is one of the major causes of mortality around the world. The disease is particularly severe because cancer cells can spread through the bloodstream and lymphatic system to other parts of the body, where they can damage vital organs and cause systemic effects that make the immune system less effective, change metabolism, and make it harder for the body to handle stress (Gil et al., 2008). These effects are particularly significant in hematologic malignancies, which arise from blood-forming tissues critical for immune defense and oxygen transport (Bair et al., 2020). Even though oncology has come a long way, many treatments we have now cause significant damage to healthy tissues, especially the bone marrow, and do not restore biological function to normal levels after treatment (National Cancer Institute, 2023). 

Hematopoietic stem cell transplantation offers a potential solution to this limitation by restoring blood and immune cell production following high-dose cancer therapy. In allogeneic contexts, it can also provide an immune-mediated anticancer effect by enabling donor cells to detect residual malignant cells. Stem cell transplantation differs from other treatments because it can both regenerate the hematopoietic system and contribute to cancer control, making it a significant approach in the treatment of blood malignancies. 

This paper explores the biological mechanisms, clinical applications, and therapeutic significance of hematopoietic stem cell transplantation as a strategy to address the limitations of conventional cancer treatment.

Problems with Current Cancer Treatments 

Chemotherapy, radiation therapy, surgery, and, more recently, targeted therapy and immunotherapy are all essential aspects of how cancer is treated today (National Cancer Institute, 2023). In some cases, these strategies work well, but they all have huge problems. Chemotherapy stops cells that are quickly dividing from making copies of their DNA or going through mitosis. But this lack of specificity harms the proliferation of healthy cells, especially hematopoietic stem cells in the bone marrow (National Cancer Institute, 2023). This is why patients have anemia, neutropenia, thrombocytopenia, and very weak immune systems, which makes them more likely to get sick and hemorrhage (Bair et al., 2020). Chemotherapy can have long-term complications, including making it hard to get pregnant, hurting the heart, and helping other tumors grow. Radiation therapy affects DNA by using high-energy particles or electromagnetic radiation. Radiation is effective for tumors that are only in one place, but it can also injure healthy tissues nearby, leading to fibrosis, persistent inflammation, and organ malfunction (National Cancer Institute, 2023). Radiation is harmful to bone marrow, which makes hematologic toxicity worse. Surgery is effective for numerous solid tumors, but it is ineffective for cancers that disseminate throughout the body, such as leukemia and lymphoma.

When tumors have spread or are close to essential organs, surgery may not be able to help with solid tumors. Targeted therapies and immunotherapies are major advances, as they aim to block specific oncogenic pathways or enhance the immune system’s ability to identify neoplastic cells. Some individuals respond well to these medicines, but if the tumor becomes resistant to them, they may stop working as effectively. Immunotherapies can make the immune system exceedingly sick. Damage to the hematopoietic system remains a major problem despite various treatments. People can’t create new blood and immune cells when their bone marrow isn’t working well (Appelbaum, 2007). This fundamental issue is what makes stem cell transplantation a beneficial approach in medicine.

Stem Cells and Their Role in Hematopoietic Recovery 

Stem cells are undifferentiated cells that have two crucial biological traits:

• The power to keep renewing themselves

• The capacity to differentiate into several cell types.

Hematopoietic stem cells produce blood cells throughout a person’s life through a process called hematopoiesis. To be more specific, they make:

• Red blood cells that deliver oxygen

• White blood cells that keep the body healthy

• Platelets that help repair tissue and make clots

Most of the time, these stem cells live in the bone marrow microenvironment. However, they can also be transported into the peripheral circulation or retrieved from the umbilical cord blood (Appelbaum, 2007). They are invaluable in cancer treatment because they can fully restore the blood and immune systems. This is especially true when chemotherapy or radiation has killed off the body’s own stem cells.

Using Stem Cell Transplants to Treat Blood Malignancies 

Leukemia, lymphoma, multiple myeloma, and myelodysplastic syndromes are the most frequent types of blood cancer that stem cell transplantation is used to treat. Since these malignancies arise in blood-forming organs, it is typically necessary to replace the damaged hematopoietic system to achieve long-term remission. There are three primary types of stem cell transplantation. In autologous transplantation, stem cells are taken from the patient before rigorous therapy and then returned to the patient after treatment. This approach prevents the immune system from rejecting the cells, but it could also allow cancer cells to return. In allogeneic transplantation, stem cells are obtained from a donor whose human leukocyte antigen markers closely match those of the recipient (Appelbaum, 2007). This allows for the complete replacement of the diseased marrow and induces an immune-mediated anticancer effect. Syngeneic transplantation, which uses an identical twin as a donor, is the best way to match, but it doesn’t happen very often. You can obtain stem cells from the blood in your bone marrow, peripheral blood, or umbilical cord. Peripheral blood stem cell transplantation is currently the most popular procedure because it speeds engraftment and immunological recovery (National Cancer Institute, 2023).

How Transplantation and Immune Reconstitution Work 

Before the transplant, patients go through a conditioning program that may or may not include high-dose chemotherapy (Appelbaum, 2007). This treatment kills off existing bone marrow and gets rid of any cancer cells that are still there. After training, healthy stem cells are released into the bloodstream and travel to the bone marrow, where they connect and begin making new blood cells.

The biological steps of transplantation are as follows:

• Conditioning-induced marrow ablation

• Infusion of stem cells and their migration to marrow niches

• Engraftment and the first restoration of blood cells

• Rebuilding the immune system over time

Blood cell counts usually return to normal within weeks, but it might take months to years for the immune system to recover, especially following a fully allogeneic transplant. One of the best things about allogeneic transplantation is that it can help fight leukemia. Donor immune cells recognize remaining cancer cells as foreign and actively destroy them (Appelbaum, 2007). This lowers the likelihood of relapse and improves long-term outcomes. But this same immune response can cause graft-versus-host disease, in which donor immune cells attack healthy tissues in the recipient, most often the skin, liver, and digestive tract (National Cancer Institute, 2023).

Issues and Plans for the Future

Even if stem cell transplantation could heal people, it is still an extremely risky and complicated surgery. Patients endure prolonged immune suppression, increased susceptibility to infections, organ damage resulting from conditioning treatments, and protracted recovery periods. Transplantation incurs significant psychological and financial burdens. Current research seeks to optimize donor matching, promote stem cell engraftment, alleviate immune-mediated problems, and develop reduced-intensity conditioning regimens (Aljagthmi et al., 2025). Advancements in stem cell biology and immune control present opportunities to improve safety while preserving therapeutic effectiveness.

Conclusion 

Stem cell transplantation is one of the most advanced biological treatments for cancer, especially for malignancies that develop in the blood system. Stem cell transplantation brings back the important biological systems needed for long-term life, unlike standard treatments that just destroy cancer cells. This medication involves a lot of hazards, but scientists are constantly finding new ways to improve it and make it more useful. Stem cell transplantation will remain an essential aspect of cancer treatment and a promising area for future medical advances as we learn more about how stem cells and the immune system function.

References

Gil, J., Stembalska, A., Pesz, K. A., & Sasiadek, M. M. (2008). Cancer stem cells: The theory and perspectives in cancer therapy. Journal of Applied Genetics, 49(2), 193–199.

Liu, P., & Hu, Q. (2024). Engineering cells for cancer therapy. Accounts of Chemical Research, 57, 2358–2371.

Zhang, C. L., Huang, T., Wu, B. L., He, W. X., & Liu, D. (2017). Stem cells in cancer therapy: Opportunities and challenges. Oncotarget, 8(43), 75756–75766.

Zhang, Y., et al. (2023). Intracavity generation of glioma stem cell–specific CAR macrophages primes locoregional immunity for postoperative glioblastoma therapy. Nature Biomedical Engineering.

Emerging strategies for engineering Escherichia coli Nissle 1917-based therapeutics. (2014). Wiley Interdisciplinary Reviews: Systems Biology and Medicine.

National Cancer Institute. (2023). Stem cell transplants in cancer treatment. https://www.cancer.gov/about-cancer/treatment/types/stem-cell-transplant

Appelbaum, F. R. (2007). Hematopoietic cell transplantation. New England Journal of Medicine, 357(15), 1472–1485.

Bair, S. M., Brandstadter, J. D., Ayers, E. C., & Stadtmauer, E. A. (2020). Hematopoietic stem cell transplantation for blood cancers in the era of precision medicine and immunotherapy. Cancer, 126(9), 1837–1855. https://share.google/rA0DvxNWjRkbf1Gjq

Aljagthmi, A. A., & Abdel-Aziz, A. K. (2025). Hematopoietic stem cells: Understanding the mechanisms to unleash the therapeutic potential of hematopoietic stem cell transplantation. Stem Cell Research & Therapy, 16(1), 60. https://share.google/tsfuAPVczotv1G5lr

About the author

Sophia Zheng

Sophia is a junior, currently interested in biology and various areas of medical research. She really enjoys reading scientific papers on cells, diseases, genes, and multiple treatments. In the future, She hopes to be a part of the medical system and help society find more ways to treat diseases.

The post Stem Cell Transplantation: An Emerging Therapeutic Approach for Cancer appeared first on Exploratio Journal.

Abstract

India’s Unified Payments Interface (UPI) has reshaped the country’s financial landscape, establishing India as the global leader in real-time digital payment adoption. However, this rapid expansion has been accompanied by a corresponding rise in digital payment fraud, raising concerns for consumer protection and the effectiveness of structural systems. Applying Routine Activity Theory, the paper analyzes literature to explain this outcome as the product of a convergence between motivated offenders, suitable targets, and absence of capable guardians. Recent studies show that victimization remains consistent despite awareness of digital payment frauds. Educated and technologically aware users continue to fall victim to complex psychological manipulation. Once targeted, victimization largely depends on the exploitation of cognitive biases. Institutional guardianship remains India’s most persistent weak point. Victims recover a fraction of the stolen funds, redressal mechanisms are largely unsatisfactory, and legal loopholes leave a majority of victims outside of the protection system. The paper concludes with recommendations to put a stop to legal loopholes, modernize legal frameworks, and add an additional layer of protection at the point of transaction rather than relying solely on user vigilance.

Introduction

India’s creation and adoption of the Unified Payment Interface (UPI) is an example of one of the most rapid financial digitisation initiatives in recent history, expanding from 20 million transactions in  FY 2016-17 to over 241.62 billion in FY 2025-26 (Ministry of Finance, 2026). This represents a 12,000-fold increase.  In 2012, over 87% of all transactions in India were conducted in cash, and fewer than 10% of the population had ever used any non-cash payment instrument (Iyer, 2017). Today, India accounts for 48.5% of worldwide real-time digital payment transactions, making it the global leader in digital payments (Singh and Agarwal, 2025). Furthermore, of the total retail digital payment transactions, UPI accounted for over 81% of them, emerging as the largest real-time retail payment system globally (Ministry of Finance, 2025). Between April 2016 (at launch) and March 2026, the number of banks live on UPI increased from 21 to 703. These statistics prove that UPI has evolved from merely being a means of payment to a financial infrastructure critical to India’s economy.

However, the same expansion that produced this success also generated a fraud crisis of proportionate scale. According to the Association of Certified Fraud Examiners, fraud is any activity that relies on deception in order to achieve a gain. Fraud becomes a crime when it is a knowing misrepresentation of the truth or concealment of a material fact to induce another to act to his or her detriment. Within the UPI ecosystem, fraud commonly involves social engineering and other deceptive practices that manipulate users into authorising transactions or disclosing sensitive credentials. The number of reported UPI fraud cases rose from 195,000 in 2020-21 to 7,25,000 in 2022-23, while the amount lost increased from ₹111 crore (approximately $15 million as per the exchange rates for FY 2020-21) to ₹573 crore (approximately $72.4 million as per the exchange rates for FY 2022-23) in the same period (Agrawal, 2026). Across all digital payment modes, fraud cases increased from 1,19,699 in 2020-21 to 14,57,000 in 2023-24, recording a compound annual growth rate (CAGR) of 130.03%, which outpaces the 40-105% annual growth in UPI transactions (Kumar and Dharshan, 2026). Iyer (2017) documented that card fraud in India increased in direct proportion to card adoption throughout 2011–2016, with losses doubling from ₹4 billion ($89.58 million as per the 2011 exchange rates) to ₹7.68 billion ($113.56 million as per the 2016 exchange rates) as card usage expanded. Moreover,  according to Wadkar et al. (2025), UPI fraud accounts for 47.25% of all cybercrime cases reported in India since 2020.Understanding why fraud has scaled at this rate requires examining not only the architecture of UPI itself, but the behavioral and structural conditions that enable exploitation. Statistics alone cannot explain why UPI fraud has scaled at this rate or why users remain vulnerable despite high awareness. This paper applies Routine Activity Theory (Cohen and Felson, 1979) to examine the structural and behavioral conditions that enable this crisis.

Theoretical Framework:

According to Routine Activity Theory (Cohen and Felson, 1979), a criminal event occurs when three elements converge in both time and space: a motivated offender who perceives an opportunity for gain, a suitable target, and the absence of a capable guardian who can prevent the crime. Additionally, Cohen and Felson argue that changes in routine activities, such as shifts in work patterns, leisure activities, and household arrangements, impact the convergence of motivated offenders, suitable targets, and the lack of capable guardians. This, in turn, affects crime rates. Scholars have since extended RAT beyond street crime to fraud contexts. Holtfreter et al. (2008) conducted a study that extended the Routine Activity Theory (RAT) to the context of consumer fraud victimization,  using a sample of 922 participants. Their study found that a one-unit increase in the buying routine index (a measure of how frequently respondents engaged in consumer activities such as online shopping) was associated with a 61% increase in the odds of being targeted for fraud.

Holtfreter et al. (2008) also tested Self-Control Theory alongside RAT, finding that while low self-control significantly increased the probability that a targeted individual would become a victim (302% per unit increase), it had no effect on whether the individual was targeted in the first place. This targeting/victimization distinction is directly relevant to the UPI fraud paradox examined later in this paper: why do 52.5% of users become victims despite 96.2% awareness? Once targeted, a user’s capacity for deliberate, cautious decision-making (rather than prior knowledge alone) determines victimization outcomes.

DeLiema (2018) reinforces this by applying RAT to study financial fraud against older adults, examining 53 cases of financial exploitation. The study investigated interpersonal financial exploitation amongst adults aged 65 years or older. The research found that social isolation, which means a lack of capable guardians like trusted friends, family, or oversight from institutions, was the main factor allowing these crimes to happen. The study also highlighted that perpetrators actively worked to create this isolation. They limited the victims’ social interactions to increase dependence before taking advantage of them.

Scale and Nature of the Problem:

Before examining specific fraud techniques, it is important to establish why the architecture of the Unified Payments Interface (UPI) itself creates a structurally suitable target for fraud. Understanding these design characteristics provides the foundation for explaining how offenders exploit the system. The simple and easy-to-use nature of UPI makes it an appealing option for online banking: the user’s phone number is linked to their bank accounts, debit cards, and credit cards through a platform, which allows for instant transactions at no cost. Singh and Agarwal (2025) state that while this attribute provides convenience, it also means that a single phone number becomes the single point of compromise that gives the attacker access to all of the user’s financial information simultaneously.

Sharma, Matharu and Sinha (2026) surveyed 183 UPI users in the Shimla district. As the study was confined to a single district and employed a relatively small sample, its findings have limited generalisability to the wider Indian population. The study found that although 96.2% of respondents reported being aware of UPI fraud, 52.5% had nevertheless become victims. The authors concluded that awareness alone may not translate into safe behaviour, highlighting behavioural factors such as overconfidence and susceptibility to social engineering. By contrast, Singh and Agarwal (2025) surveyed 280 respondents in Varanasi, excluded 30 inconsistent responses, and analysed 250 valid cases. Using the Friedman ranking test, the study identified awareness (mean rank = 6.72) and education (mean rank = 6.42) as the most effective countermeasures against UPI fraud. The two studies are not necessarily incompatible. Rather, when taken together, they suggest that awareness and education are seen as the best defences, but they do not always prevent fraud in practice. The difference in district, sample size, and research design may also contribute to the contrast.

In a comparative study of India, Sweden, and the United States of America, it was found that losses due to card fraud in India grew at an average annual rate of 12.19% between 2011 and 2016 (Iyer, 2017). Sweden, however, despite having the highest percentage of card transactions among the countries studied, demonstrated the lowest average annual fraud growth rate of 4.83%, which is nearly a third of the growth rate in India (Iyer, 2017). According to the study, India’s higher growth rate could be a result of card payment systems being introduced relatively recently, when compared to Sweden and the USA.

Furthermore, Sweden’s digital payment ecosystem functions within a complex regulatory framework managed by Finansinspektionen, the Swedish Financial Supervisory Authority. This agency sets security rules, checks compliance, and inspects payment platforms. Digital payment providers must follow the Payment Services Act, anti-money laundering laws, counter-terrorist financing regulations, the General Data Protection Regulation, and the Payment Card Industry Data Security Standard. They are also required to implement strong authentication, encryption, tokenization, and transaction monitoring systems as necessary security measures (Abraham et al., 2023).

According to Mukhopadhyay and Mukhopadhyay (2024), despite becoming victims, a large proportion of people were unwilling to leave UPI as a means of payment. Only 7.4% of fraud victims switched entirely to cash, 8.8% reduced their use of digital payments, and 51.5% reported no reduction in UPI usage. The Pearson correlation coefficient between fraud victimisation and reduced UPI usage is -0.408, a moderate negative relationship that confirms victims are largely unwilling to exit the platform. Holtfreter et al. (2008) demonstrated that a 1-unit increase in routine consumer activity, such as purchasing something online, translates into a 61% increase in the odds of being targeted for fraud. The data indicates that since victims themselves are substantially reluctant to use different means of payment, people will almost always be exposed to and within the environment in which motivated offenders operate.

It is difficult to determine the true scale of the problem due to issues regarding under-reporting. Mukhopadhyay and Mukhopadhyay (2024) found that 20.6% of respondents who had experienced UPI fraud did not report the incident to the concerned authorities. This finding is based on a survey of 70 valid respondents conducted over a five-day period (20–25 April 2023). Sirajutheen and Abirami (2026) found that, during the “Golden Hour” (the critical period immediately following a cyber fraud during which prompt reporting maximizes the likelihood of freezing fraudulent transactions and recovering funds) of a fraud incident, 40% of fraud victims would incorrectly call their bank’s IVR (Interactive Voice Response) helpline instead of the National Cyber Crime Helpline (1930). Meanwhile, post-fraud grievance redressal mechanisms prove to be largely ineffective, with 33% of people finding them unsatisfactory and only 6% describing the bank’s response as very satisfactory (Sirajutheen and Abirami, 2026). While under-reporting is an issue in and of itself, the low recovery rate of assets further highlights the shortcomings of guardianship. According to Sharma and Singh (2024), ₹2294 crore ($274.14 million as per the 2024 exchange rates) was lost in cyber fraud, but only ₹57 crore ($6.81 million as per the 2024 exchange rates) was successfully recovered, showing a recovery rate of 2.5%. The low recovery rate indicates that the existing institutional mechanisms for recovering funds after UPI fraud are objectively ineffective. Such limited recovery could reduce the effectiveness of formal reporting systems and suggests that current guardianship mechanisms provide insufficient protection to victims.

How Fraudsters Operate and Why People Become Victims

One of the most prominent modalities of UPI fraud is social engineering (the psychological manipulation of the target’s behavior). Sharma and Singh (2024) present social engineering attacks in which fraudsters impersonate trusted authorities and create a sense of urgency regarding issues such as KYC compliance, blocked accounts, or failed transactions. Similarly, Wadkar et al. (2025) studied two UPI fraud cases involving educated and technologically aware victims who were deceived by messages warning of imminent electricity disconnection and directing them to malicious links. These studies demonstrate that digital payment fraud takes advantage of psychological pressure and trust rather than being a result of mere technological ignorance.

Other prevalent techniques include phishing and vishing (voice phishing), fake QR code scams, fraudulent payment request schemes in which fraudsters send payment requests disguised as payment receipts, and SIM swap attacks that redirect incoming OTPs to the fraudster’s device (Sharma and Singh, 2024; Singh and Agarwal, 2025). Sirajutheen and Abirami (2026) report that while awareness of phishing (67%) and vishing (70%) is relatively high among UPI users, awareness of more complex schemes (such as mule account operations, in which victims unknowingly launder money by renting their bank accounts to fraudsters) is relatively low, with only 6% of respondents being familiar with the term.

Singh and Agarwal (2025) point out that the platform connects a debit card, credit card, and bank account to one phone number. This means that one successful manipulation gives the offender access to several financial tools at once. Sharma and Singh (2024) also mention that digitization has not only widened the market for fraud but has also made criminals more skilled. Offenders keep adapting their techniques to keep up with the changes in the platforms they target.

Survey data from Sharma, Matharu, and Sinha (2026), drawn from 183 UPI users in Shimla district, indicates that 68.3% of fraud victims are graduates or postgraduates, challenging the assumption that higher educational attainment inherently protects individuals from digital financial fraud. As established above, 96.2% of the same respondents reported being aware of UPI fraud, yet 52.5% had nevertheless experienced victimization (Sharma et al., 2026). Singh and Agarwal (2025), meanwhile, identified fraud awareness as the highest-ranked preventive measure among surveyed stakeholders, assigning it a mean rank of 6.72. Taken together, these findings expose a structural gap: awareness and education are the most widely recommended individual-level defences against digital payment fraud, yet their presence has failed to prevent victimization at scale. Sirajutheen and Abirami (2026) conducted a correlation analysis that revealed a strong positive relationship between the frequency of digital usage and victimization (r = .825). This finding aligns with the Routine Activity Theory, which suggests that increased routine exposure creates greater opportunities for motivated offenders. Holtfreter et al. (2008), in a study of 922 adults, established a crucial distinction between the determinants of fraud targeting and the determinants of fraud victimization. Their logistic regression models demonstrated that routine consumer activity predicted whether individuals were selected as targets, producing a 61% increase in targeting odds per unit increase in activity. Individual traits, however, did not affect targeting at all.

Holtfreter et al. (2008) established that while RAT explains targeting, individual factors determine whether a targeted person becomes a victim. In the UPI context, fraudsters systematically exploit cognitive biases.

Several cognitive and social biases contribute to vulnerability to fraud. Overconfidence bias leads users to underestimate fraud risk and disregard security protocols and updates. FOMO (Fear of Missing Out) is exploited through fabricated urgency, such as claims of limited offers or imminent account suspension, to push users toward hasty decisions. Reciprocity bias, along with sympathy exploitation, is described as common in romance and distress scams. Confirmation bias, anchoring, authority bias, and groupthink are identified as social influences that interact with these factors to further increase susceptibility to manipulation and fraud (Sharma, Matharu, & Sinha, 2026).[1]  This reveals that exploiting the architecture of human decision-making has a vital role in achieving UPI fraud.

The comparison with DeLiema’s study is instructive here. In elder fraud, cognitive vulnerability arose from neurological decline, which is a form of diminished capacity that reduced the victim’s ability to evaluate threats accurately (DeLiema, 2018). In UPI fraud, cognitive vulnerability is manufactured when fraudsters design scenarios that activate the biases listed above, effectively inducing a temporary incapacity in otherwise competent individuals. The complex nature of the attack is what converts the targeted individual into a victim, regardless of education and awareness.

Victims and non-victims attribute fraud causation differently. Victims emphasize financial illiteracy and technological complexity, while non-victims emphasize cybersecurity vulnerabilities and slow investigation. This divergence suggests that victimization is associated with a shift in how individuals understand the threat (Singh and Agarwal, 2025).

Systematic Failure

The institutional response to UPI fraud victimization has largely been a refusal to accept responsibility. Wadkar et al. (2025) document that when two fraud victims reached out to a pseudonymous Bank ABC, the bank maintained that the transactions were authorized because the victims had shared their UPI PIN or one-time password (OTP). This was the case regardless of whether the victims did so under deceptive circumstances, leading the bank to conclude that the transactions were non-refundable. The RBI’s own framework reinforces this position, specifying that if a transaction occurs as a result of the customer sharing their credentials, the loss rests with the customer until the bank is formally notified (Wadkar et al., 2025).

Sharma and Singh (2024) state that social engineering attacks trick customers into disclosing information by impersonating legitimate authorities and manufacturing crises. However, the victim does not share credentials negligently. Rather, they are deceived into doing so. Yet the legal framework treats engineered deception and careless disclosure as equivalent, and holds the victim equally liable in both cases.

One of the most prominent legal gaps in India’s UPI consumer protection framework is the APP (Authorized Push Payment) loophole. According to Agrawal (2026), as per RBI[1] ’s 2017 Consumer Protection Circular, banks are obligated to cover losses from unauthorized transactions if reported within three days. APP fraud, however, operates by deceiving the victim into authorizing the transfer themselves, so the transaction is technically classified as authorized and falls entirely outside the protection framework.

The failure to provide an appropriate institutional response is evident in the recovery statistics. In 2022, the recovery rate was determined to be 2.5% (Sharma and Singh, 2024). This statistic highlights a significant failure of systemic guardianship. Furthermore, it was revealed that only 6% of respondents rated their bank’s grievance redressal process as “very satisfactory,” while 33% found it “unsatisfactory.” These perceptions align with the recovery outcomes, indicating that the current system is not effectively providing reliable protection  Sirajutheen and Abirami, 2026, p.301).

The 20.6% non-reporting rate among UPI fraud victims is not clear evidence of a lack of action. The authors suggest two possible explanations for non-reporting: that the grievance redressal mechanism may be difficult for victims to access, or that victims may lack sufficient awareness of how and where to report UPI fraud (Mukhopadhyay and Mukhopadhyay, 2024).

Furthermore, in elder fraud cases, capable guardians (financial institutions, social services, family members) consistently failed to identify victimization until losses had already occurred, at which point intervention was too late (DeLiema, 2018). When we look at UPI, the transactions are much more rapid, and the window for intervention is narrower. Therefore, the convenience of quick transactions also brings forth the issue of timely prevention by guardians.

Recommendations

The most significant reform that Indian policymakers can make is to close the APP liability loophole. ​​The United Kingdom introduced a mandatory reimbursement of up to £85,000 (approximately $113,000 as per the 2026 exchange rates) for APP fraud, requiring payment service providers to compensate eligible victims and strengthening incentives for fraud prevention (Agrarwal, 2026). A comparable reform in the Indian context would require banks and payment platforms to compensate victims of social engineering fraud, removing the current presumption that authorized transfer equals voluntary consent.

Legal frameworks governing digital payments must evolve continuously to match the pace of technological change (Ballaji, 2024). Amending the Information Technology Act (2000) and the Payment Settlement Systems Act (2007) is recommended to address evolving forms of digital payment fraud, support a fraud management framework, and implement AI for fraud detection (Banerjee et. al, 2025).

It has been established that a majority of users aware of digital payment fraud have become victims (Sharma, Matharu, and Sinha, 2026). Therefore, policymakers should design interventions that reduce the probability of a person falling victim to fraud at the moment of the transaction. An example of such could be a form of QR authentication before the approval of a transaction.

Furthermore, since 40% of users were unaware of the National Cybercrime Helpline (1930) during a fraud incident (Sirajutheen and Abirami, 2026), a simple display of the helpline in any UPI platform (such as Google Pay) would allow for timely action and early intervention.

Additionally, while many studies support using AI in fraud detection, there is little guidance on how to implement these systems effectively. Future research should focus on building and testing practical AI-based fraud detection frameworks that can be integrated into digital payment systems.

Several methodological limitations in the current literature also need to be addressed. Future research should use nationally representative samples rather than regional ones, as this would allow the findings to be generalized across India. Furthermore, future work should test which specific cognitive biases result in relatively greater victimization, so that prevention efforts can be prioritized. Finally, comparative research across a wider set of countries would help identify which regulatory and infrastructural conditions most effectively curb fraud growth.

Conclusion

This paper explains why India’s digital payment revolution driven by UPI has produced a growing class of fraud victims. The observations suggest that UPI fraud stems from structural factors that create opportunities for fraud, rather than solely from individual negligence. UPI’s architecture, in which a single phone number links a user’s bank accounts, debit cards, and credit cards, creates a single point of vulnerability, while its convenience ensures routine digital activity that places users in the realm of motivated offenders.

Routine activity governs who is targeted, but awareness and education alone do not determine who becomes a victim. Victimization depends on whether fraudsters succeed in manufacturing the cognitive conditions, such as urgency, authority, and overconfidence, that override otherwise informed judgment.

One of the most critical issues is the absence of effective guardianship. Recovery rates for stolen funds remain minimal, and redressal mechanisms are widely regarded as unsatisfactory. Additionally, the Authorized Push Payment loophole allows banks to treat engineered deception and careless disclosure as equivalent, leaving a large category of victims without reimbursement.

Therefore, closing the gap between UPI’s convenience and its vulnerability will require structural changes, not merely individual reforms. Tackling the APP loophole, updating the legal frameworks governing digital payments, addressing methodological limitations, and introducing safeguards at the point of transaction are of utmost importance. As UPI continues to expand as the backbone of India’s digital economy, the strength of its guardianship mechanisms is likely to determine whether this expansion will occur without a proportionate rise in victimization.

References

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About the Author

Aadhavan Singh

Aadhavan is currently a Grade 12 student at La Martiniere College, India. He is a national-level shooter and passionate trekker.

The post Exploring How India’s Digital Payment Revolution Created a New Class of Fraud Victims: An Analysis of UPI Scams appeared first on Exploratio Journal.

Abstract

Prescription pet food is one of the most commonly used conservative therapeutic methods in the treatment of various pet diseases. However, it has not yet been fully utilized, and many pet owners are not aware of its efficacy. In this article, the therapeutic effect of traditional prescription pet food and herbal prescription pet food on the treatment of common pet diseases, especially metabolic, kidney, gastrointestinal, and urinary system diseases, will be discussed. Relevant literature was retrieved through searching various databases such as PubMed, Google Scholar, and CNKI.

The studies reviewed in this paper show that traditional prescription pet food can be effective in the management of pet obesity, kidney disease, and diarrhea. For example, disease-specific formulations such as protein and phosphorus content, omega-3 fatty acids, prebiotics, and functional derivatives of amino acids were effective in slowing disease progress, improving clinical signs, and increasing recovery rates compared to regular pet food. Prescription diet foods were also effective in the management of weight in pets using caloric restriction, fiber content, and metabolic modulation. Although less common, herbal prescription food has been effective in the management of urinary system diseases in pets using anti-inflammatory effects, diuretic effects, and mucosal healing potential. The anti-inflammatory effect may be due to the polysaccharide content in the food.

In conclusion, prescription pet food can be considered effective in the management of diseases in pets. Although traditional prescription pet food has been widely recognized and researched, herbal prescription food has great potential in the management of urinary system diseases in pets. Prescription pet food can be considered effective in the management of diseases in pets.

Key words: Pet prescription food, Herbal medicine, Chronic disease management, Urinary system disease

Introduction

Prescription food, as a conservative treatment method for pets, offers a convenient and effective dietary therapy. Other than the basic ingredients in pet food such as carbohydrate, protein sources, and vitamins, pet prescription food will also add some specific supplements for illnesses. For example, in the prescription food for joint disease, supplements like omega-3 fatty acid, glucosamine, and chondroitin sulfate is added. Prescription food could help with the treatment of many chronic diseases, such as kidney disease, urinary system disease, allergies, etc. It also has the function of disease prevention for senior dogs. (Shen 2020) Although there are some limitations that may hinder its usage, prescription pet food provides significant benefits for maintaining the pet’s health. 

Section 1: Uses of Prescription Medication for Specific Illness

Prescription food was invented by Dr. Mark Morris Sr. in the 1930s, he licensed the first therapeutic diet to Hill Packing Company, which started the development of prescription food in the western countries. (Lederhouse 2023) Therefore, its popularity and rate of utilization abroad are much higher than those in China. At the same time, many western brands have accumulated profound technical expertise in prescription grains. In contrast, most veterinarians still prefer to use medicine directly for treatment in China. However, in recent years, Chinese domestic prescription foods have also been gradually developing, opening up diversified and differentiated development channels. (Chen 2025) (Jia 2020)

Prescription food had several benefits in dealing with specific illness, such as decelerating the progression of some chronic diseases, providing some specialized nutrients for certain diseases, and helping with weight management.

A. Slowing Disease Progression

For the cats that had kidney disease, for example, renal failure, the purpose of treatment is to decrease the burden of their kidney, so their food-intake should be limited because then their kidneys’ workload could be reduced. Under these circumstances, the energy and nutrients of their food should be abundant enough to fulfill their daily requirements. Therefore, the prescription food would be a suitable option since it could provide a high amount of digestible protein, so that it could reduce the amount of protein that needed to be metabolized and excreted. Moreover, the prescription food contains EPA and DHA, which has an antioxidant effect to reduce inflammation. The lower level of phosphorus could also help support the function of the kidney as well. (Li 2021)

Moreover, for the dogs with kidney damage, prescription food could also be beneficial to their recovery. Research supported that prescription food could significantly increase the rate of recovery for the kidney-damaged dogs. According to the research, the researchers applied normal food and prescription food for two groups of dogs and the result showed that the group with prescription food had a rapid decrease in CREA value compared to the normal food group. CREA value is an indicator for evaluating renal injury in clinical practices, which higher value means kidney damage. Tubular type is also an important indicator to measure the damage on renal tubular cells. The amount of tubular cells shows a positive relationship with aggravation of renal injury. According to the research, on the 8th and the 10th day, Group A (which is the group with prescription food) had a smaller amount of tubular cells in contrast to Group B (the group with regular pet food). (Lin 2025) From these two examples, the experimental data and results can both suggest that pet prescription food would accelerate the pets’ recovery from kidney diseases.

B. Providing Some Specific Nutrient Supplement

Studies show that many hospitalized sick dogs suffer from malnutrition, but by feeding corresponding prescription foods, the discharge rate of sick dogs can be greatly improved. From this, it can be seen that the auxiliary effect of prescription grains is still quite considerable. (Jia 2020)

Diarrhea, as a common disease for dogs, occurs frequently between the ages of three months and six months. Usually, the dogs will infect several enteropathogens when they suffer from diarrhea, and the most common ones are hookworms, ascarids, Giardia spp, Cryptosporidium spp, and Salmonella spp. (Tupler 2012) To solve with these enteropathogens, prescription food could be a good option. Since that intestinal prescription food would contain a kind of prebiotic called fructooligosaccharide, which could adjust the pH value inside, it could benefit the dogs to recover from diarrhea. Fructooligosaccharide will be broken into various short-chain fatty acids which could lower the high pH caused by diarrhea. With this change, bacteria that is beneficial to the intestine will increase, such as bifidobacteria, and the pathogenic bacteria will be inhibited. One of the volatile fatty acids that fructooligosaccharides will be broken down into is butyric acid. As an energy provider for the intestinal mucosa, butyric acid could help with mucosal repair. (Zhong 2018) Therefore, it could be shown that prescription food is beneficial for the pets by providing some nutrients for the targeted disease. 

C. Weight Management

Obesity among pets results from the excessive calories they consumed fewer than they need. Excessive calories lead to obesity, which will ultimately cause impairment of health and body function. In general, 20% to 25% above ideal body weight is considered as obesity. Research showed that overweight dogs were at higher risk for having chronic health problems earlier than dogs with normal weight. For cats, obese cats also faced increased risks for lots of health problems such as arthritis and diabetes mellitus, which the risk to have diabetes is about four-fold in comparison with normal cats. (Laflamme 2011) To control the weight in an optimal range, prescription food became a recommended option for many of the veterinarians. Research has shown that prescription food is effective for losing weight. On average, dogs lost weight at the rate of 1.02% per week and cats lost at the rate of 0.92%. (Vendramini et al. 2024) The prescription food uses high fiber and low fat to reduce the caloric intake and body weight. Some added L-cartinine for helping cats to lose weight safely and effectively. (Roudebush 2008) L-cartinine was a non-toxic amino acid derivative that significantly increased plasma carnitine analyte concentration and rate of weight loss at the same time. (Center et al. 2000) Another concept of weight management in cats suggested that altering animals’ metabolism by using low-carbohydrate, high-protein foods increases the concentration of serum beta-hydroxybutyrate. The increase of serum beta-hydroxybutyrate contributes to change in metabolism, which could lead to weight loss. (Roudebush 2008)

Sections 2: Limitations

Some limitations may stop people from using prescription food for their pets such as the high prices, its nature as an auxiliary product and the refuse from the animals. Nonetheless, there are some methods to mitigate the limitations of prescription pet food.

A. Only Supplement for Disease Treatment

Although prescription food had an impact to benefit the pet’s health, it could not replace medicine or surgery as a main treatment method for diseases. For example, for cats that had urethral obstruction, most cases need surgery to remove the blockage, because otherwise it may threaten their lives. However, after they had the surgery, veterinarians may suggest the cat to have prescription food to prevent further blockages. (Grossmann 2024)

B. High Price & Limited Availability

One of the limitations of prescription food is the high price of the prescription food. Because prescription food is highly specialized for each disease, the low demand may lead to high prices, which further hinder the steps toward prescription food for some customers. In China, the smallest packaging of prescription food is 1.5 kg which cost around 200 yuan (around 29 dollars) and these foods would only last for 2-3 days for a medium-sized dog. Therefore, it may need some consideration for some pet owners. (Zhong 2018)

C. Poor Palatability for Pets 

Other than high prices, the lack of palatability also leads to the refusal of pets. The ingredients may not be tasty for them. However, there are some solutions for this situation. For example, the pet owner could switch brands after communicating with the veterinarians. Different brands provide different flavors and there will be some that can attract the pets. Moreover, the pet owner could also choose to change to canned food because the canned food may provide stronger odor and meatier flavor. Kidney and urinary issues could also be mitigated by wet foods. Nevertheless, the wet foods may provide less calories than dry foods, so the pets may need to eat more which increases the cost. Therefore, it would also be a good choice to mix the dry foods with canned foods which dry foods could provide enough calories, and canned foods could increase the palatability. (Cornell University College of Veterinary Medicine n.d.)

Section 3: Pet Prescription Food with Herbal Medicine

A. Current application of herbal medicine

Herbal medicine, which is the application of plants for medical treatments, is a newly emerged ingredients in prescription diets, played effective roles on anti-inflammtion and immunoregulation. Urinary disease, especially urinary system disease, is common among pets, and the regular methods for treatments are antibiotics administration, specific prescription diets, and surgery in severe cases. The prescription diet incorporates herbal medicine, including Citri Reticulatae Pericarpium (chenpi), Poria cocos (fuling), Plantaginis Semen (cheqianzi), Alismatis Rhizoma (zexie), and Ophiopogonis Radix (maidong). Among these, Poria cocos and Alismatis Rhizoma had diuretic properties. Furthermore, Plantaginis Semen, Ophiopogonis Radix, Poria cocos, and Alismatis Rhizoma were able to possess anti-inflammatory and anti-tumor activities. Citri Reticulatae Pericarpium and Alismatis Rhizoma demonstrate the effects in clearing heat and resolving turbidity. The experimental results show that the herbal prescription diet could repair the bladder mucosal layer, which has an obvious improvement effect on bladder inflammation, and enhances the repair of the lower urinary tract mucosal barrier. Experiments have shown that the adjuvant treatment of herbal prescription food can accelerate the recovery of canine bladder inflammation and effectively shorten the duration of damage to the lower urinary tract caused by inflammation. (Shen 2020)

B. Future Application

Although the fact that the application of herbal medicine with pet prescriptions food is rare, it also implied that there is huge space for exploration and inventions. Many herbal medicine had proven their function on anti-inflammation and immunoregulation, since they include core active ingredient such as polysaccharides. For example, herbal medicines such as licorice and astragalus membranaceus include glycyrrhizic acid and astragalus polysaccharides respectively, can regulate key targets such as CASP3, TLR4, STAT3, IL6 and IL1B, and also signaling pathways such as MAPK, NF-κB and FoxO. (Yin et al. 2024) Since polysaccharides are one of the main active ingredients of medical and edible homologous traditional Chinese medicines (MEHTCMs) (Zhang et al. 2024), which means that they are also ingredients appear in daily food that has medical value, it could be proven that their safety as ingredients for pets prescription food. 

However, the main challenge that the quality of raw materials in herbal medicine. Because that their quality would largely affected by several factors such as its processing methods and storage conditions, which lead to unstable amount of the active ingredient. Therefore, it is important to establish a standardized system from cultivation to production for controlling the quality of herbal medicine. Moreover, it is also necessary to limit the pollutants in the plants. Harmful heavy metal such as lead, arsenic, cadmium and mercury were possibly accumulated in soil, and enter food cycle through the absorption of plants. (Pan 2024) These heavy metal promoted the production of reactive oxygen species, which is toxic to animals since it would result in endothelial dysfunction lipid metabolism distributions and disruption of ion homeostasis. Therefore, it is significantly important to establish a standard for evaluating the safety of the herbal medicine.

Discussion

The review synthesizes current evidence on the therapeutic roles of traditional pet prescription food and the emerging herbal prescription food in treating animal diseases. Overall, the findings suggest that pet prescription food is effective on adjuvant therapy, especially for chronic and metabolism-related diseases such as kidney disease, obesity and diarrhea (cite these three studies). The evidence suggested that prescription diets shows its ability to slow disease progression and enhance recovery outcomes compared to regular pet food. These effects are benefited from specific nutrient supplement such as omega-3 fatty acids, prebiotics, and functional amino acid (e.g. L-carnitine). 

In addition to traditional prescription diets, herbal prescription foods also show great potential in urinary system diseases and metabolic regulation. Evidence showed that a diet containing traditional herbal ingredients can exert anti-inflammatory, diuretic and mucosal repair effects, which accelerates the recovery of lower urinary tract inflammation. The bioactive polysaccharides and other active compounds found in some medicine and edible homologous traditional Chinese medicine provide a reasonable mechanism basing on these therapeutic effects, and also support their relative safety as dietary components. However, comparing with traditional prescription foods, the clinical evidence of herbal prescription diets remains limited, and their application in veterinary practice is still in its early stages.

Despite these benefits, there are barriers that restrict the wide adoption of pet prescription food. For example, high cost, limited accessibility, palatability issues, and their role as a supplement all limit their utilization. Although strategies such as mixed feeding could mitigate these obstacles, economic concerns remain significant consideration for pet owners.

Conclusion

In conclusion, both traditional prescription pet food and herbal prescription diets play significant supporting roles in the treatment and management of pet disease. Traditional prescription foods are recognized for their efficacy in disease such as kidney disease, obesity and gastrointestinal disease, mainly supplementing medical or surgical interventions by providing nutritional regulation for specific disease. Although herbal prescription foods are not very commonly used, they show considerable potential in treating urinary disease and inflammation and represent potential direction for future innovation in veterinary nutrition.

However, prescription food should be regarded as an auxiliary means of primary care rather than a substitute. Future research should focus on expanding high-quality clinical trials, especially those of herbal prescription diets, and establishing a standardized production and safety evaluation system for herbal ingredients. By addressing issues of cost, quality control and public awareness, prescription pet food may gain wider acceptance and play a more significant role in care.Declaation

References

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About the author

Selina Zhang

Selina is an independent student researcher with a strong passion on veterinary science, wildlife conservation and animal welfare. She is the leader of her school’s biology club and has gained extensive practical experience in animal care through clinical volunteering at animal clinics, including Concordia Pet Care Center. Her active leadership in the biology club and her clinical internship experience have sparked her strong interest in scientific research, driving her to explore academic solutions to veterinary challenges.

Driven by her commitment to promoting evidence-based veterinary care, her current research focus on evaluating the therapeutic effects of traditional and herbal prescription pet food in chronic disease management. She hopes to continue exploring the intersection of traditional medicine, pharmacology and veterinary nutrition to provide practical solutions for animal health.

The post Effects of Traditional and Herbal Pet Prescription Food on Treatment of Disease on Pets appeared first on Exploratio Journal.

Abstract

The automotive industry is rapidly changing together with increasingly tougher environmental regulations; including, among others, the shift towards use of low-carbon-footprint products, and ban of per- and polyfluoroalkyl substances (PFAS). The remarkable thermal stability, chemical resistance, and mechanical durability of fluorocarbon elastomers (FKMs) are essential in seals, hoses and tubings of internal combustion engines. These are difficult to match by any other materials making them so far irreplaceable.  The new regulations on PFAS, however, stimulated researchers to test the alternative non-fluorinated elastomers. These materials are additionally challenged by developments of new fuels, i.e. higher energetic synthetic e-fuels, which compromise their long-term performance. This manuscript summarizes use of FKMs in passenger cars, regulatory challenges and environmental concerns on PFAS, as well discussed technical challenges associated with substitution of fluorinated materials to be exposed with ethanol-based fuel, such as E85. Material replacement strategies, implications for road and race cars, and future developments in sealant technologies are discussed.

Introduction

Fluoroelastomers in Automotive Sealing Applications

Fluoroelastomers (FKMs) are a class of high-performance synthetic elastomers characterized by a fluorine-rich polymer backbone (60-75 wt.% fluorine), typically comprising vinylidene fluoride and hexafluoropropylene units (Scheme 1). FKMs were specifically developed to address sealing challenges in environments involving high temperatures, aggressive chemicals, and long service lifetimes, particularly in the automotive and aerospace sectors (Drobny, 2007). Places where, FKM-based parts are used in a combustion engine car are presented in Figure 1. Sealing materials used in the automotive engines and fuel systems are subjected to high temperatures, variable pressure, aggressive lubricants, and fuel compounds that are growing in complexity (Nishina, 2008). The FKMs, which are elastomers with fluorine-based polymer backbone, are characterized by an exceptional mix of heat resistance, chemical inertness, low permeability, and long service life (Moon et al., 2021). These properties have made FKMs the material of choice in critical components like O-rings, gaskets, shaft seals, injector seals, and fuel hoses, especially in areas of the engine where a failure would lead to leakage, emissions, or even safety hazards. Their elasticity and resistance to swelling and degradation have seen them become a standard material in the sealing technology of automobiles. However, currently FKM alternative sealants are also considered, such as silicone-based or nitrile-butadiene rubbers (Scheme 1). Their performance will be discussed in the sections below.

Influence of Alternative and Ethanol-Based Fuels on Elastomer Performance

The relevance of FKMs has escalated further with the diversification of automotive fuels. Blended fuels like E5, E10, and E85, biodiesel, and new synthetic e-fuels have been progressively replacing or supplementing conventional gasoline and diesel (Czerwinski et al., 2016; Muelaner, 2023). Fuel containing alcohol, e.g. higher concentration of ethanol, poses new threats to elastomeric materials because they are polar, hygroscopic, and may behave as swelling solvents. Ethanol can enter into elastomer networks, remove additives, and enhance swelling or embrittlement, ultimately degrading seal integrity (U.S. Department of Energy (DOE), n.d.). FMKs with high concentrations of fluorine (usually 69-71% fluorine) are less susceptible to these effects and have longer-lasting dimensional stability and mechanical performance even in long-term contact with alcohol-containing fuels (Stevens, 2006). This is the reason why they find extensive application in the fuel system parts, such as in-tank seals and tubing, where they are likely to be subjected to hostile fuel environments over extended periods.

PFAS Regulatory Pressure and Environmental Concerns

Although fluorinated materials have technical benefits, there is a growing wave of questioning the use of fluorinated materials because of the environmental and regulatory factors related to per- and polyfluoroalkyl substances (PFAS) (Améduri, 2023). PFAS have been referred to as “forever chemicals” due to their environmental persistence and their resistance to degradation and low bioaccumulation possibilities (Brun et al., 2023; Lee et al., 2025). Regulatory agencies, especially the European Union, are working towards general bans on PFAS that have cast doubt on the future use of fluorinated polymers in industries (European Commission, 2025). One of the main controversies is whether high-molecular-weight fluoropolymers like FKMs should be considered together with low-molecular-weight PFAS that are already known to be dangerous to the environment and health. Fluoropolymer can be subdivided into polymers where perfluorination resides either in the main chain or in the side chain (Figure 2). This differentiation is important, as in the advent of hydrolytic degradation, the perfluorinated side chains are released to the environment and their mobility and accumulation may be similar to other small molecular weight PFAS. In contrast, main-chain fluoropolymers, such as FKMs, do not hydrolyse and the risk of release of similar small molecular weight compounds is rather small.In turn, FKMs do not have high mobility and have low bioaccumulation; nevertheless, they are fluorinated in nature, which puts them in an ever-tighter regulatory environment.” FKMs do not have high mobility and have low bioaccumulation; nevertheless, they are fluorinated in nature, which puts them in an ever-tighter regulatory environment.

Sustainability Challenges and the Need for Material Substitution

This regulatory pressure is in line with an overall shift to sustainable mobility, such as the use of biofuels and e-fuels as solutions to lower greenhouse gas emission levels in internal combustion engines (Yılbaşı, 2025). Although these fuels have environmental advantages, they tend to cause compatibility challenges with materials, because they are more corrosive and chemically aggressive. This, in effect, makes it a significant technical challenge to replace fluorinated materials, as their unique performance characteristics remain difficult to replicate with non-fluorinated alternatives (Améduri, 2023). Any alternative substance cannot be inferior in terms of thermal stability, chemical resistance, elasticity, low permeability, and long durability, especially when subjected to fuels that are ethanol-based, like E85 (Sahu et al., 2022). This paper discusses the multifaceted nature of the relationship between material operation, fuel development, and regulatory requirements and explains why the substitution of FKMs remains one of the most pressing and unsolved issues in automotive material development.

Discussion

Currently Used Sealant Materials

Elastomers in Use: FKM and NBR

Fluoroelastomers (FKM) and nitrile butadiene rubber (NBR) (Scheme 1) are the most common elastomers used in automotive fuel and engine systems, chosen through a performance-to-cost ratio (Białecki et al., 2021). FKMs are normally utilized in the critical sealing processes that are subjected to high temperature, aggressive fuel, and extended working time (Asthana  et al., 2025). Their fluorine content is high, and this gives them high thermal stability, which means that they can run continuously above 200 C, and are also highly resistant to oils, fuels, and additives. Such characteristics render FKMs applicable in injector seals, O-rings, shaft seals, and in-tank fuel components. Conversely, NBR has found extensive use as a cost-sensitive component in certain applications because it is less expensive and good enough for non-polar hydrocarbon resistance of petroleum-based fuels and lubricants.

NBR reveals evident limitations despite the benefits in its economy, which is relevant in the contemporary operating conditions (Akhlaghi et al., 2015). It is rather thermally unstable with a maximum service temperature of approximately 120 °C, above which it becomes hardened and loses elasticity and acquires a high compression set. “Compare static thermal stability of NBR with FKM in TGA, Figure 3.” NBR is also highly susceptible to polar solvents and, therefore, is prone to swelling and degrading when subjected to fuels containing ethanol, like E10 or E85, which reduces the life of seals. On the other hand, FKMs are highly mechanically stable, have low fuel permeability, recover elastically at high temperature and alcohol-based fuels, and have a high service life covering mileage of over 100,000 km (Lee et al., 2022). Compare mechanical properties of NBR and FKM in Table 1. In turn, NBR is suitable for low-stress, low-cost applications, whereas FKMs are needed in high-performance, high-reliability automotive sealing system applications.

Fuel Types and Their Impact

The modern internal combustion engines use a wider variety of fuels, such as gasoline-ethanol mixtures (E5 and E10), high-ethanol fuels (E85), biodiesel, and synthetic fuels (Laskowski & Zimakowska-Laskowska, 2025). Chemical and physical characteristics of each fuel also have different values that affect engine performance and material behavior (Table 2). The ethanol fuels have a lower caloric value compared to gasoline, and thus, they consume more fuel to produce the same amount of energy (Yakın et al., 2022). On the other hand, higher octane rating of ethanol enables some engines to operate at higher compression ratios and even better efficiencies. Although E5 and E10 produce moderate variation of fuel system conditions, E85 produces a significant exposure of polar solvents and water absorption that can aggravate the chemical stress on elastomeric fuel-system components.

Synthetic fuels and biodiesel present a new range of problems. Biodiesel is more lubricious than standard diesel, which may be advantageous in fuel pumping and injecting, but it oxidizes more easily and is susceptible to microbial growth where there is water. These may result in the production of acidic by-products, which hasten the breakdown of elastomers, especially those of low chemical resistance. Biofuels are classified according to their physical state, technology maturity, the generation of feedstock, and the generation of products (Awogbemi et al., 2023). Synthetic fuels, such as e-fuels made of renewable electricity and captured carbon dioxide, are developed to attain similar properties to the conventional hydrocarbons without causing lifecycle greenhouse emissions (US Department of Energy, 2019). Despite being relatively cleaner and relatively chemically inert, they react to certain extent with elastomer alternatives according to certain formulations and additive packages

FKMs, PFAS, and Regulatory Debate

Fluoroelastomers are extremely resistant to alcohol-based fuels, have low permeability, and have a long service life, so they are an essential component in the automotive fuel system (Ameduri and Sawada, 2016). However, their fluorinated structure makes them the object of the extended regulations of PFAS. Even though the FKMs do not have the same mobility, bioavailability, and environmental behavior as low-molecular-weight PFAS, the regulatory definitions are generic. This has brought about confusion among manufacturers and suppliers, where alternative materials and fluorine-reduction strategies are being researched, even though there are no similar non-fluorinated substitutes with the same level of performance.

Strategies to Replace FKMs

Several alternatives are under development that will eliminate or limit the use of FKMs in automobiles. These consist of hydrogenated nitrile butadiene rubber (HNBR, Scheme 1), high-performance thermoplastic elastomers, and silicone elastomers that have improved fuel resistance (Joshi, 2025; Gao et al., 2025). Other mitigation strategies include hybrid seal or multilayer seal construction or surface coating, whereby the traditional elastomers are coupled with thin barrier seals to restrict fuel permeation (Figure 4). The compatibility of elastomers with various fuels indicates that most alternative rubbers (including HNBR and other engineered types) change their mechanical properties significantly under exposure to renewable and blended fuels, which makes it challenging to find alternatives with more or less the same performance as FKM ( Müller et al., 2024). Although they may partially reduce the effects of chemical attack and swelling, none of these solutions can be considered as having the same thermal stability, chemical resistance, and durability as FKMs, especially when exposed to ethanol-rich fuels like E85. Other alternative engineered elastomers such as HNBR, as well as most alternative rubbers, vary their mechanical properties greatly when in contact with renewable and blended fuels, especially when in extended contact with fuels high in ethanol (Conen, Haefele and Dahlmann, 2025).

Road Cars versus Race Cars

The operational and regulatory aspects vary significantly, and this difference in priorities leads to a significant difference between the selection of materials used in road vehicles and race cars. In racing use, performance, high temperature performance, and high temperature performance are crucial factors, and the need to match rapidly evolving fuel compounds is often the reason why FKMs or perfluoro elastomers are continued to be employed despite the cost or regulatory pressure (Katon, 2025). In contrast, road vehicles have to weigh between performance and cost-effectiveness, compliance with regulations, and durability during the long run due to the long service life (Adebowale, 2025). Although race cars still almost entirely use FKMs, the continued development of fluorine-free polymers and multilayer seal technologies implies that road vehicles can use other types of elastomer systems in the future (Mandlekar, Joshi & Butola, 2022; Valentini & Lopez-Manchado, 2020).

Summary

Fluor elastomers have been very important in providing reliability and safety of automotive engines, especially those with high temperatures in the fuel system and hostile fuels (Drobny, 2023). The shift to the use of ethanol-based and synthetic fuels, coupled with stricter regulations on PFAS, has put a lot of pressure on the need to substitute or redesign fluorinated substances. Although the FKMs are still unrivaled in terms of total performance, more so when it comes to accommodating E85 compatibility, regulatory and environmental issues are driving the development of alternative elastomers and hybrid solutions. Even in the near future, it is not probable that FKMs can be fully replaced without performance loss (Puga et al., 2025). Rather, the future of automotive sealants will be characterized by incremental changes in materials and an application-specific approach in both road and race applications. 

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About the author

DongHan Li

DongHan is currently Grade 11 student, studying in Beijing, China. During the course of high school, he has studied the A-level Curriculum in subjects such as physics and chemistry. DongHan has a strong interest in Engineering, especially in Chemical Engineering. Additionally, he has also found himself very intrigued by theories related to Nanotechnology along with fluid dynamics that have facilitated the process of completing this dissertation.

At the same time, DongHan’s interest in motorsports (Grand Touring 3 race cars ) has helped develop the general aim of this dissertation. As motorsports develop continuously over time, it is very promising and exciting to dig into and challenge the theories that already exist, hoping to offer alternative solutions to existing engineering challenges.

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Introduction

Millions of people return to video games after a day of work or school. Most of them replay the same game over and over, even after they’ve “finished” it or reached a high level. According to the Entertainment Software Association (ESA), 61% of the U.S. population (ages 5–90) plays video games at least one hour per week. Among many reasons, one of the main things that makes video games so popular is the competitive system behind them. These systems can take forms like ranked ladders, leaderboards, or PvP (player versus player) matches. They turn gaming from a solo hobby into a social competition. Competitive systems give players goals, opponents, and recognition, and because of this, they hold attention more than other hobbies.

Take League of Legends as an example. The game builds everything around competition. Players team up with four others and face another team of five. After each match, a player’s rank points go up or down based on whether they win or lose, which places them in tiers from “Iron” all the way to “Challenger.” This system gives players a clear goal to work toward and a platform to prove themselves. When players improve, they see it directly in their rank. They also get matched with others at similar skill levels. These features help explain why League of Legends has stayed popular for so long.

Studying how competition keeps people engaged in games can help us build healthier systems: not just in video games, but in companies, schools, and other real-world situations. Competition becomes toxic when it increases hostile behavior, stress, and frustration, or when it pushes players toward unhealthy addiction. Research on online multiplayer communities shows that unclear feedback and harsh punishment systems lead to more hostility and verbal aggression (Kordyaka, Jahn, & Niehaves, 2020; Kou & Nardi, 2014). A healthy competitive system, on the other hand, offers fair challenges, clear rewards, and chances to cooperate. These features tend to build intrinsic motivation and teamwork, helping players stay engaged in positive ways (Deci & Ryan, 2000; Hamari & Keronen, 2017). By understanding what keeps people playing, we also learn what pushes them away. Knowing how competition works in games can help us create environments where people grow under pressure instead of getting hurt by it.

The results show that different competitive systems create different patterns of engagement. PvP attracts the most players, while Ranking leads to the highest time and money investment. Systems that emphasize status pull players in deep, but they also raise emotional pressure: stress, burnout, and addictive behaviors are clearly higher in Ranking and PvP. Timing-based and achievement systems draw smaller but dedicated groups who care about self-improvement and finishing goals, though they struggle with repetition and quitting tied to cost. Non-competitive systems, while showing the lowest engagement levels, give casual players important spaces without pressure. Getting the right mix between these systems matters, and choosing the right combination could encourage sustainable, healthy engagement in competitive environments. 

This study used an anonymous online survey distributed through social media. The survey included structured questions designed to capture participants’ gaming behaviors, preferences, and motivations. To measure gaming intensity, the survey asked about weekly playtime. Genre preference came from asking participants to name their most-played game type. Financial investment was measured through a question about average monthly spending on games and in-game purchases.

To understand competitive orientation, the survey asked whether participants preferred games with strong competitive features like ranked ladders, leaderboards, or PvP modes. Game attachment was measured by asking how often players replay the same game instead of switching to new ones. Motivational factors came from asking respondents to pick their main reason for playing: competition, story immersion, social interaction, or relaxation. The survey also included questions about emotional responses to winning and losing, capturing how competitive play affects players emotionally. Another question asked about how much players value visible recognition systems like rankings, badges, or achievements. Finally, the survey collected demographic information: age group, gender, and participation in other activities: to use as control variables. The whole survey took about three minutes to finish.

Participants came from a Chinese college population. Recruitment happened mostly through online channels commonly used for campus surveys, including student social media groups and course-related platforms. In total, the study collected 1,114 valid responses. The sample was 60.14% female (n = 670) and 39.86% male (n = 444). Age skewed young: 58.89% were 18–25, followed by 17.06% under 18, and 9.96% aged 26–30. The rest were spread across older groups. Education levels matched the college setting: 44.34% were undergrads, 23.25% had associate degrees, and smaller groups reported junior high or below (10.68%), high school or technical school (7.56%), or graduate degrees and above (12.39%). This makes the sample mostly young, educated, and slightly more female, which fits what you’d expect in Chinese universities today.

Literature Review

To better understand whether my observations about competitive games are shared by researchers, it is helpful to look at how previous studies have examined competition, motivation, and gaming behavior.

Previous research has shown that competition plays an important role in shaping player behavior in digital games. Studies on online multiplayer communities suggest that competition system can significantly increase player engagement, but may also lead to negative outcomes such as stress, frustration, and hostile interactions when design elements are unclear or overly punitive (Kou & Nardi, 2014; Kordyaka, Jahn, & Niehaves, 2020). These findings indicate that competition itself is not inherently harmful, but its effects depend largely on how it is structured and experienced by players.

From a motivational perspective, research has emphasized that players are driven by more than simple external rewards. According to self-determination theory, motivation in games can be supported when players experience autonomy, competence, and relatedness (Deci & Ryan, 2000). Building on this idea, studies on game engagement and gamification suggest that transparent feedback, achievable challenges, and meaningful recognition systems are more likely to promote sustained and positive participation (Hamari & Keronen, 2017). In contrast, competitive environments that focus excessively on ranking, punishment, or exclusion may undermine intrinsic motivation and increase emotional pressure.

Research on adolescents and online games further highlights the complexity of game engagement. Large-scale studies in China have shown that online games have both positive and negative impacts on minors. On the one hand, games can provide enjoyment, stress relief, and opportunities for social interaction; on the other hand, a proportion of adolescents display signs of excessive use and difficulty disengaging from games (Tian & Wang, 2022). Importantly, this research suggests that problematic gaming behavior cannot be explained by a single factor, but is related to a combination of individual experience and game structure.

While existing studies have examined gaming addiction, motivation, and social effects separately, fewer studies focus specifically on how differentcompetition system —such as player-versus-player modes, ranking systems, or goal-based challenges—shape patterns of engagement and risk. Most discussions treat competition as a general feature, rather than distinguishing between its different forms.

Based on this gap, the present study focuses on how various competition system are associated with player engagement, motivation, and potential addictive experiences. By comparing different gameplay structures, this research aims to provide a more detailed understanding of how competition operates within games, especially among younger players.

Method and Participants

This study employed an anonymous questionnaire-based survey to examine the relationship between competition system and player engagement in digital games. 

Gaming intensity was measured through self-reported weekly playtime, while genre preference was assessed by asking participants to identify the type of games they played most frequently. Financial investment in gaming was measured using a question on average monthly spending on games and in-game purchases. To examine competitive orientation, the survey included items assessing participants’ engagement with competitive features such as ranked ladders, leaderboards, player-versus-player modes, achievement systems, and time-based challenges. Participants were also asked to report their primary motivation for playing games, including competition, narrative immersion, social interaction, relaxation, and self-improvement.

In addition, the questionnaire included items measuring emotional responses to winning and losing, as well as the perceived importance of visible recognition systems such as rankings, badges, or achievements. Experiences related to gaming addiction were assessed using a series of frequency-based items capturing behaviors such as excessive play, difficulty stopping, emotional dependence, and perceived impact on daily life. Most attitudinal items were measured using five-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree). Demographic variables, including age group, gender, educational background, and participation in other activities, were also collected to serve as contextual variables.

Participants were recruited using a non-probability convenience sampling method, primarily through online distribution channels commonly used for campus surveys, including student social media groups and course-related communication platforms. In addition, some responses were collected through school-based channels, allowing access to younger participants. As participants were recruited based on accessibility rather than random selection, the sample does not represent a probability-based population.

A total of 1,114 valid responses were collected. The sample was 60.14% female (n = 670) and 39.86% male (n = 444). The age distribution was heavily concentrated among young people, with 58.89% aged 18–25, followed by 17.06% under 18 (n = 190) and 9.96% aged 26–30, while the remaining respondents were distributed across older age groups. Educational attainment reflected the recruitment context, with most participants reporting undergraduate or college-level education.

Due to the relatively smaller number of adolescent respondents, comparisons between adolescents and adults in this study are presented as exploratory analyses, focusing on identifying patterns and tendencies rather than making population-level generalizations. As the sample was not randomly selected, the findings of this study should be interpreted with caution and are intended to provide insight into possible relationships between competitive game design and player engagement, rather than to represent all player populations.

Results

The study yielded 1,114 valid responses from adolescents and young adults (under 30), providing a broad overview of how players experience different competition system. When categorized by primary competition type, 578 respondents reported most frequently engaging in player-versus-player (PvP) modes, followed by 417 respondents engaging in cooperative-task or player-versus-environment (PvE) modes. Smaller proportions reported primarily engaging in ranking systems (n = 184), achievement-based systems (n = 215), timing-based challenges (n = 130), and non-competitive modes (n = 473). The analysis incorporates both behavioral indicators—such as time spent and money spent—and psychological indicators, including motivation, quitting intention, and addictive tendencies.

Player Investment Across Competitive Systems

Table 1

As shown in Table 1, different competition systems are associated with distinct levels of player investment. Among all gameplay modes, ranking-based systems exhibit the highest average playtime (41.25 minutes) and the highest average financial spending (522.66). These values are notably higher than those observed in the two most common systems, PvP (33.32 minutes; 431.39) and PvE (32.55 minutes; 426.08).

PvP and PvE represent the two most fundamental competition systems in the dataset, capturing direct social confrontation and cooperative performance respectively. They also function as foundational building blocks for other systems, as ranking, timing, and achievement-based modes often incorporate elements of either PvP or PvE. Therefore, comparisons below focus primarily on these two systems, while highlighting how ranking systems amplify their competitive intensity.

The higher levels of time and monetary investment associated with ranking systems suggest that structured competition, particularly when tied to progression ladders and status indicators, is especially effective in sustaining prolonged engagement.

In contrast, achievement-based and non-competitive systems show lower investment levels. Achievement players spend an average of 33.14 minutes and 411.78, while non-competitive players spend 34.22 minutes but only 377.23, the lowest spending among all modes. This suggests that without competitive pressure or visible status rewards, players are less motivated to invest financially.

Motivational Structures in Competitive Gameplay 

Table 2

Beyond behavioral investment, Table 2 illustrates that competitive engagement is driven by multiple motivations rather than the pursuit of victory alone. In ranking systems, players report high motivation not only for ranking and winning (3.76), but also for emotional immersion (3.98), stress relief (4.11), and recognition (3.54). Motivation related to learning and self-improvement (3.35) also remains relatively strong.

PvP modes show a similar but slightly less intensified pattern, with particularly high scores in achievement (3.99) and immersion (3.83). PvE systems, in contrast, emphasize collaboration and immersion, with immersion reaching 4.10 and friend-related motivation at 3.99, reflecting a more stable and predictable engagement structure.

These findings suggest that competition operates as a multi-dimensional motivational environment, fulfilling players’ needs for progress, validation, emotional release, and social connection, rather than functioning solely as a win–lose mechanism.

Timing-based systems stand out for their strong learning motivation (3.846) and freedom (3.7), reflecting their appeal to players who enjoy self-improvement and efficiency. Achievement systems show more moderate scores across all motivational dimensions, while non-competitive modes score lowest on most items except freedom (3.691), confirming their role as pressure-free spaces for casual play.

Addictive Experiences and Competitive Intensity 

Table 3

In addition to increased engagement, competitive and ranking-based systems show a higher concentration of addictive experiences, as presented in Table 3. Players primarily engaged in ranking modes report higher levels of excessive play (2.89), perceived impairment in daily life (2.30), and emotional escape (2.87) compared to other gameplay structures. Financially related addictive behavior is also most prominent in ranking systems, with spending reaching 2.36, the highest among all modes.

PvP modes also display elevated scores on several addiction-related indicators, including overplay (2.79) and craving (2.25), though these values remain lower than those observed in ranking systems. Importantly, while average scores across all modes remain below the threshold of severe addiction, the clustering of higher values in ranking-based gameplay suggests that competitive intensity may increase the likelihood of addictive tendencies emerging, particularly when repeated match cycles and ranking pressure are present.

Among less intensive systems, achievement-based play shows moderate addiction scores, with overplay at 2.753 and craving at 2.163. Timing-based systems have relatively low spending addiction (1.885), the lowest among all modes, suggesting that players focused on efficiency are less likely to spend money excessively. Non-competitive systems consistently score lowest on most addiction indicators, including impairment (1.907) and withdrawal (1.879), supporting their image as healthier play options.

Quitting Intentions and Structural Pressure 

Table 4

Finally, Table 4 demonstrates that players’ intentions to quit are more closely associated with structural pressures than with a simple loss of interest. In ranking systems, players report relatively high levels of stress (3.59) and financial cost (3.92) as reasons for considering withdrawal. The pressure of unmet achievements is particularly notable, with ranking modes scoring 4.02 on “no achievement,” the highest among all systems.

PvP and PvE modes show similar patterns, where quitting intentions are more strongly linked to cost, stress, and unmet progression rather than boredom alone. In contrast, purely non-competitive modes display higher quitting scores related to difficulty and lack of achievement, but comparatively lower stress levels.

These results indicate that players are often driven away not because games become uninteresting, but because competitive structures introduce sustained emotional, financial, and performance-related pressures that gradually outweigh enjoyment.

Summary of Results

The results of this study suggest that competitive and ranking-based game modes are strongly associated with higher player engagement. One possible explanation is that these systems provide players with clear goals and immediate feedback. When players win a match or improve their rank, they receive a sense of progress that encourages them to continue playing. Even after losing, the desire to recover lost points or improve performance may encourage players to play “one more round.” Both of these responses happen in real time during and right after the game. From the players’ perspective, ranking systems make progress visible and measurable, which may help explain why they are linked to longer playtime and higher investment.

Another important finding of this study is that competition is driven by multiple motivations rather than only the desire to win. The data shows that players in competitive modes report strong motivations related to improvement, recognition, emotional release, and immersion. This suggests that competitive gameplay satisfies different psychological needs at the same time. For some players, competition offers a way to test their skills and gain recognition, while for others it provides a way to relieve stress or feel more emotionally involved. As a result, competitive systems may be especially engaging because they combine achievement, emotion, and social interaction into a single experience.

However, the findings also indicate that higher engagement in competitive systems is often accompanied by increased pressure. Players in ranking modes report higher levels of stress, spending, and addictive experiences compared to other gameplay structures. This suggests that competition has pros and cons. While it makes games more exciting and meaningful, it may also increase the difficulty of stopping or taking breaks, especially when progress and performance are constantly evaluated. In addition, the results related to quitting intention show that players are more likely to consider quitting due to stress and cost rather than a lack of interest. This implies that players do not necessarily lose enjoyment, but may feel overwhelmed by the demands of competitive systems.

Beyond the major competitive systems, timing-based and achievement-based modes serve more specialized functions. Timing systems appeal to players who value efficiency and measurable self-improvement, as shown by their high learning motivation scores. However, their narrow player base and high cost-related quitting suggest they work best as complementary features rather than core gameplay loops. Achievement systems, while effective for short-term goal completion, struggle with long-term retention once objectives are exhausted. This explains their high quitting scores related to lack of interest, suggesting that players simply run out of things to do.

Non-competitive systems, despite their low engagement intensity, play an important balancing role in the gaming ecosystem. They provide pressure-free spaces for relaxation, creative expression, and casual social interaction. The high freedom scores in this mode suggest that some players actively seek environments without performance evaluation. However, the elevated quitting motivations related to lack of achievement indicate that pure sandbox experiences may need occasional structure or events to maintain long-term interest. For game developers, this suggests that offering a mix of competitive and non-competitive modes within a single game could support a wider range of player needs: intense ranked play for some, casual exploration for others.

This study has several limitations. First, the data was collected through an online questionnaire, which means the results rely on self-reported experiences and cannot determine cause-and-effect relationships. Second, although the sample size is relatively large, the number of adolescent respondents is smaller compared to adults, so comparisons between age groups should be interpreted cautiously. Future research could use interviews or experiments to better understand how players experience competition over time, or focus specifically on younger players to explore how competitive systems influence their gaming habits.

Overall, this study suggests that competition plays an important role in shaping player engagement. Competitive systems appear to increase both enjoyment and pressure, making them powerful but complex elements of game design. Understanding how different competitive structures influence player behavior may help players, developers, and researchers better reflect on how games are designed and experienced.

This study explored how different competitive systems in digital games are related to player engagement, motivation, and potential addictive experiences. Based on data collected from 1,114 players, the results show that competitive structures: especially ranking-based systems: are associated with higher levels of time investment, financial spending, and emotional involvement. Compared to other gameplay modes, ranked competition appears to intensify both engagement and pressure.

One key finding of this research is that competition motivates players in multiple ways. Rather than being driven solely by the desire to win, players in competitive systems also report strong motivations related to self-improvement, recognition, immersion, and emotional release. This is especially true in timing-based systems, where learning and freedom emerge as primary drivers, and in achievement systems, where goal completion provides structure and direction. Even non-competitive modes serve a purpose, offering players autonomy and relaxation without performance pressure. This suggests that different competitive structures fulfill different psychological needs, and no single system works for all players.

At the same time, the results indicate that higher engagement in competitive modes often comes with increased stress and addictive tendencies. Players in ranking systems are more likely to report excessive play, difficulty stopping, and concerns related to spending and pressure. Importantly, quitting intentions are more closely linked to stress and cost than to a loss of interest, suggesting that players may still enjoy the game but feel overwhelmed by the demands of competitive structures.

This study considers competitive systems as both helpful and potentially dangerous. They can stimulate enjoyment and engagement in the gaming process, but they may also increase the risk of unhealthy play behaviors associated with higher levels of addiction. Based on an online questionnaire survey of 1,114 participants and further analysis, this research provides a clearer understanding of how different competitive systems influence player engagement.

References

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.

Hamari, J., & Keronen, L. (2017). Why do people play games? A meta-analysis. International Journal of Information Management, 37(3), 125–141.

Kou, Y., & Nardi, B. (2014). Regulating anti-social behavior on the Internet: The example of League of Legends. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW), 616–628.

Kordyaka, B., Jahn, K., & Niehaves, B. (2020). Towards a unified theory of toxic behavior in video games. Internet Research, 30(4), 1081–1102.

Tian, F., & Wang, L. (2022).  A study on minors’ online game use and its impacts.  Youth Research, (3), 45–57.

About the author

Charles Shang

Charles is a 12th grade student based in San Francisco, CA.

The post The Role of Competition in Player Engagement: Evidence from Different Competitive Systems in Video Games appeared first on Exploratio Journal.

Introduction

“87% of neighborhoods in San Francisco undergoing gentrification were once redlined as hazardous” (“From Redlining to Gentrification: The Policy of the Past that Affects Health Outcomes Today”). Redlining was a discriminatory practice in which banks and government agencies denied services such as insurance and mortgages to residents of neighborhoods with large African American and other minority populations. Over time, this practice caused significant disinvestment and economic deterioration of neighborhoods it affected, leaving many people in a fixed place of poverty that prevented upward socioeconomic mobility.

Established in 1933 as a part of Franklin Delano Roosevelt’s “New Deal” program to lift the country out of economic depression, the Home Owners’ Loan Corporation (HOLC) provided mortgage relief to homeowners at risk of losing their homes through foreclosure, in order to stabilize the housing market. As a part of this process, the HOLC created numerous residential security maps, grading different neighborhoods A-D (A being a highly secure and desirable neighborhood & D being a hazardous neighborhood) in about 200 different U.S. cities. Though aiming to create stability in the housing market, these letter grades were primarily based upon the socioeconomic, racial, and ethnic makeup of the neighborhoods’ residents, which facilitated the development of discrimination in the U.S. housing market. With the Johnson administration’s passage of the Fair Housing Act in 1968, any means of housing discrimination on the basis of race, sex, familial status, nationality, and disability were prohibited, thereby marking the closure of the HOLC’s rampant redlining practices of the mid-20th century. However, despite the practice’s prohibition, redlining had already caused a large amount of socioeconomic damage on the communities it had impacted, through a ripple effect of discrimination prevalent in many of society’s pillar institutions (ie schooling, employment, healthy food access)

In this paper I aim to answer the following question: What are the long-term economic impacts of 1930s-era HOLC redlining in the San Francisco Bay Area? By focusing on the quantitative relationships between redlining tract coverage and census data of racial demographics, household income, educational access, and employment, this paper aims to quantify the extent of redlining’s association with economic disparity throughout the late 20th and early 21st centuries through distributional impacts, categorical comparisons, and regression analysis.

From conducting distributional, categorical, and regression analyses, there are several patterns that can be concluded about the long-term economic impacts of New Deal-era HOLC redlining in the Bay Area. Firstly, the distributions holistically showed minimal change in shape over time indicating that the economic indicators measured stayed consistent over the course of several decades. This consistency goes to show that the economic impacts caused by redlining are solid and aren’t weak enough to change over time. Secondly, the regression analysis showed a negative relationship between income and redlining tract coverage with an increasing slope magnitude over time and a statistically significant relationship between the two variables indicated by the very low p-values.  

Section 1: Distributional Impacts

To examine the persistent long-term economic impacts of redlining across different HOLC-graded neighborhoods (graded on desirability), I first generated histograms of median household income (1980-2000), unemployment (1980-2000), and white-occupied housing (1980-2020) across HOLC grades A-D, which reveal several long term distributional economic trends.

Histograms were chosen because they allow one to see the persistence or change of these economic patterns over time by noticing a persistence of change in the spread and shape of the census data across HOLC grades. The incorporation of histogram series by time period with the four different grades per serie allows one to see variation within each grade and how the socioeconomic indicator of one grade changed over time relative to another. Each histogram plots the relative frequency of a given census variable across HOLC tracts A-D as defined by the 1930s HOLC redlining maps. By using relative frequency histograms rather than raw count histograms, the distributions are normalized which allows for fair and accurate comparison across grades that have a different number of census tracts. To ensure accurate comparability over time, the relative frequency histogram that plots median household income (1980-200) across HOLC grades A-D were converted from nominal US dollars to real 2020 US dollars using the Bureau of Labor Statistics’ annual average CPI values.

Subsection 1A: Median Household Income

The first socioeconomic indicator is real median household income. This variable measures the typical earning level of all households in a neighborhood, which is a strong indicator of overall economic opportunity in that neighborhood. Because nominal incomes change with inflation, the median household incomes were converted from nominal to real 2020 U.S. dollars using annual average CPI values from the U.S. Bureau of Labor Statistics. 

The first observation that can be drawn from Figure 1.1 is that the shape of the distributions of median household income for each HOLC grade remains approximately the same consistently across all three dates of measure. This indicates that aggregate household income for each grade persisted over the period of measure.

The second observation that can be drawn from Figure 1.1 has to do with the differences in intergrade clustering and spread of the data. Grade A’s distribution has a slight right-skew with a moderately-large income range that persists across the three dates of measure. Moving from the Grade A distribution to the Grade D distribution, the data gradually shifts left with each grade closer to D. Grade D’s distribution shows one, a high level of clustering at the left side of the left side of the histogram and two, an income range that is significantly smaller than that of Grade A’s distribution. These two observations continue throughout the three dates of measure.

From these observations, it can be concluded that Grade A Bay Area neighborhoods have a larger range of median household income values with fewer observations of lower median household income values. This conclusion is in accordance with the inference that those in a grade with greater security and desirability tend to have higher job opportunity, educational access, and healthcare access. The subtle right-skew in Grade A’s distribution indicates that a smaller proportion of tracts have exceptionally high incomes which increases the mean and pulls the distribution’s tail rightward. The gradual leftward shift of the distributions from Grade A to Grade D indicates that as the magnitude of risk assessed by the HOLC for the tracts is inversely proportional to the median household income for the tracts. Moving to Grade D, it can be concluded that tracts judged to have lower security and desirability by the HOLC have a smaller range of median household income values and a high proportion of observations of lower median household income. This conclusion is in agreement with the inference that individuals in this grade tend to have lesser job opportunity, lesser educational access, and lesser healthcare access, traits which undermine ability to achieve high household income.

In addition, the moderately-large range of income denoted in Grade A’s distribution hints at a more discrete but key principle of 1930s HOLC redlining: racial prejudice. As observed in Grade D’s distribution, there was a visible clustering of tracts toward the left of the histogram around the lower income values. Intuitively, this logic should apply to Grade A as well but in an opposite fashion: clustering of tracts toward the right of the histogram around the higher income values. However, in Grade A’s distribution there is a significantly-sized range and spread of income, much larger than that of Grade D’s distribution, indicating that Grade A neighborhoods contained households with a variety of median household income values. From this distributional observation, it can be understood that HOLC redlining grading was deeply influenced by racial prejudice, more so than objective economic data for a lot of the time.

Subsection 1B: Unemployment

The second socioeconomic indicator is percent of civilians unemployed (16+). This variable measures the share of the working-class population per grade that are actively seeking employment but cannot find work. Redlined neighborhoods often see disinvestment and lower amounts of socioeconomic mobility so distributional unemployment is a strong indicator of the severity of redlining experienced by grade.

Similar to the distribution of median household income across HOLC grades, the first observation drawn from Figure 1.2 is that consistently across all three dates of measure, the shape of the distribution for each respective HOLC grade remains approximately the same, with only a few minor shifts, which indicates that unemployment shares for each grade persisted over time. This persistence in unemployment suggests that the socioeconomic division caused by 1930s HOLC redlining has stayed relatively intact in the latter half of the 20th century, with census level unemployment shares continuing to mirror structural inequalities imposed by HOLC redlining maps.

The second observation drawn from Figure 1.2 is concerned with the differences in intergrade clustering and spread of the data as well. Generally, these differences are the same as in Figure 1.1 but a mirror flip. Grade A’s distribution shows high left-clustering, a small right-skew, and a very high proportion of observations on that side of the histogram. Moving from the Grade A distribution to the Grade D distribution, the data gradually gains overall spread/range and magnitude of right-skew while also losing peak height. Grade D’s distribution shows a range and median greater than that of Grade A. This pattern of inter-grade shifting from A to D remains approximately the same over the period of measure.

From these observations, it can generally be concluded that the magnitude of risk assessed by the HOLC for the tracts is directly proportional to the percent of civilians (16+) unemployed for the tracts. The association between these two variables is consistent with the inference that redlined neighborhoods tend to have lower economic opportunity, in this case job access, resulting in a higher unemployment rate in these tracts. While the median percent of civilians (16+) unemployed increases moving from Grade A to Grade D, the magnitude of right skew also increases which means that there is a higher degree of variability in tracts deemed less secure and desirable by the HOLC. Economically, this increase in variability in percent of civilians (16+) unemployed from Grade A to Grade D means that the magnitude of risk assessed by the HOLC for the tracts is associated with a higher degree of economic volatility in the tracts. These tracts that have experienced a high amount of redlining have also seen uneven patterns of disinvestment, reinvestment, demographic/population change, gentrification, industrial restructuring, factors which are greatly responsible for a high variation in unemployment in these tracts. In addition to an increase in magnitude of right skew from Grade A to Grade D, a decrease in peak height is also observed. This means the magnitude of risk assessed by the HOLC for the tracts is inversely proportional to the proportion of observations made. Similar to an increase in spread, a decrease in peak height also denotes a proportional relationship between degree of redlining and degree of economic volatility observed. From a statistical standpoint, the decrease in peak height, or flattening, of the distribution means that a fewer proportion of tracts share a common unemployment rate for civilians (16+). This flattening represents a growing internal heterogeneity suggesting that redlining produced a fragmented and nonuniform economic landscape in the areas it greatly impacted.

Subsection 1.C: White Share

The third socioeconomic indicator is percent of white-occupied houses. This variable measures a specific racial composition of occupied housing units. Historically, redlined neighborhoods in the mid 20th century saw a large amount of urban flight in which primarily caucasian residents departed urban areas for suburban neighborhoods. As a result of this exodus, over time, redlined neighborhoods saw a change in white residents so by measuring this variable, we will see how the magnitude and direction of this change across grades and the fluctuations in change over time. 

Similar to the previous two distributions, the first observation that can be drawn from Figure 1.3 is that across the five dates of measure, the shape of the distribution for HOLC grades A, B, and C remain approximately the same, with only a few minor shifts. This indicates that for these grades, the distribution of percent of white occupied homes persisted over the course of 4 decades. Likewise to the other socioeconomic indicators measured in Figures 1.1 and 1.2 respectively, the persistence of shares of white occupied homes suggests that the census-level outcomes of the racial bias  ingrained into HOLC redlining maps has seen very minimal change in the highly to moderately secure and desirable tracts (A-C), indicating that housing segregation on the basis of race has persisted over time in the Bay Area.

The second set of observations that can be drawn from Figure 1.3 concerns the shape of each grade’s distribution over the period of measure: what the shape means and for Grade D, what a shift in the distribution’s shape means. Firstly, moving left to right from Grade A to Grade D, there is a gradual flattening of the distribution, most significantly moving from Grade A to Grade B, and there is a shift from a right-skew to a left-skew. Moving vertically down the figure from 1980 to 2020, the distributions of Grades B, C, and D see a subtle and gradual increase in aggregate peak height over time. In Grade D, there is a sharp increase in proportion of observations over time at the 7-20% range of the 2020 distribution.

From these observations, it can be concluded that magnitude of risk assessed by the HOLC for the tracts is inversely proportional to the percent of white-occupied houses for the tracts over time. As the HOLC grade declines in security and desirability, the distribution becomes increasingly concentrated toward lower percentage values of white-occupied houses. This inverse association, shown by the shift of a right-skew to a left-skew from Grade A to Grade D in each date of measure, confirms our understanding of HOLC redlining having a basis of racial prejudice, as lower-tier grades and areas with higher levels of redlining tend to have a greater minority demographic and a lower majority, in this case caucasian, demographic. The flattening of the distributions in the earlier dates of measure (1980 and 1990 primarily) indicate an increase in demographic variability as tracts become more “hazardous.” While secure and desirable areas remain racially homogenous with a high proportion of caucasians, areas with lower security and desirability are more racially heterogenous reflecting both racial segregation’s influence in HOLC redlining as well as subsequent population shifts driven by disinvestment and suburbanization. The increase in overall peak height for Grades B, C, and D but retainment of a roughly flat shape relative to Grade A’s distribution indicate that there is a greater amount of census data concerning white-occupied housing moving forward in time, but the demographic trends revealed by this data remain persistent as mentioned in detail above. The sharp increase in peak height in 2020 at the 7-20% range of the Grade D histogram indicates that there is a significantly higher proportion of observations concentrated around the lower percent of white-occupied houses in recent times, a trend that can be explained by “white-flight” and suburbanization. This migratory occurrence is defined as the migration of primarily caucasian residents from urban to suburban areas, which results in the economic degradation of urban areas through decreased funding and overall maintenance.

Section 2: Categorical / Group-Level Comparisons

To further explore the economic impacts of 1930s HOLC redlining in the Bay Area, I used categorical plots to visualize redlining’s impact through median household income and educational attainment across HOLC grades A-D.

Definitionally, a categorical plot allows for comparison of a single numerical variable across four levels of categorical variables, in this case, each level corresponding to a HOLC grade A-D. The important distinction that needs to be made in order to understand what the differences are in the displaying of categorical variables (HOLC grades) is the difference in interpretation over time for the histograms vs. the categorical plots. The histogram focuses on distributional change within a singular HOLC grade over time, which provides insight into intra-grade socioeconomic change over the period of measure. On the other hand, the categorical plot focuses on the change in inter-grade variation over time for a given socioeconomic indicator.  Due to this difference, I chose to incorporate categorical plots to supplement the distributions in order to visualize comparative disparities between HOLC grades.

Furthermore, in connecting the two models, a single bar within a categorical plot can be interpreted like a distribution for the HOLC grade that the bar represents, through the spread of data points within the bar.

Subsection 2A: Median Household Income

The first socioeconomic indicator to be analyzed categorically is median household income (real 2020 USD) across HOLC grades A-D. In the figure that assesses this variable, there are three categorical plots, one plot for each period of measure, 1980, 1990, and 2000 respectively.

The first observation that can be drawn from Figure 2.1 is the clear downward trend from Grade A to D that is consistent across all three dates of measure, with Grade A having the highest peak and Grade D having the lowest peak. The second observation that can be drawn from Figure 2.1 concerns intra-grade variability indicated by the distribution of data points within each HOLC Grade’s bar. Consistently across all three dates of measure, Grade A tracts show the greatest intra-grade variability, indicated by a greater spread of data points within the bar. As the HOLC grade declines however, there is a decrease in intra-grade variability, indicated by the increasing clustering of data points within each bar. The third observation that can be drawn from Figure 2.1 is an increase in the peak height for each HOLC Grade’s bar over time, while the vertical separation between each bar remains approximately equivalent over time. 

From the first observation, it can be roughly concluded that magnitude of risk and insecurity determined by the HOLC and the median household income are inversely proportional for the tracts. This observation is consistent with the basis of redlining, in which redlined neighborhoods saw high levels of disinvestment and economic deterioration, preventing upward socioeconomic mobility for residents of these tracts, therefore explaining why residents of redlined tracts tended to have lower median incomes than those of non-redlined tracts. From the second observation concerning intra-grade variability, the higher levels of variability in tracts deemed less risky and more secure by the HOLC indicate that in these tracts, there is a greater degree of economic opportunity and mobility, providing an explanation for a wider spread in Grade A and a tighter clustering in lower grades, where many residents of redlined tracts are stuck in a lower socioeconomic position and unable to advance upwards. From the third observation concerning the increase in relative heights of each HOLC Grade’s bar over time, we can conclude that across all tracts, redlined or not, general development over the period of measure through technological innovation and globalization promoted the median household incomes for each HOLC Grade. Despite this overall increase, the vertical separation for each catplot between each HOLC Grade remains approximately equivalent suggesting that relative disparities between grades persisted despite overall income growth over the period of measure.

Subsection 2B: Educational Attainment

The second socioeconomic indicator to be analyzed categorically is educational attainment across HOLC grades A-D. This variable will be measured by the percent of adults 25 and older with 4 or more years of college education. In the figure below, there are three categorical plots, one plot for each period of measure, 1980, 1990, and 2000 respectively.

The first observation that can be drawn from Figure 2.2 is the clear downward trend from Grade A to Grade D across all three dates of measure, with Grade A having the highest peak and Grade A having the lowest peak. The second observation that can be drawn is the gradual upward shift in each grade over time, shown by the increasing peak height for each HOLC Grade’s bar. In connecting these two observations, we see that the difference in peak heights between the HOLC Grades decreases over the period of measure, with the bars for Grades B-D increasing by a greater amount than the bar for Grade A, revealing a flattening of the downward trend from Grade A to D.

From the first observation, it can be concluded that magnitude of risk and insecurity determined by the HOLC is inversely proportional to the percent of adults 25 years of age and older with 4 or more years of college education for the tracts. This conclusion aligns with the economics of redlining in which neighborhoods that experienced the negative implications of the practice saw less economic opportunity as well as overall disinvestment in their communities. Tracking back to Figure 2.1, we see that neighborhoods in lower tier HOLC grades have generally a lower aggregate median household income. Due to the aggregate financial status of these neighborhoods, we can infer that one of the reasons that households in redlined neighborhoods saw lower degrees of educational attainment was they comprehensively had less disposable income to spend on privileges like textbooks, tutoring services, or in this case, a college education.

From the second observation, it can be concluded that the moderate to lower tier grades saw upward educational mobility over the period of measure. This pattern can be primarily attributed to neighborhood redevelopment and gentrification throughout the Bay Area, particularly in tracts close in proximity to university centers, like UC Berkeley, or booming industries that have attracted more college-educated residents in search of work on a domestic and international level, like Silicon Valley and the South Bay. Coinciding with these inferences, the Bay Area saw several urban redevelopment projects in parts of Oakland, Emeryville, and San Francisco’s Mission District in which communities experienced infrastructural improvement in housing and schools. In a research report titled Engaging Schools in Urban Revitalization: The Y-PLAN (Youth – Plan, Learn, Act, Now!), authors Deborah L. McKoy and Jeffrey M. Vincent define the Y-PLAN initiative in the context of the urban Bay Area: 

“West Oakland, California, is an industrial area suffering the abandonment and blight common to other neighborhoods after the loss of manufacturing employers, a process that began in the 1950s…Stepping into this environment in 2000 was the Y-PLAN (Youth—Plan, Learn, Act, Now!), a model for youth civic engagement in city planning that uses urban space slated for redevelopment as a catalyst for community revitalization and education reform. Sponsored by the Center for Cities & Schools at the University of California (UC), Berkeley, Y-PLAN facilitates positive community outcomes by partnering graduate student “mentors,” local high school students, government agencies, private interests, and other community parties to work on a real-world planning issue. The Y-PLAN is both a pedagogical tool and a planning studio that addresses specific issues in local communities” (McKoy & Vincent, 2007, p. 1).

Due to the implementation of programs such as the Y-PLAN that educationally mobilized the Bay Area’s youth in impoverished areas, many of these neighborhood areas saw educational improvement reflected by the increasing peak heights for tracts in HOLC Grades B-D in the categorical plot. As a result of the flattening of the downward trend, it can be concluded that the educational disparities within the Bay Area were ameliorated over the period of measure, in part due to youth initiatives and other programs seeking educational improvement.

Section 3: Regression Analysis of Raw Median Household Income

To determine the impact of redlining tract coverage on median household income, I also conducted ordinary least squares (OLS) linear regressions for both the raw and logarithm of median household income as a function of redlining tract coverage from a 1930s HOLC map. While the histograms and catplots provide distributional insight and categorical comparison, respectively, into disparities across HOLC grades, the regression allows for high quantitative precision in determining the extent to which redlining coverage can accurately predict income for a Bay Area neighborhood. More specifically, the categorical plots allowed us to see a general slope trend based on the peaks of each bar, but the regression expands upon this observation by making it more specific numerically.

Because nominal incomes change with inflation, the median household incomes were converted from nominal to real 2020 U.S. dollars using annual average CPI values from the U.S. Bureau of Labor Statistics. The purpose of conducting a log-transformed regression was to linearize the relationship and allow for the slope coefficient to be interpreted as a percent change in income for a one-unit increase in tract coverage.

The two key regression outputs that will be analyzed in this section are slope coefficient and p-values, which together, can assess the magnitude and certainty, respectively, of the relationship between redlining tract coverage and median household income over time. For the raw income model, the slope coefficient is interpreted as the predicted change in real median household income by the OLS regression line per one-unit (or 100%) increase in redlining tract coverage. For the log-transformed income model, the slope coefficient is interpreted as the predicted percent change in real median household income by the OLS regression line per 1.0 (or 100%) increase in redlining tract coverage. Given that the distribution of income is skewed, taking the logarithm of income compresses high-income and low-income outliers, which creates a more symmetric and homoscedastic residual distribution. The patterns observed in the logarithm-transformed regression are largely identical to those observed in the untransformed regression, so I decided to include logarithm-transformed regression analysis in the first appendix of the paper. The p-values for each model (raw income and log-transformed income) represent the probability of observing each respective slope by random chance. A low p-value (less than the alpha level of 5%) indicates that the observed relationship is unlikely due to random chance.

Several neighborhood wealth-related trends can be concluded from OLS regression analysis of raw and log-transformed real median household income (2020 dollars) as a function of redlining tract coverage from 1980-2000. While quantitative, these patterns share similarities with the distributional trends derived from the histograms showing real median household income (2020 dollars) across HOLC grades A-D described in detail earlier in the paper.

Here, our parameter beta represents the true slope of the population regression line relating the explanatory and response variables of redlining tract coverage and raw income respectively. The null hypothesis is that beta is equal to zero and the alternative hypothesis is that beta is less than zero.

Figure 3.1 Raw Income as a function of Redlining Tract Coverage

Figure 3.1 confirms a negative association between income and redlining tract coverage based on the slope coefficients. Firstly, the slope coefficient for the 1980 OLS regression is -15,205.29. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a $15,205.29 decrease in real median household income (2020 dollars) in 1980. Secondly, the slope coefficient for the 1990 OLS regression is -29,742.12. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a $29,742.12 decrease in real median household income (2020 dollars) in 1990. Thirdly, the slope coefficient for the 2000 OLS regression is -46,819.90. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a $46,819.90 decrease in real median household income (2020 dollars) in 2000.

While I noted that the association between income and redlining tract coverage remains negative, from the slope coefficients in Figure 3.1, it can also be observed that the magnitude of the decrease in income increases with each subsequent date of measure. From a graphical standpoint, this can be understood as an initially negative regression line that gets steeper and steeper in the negative direction over time. So what does this mean in the context of redlining? From this observation, it can be concluded that the negative impact of redlining coverage on household income worsens overtime, as the same increase in coverage is met with a greater magnitude of decrease in income over the period of measure. This trend can be best explained by the extensive impact of disinvestment in higher redlined areas. Following the HOLC’s classification of neighborhoods as “hazardous” via their redlining maps, banks and financial institutions chose not to lend money to businesses and individuals, insure mortgages, or fund development in these areas due to the high-risk attributed to these areas by the HOLC. As a result of this lack of financial and infrastructural support from banking institutions, these areas deteriorated over time through an aggregate decrease in property values as well as less socioeconomic mobility and in this case, access to high-paying jobs. Due to the extensive decline of the economic makeup of these areas caused by disinvestment, it makes sense why the slope coefficient of the income vs. redlining tract coverage regression decrease over time.

Figure 3.1 also displays very low p-values that all fall significantly below the general alpha level of 5%. Therefore, we can ascertain a relationship between redlining tract coverage and income. Firstly, the p-value for the 1980 OLS regression with raw income as the dependent variable is  . Assuming that there’s no association between redlining tract coverage and real median household income in 1980, the probability of obtaining a sample of this size and observing a negative relationship between redlining tract coverage and real median household income (2020 dollars) as or more extreme than a magnitude of approximately $15,205.29 per percentage point of redlining tract coverage by random chance is less than approximately   (rounded 0.0002%). Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower real median household income in 1980 for this sample. Secondly, the p-value for the 1990 OLS regression with raw income as the dependent variable is  . Assuming that there’s no association between redlining tract coverage and real median household income in 1990, the probability of obtaining a sample of this size and observing a negative relationship between redlining tract coverage and real median household income (2020 dollars) as or more extreme than a magnitude of approximately $29,742.12 per percentage point of redlining tract coverage by random chance is less than   (rounded 0.0008%). Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower real median household income 1990. Thirdly, the p-value for the 2000 OLS regression with raw income as the dependent variable is  . Assuming that there’s no association between redlining tract coverage and real median household income in 2000, the probability of obtaining a sample of this size and observing a negative relationship between redlining tract coverage and real median household income (2020 dollars) as or more extreme than a magnitude of approximately $46,819.90 per percentage point of redlining tract coverage by random chance is less than   (rounded 0.0002%). Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower real median household income 2000.

The consistently low p-values across all three dates of measure (less than the alpha level of 5%) confirms the high statistical strength between redlining tract coverage as an independent variable and median household income as a dependent variable. Furthermore, this aspect of the regression corroborates the trends observed from the slope coefficients of the decreasing negative association between these two variables, indicating that it is extremely unlikely this relationship occurred by random chance.

Conclusion

From distributional impacts, to categorical plots, to regression analysis, we can conclude several long term economic impacts that vary by extent to which a given census tract was redlined and how it changed over time. With the histograms, we saw that a lot of the economic patterns surrounding income and unemployment maintained shape for a given grade distribution over time indicating that a lot of the economic impacts stayed stagnant over the course of a few decades. With the regression analysis, we saw that the magnitude of negative slope for raw income as a function of redlining tract coverage greatly decreased in the 20 year period from around $30,000 to $42,000 in 1980 and 2000 respectively. With the log transformed regression, the slope coefficients were approximately equivalent for each year indicating that income dropped by the same percentage each year which shows relative stability in the influence of redlining, not due to external factors.

Looking forward, I would love to dive deeper into how gentrification interacts with these socioeconomic patterns of redlining. By definition, gentrification is the process in which a poorer area is infrastructurally improved as a result of a wealthier demographic of people moving in. This economic improvement is seen over time through improved housing, healthcare, and new business. By exploring gentrification in the context of redlining, I would ask whether modern reinvestment in previously redlined areas resulted in economic growth, ameliorating the negative effects of 1930s-era HOLC redlining.

While in this paper, I primarily studied solely socioeconomic indicators measuring the effects of redlining (unemployment, income, racial demographics, etc.), I would be curious to explore more health-related effects of this same era of redlining. By conducting research on food deserts, urban areas with limited access to good-quality fresh food, through measuring, for example, the number of fast food restaurants in a given census tract as well as illnesses through measuring, for example, the number of diabetes occurrences or hospital visits in a given census tract, I would bring in a new dimension of human health and biology in association with the redlining I explored in this paper. 

Bibliography

De los Santos, H., Jiang, K., Bernardi, J., & Okechukwu, C. (2021, May 26). From redlining to gentrification: The policy of the past that affects health outcomes today. Harvard Medical Journal. Retrieved October 28, 2025, from https://info.primarycare.hms.harvard.edu/perspectives/articles/redlining-gentrification-health-outcomes

Jonathan Schroeder, David Van Riper, Steven Manson, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 20.0 [dataset]. Minneapolis, MN: IPUMS. 2025. http://doi.org/10.18128/D050.V20.0

McKoy, D. L., & Vincent, J. M. (2007, June). Engaging schools in urban revitalization: The y-PLAN. Association of Collegiate Schools of Planning. https://doi.org/10.1177/0739456×06298817

Nelson, R. K., Winling, L, et al. (2023). Mapping Inequality: Redlining in New Deal America. Digital Scholarship Lab. https://dsl.richmond.edu/panorama/redlininghttps://dsl.richmond.edu/panorama/redlining.

Appendix A: Regression Analysis of the Logarithm of Median Household Income

While the raw-income regression shows the dollar change in income as a function of redlining tract coverage, by taking the logarithm of income and performing the same regression function, we see the percent change in income as a function of redlining tract coverage for the same three dates of measure. Though not visible from the regression outputs, the log-transformed regression mitigates the effect of outliers and influential points that undermined the strength of the relationship between raw-income and redlining tract coverage, providing us slope coefficients and p-values that are optimal for ensuring an accurate relationship between the two variables.

Here, our parameter beta represents the true slope of the population regression line relating the explanatory and response variables of redlining tract coverage and logarithm of income respectively. The null hypothesis is that beta is equal to zero and the alternative hypothesis is that beta is less than zero.

Figure A1 Logarithm of Income as a function of Redlining Tract Coverage

Similar to the raw income regression, the results of the log-transformed regression shown in Figure A1 confirm a negative association between redlining tract coverage and income as well. The slope coefficient for the 1980 OLS regression is -0.30. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a 30% decrease in real median household income (2020 dollars) in 1980. The slope coefficient for the 1990 OLS regression is -0.28. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a 28% decrease in real median household income (2020 dollars) in 1980. The slope coefficient for the 2000 OLS regression is -0.32. This means that for each additional one-unit (or 100%) increase in redlining tract coverage, the OLS regression line predicts about a 32% decrease in real median household income (2020 dollars) in 1980.

Similar to the raw income regression, the results of the log-transformed income regression evidenced in Figure A1 show very low p-values. Therefore, we can ascertain a relationship between redlining tract coverage and income. The p-value for the 1980 OLS regression is  . Assuming that there is no relationship between redlining tract coverage and the logarithm of real median household income in 1980, the probability of obtaining a sample of this size and observing a linear relationship between redlining tract coverage and the logarithm of real median household income (2020 dollars) with a slope coefficient of -0.30 or less by random chance alone is less than  %. Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower proportional levels of real median household income in 1980. The p-value for the 1990 OLS regression is  . Assuming that there is no relationship between redlining tract coverage and the logarithm of real median household income in 1990, the probability of obtaining a sample of this size and observing a linear relationship between redlining tract coverage and the logarithm of real median household income (2020 dollars) with a slope coefficient of -0.28 or less by random chance alone is less than  . Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower proportional levels of real median household income in 1990. Lastly, the p-value for the 2000 OLS regression is  . Assuming that there is no relationship between redlining tract coverage and the logarithm of real median household income in 2000, the probability of obtaining a sample of this size and observing a linear relationship between redlining tract coverage and the logarithm of real median household income (2020 dollars) with a slope coefficient of -0.32 or less by random chance alone is less than  . Therefore, the OLS regression model provides significant statistical evidence that tracts with higher redlining coverage are associated with lower proportional levels of real median household income in 2000.

For the most part, these two outputs of the regression reveal largely similar or identical patterns as the raw-income regression. Firstly, the slope coefficients reveal a negative relationship between redlining tract coverage and median household income that steepens over time. This steepening means that the percent decrease in median household income per 100% increase in redlining tract coverage increases with each subsequent date of measure.

Again, the p-values for this regression are far below the alpha level of 5% meaning that a relationship between redlining tract coverage and the logarithm of income is statistically significant across all three dates of measure, and not due to random chance.

About the author

Rishi Haldar

Rishi is a senior at Miramonte High School with interests in economics, mathematics, statistics, and history. He plans to attend Cornell University in the fall, where he will be studying economics in the College of Arts and Sciences. Apart from academics, Rishi is a guitarist in a band that plays local gigs (restaurants, fundraisers, etc.) and plays soccer for a club team and his high school team.

The post Redlining: Quantifying the Economic History of the San Francisco Bay Area appeared first on Exploratio Journal.

Abstract

This paper focuses specifically on terrorism in India, with particular reference to Kashmir insurgency and the Naxalite (Maoist) movement, examining the structural, political and socio-economic factors that contributed to the emergence of these insurgent groups. 

The study is further situated within two principal theoretical lenses identified by Jeff Goodwin: the traditional perspective, which conceptualizes terrorism as a “weapon of the weak”, and the radicalization perspective. In addition, the paper critically reviews the work of prominent scholars whose analysis highlights diverse motivational factors. 

Building on this framework, it also identifies and analyses the principal causes underlying the Kashmir insurgency, highlighting political marginalization, cross-border influence, identity-based grievances, the legacy of the unresolved 1947 Partition dispute, and the impact of Hindutva politics have shaped the trajectory of conflict in the region. 

Extending this analysis, the paper examines the Naxalite Movement (Maoist) movement, tracing its roots to socio-economic inequalities, agrarian exploitation and the persistent marginalization of tribal communities, alongside the ideological influence of contested claims regarding external (particularly Chinese) involvement.

However, in reference to the Kashmir insurgency and the Naxalite (Maoist) movement, Terrorism in India cannot be narrowly confined to religious extremism alone. Rather, it must be understood as the outcome of intersecting structural, ideological and political factors. India’s complex geopolitical location and its history entrenched conflicts have further vulnerabilities in the internal security framework. In light of these findings, it’s important for the enforcement of inclusive governance, credible institutions and sustained socio-economic development to address root causes that perpetuate conflict and instability instead of militarization. 

1. Introduction

What are the causes and consequences of terrorism in India with reference to the Kashmir insurgency and Naxalite movement today? India, like many other countries around the world, has suffered several terrorist attacks over the last two decades. In this research paper, I aim to explore some of the factors that contribute to the rise of terrorism, and to examine its consequences on domestic politics and foreign relations. 

Terrorism has emerged as one of the most persistent and complex challenges in contemporary states, particularly in developing democracies such as India. Several studies identify socio-economic disparities, political exclusion, regional imbalances and perceived injustice as certain factors contributing to the emergence of terrorism. In this model, terrorism is often conceptualized not merely as indiscriminate violence, but as a strategic instrument employed by non-state actors.

Two prominent theoretical perspectives can help in contextualizing the phenomenon. The traditional view portrays terrorism as the “weapon of the weak”, suggesting that downtrodden groups resort to asymmetric violence and in contrast, the radical perspective interprets terrorisms as part of a wider process of escalating political violence, where ideological mobilization, identity-based politics and structured inequalities over the time.

This study points out two major challenges that have continued to shape India’s territorial security landscape: the Kashmir conflict and the Naxalite (left wing extremist) movement. The Kashmir issue represents a multifaceted conflict rooted in historical disputes, regional aspirations and cross-border interactions. Motivations which link to terrorism in this region include separatist aspirations, the influence of global jihadist movement, ideological divergences, perceived political marginalization, etc.

Arising from the 1960s in Naxalbari village of West Bengal, the Naxalite movement reflects a different but also an important form of political violence. Emerging from a radical communist ideology, the movement initially mobilized marginalized rural populations around issues of land redistribution, class exploitation and state neglect

By examining these two cases, this paper seeks to address our central research question: What are the principal causes of terrorism in India? Through a critical analysis of structural conditions, ideological motivations and state responses, the study aims to contribute to an understanding of terrorism as a product of intersecting political, economic and identity-centric factors. This study examines these two cases and offers policy recommendations. 

2. Defining Terrorism

It is important first to define the concept of “terrorism” before addressing my main research question. Terrorism is a highly contested term, and there is no common argument among the scholars on how to define it. However, the purposes of this paper, is to define terrorism in line with Goodwin (2019): “any tactic or set of tactics used by any government, group, organization, or individual, in pursuit of a political goal (broadly defined), which is intended to kill or harm civilians or noncombatants (as opposed to soldiers or political leaders) so as to frighten, intimidate, demoralize, provoke, or pressure other civilians and/or political leaders.” In short, this definition prioritizes that terrorism is primarily about the harming of civilians in order to intimidate others for political ends.

3. LITERATURE REVIEW: CAUSES OF TERRORISM

The section reviews literature on the causes of terrorism, highlighting two major arguments: one links to individual factors like religious fundamentalism and psychological traits while other traces to political marginalization and socio-economic inequalities and ethnic divisions. 

3.1 Individual-Level Explanations: Ideology and Psychology

To start with the first argument, older literature tends to emphasize that terrorists- were motivated by ideology, psychological predispositions, or religious fundamentalism (especially Islamic fundamentalism) (Pape 2003; Kramer 1990; Merari 1990; Post 1990). 

However, Pape (2003) argues that modern day terrorist attacks have not just limited to religious fundamentalists or to isolated men with certain psychological variations, but he gives the example of the LTTE (Liberation Tigers of Tamil Eelam), a group which recruited individuals who were Hindu Tamils but who were inclined towards Marxist ideology.

3.2 Structural and Political Explanations

The second type of argument focuses on some social grievances for example: political – and not religious, or individual psychological – motives of the terrorists. It has highlighted the motives i.e., to achieve specific political motives, change the policies in favour of them, to get financial backing (Crenshaw 2012; Abrahms 2008; Goodwin 2019). Crenshaw (2012) argues that violent acts are intentional political and strategic choices, rather than the inevitable result of social or psychological factors. In her view, terrorism functions as a “weapon of the weak,” used by actors who lack conventional means to influence government policy. Groups such as Euskadi ta Askatasuna (ETA) in Spain and the Provisional Irish Republican Army (PIRA) in Northern Ireland have used violence to pursue ethnic and religious rights Abrahms (2008) similarly argues that terrorism is the last and only resort for some groups to achieve their political goals.

Although the act of suicide terrorism might be irrational on an individual level. Thomas Schelling (1996) may have called it “the rationality of irrationality” – that is, the individual dies but the group is able to strategically demonstrate to the audience that it is serious about its political goals (Pape 2003). Terrorist groups are more likely to use suicide terrorism when there is a possibility of changing public opinion in favour them (i.e., in democracies). In the words of Boix and Rosato (2001): “The target state of every modern suicide campaign has been a democracy. The United States, France, Israel, India, Sri Lanka, Turkey, and Russia were all democracies when they were attacked by suicide terrorist campaigns, even though the last three became democracies more recently than the others.” Crenshaw (2012) similarly argues that terrorists do not just target communist regimes, but also target democracies.

Terrorism is closely linked to social and political conditions that create exclusions and injustice, which can be explained by some contextual examples on socio-political grievances. For example, when a minority group feels isolated in their own homeland, it forces them to retaliate or harm the civilians of their own nation or others. To make it clearer, we can take the example of attacks on Muslim community in India whether in Kashmir, Ayodhya or Godra Kand, which scorn the wounds of the neighbouring states which led to 26/11.  In short, historical rifts are also the causes which never cure the wounds of the nation. 

According to Edward Newman (2006) “These conditions-such as poverty, demographic factors, social inequality and exclusion, dispossession, and political grievances can be either permissive or direct.” This shows how terrorism is directly linked with environmental factors which degenerates the equality, rights and dignity of a human being. Ongoing social inequality and exclusion contribute to perception of injustice, while political grievances and dispossession weaken trust in state institutions. 

Furthermore, Terrorism is closely linked with the failures of human securities which can be understood as a consequence of insecurity and modernization where marginalized individuals resort to violence due to lack of opportunities and social breakdown for which O’Neill (2002) says, “human insecurity, broadly understood, provides the enabling conditions for terrorism to flourish”. As a result, insecurity of a human leads to its action which might harm the community. Also, terrorism is considered as the last resort to accomplish their needs and desires when peaceful methods don’t pay off. To understand with a few examples: Afghanistan, Sudan, Algeria, Yemen, Georgia, Pakistan. Somalia- which are the fundamental core of the operations conducted by the groups all over the world because of poverty.

In accordance with earlier explanations, this perspective offers a different account of the causes of terrorism. As per Martha Crenshaw (2012), she firstly makes a point on Modernization which creates a link between the individuals via easy transportations and communications.   Urbanization is one of the best tools which comes under modernization, as it increases the number of mobility and accessibility of targets and methods, which she termed as “urban guerilla warfare”, which was seen in Latin America in the 1960s.

This approach indicates that terrorism is not just driven by ideology or political grievances but is also shaped by structural transformations in society. Processes such as modernization and urbanization create environments that increase connectivity, mobility and the concentration of targets, thereby making terrorist strategies more feasible. 

4. THEORETICAL FRAMEWORK

Theoretical framework explain terrorism through two perspectives- first is Traditional theory, finds terrorism as “the product of weakness and/or desperation of some rebel or state (a ‘weapon of the weak’)” when there is a lack of capacity in pressurizing their opponent by their non-violent or conventional acts. The second, The Radicalization Perspective, which believes that “not all radicals may be terrorists, but all terrorists are radicals”. Radicalization is understood as “a process leading towards increased use of political violence”. Both theories ultimately points to a common goal i.e. Political motives. (Jeff Goodwin, 2019). Together, these perspectives indicate that terrorism is not merely a tactical choice but also a political constructed strategy shaped by both opportunities. 

5. INDIAN TERRORISM LANDSCAPES

This section focuses on the insurgencies faced by India.  India is a secular and multicultural nation. Since 1947, conflicts with Pakistan have led to wars. The Kashmir dispute emerged after the 1947 Instrument of Accession, leading to prolonged conflict between India and Pakistan. But India is not just facing such insurgencies by Pakistan, internal insurgencies continue which harm the peace and harmony of the nation. Apart from Kashmir related issues there is another big issue, i.e. The Naxalite or Maoist movement began in 1969 in Naxalbari and spread across states.

5.1 On Kashmir Conflict

Historical Background

The Kashmir dispute emerged during the 1947 partition of British India. Princely states were permitted to join either country. Jammu and Kashmir, ruled by Maharaja Hari Singh, had a Muslim-majority population but was governed by a Hindu ruler who initially attempted to remain independent. In October 1947, tribal fighters from Pakistan entered the region, prompting the Maharaja to seek military assistance from India, leading to the first war between India and Pakistan. This conflict was referred to the United Nations, which called for a ceasefire and proposed a plebiscite to allow Kashmir to determine their future. Unfortunately, the plebiscite was never conducted and the region was divided. In Curfewed Night, Basharat Peer presents this history as lived trauma. The memoir briefly outlines the unfulfilled promise of self-determination. Rather than analysing diplomatic negotiations, the book highlights how unresolved political conflict translated into militarization, curfews and insecurities in the Kashmir Valley. Having outlined the historical foundations of the dispute, the discussion now moves to the factors that have shaped the rise and continuity of militant violence in Kashmir. 

Drivers of the Kashmir insurgency

Many possible motives can be drawn for attacks in India, which is provided by K. Alan. Kronstadt (2008, pg. 6), according to some reports, radical Islamic sentiments play a crucial role, as radicals wanted a certain revenge for their Indian Muslim community in Ayodhya, Uttar Pradesh- 1992 and at Godra, Gujarat- 2002. And it is known that the radicals were inspired by Al-Queda’s brand of global jihadi ideology.  Some views indicate LeT has aimed for Kashmir’s separatism as their primary goal.  The Kashmir problem is related to the state’s claim by which state, which was very clearly demarcated since 1947. By military, Line of Control separated Muslim majority Jammu and Kashmir from Pakistan which controlled Azad Kashmir. Secretary Rice has speculated that the goal of the attackers was “probably to stir up trouble between Pakistan and India.” 

Terrorism violence in India cannot be explained by a single ideology; it results from the interaction of religious radicalization, geopolitical rivalries and unresolved territorial disputes. The Kashmir conflict and India-Pakistan tensions provide a strategic environment where extremist groups try to internationalize the issue and escalation between the two states. Thus, terrorism also functions as a strategic tool to influence regional politics.

In line with Shivaji Khemnar (2018), there are ethno-nationalist terrorism, religious terrorism, left wing terrorism, Narco terrorism. Roots of massive terrorism in India, are various in ideology, poverty, regional imbalance, strong worship about religion. India suffers attacks from Bangladesh, Nepal and Pakistan. 

Following this, Vani. K. Borooah (December 2008), provided some factual data of the attacks held in Kashmir. Over the period of 1998-2004, 784 terrorist incidents in India which resulted in 3008 deaths, and 61% of them and 55% of casualties occurred in Jammu and Kashmir alone. Apart from native-born terrorism, Kashmir conflict has attracted Al-Qaeda who have viewed this as a large part of Islamic strategy. A pamphlet titled “Why are we waging jihad” by Lashkar-e-Taiba includes the spread Islam in every corner of India. (Haleen 2004; Borooah 2008). Islamist groups were responsible for 126 attacks out of which 12 were suicidal attacks, out of which 6 were by Lashkar-e-Taiba, 2 were by Al-Mansurian (LeT) and 2 were by Jaish-e-Mohammad.

This is important statistical evidence which shows the concentration of terrorist incidents in J&K, the analysis largely focuses on quantitative data and military group involvement. Hence, the data highlights the scale and external linkages of terrorism. 

The Kashmir revolt is traced from the middle class and their frustration as it lacks employment opportunities (Sumit Ganguly; Prem Shankar Jha). Why do democratic political systems tend to experience higher incidents of terrorist attacks? 

It is explained in the lines “The growth of political assertiveness of minority communities in democratic states is virtually inevitable. As minorities acquire increased literacy and education, they will become more conscious of their political rights and will seek to assert them. Nondemocratic, poly-ethnic states can suppress minority demands for political participation through co-optation, coercion, or repression.” (Sumit Ganguly, 1996). 

Hence, this argument can be interpreted as rising awareness and mobilization especially among educated minorities– can create pressures that political institutions struggle to manage. However, this explanation may overemphasize political participation as a cause of terrorism, while underestimating other factors such as regional, external support to militant groups and state responses. Thus, democratic openness may create space for dissent, but it does not necessarily make democratic the only and primary cause for terrorism. 

Terrorism cannot be explained solely in terms of social and political factors; rather politics plays a crucial role in shaping and concealing issues that are deliberately prevented from being revealed. In Kashmir there are 3-way perspectival divisions: Hindus in Jammu, Buddhist in Ladakh and Muslims in Kashmir and it has created a paradigm that Kashmir is Muslim and is Anti-National. It gives a reflection of the Kashmiris struggle “against India’s brutalities”, where they were illegally detained, tortured, imprisoned and exploited. 

Under the governance of NDA, ideology of Hindutva was followed and spread in Kashmir without considering Muslims in Kashmir, which has resulted in the rise of attack frequency.

Hindutva-driven political strategies have indirectly increased tensions in Kashmir by making many Muslims feel excluded and unheard. Debates around Article 370 and 35A, along with efforts to fully integrate the region have added to this feeling of insecurity. At the same time, the growing support between Hindu nationalist groups and some Kashmiri Pandits has deepened divisions. These tensions and grievances are used by militant groups, which keep the cycle of conflict and violence going. (Mridu Rai, 2019)

Her focus is on political narratives, identity politics, and ideological agendas- particularly debates around Article 370 and 35A- which has shaped perspectives of marginalization in Kashmir. However, this argument primarily emphasizes the role of Bhartiya Janta Party and Hindutva politics. Nevertheless, political factors play a significant role, since politics shapes governance and national decisions-making which remains crucial in understanding terrorism and instability. 

The BJP’s Kashmir Policy: Continuity and Change from Vajpayee to Modi

While the Kashmir conflict is often explained through historical, social and security-related factors, political dynamics also constitute a significant dimension, as political parties frequently shape policies and narratives in alignment with their ideological orientations and strategic interests. 

In line with Mridu Rai (2019) there are 2 eras of the BJP government, which depicts the conditions in the Kashmir valley since the 1990s. According to the report, since 1990 Kashmir has experienced less governance in the conventional sense and more population control enforced through fear and violence. The passage further argues that the Indian state under the Modi government has intensified this security-oriented approach, framing it within the ideological vision of the Hindu Rashtra.

It presents a comparative analysis of the BJP under Atal Bihari Vajpayee and under Narendra Modi, particularly in relation to Kashmir and Pakistan. It suggests that Vajpayee’s tenure represented a relatively peaceful phase in India’s Kashmir policy. Vajpayee included this approach in the slogan “Insaaniyat, Jamhooriyat aur Kashmiriyat”, signalling a framework attached with humanity, democratic process and recognition of Kashmir identity.

In contrast, the Modi government has described as adopting a more assertive and militarised way toward Pakistan. Public celebration of “surgical strikes” and repeated warnings of retaliations show increased aggression. This external posture is said to be mirrored internally in Kashmir, where stronger security measures have been justified in the name of countering cross-border terrorism. 

 It argues that organisationally, the BJP has undergone significant transformation since 2014. While centralized under Vajpayee, but more monolithic under Modi’s leadership, with reduced space for internal dissent and increased subordination of regional leadership to central command. Since 2014, the author suggests, incidents of hostility and violence against such groups have been more visible. 

Cross-border Militancy in Jammu & Kashmir

Numerous attacks in Kashmir are observed, like Mumbai Train Blast (2006), Mumbai Attack 26/11(2008), Mumbai Bombing (2011), Pathankot Airbase Attack (2016), Uri Attack (2016), Pulwama Attack (2019), Bengaluru Attack (2023), Pahalgam Attack (2025). 

The most recent Pahalgam attack (22nd April 2025), which resulted in the death of 27 security personnel and injury to over 40 civilians (Adil, Sohail & Farid, 2025).  Pahalgam was a core of tourism along with the transit point for Annual Amarnath Yatra pilgrimage, which was then considered as the attack on for both its scale and its symbolism. By the Indian Ministry of Home Affairs, Pakistan’s military group Lashkar-e-Taiba was accused of this insurgency. (Tanveer, 2025). This time India retaliated with fearlessness by its operation named “Operation Sindoor” on 7 May 2025.

The paragraph concludes with the call of, that Kashmir has been called by Arundhati Roy, a ‘real theatre of unspeakable violence and moral corrosion.’ (Muhammad Feyyaz 2019). Geopolitics plays a significant role with all events happening in and around the world. Geography is what stabilizes or destabilizes a nation’s security and development. Pakistan’s desire to cover Kashmir in its territory is not just in the interest of Islamic fundamentalism, instead, its geographical, hydrological and strategic factors. It is important for Pakistan to maintain a powerful position in Kashmir, essentially for water security and advantageous position against India.  

Repercussions of Armed insurgency

Since 1947, this prolonged conflict in Jammu and Kashmir has had wide- ranging impacts beyond security. It has led to environmental damage, economic decline (majorly tourism), has disrupted education and regular life, along with serious mental health issues as per Dr. Mushaq Margoob, Professor & Head, Post Graduate Department of Psychiatry, Government Medical College, Srinagar. Taken together, these have weakened the region’s social and economic stability. Overall, terrorism has deeply affected Kashmir’s development and the life-style of the people. 

5.2 THE NAXALITE (Maoist) MOVEMENT

Historical Background and Origins

After examining the conflict in Kashmir, it is equally important to turn to another major internal security challenge persisting in India–the Naxalite Movement. The Naxalite movement, also known as Maoist insurgency in India, originated in 1967 in the village of Naxalbari in West Bengal as a radical peasant uprising against those land inequalities and exploitative agrarian structures. Inspired by Maoist ideology and the principles of the armed revolution, the movement sought to challenge what is known as a semi-feudal and oppressive state system. The movement was ideologically influenced by revolutionary doctrine linked with Mao Zedong and political development in China during the 1960s. However, this was largely evolved as a domestically rooted response to socio-economic inequalities, tribal marginalization and governance deficit within India. Initially driven by demands for land redistribution and social justice for marginalized communities–which then gradually expanded into the central and eastern parts of Indian states, forming what is often referred to as the “Red Corridor”. Over the period of time, the movement evolved from localized agrarian resistance into an organized armed insurgency. Manohar Parrikar Institute for Defence studies and Analysis (2010) states that the Naxalite movement was driven by radical Marxist- Leninist ideology which led to the formation of the CPI (M-L). Multiple internal challenges seen in: the Central-Eastern area (Red Corridor), North-East India by groups like NSCN & ULFA. (Aditya Dasgupta, Kishor Gawande and Devesh Kapoor; 2017)

Understanding the structural and immediate causes that helps in the emergence and persistence of this movement becomes essential to comprehend its relevance in India’s internal security landscapes. 

Structural and Socio-Economic Determinants

In 2008, Prime Minister-Manmohan Singh described Naxalism as “the greatest threat to our internal security”. The pace of the Naxalite movement suggests not merely state incapacity, but deeper structural contradiction within India’s developmental model, has been the driving force to such insurgent acts. While the Indian state pursued parliamentary democracy and economic modernization, large parts of rural and tribal populations remained excluded from land reforms, political representation, and access to justice. 

In line with European Foundation for South Asian Studies Publications: Naxalbari, the village that gave its name to the movement, was the site of the peasant revolt, started by communist leaders against owners of the state. The Naxalbari uprising was not a mere reason for agrarian anger but an ideological rupture within Indian communism. It marked a rejection of parliamentary gradualism and signalled the re-emergence of revolutionary violence as a legitimate political instrument among sections of the left. Members of Communist Party of India (Maoist), notably Charu Majumdar, Kanu Sanyal and Jangal Santhal who initiated this movement. Drawing inspiration from Maoist ideology, these leaders advocated a strategy of long armed struggle aimed at overthrowing the existing socio-economic order through agrarian revolution. The uprising reflected a vision articulated by the so-called “Siliguri Group”, which called for a militant path to political transformation. The formation of CPI in 1925 institutionalized communist ideology in India drawing inspiration from the global Marxist-Leninist movements and adapting them to the colonial Indian context. In its early years, the CPI was committed to a Marxist-inspired mass revolution but later faced internal and external pressures that reshaped its strategy. In 1951, after consultations with Stalin- who advised participation in democratic politics rather than armed revolt–the CPI reconsidered its revolutionary path. The ideological reshaping laid the groundwork for subsequent fractions within the left movement ultimately which contributed to the emergence of radical Maoist. Disillusionment with the CPI’s gradual and pro-soviet orientation- particularly its commitment to parliamentary democracy and perceived abandonment of revolutionary militancy–led to the break away at the Seventh Party Congress in Calcutta. The CPI-M thus emerged as a distinct political formation that rejected what it characterized as soviet “revisionism” and aimed to reaffirm a more orthodox and militant Marxist line, drawing ideological inspiration from the Chinese Communist model. 

Naxalite emerged as a radical response to deep rooted socio-economic inequalities. Its primary causes according to Sanjay Seth (1995), were as follows: The very first is, Structural Contradictions of Capitalist Development- Capitalist expansion generates contradictions between socialized productionand private appropriation leading to crises which intensify exploitation and weaken legitimacy, creating conditions for resistance.Second is seen as the Formation and Organization of the Proletariat- capitalist modernization concentrates workers in the factories and cities, fostering class solidarity and collective resistance and also creates a basis for revolutionary mobilization. Moving forward, Marxist Reorientation towards Peasant Agency in ‘Backward’ Societies- Marxist revolutionary theory shifts emphasis to peasant agency in backward societies, viewing peasant struggles against feudalism as a key driver for revolutionary change.Along with that Gap between Consciousness and Marxist Political Objectives- Peasant resistance, shaped by local and traditional identities, which differ from Marxist class ideology, creating a gap that leads to ideological tensions. In addition to the initial factors, Misrecognition of Peasant Subjectively by Communist Parties- where communist parties misread peasant consciousness, projecting revolutionary intent, leading to a strategic and ideological disconnect. And last but not the least, Instrumentalization of Peasant Struggles by Vanguard Parties- communist parties often used peasant struggles for their own goals, directing movements beyond peasants’ intent, leading to dissatisfaction.

While Sanjay Seth highlights contradictions between peasant consciousness and Marxist political objectives, the Naxalite movement demonstrates how ideology and lived experience interact dynamically rather than mechanically. Peasant resistance in India was not purely “false consciousness” nor fully revolutionary; rather, it evolved through local grievances–land alignment, state repression and caste hierarchies–that were gradually reframed within Maoist political language. Thus, the movement’s radicalization can be understood as a process of ideological translation.  

It follows that the persistence of Naxalite exposes the gap between India’s procedural democracy and substantive socio-economic justice, particularly in marginalized regions such as Chhattisgarh and Jharkhand. Governance failures, extractive development and weak reforms lead the insurgent appeal, framing it as internal security issues. The growth of Naxalism reflects deeper structural inequalities in India’s development model rather than only a security issue.              

Integrated approaches to mitigate Naxalite insurgency

Although the Naxalite insurgency has emerged from the multiple structural and socio-economic causes discussed above, the Indian government has also introduced several strategies to mitigate the movement. In line with Manohar Parrikar Institute for Defence Studies and Analyses (April, 2010) The Government of India, has adopted a two-pronged strategy, to address Naxalism, combining security measures and governance interventions.The government set up an Empowered Group of Ministers, a Coordination Centre, and a special task force to improve coordination. It focused on a Law-and-Order Approach through police modernization and long-term deployment of centre forces, with the centre bearing costs (around  ₹1,100 crore) to support states. Another approach the government adopted is Social Integration Approach, focusing on development through schemes like the Backward District initiative and BRGF, allocating funds to affected areas. It also implemented state-level support and infrastructure programs like PMGSY to improve living conditions and reduce Naxal influence. 

Sino- Maoist connections

After examining the internal causes and state responses to Naxalite insurgency, it also becomes important to assess whether external factors contribute to these insurgent activities. This analysis helps determine whether policy responses should extend beyond domestic measures to include broader strategic and foreign policy considerations. 

There are two principal stands of argument, one contends that China has no involvement with Naxalite or Maoist movement in India, while the other claims that China bears responsibility for supporting or facilitating these insurgencies.

First argument notes, Maoist groups’ linkages with militant, Northeast insurgent, and radical Islamist organizations for logistical support pose a significant internal security challenge to India, while multiple factors suggest Chinese involvement in sustaining Left-Wing Extremism (LWE). An analysis by the Vivekanand International Foundation quotes The Home Secretary, G.K.Pillai in November 2009, where he was confident with the supply chains of arms by China to Maoists in India, “Chinese are big smugglers and suppliers of small arms. I am sure that the Maoists also get them.”

The Home Minister, P. Chidambaram, acknowledged that Naxalites procure weapons through cross-border routes via Nepal, Myanmar, and Bangladesh, but expressed uncertainty regarding any direct assistance from Chinese state actors. Nevertheless, repeatedly Chinese made arms and communication equipment from Maoist cadres are often cited as indicative of possible linkages between Maoist groups and China. 

To line RSN Singh (2010), the Home Secretary has asserted that China is a major supplier of small arms and suggested that Maoist Groups may procure weapons of Chinese origin. In 2004, a significant supply of light and medium machine guns and ammunition were seized at Chittagong port in Bangladesh, reportedly sourced from China. Bangladesh intelligence officials further alleged that leaders of United Liberation Front of Asom and National Socialist Council of Nagaland (Isak-Muivah) visited Kunming to obtain arms, some of which were allegedly routed to Maoist groups operating in Nepal and India. 

Second, it argues that China has nothing to do with any insurgency, according to the statement issued by the Ministry of External Affairs (May 17, 2012) that India raised concerns with China regarding reported support for anti-India armed groups, prompting China to assure a policy of non-interference and deny such support. China has officially denied supporting anti-government forces and emphasized its policy of non-interference; the dialogue mechanism indicates that India remains cautious and will continue to monitor dimensions of internal threats.

6. Conclusion

In conclusion, the study demonstrates that terrorism is a complex and evolving phenomenon that cannot be understood through a single framework. The discussion of various scholarly definitions, theoretical perspectives and the examination of cases such as Kashmir and Naxalite illustrate that terrorism emerges from the interaction of ideological, political, socio-economic, and historical factors, earlier, terrorism was often narrowly associated with religious extremism; however, contemporary conflicts increasingly reveal an overlap between terrorism and civil wars. This growing convergence has created distinguish terrorism from other forms of political violence, consequently, the absence of a universally accepted and precise definition of terrorism continues to challenge the international community in formulating consistent responses.

From the Indian perspective, geography plays a dual role. While India’s geographical position has historically facilitated cultural exchange, economic interaction and strategic connectivity. It has also exposed the country to security vulnerabilities and cross-border tension. In addition to these historical and geographical factors, ideological influences have also shaped certain forms of insurgency in India. The influence of socialism and communalism, particularly in regions characterized by socio-economic disparities and governance gaps, has contributed to the emergence of structural grievances, including poverty, land alienation and the neglect of marginalized communities. 

The findings of this research suggest that addressing terrorism requires a multidimensional approach. While security measures remain important, long term solutions must also focus on ensuring inclusive development for all sections of society. Future research should therefore explore innovative perspectives that move beyond purely historical explanations and instead examine how structural reforms, effective governance and socio-economic justice can help mitigate the conditions that sustain violent movements. 

Although the complete eradication of terrorism may be unrealistic, meaningful progress can still be achieved through stronger international cooperation, stricter accountability for states that have terrorist networks, and institutional reforms. Finally, the persistence of terrorism highlights not only individual responsibility but also government failures. When the government is unable or unwilling to address citizens’ aspirations and grievances, extremist ideologies find space to grow. In this context, terrorism may be defined as “a strategic and radical practice aimed at influencing state policy through coercion or violence.” 

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ACKNOWLEDGEMENT

I would like to express their sincere gratitude to Dr. Mashail Malik, Assistant Professor, Harvard University for her invaluable guidance, intellectual support, and constant encouragement throughout this research. I also thank her for the insightful discussions and helpful feedback on the initial drafts of this manuscript.

I am grateful to the Department of Political Science at Banasthali Vidyapith for providing the necessary institutional resources to conduct this study.

On a personal note, I wish to thank my parents and friends for their unwavering moral support, patience, and encouragement during the course of this work. Their belief in my efforts was a constant source of motivation.

Finally, I thank the anonymous reviewers for their constructive comments and suggestions, which significantly contributed to improving the quality of this paper.

About the author

Disha Tyagi

Disha Tyagi is currently pursuing a Master’s degree in Political Science from Banasthali Vidyapith. Her academic interests revolve around International Relations, South Asian regionalism, geopolitics, diplomacy, public policy, global governance, trade relations, conflict studies, security studies, and international cooperation. Among these, terrorism and regional instability remain one of her strongest areas of interest and research. She is particularly interested in studying how terrorism impacts regional integration and development, especially in areas such as Kashmir and Left-Wing Extremism in India.

Her academic journey has been enriched through active participation in seminars, conferences, webinars, debates, and international academic engagements. She attended the BRICS International School: New Generation in March 2026 and participated in the Global Studies Program organized by the Russian Federal University in February 2026. She also attended the seminar on “WTO MC14 Outcomes: Future of Multilateralism and Implications on India’s Trade Agenda” held on 5 May 2026, which deepened her understanding of trade diplomacy and multilateral institutions. Additionally, she participated in webinars such as “The Structural Causes of World Poverty” by Thomas Pogge on 23 April 2026 and “India’s BRICS Presidency 2026” by Antara Ghosal Singh on 8 May 2026. These academic interactions enhanced her understanding of global justice, development politics, and emerging geopolitical transformations.

Beyond academics, she has actively participated in Model United Nations and parliamentary simulations, including the Youth Parliament (UNGA Portfolio) held during SANSAD 2025 in Delhi. She has also completed online courses including “Successful Negotiation: Essential Strategies and Skills” by the University of Michigan, “Learning How to Learn” by the University of Arizona, and “Writing in the Sciences” from Stanford University, along with an offline communication course at the British Council, Delhi.

The post From Global Causes to Local Realities: Terrorism in India with Reference to Kashmir and the Naxalite Movement appeared first on Exploratio Journal.

Abstract

Dissociative identity disorder (DID) is a treatable mental disorder that has a proven neurochemical basis. However, public misunderstanding prevents patients from getting appropriate and timely treatment, and medication focusing on dissociation is limited. Therefore, this paper proposes an approach of delivering JDTic and paroxetine by lipid nanoparticles (LNP) through the intranasal route. JDTic and paroxetine target the dysregulated neurotransmitter system. LNPs improve delivery efficacy by increasing bioavailability and drug stability. The intranasal route was chosen as the administration route for its ability to target the brain directly. Limitations and development visions are discussed to inspire further research. 

1. Introduction

DID is a mental illness that affects approximately 1% of the general population. Studies suggest that it is strongly related to severe childhood trauma, most often caused by caregivers. Typical symptoms include subjective experience of identity alteration, memory gaps, frequent experience of depersonalization, along with post-traumatic stress disorder, depression, and anxiety (Purcell et al., 2024). Due to the special symptoms it displays, the disorder and DID patients are receiving public attention, yet few people know the scientific facts about the disease, which has led to misunderstandings about DID patients. This mental disorder has been proven to be treatable, with multiple reported structural and neurochemical basis. However, current medication mainly targets the depression and anxiety symptoms of DID, and focuses less on its dissociations, which are the symbolic symptoms. This study aims to discuss DID from a biological perspective and dive into its dissociation aspect. 

In this paper, we explored a new way of DID treatment. We targeted the opioid system and serotonergic system, whose dysregulations are believed to be the main cause of dissociation. Medications are chosen according to the targeted receptors, namely JDTic for opioid receptors and paroxetine for serotonin receptors. Lipid nanoparticles are then designed to deliver the drugs to the brain. We proposed to administer the drugs intranasally, which was reported to target the brain through olfactory neurons, circumventing the blood-brain barrier (BBB). This could enable more efficient drug administration as well as reduce systemic side effects. Furthermore, we envision several directions related to the theme of this paper. Future studies in these directions could improve medical treatments for DID. We hope to provide new insight into treating this mental illness based on its neurotransmitter basis.

2. The Neurotransmitter Basis of DID

The dissociative symptoms of DID are related to multiple neurotransmitter systems. In this section, we will discuss two neurotransmitter systems that are believed to contribute to dissociative experiences (including depersonalization, derealization, amnesia, etc.), namely the opioid system and the serotonergic system. 

2.1.Opioid System

The opioid receptors are a kind of G protein coupled receptors (GPCR). The system regulates multiple biological and cognitive processes, such as nociception, analgesia, endocrine and immune functions, stress and emotions, and more (Reeves et al., 2022). Opioid receptors are differentially expressed throughout the brain, with higher concentrations in the cortex, limbic region (including amygdala, hippocampus, hypothalamus, and cingulum gyrus), and brainstem (Purcell et al., 2024) . The receptors are categorized into three subtypes: mu (MOR), kappa (KOR), and delta (DOR). The subtypes accept different signaling peptides (Reeves et al., 2022). Exposure to stressful stimuli activates kappa opioid receptor (KOR) signaling, a process known to produce aversion and dysphoria and related to stress-related disorders(Jacobson et al., 2020). A study conducted on nine adult volunteers suggests that enadoline, a kind of KOR agonist, leads to confusion, dizziness, visual distortions, and depersonalization reported by participants (Walsh et al., 2001). Other related studies also suggest that salvinorin is a KOR agonist that relates to depersonalization (Roth et al., 2002). This might indicate that kappa-opioid receptors might be helpful in regulating certain dissociative symptoms. While naloxone, naltrexone, and nalmefene were reported to reduce the dissociative symptoms, these therapeutics have yet to be systematically studied, and conflicting results were shown in recent studies (Purcell et al., 2024). Therefore, we propose JDTic, an alternate medication that targets the KOR, thus in theory can alleviate dissociation. 

2.2.Serotonergic System

The serotonin receptors can be classified into six different GPCR populations (namely 5-HT₁, 5-HT₂, 5-HT₄, 5-HT₅, 5-HT₆, and 5-HT₇) and one family of ligand-gated ion channels (5-HT₃). Every receptor class regulates neural processes through a different mechanism. Under each class, they can be further divided into specific subtypes, each of which has a different function, agonists, and antagonists (Pytliak et al., 2011). The receptors are distributed throughout the brain, regulating affect, cognition, autonomic, and other behavioral activities (Purcell et al., 2024). Sixty-seven subjects received depersonalization ratings after double-blind, placebo-controlled experiments with the partial serotonin agonist meta-chlorophenylpiperazine (m-CPP). Results show that m-CPP significantly induced more depersonalization than the placebo did (Simeon et al., 1995). Another study performed a positron emission tomography scan on a patient with dissociative amnesia. Images were compared between the patient in amnestic state and recovery state and 14 other healthy subjects. The patient in the recovery state displayed significantly higher 5-HT_1A receptor bindings than healthy subjects in multiple cortical regions (Kitamura et al., 2014). This might indicate that the serotonin system is possibly related to dissociative experiences. Study suggests that paroxetine, a selective serotonin reuptake inhibitor, might relieve dissociation. 

3. Possible Therapeutics

In this part, we mainly give two medications that seem promising in dissociation treatment and introduce their mechanisms and metabolism. Namely, we will introduce JDTic and paroxetine, targeting the opioid system and the serotonin system, respectively. 

3.1.JDTic

Currently, JDTic is not used clinically. Therefore, we only discuss it as a possible treatment of dissociation due to its medical mechanism. Further study is still needed to enable its clinical application. JDTic is a kind of highly selective competitive antagonist of KOR. Notably, it is not derived from the opiate class of compounds. The potent and selective κ antagonist properties can be explained by the “message−address” concept, a theory that explains the interaction between the ligand and receptor. The “message” component of the ligand specifies primary receptor recognition, and the “address” portion enables selectivity by specifically recognizing a particular receptor subsite (Thomas et al., 2003). 

After JDTic was intraperitoneally administered to mice, its brain level peaked within 30 minutes and declined gradually over a week. JDTic did not show high lipophilicity, demonstrating high water solubility and low distribution into octanol. Brain homogenate binding was within the range of many shorter-acting drugs. JDTic displayed P-glycoprotein (P-gp)-mediated efflux. The surprisingly slow elimination of the drug is postulated to result from the drug lingering in cellular compartments such as lysosomes (Munro et al., 2012).

There is no current evidence that directly proves that JDTic is effective in dissociation treatment. Being an opioid receptor antagonist, JDTic is believed to restore neuron functions that were overly inhibited by opioid receptors. Considering the relationship between opioid receptors and dissociation symptoms mentioned above (KOR especially), it is reasonable to conclude that JDTic might be conducive to dissociation treatment. However, further researches are needed to validate this assumption.

3.2.Paroxetine

Paroxetine is a drug for a variety of anxiety disorders. It is worth emphasizing that in the treatment of PTSD, there are only two approved pharmacotherapies based on SSRIs, including Sertraline and Paxil (paroxetine hydrochloride). It is a potent, selective serotonin reuptake inhibitor (SSRI). It exhibits the highest known binding affinity for the central site SERT compared to any other currently prescribed antidepressants. Paroxetine acts on the serotonin transporter (SERT) in the CNS, which is a type of monoamine transporter that transports serotonin from the synaptic cleft back to the presynaptic neuron. Inhibiting the SERT increases the serotonin concentration in the synapse, enhancing the activation of postsynaptic receptors.

Paroxetine hydrochloride salt ingested orally is almost completely absorbed, with only 2% of the dose recovered in feces. Its absorption was insusceptible to the influence of food or concomitant antacid treatment. It reaches saturation during the pass through the liver. With repeated administration, the steady-state concentration of the drug is achieved within 4 to 14 days. There is no further accumulation of the compound. The distribution of paroxetine in the body is extensive, aligning with its lipophilic amine character, with only 1% of the drug remaining in systemic circulation. After administered intravenously, the volume of distribution ranges from 3.1 to 28.0 L/kg. The mean elimination half-life is estimated to be about 21 h. Almost two-thirds of the drugs are eliminated through the kidneys. Up to 95% of the drug is bound to proteins, mainly P-gp, which is involved in transportation through the blood–brain barrier (BBB) (Data from reference (Paroxetine—Overview of the Molecular Mechanisms of Action, n.d.)(Paroxetine—Overview of the Molecular Mechanisms of Action, n.d.)). 

Paroxetine is mainly metabolized by cytochrome P450 family 2 subfamily C member 19 (CYP2C19) and CYP3A4, producing a catechol metabolite (Benedetti et al., 2009). It is one of the most potent inhibitors of CYP2D6 and CYP2B6, members of the CYP450 family, among SSRIs (Paroxetine—Overview of the Molecular Mechanisms of Action, n.d.). Autoinhibition is observed in the metabolism of Paroxetine, which means that some drugs can inhibit the enzyme(s) involved in its metabolism. When autoinhibition occurs, observed plasma concentrations are higher than the value expected by linear accumulation, which could cause adverse effects or an enhanced therapeutic effect (Benedetti et al., 2009).

In a study conducted to determine the efficacy of paroxetine on PTSD symptoms, results show that paroxetine continued to perform superior to placebo in reducing dissociations during the 12-week maintenance phase. Specifically, significantly more patients treated with paroxetine were rated as responders compared to patients treated with placebo. Mixed effects models showed greater PTSD feature reductions in the paroxetine groups than the placebo groups. Paroxetine was also superior to placebo in reducing dissociation and self-reported interpersonal problems (Marshall et al., 2007). This suggests that paroxetine is a promising medication against dissociation. 

4. Lipid Nanoparticle

To deliver therapeutics for DID to the brain, there are multiple approaches used. The use of lipid-based nanoparticles first came from the biocompatible concept, where the tiny lipid cholesterol molecules and phosphatidylcholine are popular. Lipid-based nanoparticles enable easy cellular uptake of drugs because of the lipid coat. Two of the most important lipid-based nanomaterials are liposomes and solid lipid nanoparticles (SLN). Liposomes are composed of lipid bilayers and enclosed aqueous cores, while SLN consists of a lipid monolayer enclosing a solid lipid core. While they are slightly different in structure, both can be effectively used in drug delivery applications, and nasal administration studies on both types of nanoparticles have been done (Trapani, A et al., 2021). According to existing research, we propose that the medication for DID could be delivered by lipid nanoparticles through the intranasal route. By applying adhesive components such as chitosan to nanoparticles, we can improve the drug internalization of olfactory neurons, increasing bioavailability. The molecule size and surface properties of nanoparticles can be accommodated to adjust to both passive and active drug targeting, which might be helpful in drug administration. Additionally, nanoparticles protect drugs from degradation, improving stability and delivery efficacy (Chenthamara et al., 2019). 

In the study of (Wang et al., 2025), the authors designed and synthesized ionizable lipids. Specifically, small-molecule ligands known to traverse the BBB, such as MK-0752, were chemically coupled with amino lipids to synthesize a series of structurally diverse BBB-crossing lipids. The BBB-crossing module enables the nanoparticle to cross the BBB. The amino groups function to provide ionization properties, enabling the lipids to exhibit variable charge states, determining their apparent pKa, a critical parameter and the basis of endosomal escape. The lipid tails form the hydrophobic core of the nanoparticle, the length of which influences the efficiency of MK-series BLNPs in delivering mRNA to the brain. The study administered the drug intravenously into mice. MK16 BLNPs are distributed throughout the body, with most of the dose being absorbed by the liver and part of the dose (15.3±0.4%) in the brain. Notably, the study also demonstrated that caveolae and γ-secretase might be critical mediators in facilitating the BBB crossing of MK16 BLNPs (Wang et al., 2025). The study proved that lipid nanoparticles are capable of carrying large organic particles like mRNA, providing a theoretical possibility for delivering paroxetine and JDTic. However, it also showed a high rate of missing the targeted administration region, which could cause systemic side effects or increased metabolic stress. In order to target the brain better, we referred to an alternate model of LNP. In “Dopamine-loaded lipid-based nanocarriers for intranasal administration of the neurotransmitter: A comparative study”, the researchers structured the SLN based on the self-emulsifying lipid Gelucire® 50/13, coated with stearoyl polyoxyglycerides, surrounded by a hydrophilic polyoxyethylene chain shell. The study evaluated two types of liposomes, which were composed of an aqueous drug solution core and the coating fat phase, which was made of a mixture of phosphatidyl choline, phosphatidyl glycerol, and cholesterol. Lip 1 was uncoated, while Lip 2 was coated with Chitosan-glutathione conjugate. Both liposomes were outperformed by SLNs in encapsulation efficiency. The two types of SLNs are unmodified DA-SLN (SLN 1) and Glycol Chitosan-associated GCS-DA-SLN (SLN 2). In the case of SLN 2, the GCS component contributed to the network on the shell of the nanoparticle, reducing the leakage of the drug. Therefore, SLN 2 outperformed SLN 1 in encapsulation efficiency. The drug was administered intranasally. The GCS component increased the mucoadhesive ability of SLN 2, allowing the nanoparticles to be taken up with high efficiency by olfactory ensheathing cells (OECs), enabling better drug administration. The study also proved that the SLNs lack cytotoxicity towards OECs, rendering it a rather safe route (Trapani, A et al., 2021). 

Inspired by SLN 2, the general image of our proposed lipid nanoparticle carrier of paroxetine is shown Figure 1. We surrounded the lipid basis and its stearoyl polyoxyglycerides shell with a hydrophilic layer and GCS in order to increase the mucoadhesive ability of the nanoparticle. Notably, according to the structure of paroxetine, it will be involved in the outer network of hydrogen bonds and molecular interactions, as dopamine was in the paper. The methods of preparation were recorded in the reference (Trapani, A et al., 2021). We are uncertain if chemicals like Gelucire® 50/13 or stearoyl polyoxyglycerides still apply here. Specific components of the designed SLN need to be determined in further research. 

JDTic does not display good lipophilic properties. To avoid the challenge of encapsulation of hydrophilic compounds in SLN, as mentioned in the paper, since the paper suggests that liposomes have aqueous cores (Trapani, A et al., 2021), we propose to use a liposome carrier to deliver JDTic, as shown in Figure 2. Notably, although in the study of Wang et al., the delivered mRNA is aqueous, the following studies and property determination are all done in the context of intravenous delivery. We are not sure if the results still apply to intranasal administration. Thus, we did not adopt the LNP used in this study (Wang et al., 2025). We propose to use the same component as coating lipids as used in the reference. To increase encapsulation efficiency, an appropriate polymer coating (e.g., chitosan, alginate) may be applied to limit such drug leakage. Its preparation methods were also recorded (Trapani, A et al., 2021). Still, the exact component of the liposome requires further determination. 

5. Nasal Route

After intranasal administration, drugs primarily enter the brain by interacting with neural pathways within the nasal cavity. Specifically, drugs absorbed from the nasal mucosa can be transported along the axons of the olfactory and trigeminal nerves. Olfactory sensory neurons take up the drugs and transport them via their axons to the olfactory bulb, the first site of entry into the brain. Subsequently, drugs can diffuse further from the olfactory bulb to more distant brain regions, such as entering cortical and hippocampal areas via cerebrospinal fluid circulation. Beyond intracellular transport along nerve axons, drugs may also enter through extracellular pathways—specifically, by crossing tight junctions between epithelial cells into the lamina propria of the submucosal layer, then traversing perineural spaces to ultimately reach the subarachnoid space, thereby diffusing into brain tissue (Erdő et al., 2018). 

The primary advantage of the intranasal route of administration lies in its ability to bypass the blood-brain barrier, enabling direct delivery of drugs to the central nervous system. This opens therapeutic possibilities for large molecules or hydrophilic drugs—such as dopamine, neuropeptides, and even mRNA—that struggle to cross the BBB. Compared to oral or intravenous administration, this route typically yields higher brain bioavailability, achieving greater brain tissue exposure at equivalent doses. Simultaneously, reduced systemic circulation helps minimize peripheral side effects. Furthermore, intranasal administration offers non-invasive delivery, facilitates patient self-administration, and provides a relatively rapid onset of action. It demonstrates potential for treating both acute and chronic central nervous system disorders, including neurodegenerative diseases, epilepsy, and pain management (Erdő et al., 2018). 

6. Developments and Limits

JDTic has yet to be applied clinically, and its effectiveness in humans is still lacking validation. Also, existing study suggests that nonsustained ventricular tachycardia is a potential JDTic toxicity in humans (Buda et al., 2015), indicating potential medical safety problems. Further assessments and studies on the drug safety of JDTic are required. 

The reported effectiveness of the drugs on dissociation is conflicting, and any significantly effective drug for dissociative symptoms is not clear yet. For instance, although opioid receptor antagonists are believed to be effective in dissociation, research indicates that naltrexone, an opioid receptor antagonist, cannot cancel out the dissociative symptoms induced by ketamine (Jacobson et al., 2020). It is possible that ketamine induced dissociation is not the same as dissociation observed in DID patients in terms of mechanisms. However, further determination is still needed.

The stability and storage of lipid nanoparticles still need to be improved. The paper suggests that the liposome can be stored for 6 days under 4 degrees Celsius, while SLN can maintain stability for 1 month before undergoing particle aggregation and drug autoxidation. Also, the encapsulation efficiency of liposomes is not quite satisfying due to drug leakage, and the polymer coating can only improve this condition to a limited extent (Trapani, A et al., 2021). The refrigeration requirement might lead to increased storage and transportation costs, and the drug delivery efficacy requires further improvement. 

Due to the omnipresence of the serotonin receptors and KORs in the brain, we are not sure if the impact of the medication on the whole brain will bring any side effects. Medication safety requires future confirmation. 

As this study is about mental illness medication, which must be applied carefully, this study lacks experimental data, and the effectiveness of the therapeutics proposed in this study remains to be seen. Further experiment is needed to test its viability, and more assessments of its effectiveness are required.

The glutamatergic system might also be related to the dissociative symptoms of DID. Glutamate receptor can be classified into ionotropic glutamate receptors(iGluRs), which are ion channels, and metabotropic glutamate receptors (mGluRs), which are GPCRs. They are widely distributed in the central nervous system (CNS). They are involved in synaptic transmission and regulate learning, memory, and neural plasticity through long-term potentiation (LTP) and long-term depression (LTD) (Wang et al., 2024). Ketamine is a dissociogenic drug that influences NMDA, a kind of iGluR. Moreover, glutamate levels in the anterior cingulate cortex positively correlated with dissociative symptoms in a borderline personality disorder sample. However, evidences are still limited, and the medications acting on the glutamatergic system show conflicting results on relieving dissociative symptoms (Purcell et al., 2024). Therefore, we did not concentrate on this neurotransmitter system. However, we believe there might be a potential relationship between the glutamatergic system and the dissociation, which calls for further study. 

Clinical evidence suggests that DID is induced by childhood abuse (Purcell et al., 2024). It has been suggested that epigenetics is the link between environmental factors and physiological features (Weir, 2012), so we are convinced that DID can also be related to epigenetics, which may inspire further exploration or other treatment routes. Additionally, DID was also proven to display multiple neural structural differences compared to normal brains, such as reduced cortical and subcortical volumes in the hippocampus, amygdala, and parietal structures (Blihar et al., 2020), or dysregulation of activity in the prefrontal cortex, amygdala, and other brain areas (Shimiaie, 2025). While this paper mainly focused on the neurochemical basis of DID, this might suggest another perspective on the neurological basis of the disorder, thus leading to future research. 

In DID, the dissociation symptoms usually appear along with other disorders, namely depression, anxiety, and somatization, which could require concomitant drug application (Purcell et al., 2024). To explore drug interactions and avoid adverse effects due to concomitant drugs, an artificial intelligence application has been introduced, and a systematic review of the viability of this method has been conducted (Zhang et al., 2024). With further development, this application of AI could lead to more improvement in DID treatment. 

The treatment mentioned in this paper may also be applicable to dissociation under other circumstances. However, further research is needed to validate this hypothesis, since dissociative symptoms could appear slightly differently in different illnesses.

Considering nasal administration, there could be ways to improve the drug delivery efficiency. The nasal epithelium can be a rate-limiting barrier of drug uptake, where cells are joined together by tight junctions. Therefore, permeation enhancers, such as borneol, chitosan, and cyclodextrins, are applied to enhance drug transportation. The effectiveness of intranasal administration is more influenced by the administration technique compared to other routes. The administration technique should be efficient and feasible for the patient to administer by themselves. Nasal drops spread over a larger area than nasal sprays and display a higher deposition rate compared to nasal sprays, if administered correctly. While drops are more easily cleared from the administration region than sprays, adding mucoadhesive agents can address this problem. However, nasal drops require a highly accurate administration technique and correct head position. Some novel intranasal delivery devices include the Vianase^TM electronic atomizer and the bi-directional delivery device Opt-Powder by Optinose^®, the potential of which on lipid nanoparticle delivery requires further studies. Multiple approaches have been studied to increase brain uptake of drugs, including increasing drug lipophilicity, increasing carrier-mediated transport through the BBB, and decreasing efflux by applying transport inhibitors (Erdő et al., 2018). Studies of delivery efficacy-enhancing methods could further propel the development of intranasally administered mental disorder medications.

7. Conclusion

This paper offers a novel insight into DID medical treatment based on KOR and serotonergic system and LNP delivery through the intranasal route. Although this treatment possesses the theoretical advantage of targeting the brain and improving bioavailability, its clinical transformation still needs to overcome major obstacles. JDTic, a proposed medication, has not been put into clinical application and faces safety challenges. The stability of nanoparticle carriers could be improved. There is a lack of experimental data. Future studies could focus on medication trials, nanoparticle carrier improvement, delivery efficacy enhancers, and more exploration into the neurologic basis of DID. We hope to develop safe and accurate medical therapy for DID. 

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About the author

Kaifeng Zhu

Kaifeng is currently an 11th grade student in EHSBNU, graduating in 2027, and anticipating future study in biomedicine.

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