Evaluating ESG efficiency using DEA an analysis of Dow Jones Industrial average companies
Bibliographic record
Abstract
In today's investment climate, the integration of Environmental, Social, and Governance (ESG) factors into strategic decision-making is essential, particularly in industry performance analysis. The article employs Data Envelopment Analysis (DEA) to calculate and contrast ESG efficiency for a broad variety of industries represented in companies in the Dow Jones Industrial Average. Through adopting three other DEA methods—the Constant Returns to Scale (CCR) model and input- and output-oriented Banker, Charnes, and Cooper (BCC) models—we provide a comprehensive framework to analyze how ESG inputs are allocated across different industries to achieve stock price appreciation. The results have important variations in different sectors. For example, the Technology & Telecom, Financial Services, and Retail & Consumer Goods industries have efficiency scores calculated much higher using the input-oriented BCC approach (INBCC) compared to when the scores are derived from the CCR model. This indicates very efficient management of resources that is masked under the constant return assumption. In contrast, industries like Media and Entertainment have efficiency scores that are high across different models, while others like Aerospace and Defense perform better once, they change their priority to output maximization. The results show that the selection of DEA methodology has a strong impact on efficiency scores and that the impact differs by industry. These findings provide industry-specific benchmarks for corporate practitioners, investors, and policymakers in return for fostering sustainable practices and enhancing portfolio selection strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".