Evaluating ESG efficiency using DEA an analysis of Dow Jones Industrial average companies

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.

Authors

Publication Details

Journal
Accounting
Published
2025-01-01
DOI
https://doi.org/10.5267/j.ac.2025.5.001
Citations
2
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
4.21
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article

Evaluating ESG efficiency using DEA an analysis of Dow Jones Industrial average companies

Reyhane Sadat Mohajeri Kharaghani, Amirparsa Madadkhani
2 citations
Accounting
Efficiency Analysis Using DEA
4.21
article

Evaluating ESG efficiency using DEA an analysis of Dow Jones Industrial average companies

Reyhane Sadat Mohajeri Kharaghani, Amirparsa Madadkhani
article en
2 citations

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.

AccountingVol. 11(3)
Openalex Percentile: Top 6%
Efficiency Analysis Using DEA
4.21
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