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Evaluating the Financial Performance of CSR Strategies and Sustainable Operations in Mexican Companies: An Explainable Machine Learning Approach

メキシコ企業におけるCSR戦略と持続可能な事業の財務パフォーマンス評価：説明可能な機械学習アプローチ (AI 翻訳)

Laura Elena Jiménez-Casillas, Román Rodríguez-Aguilar, Marisol Velázquez-Salazar, Santiago García-Álvarez

Mathematics📚 査読済 / ジャーナル2026-02-04#AI×ESG経営インパクト: 資金調達対象セクター: cross_sector
DOI: 10.3390/math14030557
原典: https://doi.org/10.3390/math14030557

🤖 gxceed AI 要約

日本語

本研究は、メキシコ証券取引所上場企業を対象に、CSRと持続可能な事業が財務パフォーマンスに与える個別影響を、ランダムフォレストと説明可能なML（ICE、PDP、SHAP）を用いて測定した。環境スコアが最も一貫して財務パフォーマンスに寄与し、社会的・ガバナンス効果は指標依存であることを示した。SHAP分析により企業間の不均一性が明らかになり、説明可能なMLの有用性を強調している。

English

This study measures the individual impact of CSR and sustainable operations on financial performance for Mexican listed firms using Random Forest and explainable ML (ICE, PDP, SHAP). Environmental scores show the most consistent contribution, while social and governance effects are metric-dependent. SHAP reveals heterogeneity across firms, highlighting the value of explainable ML.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本ではSSBJ開示が進む中、ESGスコアと財務パフォーマンスの関連を個別企業レベルで分析する手法は、投資家対応や統合報告書の改善に示唆を与える。ただし、メキシコ市場のデータに基づくため、日本企業への適用には市場特性の違いを考慮する必要がある。

In the global GX context

This paper contributes to global ESG-financial performance literature by applying explainable ML to emerging market data, offering a methodological template for analyzing ESG disclosure impacts. It aligns with ISSB/CSRD trends emphasizing materiality and data-driven insights, though its Mexican context limits generalizability.

👥 読者別の含意

🔬研究者:Provides a novel methodological framework combining RF and XML to assess ESG-financial performance links, useful for replication in other markets.

🏢実務担当者:Offers insights into which ESG dimensions (environmental) most affect profitability, guiding resource allocation in sustainability reporting.

🏛政策担当者:Highlights the importance of environmental performance in financial outcomes, supporting policies that encourage environmental disclosure.

📄 Abstract（原文）

Research on how corporate social responsibility (CSR) practices linked to sustainable operations (SO) affect corporate financial performance (FP) is still limited. This study presents a novel methodological proposal to measure the individual impact of such practices on the profitability of companies listed on the Mexican Stock Exchange. The method employed consists of a Random Forest (RF) model complemented by Explainable Machine Learning (XML) techniques, namely Individual Conditional Expectation (ICE), Partial Dependence Plots (PDPs) and SHapley Additive exPlanations (SHAP), to calculate the individualized marginal effect in the return on assets (RoA), return on equity (RoE) and return on investment capital (ROIC) for each company, explained by the environmental, social, and governance scores provided by Bloomberg (Bloomberg Finance, L.P., New York, NY, USA), such as the market capitalization, debt-to-equity ratio, sales growth, and years since listing. The novelty of this model lies in the application of RF and XML, which offers a comprehensive and interpretable perspective on the CSR–FP relationship and the use of lagged explanatory variables to avoid endogeneity problems, overcoming the limitations of traditional analyses. The results indicate that environmental scores exhibit the most consistent contribution to FP, whereas social and governance effects are highly metric-dependent. The SHAP analysis reveals substantial heterogeneity in the drivers of firm FP, highlighting the relevance of XML methods.

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