Revealing the contribution of corporate sustainability practices to financial performance: Case of BIST Sustainability 25 Index companies

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The purpose of the paper is to study the impact of corporate sustainability practices on the financial performance of companies included in the BIST Sustainability 25 Index. To assess the efficiency and quality of corporate sustainability, general (ESG Disclosure Index) and partial (Environmental Disclosure Index, Social Disclosure Index, and Corporate Governance Disclosure Index) indices were used, calculated based on content analysis of sustainability reports. Based on the two given types of indices and four types of financial performance indicators (return on assets, return on equity, assets turnover ratio, and Tobin’s Q), two types of regression models (GEN models and PART models) were built, and eight analytical models were examined. Company size and leverage were included as control variables in each model. The regression analysis results were contradictory, partially confirming the conclusions of some scientists and refuting the findings of others. A study of GEN models revealed that companies implementing more effective general corporate sustainability practices have a significant positive impact only on return on equity; as for other measures (return on assets, assets turnover ratio, and Tobin’s Q), an insignificant relationship between them and ESG Disclosure Index was found. Results of the PART models analysis revealed a significant positive effect of the Social Disclosure Index on return on equity and assets turnover ratio and a negative relationship between the Corporate Governance Disclosure Index and assets turnover ratio. Using control variables for the two types of models showed a significant negative effect of company size on Tobin’s Q.

Acknowledgment
This study was supported by the Ministry of Education and Culture of Ukraine within the project “Development of a mechanism for the sustainable development of economic systems in the conditions of military operations and post-war recovery of the economy” (Registration number of the project: 0124U000463).

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    • Table 1. Variables (definition, method of calculation, and source)
    • Table 2. Regression models
    • Table 3. Descriptive statistics
    • Table 4. GEN models and PART models (ROA, ROE, ATR, and TQ) (p-value)
    • Conceptualization
      Yuliia Serpeninova, Serhii Lehenchuk, Dmytro Zakharov
    • Formal Analysis
      Yuliia Serpeninova, Serhii Lehenchuk
    • Funding acquisition
      Yuliia Serpeninova, Serhii Lehenchuk, Dmytro Zakharov
    • Project administration
      Yuliia Serpeninova, Serhii Lehenchuk, Dmytro Zakharov
    • Supervision
      Yuliia Serpeninova, Serhii Lehenchuk
    • Writing – original draft
      Yuliia Serpeninova, Serhii Lehenchuk, Nataliya Zdyrko
    • Writing – review & editing
      Yuliia Serpeninova, Dmytro Zakharov
    • Investigation
      Serhii Lehenchuk, Nataliya Zdyrko, Dmytro Zakharov, Olena Podolianchuk
    • Methodology
      Serhii Lehenchuk, Dmytro Zakharov
    • Validation
      Serhii Lehenchuk, Nataliya Zdyrko
    • Data curation
      Nataliya Zdyrko, Dmytro Zakharov, Olena Podolianchuk
    • Resources
      Nataliya Zdyrko, Dmytro Zakharov, Olena Podolianchuk
    • Software
      Nataliya Zdyrko, Dmytro Zakharov, Olena Podolianchuk
    • Visualization
      Nataliya Zdyrko, Olena Podolianchuk
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Environmental Economics
Environmental Economics