IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-05704173.html

Environmental, Social and Governance Performance and Productivity Transformation in Energy Enterprises: Evidence from Panel Data Analysis

Author

Listed:
  • Miyan Ashu

    (College of Economics and Management, Taiyuan University of Technology, China)

  • Sarwar Areej

    (College of Economics and Management, Taiyuan University of Technology, China)

  • Laraib Hussain

    (College of Economics and Management, Taiyuan University of Technology, China)

  • Mehmood Meerab

    (College of Economics and Management, Taiyuan University of Technology, China)

  • Akram Muhammad Rehan

    (College of Economics and Management, Taiyuan University of Technology, China)

Abstract

Environmental, Social, and Governance (ESG) practices have emerged as an important strategic framework for enhancing corporate sustainability, resilience, and long-term competitiveness. However, empirical evidence regarding the impact of ESG performance on firm-level productivity transformation remains limited, particularly in environmentally sensitive sectors such as energy. This study examines the relationship between ESG performance and productivity transformation in energy enterprises using panel data covering the period 2018-2025. A quantitative panel-data research design is employed to evaluate the effect of ESG performance on firm-level productivity transformation. The dependent variable is operationalized through a multidimensional composite productivity transformation index constructed using the entropy weighting method, integrating indicators related to innovation capability, operational quality, sustainability-oriented outcomes, and organizational contribution dimensions. ESG performance is measured using standardized ESG scores, while firm-specific control variables—including firm size, leverage, profitability, and capital intensity—are incorporated to mitigate omitted-variable bias. The empirical analysis adopts fixed-effects panel estimation following Hausman model selection testing. Diagnostic procedures, including multicollinearity assessment, panel unit root testing, heteroskedasticity diagnostics, and autocorrelation testing, are conducted to ensure model reliability. To address potential endogeneity arising from reverse causality and simultaneity, Two-Stage Least Squares (2SLS) estimation is implemented using lagged ESG performance as an instrumental variable. The findings indicate that ESG performance is positively and statistically significantly associated with productivity transformation in energy enterprises. Firms with stronger ESG performance demonstrate superior productivity capability, suggesting that sustainability-oriented strategic practices generate operational benefits beyond conventional compliance outcomes. Dimension-specific analysis further reveals that environmental, social, and governance performance each contribute positively to productivity transformation, although the relative magnitude of these effects varies. The robustness of the results under endogeneity correction strengthens confidence in the stability of the observed relationship. This study contributes to ESG and sustainability literature by extending analysis beyond conventional financial performance outcomes toward broader enterprise productivity transformation. The findings provide practical implications for managers and policymakers seeking to accelerate sustainable competitiveness and organizational upgrading in energy-intensive sectors.

Suggested Citation

  • Miyan Ashu & Sarwar Areej & Laraib Hussain & Mehmood Meerab & Akram Muhammad Rehan, 2026. "Environmental, Social and Governance Performance and Productivity Transformation in Energy Enterprises: Evidence from Panel Data Analysis," Post-Print hal-05704173, HAL.
  • Handle: RePEc:hal:journl:hal-05704173
    DOI: 10.59324/ejmeb.2026.3(3).14
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-05704173. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.