Green governance and investor value: An empirical study of sustainability practices among Nigerian listed industrial firms

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Type of the article: Research Article

Abstract
The study considers the impact of green governance on investor wealth through the sustainability performance of manufacturing firms in Nigeria from 2015 to 2024. The green governance variables considered in the study include environmental expense ratio, sustainability index score, social expense ratio, and environmental, social, and governance (ESG) facilitators score against earnings per share (EPS) as a proxy for investor wealth. The study adopts the ex post facto research design. This paper employed fixed-effects panel regression to analyze panel data from Dangote Cement, Lafarge Africa, and BUA Cement. The model has an overall predictive ability of 67.4%; therefore, the model was found to be appropriate (p = .001, F = 12.83). The sustainability index score (β = 0.186, p = 0.013), social expense ratio (β = 5.487, p = 0.027), and ESG facilitators scores (β = 0.212, p = 0.018) were found to be significantly and positively related to profitability, while the environmental expense ratio (β = – 1.908, p = 0.312) was found to be non-significant. It can be argued based on the findings that sustainability initiatives combined with social investment and transparent governance practices can increase the wealth of investors. But the environmental expense ratio might take more time before it turns into profitability. The research findings highlighted the theoretical and practical importance of long-term investment in sustainability practices.

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    • Figure 1. Dual-axis earnings per share (EPSₙₑ) trends for Nigerian listed industrial firms (2015–2024)
    • Table 1. Environmental, social, and governance (ESG) performance and shareholder wealth dataset (2015–2024)
    • Table 2. Panel regression coefficients for ESG predictors and earnings per share (EPSₙₑ)
    • Table 3. Model summary and diagnostic tests for fixed effects regression
    • Conceptualization
      William Inyang, Innocent Okoi, Ije Ubi, Inyang Inyang
    • Data curation
      William Inyang, Innocent Okoi, Essien Oden, James Obriku Otiwa
    • Formal Analysis
      William Inyang, Innocent Okoi, Ije Ubi, James Obriku Otiwa
    • Investigation
      William Inyang, Ije Ubi, Inyang Inyang, Femi Gabriel
    • Methodology
      William Inyang, Innocent Okoi, Inyang Inyang, Essien Oden
    • Project administration
      William Inyang, Ije Ubi, Inyang Inyang, Femi Gabriel
    • Resources
      William Inyang, Innocent Okoi, Essien Oden, James Obriku Otiwa
    • Software
      William Inyang, Essien Oden, Femi Gabriel, James Obriku Otiwa
    • Supervision
      William Inyang, Innocent Okoi, Ije Ubi, Inyang Inyang
    • Validation
      William Inyang, Ije Ubi, Inyang Inyang, Essien Oden, Femi Gabriel
    • Writing – original draft
      William Inyang, Innocent Okoi, Ije Ubi, Inyang Inyang
    • Writing – review & editing
      William Inyang, Essien Oden, Femi Gabriel, James Obriku Otiwa