The impact of board governance effectiveness on carbon disclosure in the banking sector

  • 5 Views
  • 1 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
Climate-related risks have intensified the demand for transparency in the banking sector, particularly with respect to carbon-related information disclosed to stakeholders. In emerging economies, where climate disclosure remains largely voluntary, internal governance mechanisms are expected to play a decisive role in shaping reporting practices. The aim of this study is to examine the relationship between board governance effectiveness and carbon emission disclosure in the ASEAN banking sector. The object of the study is listed commercial banks operating in six ASEAN countries over the period 2014–2023. The analysis is based on panel data and employs fixed-effects and Tobit regression models to account for unobserved heterogeneity and the bounded nature of disclosure scores. The results indicate that board governance effectiveness is positively and statistically associated with carbon emission disclosure. Accordingly, the within R-squared value for the fixed-effects model is 23.5%, while the pseudo R-squared for the Tobit model is 55%, indicating strong explanatory power of both specifications. Economically, a one-point increase in the Board Effectiveness Score corresponds to an increase of 2.630 units in carbon emission disclosure in the fixed-effects model and 4.550 units in the Tobit specification, indicating economically meaningful improvements in disclosure intensity. In addition, bank size, age, profitability, and eco-innovation activity are found to be positively related to disclosure levels. The results remain robust across alternative specifications, including panel quantile regression, panel logit estimation, and two-step system generalized method of moments.

view full abstract hide full abstract
    • Table 1. Study sample from ASEAN countries
    • Table 2. Variable definitions
    • Table 3. Descriptive statistics
    • Table 4. Pairwise correlations
    • Table 5. Variance inflation factor
    • Table 6. Results of baseline regression models
    • Table 7. Quantile regression
    • Table 8. Results of panel logit regression – fixed-effects with the Hausman test
    • Table 9. Results of dynamic panel GMM estimators
    • Conceptualization
      Marwan Mansour, Ahmad Marei
    • Data curation
      Marwan Mansour, Murad Mujahed, Mohammed Nofal
    • Formal Analysis
      Marwan Mansour, Murad Mujahed, Mohammed Nofal
    • Investigation
      Marwan Mansour, Murad Mujahed, Mohammed Nofal
    • Methodology
      Marwan Mansour, Ahmad Marei, Mohammed Nofal
    • Resources
      Marwan Mansour, Ala Albawwat, Murad Mujahed
    • Writing – original draft
      Marwan Mansour
    • Funding acquisition
      Ala Albawwat, Ahmad Marei, Murad Mujahed, Mohammed Nofal
    • Software
      Ala Albawwat
    • Supervision
      Ala Albawwat, Murad Mujahed
    • Visualization
      Ala Albawwat, Ahmad Marei
    • Writing – review & editing
      Ala Albawwat, Ahmad Marei, Murad Mujahed, Mohammed Nofal
    • Project administration
      Ahmad Marei