A Machine Learning and Panel Data Analysis of N2O Emissions in an ESG Framework

Abstract

Addressing climate change requires a deeper understanding of all greenhouse gases, yet nitrous oxide (N2O)—despite its significant global warming potential—remains underrepresented in sustainability analysis and policy discourse. The paper examines N2O emissions from an environmental, social, and governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on emissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N2O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and country-specific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, this paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of this study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N2O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emission mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets.