Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context

Drago, Carlo and Arnone, Massimo and Leogrande, Angelo (2025): Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context.

Abstract

The paper examines nitrous oxide (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, the paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of the 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 emissions 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nitrous Oxide Emissions, ESG Models, Econometric Analysis, Machine Learning, Sustainability Policy
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q53 - Air Pollution ; Water Pollution ; Noise ; Hazardous Waste ; Solid Waste ; Recycling; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming

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