A Machine Learning and Panel Data Analysis of N2O Emissions in an ESG Framework
ESGフレームワークにおけるN2O排出の機械学習とパネルデータ分析 (AI 翻訳)
Carlo Drago, Massimo Arnone, Angelo Leogrande
🤖 gxceed AI 要約
日本語
本論文は、温室効果ガスであるN2O排出をESGの観点から分析。計量経済学と機械学習を組み合わせ、森林劣化、エネルギー強度、所得不平等が主要な決定要因であることを特定。先進国は効率が良いが、産業・農業で排出が多く、途上国は構造的障壁を抱える。規制の質やグリーンボンドなどのESG金融が排出削減に寄与することを示唆。
English
This paper analyzes N2O emissions from an ESG perspective using econometric and machine learning methods. It identifies forest degradation, energy intensity, and income inequality as key determinants. Developed nations, despite better efficiency, remain significant emitters due to industry and agriculture, while developing economies face structural barriers. Regulatory quality and ESG finance instruments like green bonds are found to support emission reduction.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではN2O排出は農業・工業由来が多く、本論文の知見は日本のESG統合報告やSSBJ開示において、スコープ1・2に加えた温室効果ガス管理の重要性を示唆する。ただし、日本の政策文脈に直接結びつくものではない。
In the global GX context
Globally, this paper contributes to the understudied area of N2O emissions within ESG frameworks, complementing the CO2-centric focus of TCFD/ISSB. It highlights the role of governance quality and ESG finance, relevant for transition finance and climate disclosure standards.
👥 読者別の含意
🔬研究者:Provides a methodological framework combining panel data and ML for N2O analysis, useful for GHG research beyond CO2.
🏢実務担当者:Offers insights on ESG factors affecting N2O, potentially informing corporate sustainability strategies and supply chain management.
🏛政策担当者:Highlights the importance of regulatory quality and ESG finance in N2O mitigation, relevant for national climate policy design.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.3390/su17104433first seen 2026-05-05 19:07:52
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