Decoding CO2 emissions in South Asia: evidence from heterogeneous effects and dynamic panel estimation using AMG, CCEMG, and quantile regression
南アジアのCO2排出の解読：AMG、CCEMG、分位点回帰を用いた不均一効果と動的パネル推定からのエビデンス (AI 翻訳)
Masud Rana, Krishno Karmakar, M. Kamruzzaman, Md. Kamal Hossain, Md Abdullah Al Mamun, Sutap Kumar Ghosh, Md. Mahmudul Hasan
🤖 gxceed AI 要約
日本語
南アジア8カ国（2000-2023年）のCO2排出要因をパネルデータで分析。再生可能エネルギー消費は排出削減に有意な効果を示す一方、他の変数は国や排出分布により異なる影響。再生可能エネルギー転換の重要性を強調。
English
This study examines CO2 emission drivers in 8 South Asian countries (2000-2023) using panel econometrics. It finds that renewable energy consumption reduces emissions, while other factors show heterogeneous effects across countries and emission levels. The results underscore the importance of renewable energy transition despite complex dynamics.
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
南アジアのCO2排出要因を実証的に分析し、地域固有の政策立案に示唆を与える。日本にとっては直接関係ないが、新興国における再生可能エネルギー導入効果の事例として参考になる。
In the global GX context
This paper contributes to global climate policy literature by providing empirical evidence from South Asia, a region often underrepresented in emissions studies. Its use of second-generation panel methods highlights the need for heterogeneous approaches in emission modeling.
👥 読者別の含意
🔬研究者:Useful for empirical researchers studying emissions determinants and heterogeneous panel methods.
🏛政策担当者:Could inform South Asian policymakers on the effectiveness of renewable energy policies for decarbonization.
📄 Abstract（原文）
This study investigates the determinants of carbon dioxide (CO 2 ) emissions in eight South Asian countries over the period 2000–2023 using a comprehensive panel econometric framework. The analysis incorporates key structural drivers, including renewable energy consumption, renewable electricity output, fossil fuel consumption, foreign direct investment (FDI), technological development, access to clean fuels, population, and GDP per capita. To address cross-sectional dependence and slope heterogeneity, the study applies second-generation panel techniques, including the Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCEMG), and panel quantile regression. The empirical findings reveal substantial heterogeneity in the effects of explanatory variables across countries and emission distributions. Renewable energy consumption exhibits a statistically significant negative association with CO 2 emissions under the CCEMG estimator, indicating its role in mitigating environmental degradation. In contrast, most other variables, including FDI, fossil fuel consumption, technological indicators, and GDP per capita, do not demonstrate statistically robust effects across estimators. The quantile regression results further confirm that the impacts of key variables vary across different emission levels. Overall, the findings highlight the importance of renewable energy transition while underscoring the complex and heterogeneous nature of emission dynamics in South Asia.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43621-026-03501-5first seen 2026-07-14 04:39:39
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