Decomposing the Drivers of CO2 Emissions in India: A Dual Adjustment Approach
インドにおけるCO2排出要因の分解：二重調整アプローチ (AI 翻訳)
Jani Kinnunen, Irina Georgescu
🤖 gxceed AI 要約
日本語
本研究は1990～2024年のインドを対象に、GDP、畜産、農業・林業・漁業、再生可能エネルギー消費、都市化がCO2排出に与える影響を二重調整アプローチ（DAA）とARDLモデルで分析。長期的にはGDPと畜産が排出を増加させる一方、再生可能エネルギーは削減効果を持つ。都市化は効率性仮説を支持し排出削減に寄与するが、持続的な投資と政策が前提。短期的変動ではGDPのみが排出増加に寄与。再生可能エネルギー拡大と農業・畜産の環境効率改善、持続可能な都市開発の重要性を示唆。
English
This study analyzes the long-run and short-run effects of GDP, livestock production, agriculture/forestry/fishing, renewable energy consumption, and urbanization on CO2 emissions in India from 1990-2024 using the Dual Adjustment Approach and ARDL model. Results show that GDP and livestock increase long-run emissions, while renewable energy reduces them. Urbanization supports the urban efficiency hypothesis, reducing long-run emissions contingent on sustained investment. Short-run fluctuations only in GDP increase emissions. Findings emphasize expanding renewables, improving agricultural environmental efficiency, and sustainable urban development.
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
インドは日本のGX戦略において重要なパートナーであり、本研究成果はインドの脱炭素政策の方向性に示唆を与える。日本のSSBJやTCFD開示においても、サプライチェーン排出削減の観点からインドの動向把握は有用。
In the global GX context
This empirical study on a major emerging economy provides valuable evidence for global climate policy design, particularly on the role of renewable energy and urbanization in emissions reduction. It contributes to the TCFD/ISSB discourse by offering quantitative insights applicable to developing countries' transition pathways.
👥 読者別の含意
🔬研究者:The Dual Adjustment Approach offers a novel methodological framework for decomposing permanent and transitory effects in emissions drivers, applicable to other countries or time periods.
🏢実務担当者:Corporate sustainability teams in sectors like energy, agriculture, and urban development can use these findings to prioritize investments in renewables and efficient livestock practices for long-term emissions reduction.
🏛政策担当者:Policymakers in India and similar economies should consider the urban efficiency hypothesis and the persistent impact of livestock on emissions when designing integrated climate and development strategies.
📄 Abstract（原文）
Understanding how economic growth (GDP), livestock production (LPI), agriculture, forestry and fishing (AFF), renewable energy consumption (REN), and urbanization (URB) influence carbon emissions is essential for designing effective climate policies in rapidly developing economies such as India. This study examines the long-run and short-run effects of these factors on CO2 emissions in India during 1990–2024 using the Dual Adjustment Approach (DAA) and the Autoregressive Distributed Lag (ARDL) model. The DAA framework decomposes variables into permanent (trend) and transitory (cyclical) components, allowing a simultaneous assessment of long-run equilibrium and short-run dynamics. Both DAA and ARDL models indicate that GDP and LPI increase CO2 emissions in the long run, while REN reduces them. AFF exerts a weak effect on emissions compared with the other determinants. URB is associated with lower long-run emissions, supporting the urban efficiency hypothesis, but this depends on sustained infrastructure investment and policy support, rather than automatic results of current urbanization levels. The transitory component analysis shows that short-run fluctuations in GDP increase emissions, while the effects of the remaining variables are driven by long-run structural changes. The findings highlight the importance of expanding renewable energy deployment, improving environmental efficiency in agricultural and livestock production systems, and promoting sustainable urban development to reduce carbon emissions in the case of India.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18136531first seen 2026-07-20 04:46:04
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。