INTERPLAY BETWEEN RESIDENTIAL CLEAN ENERGY AND CLIMATE POLICIES IN INDIA: DISTRIBUTIONAL CONSEQUENCES
インドにおける住宅用クリーンエネルギーと気候政策の相互作用：分配結果 (AI 翻訳)
Arda Aktaş, Miguel Poblete-Cazenave
🤖 gxceed AI 要約
日本語
この研究は、インドの住宅用エネルギー技術が健康・経済に与える不均一な効果を因果森林法で推定し、気候政策とクリーン燃料への完全移行を組み合わせることで、特に社会的に不利な層に大きな利益がもたらされることを示した。現在の政策では削減効果は限定的だが、より厳格な気候政策と住宅部門のクリーン化を同時に実施することで、経済的利益の規模と分配が大幅に改善される。
English
This study uses causal forests to estimate heterogeneous effects of residential energy technologies on health and economic outcomes in India. Combining climate policies with a full transition to clean fuels yields larger reductions in sickness and expenditures, with disadvantaged groups benefiting most. The results underscore the need for complementary climate and clean energy policies to enhance both magnitude and distribution of economic benefits.
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
インドは石炭依存度が高く、気候政策が途上国の公正な移行にどう影響するかが注目される。本論文は機械学習で個人レベルの分布効果を定量化し、日本でも政策の分配効果を考慮する際に参考になる。
In the global GX context
This paper provides rigorous empirical evidence on the distributional impacts of climate and clean energy policies in India using causal machine learning. Globally, it demonstrates the importance of targeting clean energy transitions to vulnerable populations, relevant for just transition frameworks and international climate policy.
👥 読者別の含意
🔬研究者:Shows how causal forests can identify heterogeneous treatment effects in energy-health linkages, a methodological contribution for impact evaluation.
🏢実務担当者:Highlights that combining climate policies with residential clean energy programs can maximize health and economic benefits for the most vulnerable.
🏛政策担当者:Provides evidence that stringent climate policies plus clean fuel transition yield larger and more equitable benefits, supporting integrated policy design.
📄 Abstract（原文）
Climate policy affects households not only through aggregate emissions reductions but also through its interaction with sector-specific policies that shape exposure and economic outcomes. In this study, we use causal forests to estimate heterogeneous effects of residential energy technologies on sick days and sickness-related work absences, out-of-pocket health expenditures, and lost earnings using nationally representative individual-level data from the India Human Development Survey. We combine these with projections of ambient PM[Formula: see text] from GAINS to simulate outcomes through 2030 under alternative climate and residential clean energy policies. We find that under current climate policies, reductions are modest, whereas more stringent, but feasible climate policies, combined with a full transition to clean fuels in the residential sector, produce considerably larger reductions, with routinely disadvantaged individuals benefiting the most. The results highlight the importance of complementing climate and residential clean energy policies to enhance both the magnitude and the distributional reach of economic benefits.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1142/s2010007826400087first seen 2026-07-02 05:08:29
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