気候変動緩和策がエネルギー貧困に及ぼす空間スピルオーバー効果の地図化：途上国・先進国の実証分析
Mapping the spatial spillover effects of climate change mitigation on energy poverty: Evidence from developing and developed countries (原題)
Mohamed Sami Ben Ali, Alanoud Ali Al-Maadid, Brahim Bergougui
🤖 gxceed AI 要約
日本語
61カ国・2010〜2019年のパネルデータを用い、空間ダービンモデルで気候緩和策がエネルギー貧困に与える自国・近隣国効果を推計。緩和は国内（直接効果−0.163）のみならず近隣国（間接効果−0.296）のエネルギー貧困も低下させ、総効果の64%が国境を越える。効果は中所得国で高所得国の約4倍、財政支出が調整要因となる。
English
Using a spatial Durbin model on 61 countries (2010–2019), the study estimates direct and cross-border effects of climate mitigation on energy poverty. Mitigation lowers energy poverty domestically (−0.163) and in neighbours (−0.296), with 64% of the total effect crossing borders. Effects are ~4x larger in middle-income countries and moderated by public spending.
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
日本のGX政策・開示制度への直接の含意は薄いが、アジア域内の電力網・技術移転を通じた脱炭素協力や、日本の国際協力（JCM等）の便益評価を考える際の参照枠になる。
In the global GX context
Adds a spatial-econometric lens to the just-transition and energy-poverty literature relevant to ISSB/CSRD social-disclosure debates, showing cross-border spillovers that single-country disclosure frameworks miss.
👥 読者別の含意
🔬研究者:空間計量で緩和策の越境便益を定量化する手法と、非空間推計の過小評価という論点が参考になる。
🏢実務担当者:越境の電力網・技術移転を通じた脱炭素投資が地域のエネルギー貧困改善に波及しうる点を、国際事業の便益評価に活用できる。
🏛政策担当者:気候緩和策の便益は国境を越え、財政能力の低い中所得国で大きい点を、国際協力・気候資金配分の設計に反映すべき。
📄 Abstract（原文）
Climate change mitigation is widely expected to reduce energy poverty, yet the spatial dimension of this relationship has been largely overlooked: mitigation undertaken in one country may affect energy poverty in its neighbours through shared grids, regional markets and technology diffusion. This study estimates the direct and cross-border effects of climate change mitigation on energy poverty for a balanced panel of 61 countries over 2010–2019, using a spatial Durbin model with country and year fixed effects. Diagnostic tests show that residual spatial dependence is present in every year and takes the form of a spatial lag rather than a spatial error. The LeSage–Pace decomposition shows that mitigation is associated with lower energy poverty both domestically (direct effect − 0.163) and in neighbouring countries (indirect effect − 0.296), with the cross-border component accounting for 64% of the total effect. Non-spatial estimates capture less than half of this total. Industrial automation transmits only a small part of the effect: the mediated path is significant and reinforces the direct effect, but accounts for about 8% of the total. The benefits depend on national context. Urbanisation does not moderate the relationship, but public spending does, and the effect is absent in high-income countries with below-median fiscal capacity. The effect in middle-income countries is roughly four times that in high-income countries, indicating that the benefits of mitigation accrue where energy poverty is most severe. The results are robust to eight alternative spatial weight matrices, instrumental-variable estimation and alternative measures of energy poverty and climate action.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.seta.2026.105493first seen 2026-11-07 04:40:22
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