Assessing the Impact of Artificial Intelligence and Green Finance on Energy Efficiency: Based on Super‐Efficiency SBM and Tobit Two‐Stage Models
人工知能とグリーンファイナンスがエネルギー効率に与える影響の評価：超効率SBMとTobit二段階モデルに基づいて (AI 翻訳)
Hongji Zhou, Rong Wang
🤖 gxceed AI 要約
日本語
本研究は超効率SBMモデルでエネルギー効率（EE）を測定し、TobitモデルでAIとグリーンファイナンス（GF）のEEへの影響を検証。中国の地域別分析の結果、AIは全国・東部・中部でEEを有意に向上させるが、西部では非有意。GFはEEを促進するが弾性値は小さい。エネルギー賦存はEEを阻害し、環境規制は促進する。産業構造はEEを低下させ、技術水準は中部でのみ阻害効果を示す。
English
This study measures energy efficiency (EE) using a super-efficiency SBM model and examines the impact of AI and green finance (GF) on EE via a Tobit model. Regional analysis in China shows AI significantly improves EE nationally and in eastern and central regions, but not in the west. GF promotes EE with small elasticity. Energy endowment inhibits EE, while environmental regulation promotes it. Industrial structure reduces EE, and technology level inhibits EE only in the central region.
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
中国のデータに基づく研究だが、AIとグリーンファイナンスのエネルギー効率への影響を定量的に示しており、日本のGX政策（特にAI活用やグリーンファイナンス促進）への示唆を含む。ただし、地域特性の違いが大きく、日本への直接適用には注意が必要。
In the global GX context
This paper provides empirical evidence on how AI and green finance can enhance energy efficiency, relevant to global GX discussions on digitalization and sustainable finance. The regional heterogeneity findings offer insights for policymakers in countries with diverse economic structures.
👥 読者別の含意
🔬研究者:AIとグリーンファイナンスのエネルギー効率への影響を地域別に分析した手法と結果は、エネルギー経済学の研究者に参考となる。
🏢実務担当者:エネルギー効率改善に向けたAI投資やグリーンファイナンス活用の効果を定量的に示しており、企業のエネルギー戦略立案に活用できる。
🏛政策担当者:地域ごとに異なる政策効果を示しており、エネルギー政策の地域別設計やAI・グリーンファイナンスの活用促進策の検討に有用。
📄 Abstract（原文）
ABSTRACT To enhance energy efficiency (EE) and achieve sustainable development. This study measures EE through super‐efficiency SBM model, and verifies artificial intelligence (AI) and green finance (GF) impact on EE by Tobit model, conclusions as follows: (1) The EE of each region and the country is the spread of the low, with a lot of opportunity for improvement. The EE decreases in the following order: the regions in eastern, central, and western. (2) At the national level, AI has a significant positive effect on EE, implying that advances in AI can effectively improve EE. From different regions, AI impact on EE in both the eastern and central regions shows positive effect, and the effect in the central is larger than that eastern, while in the western region is positive but statistically insignificant. (3) At the national level, GF promotes EE but the elasticity coefficient is small; in the eastern region, GF has the biggest effect on EE, while in the central and western regions, it has weaker effects on EE. (4) Energy endowment inhibits EE; environmental regulation can promote EE at the national and regional levels, with the biggest effect in the eastern region and the least effect in the western region. The industrial structure coefficient in all regions reduces the EE. The technology level inhibits EE only in the central region. The thesis through the analysis of the relationship between the three and the reliability of the conclusions drawn from the analysis, to be able to better play the GF and AI in the energy sector of the policy implementation effect, effectively improve EE, improve the energy structure, for the comprehensive promotion of the energy transition is of great significance.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1002/ese3.70132first seen 2026-05-05 19:07:47
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