The Impact of Green Credit on Agricultural Carbon Emissions: Spatial Spillover Effects and Channels in China
グリーンクレジットが農業炭素排出に与える影響：中国における空間的波及効果と経路 (AI 翻訳)
Yuzhen Deng, Zhicheng Yang, Litian Yang, Yuping Wen, Kaixi Chen
🤖 gxceed AI 要約
日本語
本研究は、2005年から2022年までの中国30省のパネルデータを用いて、グリーンクレジットが農業炭素排出強度（ACEI）に与える影響を分析した。空間的波及効果や産業構造の高度化などの経路を検証し、グリーンクレジットがACEI低減に有意に寄与することを示した。また、地域による効果の異質性や閾値効果も明らかにした。
English
This study uses panel data from 30 Chinese provinces (2005-2022) to examine the impact of green credit on agricultural carbon emission intensity (ACEI). It finds significant spatial spillover effects, with green credit reducing ACEI both within and across provinces. The effect operates through industrial structure upgrading and reduced agricultural energy intensity. Regional heterogeneity and a threshold effect based on green credit development level are also identified.
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
本論文は、中国のグリーンクレジット政策が農業分野の炭素排出に与える効果を実証分析。日本でも農業の脱炭素化が課題となる中、地域間の波及効果や産業構造転換の役割を示唆する点で参考になる。ただし、日本のGX政策ではグリーンクレジットよりもトランジションファイナンスやSSBJ開示が焦点となっている。
In the global GX context
Globally, this paper contributes to the literature on climate finance instruments and their spatial spillover effects in emerging economies. It provides evidence for the effectiveness of green credit in reducing agricultural emissions, which is relevant for countries designing agricultural decarbonization policies. The spatial Durbin model and threshold analysis offer methodological insights applicable to other climate policy evaluations.
👥 読者別の含意
🔬研究者:This paper provides robust empirical evidence on the spatial spillover effects of green credit, useful for researchers studying climate policy instruments and agricultural decarbonization.
🏢実務担当者:Corporate sustainability teams in agribusiness and financial institutions can use these findings to understand the regional diffusion of green credit impacts and adjust their strategies for agricultural emission reductions.
🏛政策担当者:Policymakers in developing countries can draw on this evidence to design green credit policies that account for regional heterogeneity and spatial spillovers, particularly in agriculture.
📄 Abstract（原文）
Reducing agricultural carbon emissions is an important component of China’s efforts to achieve its carbon peaking and carbon neutrality goals. As an important policy oriented financial instrument, green credit can facilitate lower agricultural carbon intensity by directing resources more efficiently across regions and encouraging low carbon transformation in agriculture. Using panel data for 30 Chinese provinces from 2005 to 2022, this study measures agricultural carbon emission intensity (ACEI) from six sources. It then examines the spatial spillover effects, transmission channels, and nonlinear characteristics associated with green credit by using a spatial Durbin framework, mediation analysis, and panel threshold model. The results indicate that: (1) green credit development is significantly associated with lower ACEI; (2) green credit exhibits significant spatial spillover effect, being associated with lower ACEI both within a province and in neighboring provinces; (3) green credit exhibits marked regional heterogeneity in its impact on ACEI: it shows both direct and spillover effects in the eastern region, only spillover effects in the central region, and only direct effects without effective diffusion in the western region; (4) green credit is associated with lower ACEI through industrial structure upgrading and lowering agricultural energy consumption intensity; (5) green credit has a single threshold effect on ACEI based on its own development level. After crossing the threshold, the emission intensity reduction effect weakens but remains significant. These results offer empirical evidence for refining green credit arrangements and advancing coordinated agricultural emission reduction across regions.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3390/su18105069first seen 2026-05-20 05:55:02
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