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The Low-Carbon Efficiency Illusion in Agricultural and Rural Systems: Efficiency Measurement, Threshold Effects, and Sustainable Mitigation Strategies

農業・農村システムにおける低炭素効率の幻想：効率測定、閾値効果、持続可能な緩和戦略 (AI 翻訳)

Yuanyuan Xiong, Guoxin Yu, Xiaofu Chen

Sustainability📚 査読済 / ジャーナル2026-04-26#炭素会計Origin: CN
DOI: 10.3390/su18094299
原典: https://doi.org/10.3390/su18094299
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🤖 gxceed AI 要約

日本語

この研究は、中国30省の2000〜2022年のパネルデータを用いて、農業・農村の炭素排出効率と「低炭素効率の幻想」を分析。メタフロンティアSBMモデルとXGBoost・SHAP手法を組み合わせ、効率の幻想が排出削減コスト制約から生じることを明らかにした。技術的非効率が主な制約であり、削減コストのドライバーには閾値効果があることを示した。

English

This study uses panel data from 30 Chinese provinces (2000-2022) to analyze agricultural carbon emission efficiency and the 'low-carbon efficiency illusion' where measured efficiency does not translate into real reductions. Employing meta-frontier SBM, XGBoost, and SHAP, it finds that technical inefficiency is the main constraint, and that efficiency illusion provinces increased then decreased. Core drivers of abatement costs show heterogeneous impacts and threshold effects, informing differentiated mitigation strategies.

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

中国の農業炭素効率を分析した研究で、日本農業への直接的な示唆は限られるが、効率幻想という概念や分析手法は日本の農業分野の脱炭素評価にも応用可能性がある。

In the global GX context

This paper contributes to the global discussion on carbon accounting in agriculture by introducing the concept of 'low-carbon efficiency illusion' and using machine learning to identify threshold effects. It underscores the need for context-specific mitigation policies beyond efficiency metrics, which is relevant for international frameworks like the UN SDGs.

👥 読者別の含意

🔬研究者:Provides a novel methodological framework combining efficiency measurement with machine learning to identify drivers and threshold effects in agricultural carbon emissions.

🏢実務担当者:Useful for understanding the limitations of efficiency metrics in guiding actual emission reductions; highlights the importance of cost constraints.

🏛政策担当者:Demonstrates that efficiency gains alone do not guarantee emission reductions; policies must address underlying cost barriers and technical inefficiency.

📄 Abstract（原文）

This study examines agricultural and rural carbon emission efficiency and the underlying “low-carbon efficiency illusion” in China, where measured efficiency gains fail to translate into genuine environmental improvements. Using panel data from 30 Chinese provinces spanning 2000 to 2022, this study employs a meta-frontier slack-based measure (SBM) model to assess agricultural and rural carbon emission efficiency across meta-frontier and group-frontier benchmarks and investigates the efficiency illusion from the perspective of carbon emission reduction cost constraints. We further combine the Extreme Gradient Boosting (XGBoost) model and Shapley Additive Explanations (SHAP) explainability methods to identify core drivers of agricultural carbon emission reduction costs. We find that technical inefficiency is the primary constraint on China’s agricultural and rural carbon emission efficiency; the number of provinces with an efficiency illusion shows an initial increase followed by a decrease between 2005 and 2022; and core drivers of emission reduction costs exhibit heterogeneous impacts and significant threshold effects across the two frontier frameworks. These findings offer evidence-based guidance for designing differentiated, targeted emission reduction strategies to mitigate the efficiency illusion, advance low-carbon agricultural transition, and support the sustainable development of agricultural and rural systems in the context of the United Nations Sustainable Development Goals.

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