The Low-Carbon Efficiency Illusion in Agricultural and Rural Systems: Efficiency Measurement, Threshold Effects, and Sustainable Mitigation Strategies
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.
Authors
- Guoxin Yu (ORCID: https://orcid.org/0000-0002-4779-2805)
- Yuanyuan Xiong (ORCID: https://orcid.org/0009-0001-9495-4061)
- Xiaofu Chen
Institutions
- Xinjiang Agricultural University (CN)
Publication Details
- Journal
- Sustainability
- Published
- 2026-04-26
- DOI
- https://doi.org/10.3390/su18094299
- Primary Topic
- Efficiency Analysis Using DEA
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China