Unraveling the Estimation Bias of Carbon Emission Efficiency in China’s Power Industry by Carbon Transfer from Inter-Provincial Power Transmission
Current evaluations of carbon emission efficiency in China’s provincial power industry often neglect the impact of carbon transfers from inter-regional power transmission, leading to biased assessments that hinder the sustainable development of the energy transition. To address this, we propose an advanced efficiency evaluation model that incorporates a multi-regional input–output (MRIO) framework, refining CO2 emission calculations and correcting parameter deviations in the slack-based measure (SBM) model. This model improves both the precision and fairness of carbon emission efficiency assessments. We apply the MRIO-SBM model to evaluate carbon emission efficiency in the power industry across 30 provinces in China for 2012, 2015, and 2017, revealing the impact of carbon transfers on efficiency. The results indicate that incorporating MRIO improves the precision of SBM evaluations. Significant regional disparities are observed: eastern coastal regions achieve higher efficiencies, while northeastern and western regions typically exhibit lower values, ranging from 0.5 to 0.7. Efficiency evaluations must account for carbon transfer dynamics, as these transfers can lead to overestimations of efficiency by up to 19% in electricity-importing regions and underestimations of approximately 10% in electricity-exporting regions. Furthermore, the findings emphasize the need to foster low-carbon cross-regional collaboration to promote sustainable development in the power industry.
Authors
- Huangwei Deng (ORCID: https://orcid.org/0000-0002-8738-737X)
- Bin Zhou (ORCID: https://orcid.org/0000-0002-0315-8204)
- Yue Han (ORCID: https://orcid.org/0000-0002-1549-7924)
- Qin Ji-wei
Institutions
- Tongji University (CN)
- Xinjiang University (CN)
Publication Details
- Journal
- Sustainability
- Published
- 2025-03-06
- DOI
- https://doi.org/10.3390/su17052312
- Citations
- 1
- Primary Topic
- Environmental Impact and Sustainability
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- Natural Science Foundation of Xinjiang Province