A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China

Area of Science:

• Environmental Science
• Urban Studies
• Economics

Background:

• Accurate city-level carbon emission efficiency (CEE) evaluation is crucial for achieving dual carbon targets.
• Existing studies overlook city-level CEE and inter-unit competition, limiting policy relevance.
• Understanding urban low-carbon performance requires refined measurement models.

Purpose of the Study:

• To introduce and apply the data envelopment analysis game cross-efficiency (DEA-GCE) model for measuring urban CEE.
• To compare the DEA-GCE model with the traditional Super-SBM model at the city level.
• To analyze the spatiotemporal patterns and influencing factors of urban CEE in China.

Main Methods:

• Data envelopment analysis game cross-efficiency (DEA-GCE) model.
• Super-efficiency slack-based model (Super-SBM).
• Empirical analysis using data from 283 Chinese cities (2006-2019).

Main Results:

• The DEA-GCE model yields more intensive and stable CEE results compared to Super-SBM.
• Overall urban CEE in China showed a slight decline with fluctuations between 2006 and 2019.
• Significant spatial clustering of CEE was observed, with Northeast China improving and Northwest China lagging.

Conclusions:

• The DEA-GCE model offers an innovative and robust approach for urban CEE assessment.
• Urban CEE in China faces challenges, necessitating targeted emission reduction strategies.
• Regional disparities in CEE highlight the need for place-based environmental policies.