Policy-driven analysis of carbon emission efficiency under uncertainty and its application in Chinese industry: Hybrid delta-slacks-based model and ordinal priority approach

Regional differences in carbon emission efficiency arise from disparities in multiple factors, which are often influenced by government policy preferences. However, currently, most studies fail to consider the impact of government policy preferences and data uncertainty on carbon emission efficiency. To address the above limitations, this study proposes a hybrid model based on delta-slacks-based model and ordinal priority approach for measuring carbon emission efficiency driven by government policy preferences under data uncertainty. The proposed model incorporates constraints on the importance of inputs and outputs under different policy preference scenarios. It then develops the efficiency optimization model with Farrell frontiers and efficiency tapes to deal with the data uncertainty in inputs and outputs. This study demonstrates the proposed model by analyzing industrial carbon emission efficiency in Chinese provinces in 2021. It examines the carbon emission efficiency and corresponding clustering results of provinces under three types of policies with varying priority preferences. The results indicate that the carbon emission efficiency of the 30 provinces can mainly be categorized into technology-driven, development-balanced, and transition-potential types, with most provinces achieving optimal efficiency under the technology-dominant preferences. Ultimately, this study suggests policy recommendation for different provinces to work towards achieving the low carbon goal.