Performance measurement and policy implications of energy systems

Abstract

Global warming concerns drive the urgency to assess energy systems. This study aims to evaluate the performance of different energy system scales at the national, provincial, and sectoral levels to promote global sustainable development and identify targeted strategies and interventions for relevant organizations. Firstly, to compare performance across countries, the research adopts a decision matrix that considers dimension weights and employs half-quadratic theory to create an ensemble ranking methodology based on the Energy Trilemma Index (ETI) data of each country. Consensus index and trust level are constructed to gauge the agreement and reliability of the ensemble ranking. Then the research takes the case of China, utilizing a three-stage Slacks-Based Measure Stochastic Frontier Analysis (SBM-SFA) model to measure provincial environmental efficiency, reducing the influence of external environmental factors, and enabling a specific evaluation of group performance by Meta-frontier Data Envelopment Analysis (DEA). Additionally, an approach combining the super-efficiency DEA with the Malmquist Index is conducted to evaluate the operational efficiency of China's power grid enterprises, while the dynamic analysis is delivered as well.

Different from previous studies, for country-level comparison, this research develops a decision matrix and ranks according to performance results, thereby achieving an objective aggregation process, reducing decision bias, and improving decision quality. The inter-country comparison effectively measures aggregate energy performance, considering preferences among countries and capturing outcomes related to energy security, energy equity, and environmental sustainability. Empirical research on 30 member countries of the International Energy Agency (IEA) emphasizes the validity of the proposed method. Besides, for the provincial evaluation, the influence of external environmental variables and geographical heterogeneity is eliminated by the application of SFA regression and Tobit regression, respectively. An empirical analysis of the environmental efficiency of 30 regions in China shows the overall environmental efficiency has gradually improved from 2011 to 2020, and there is still room for improvement in scale efficiency and technical efficiency. Notably, there are large differences in the environmental efficiency of different provinces and cities in China. Moreover, the application of super-efficiency DEA makes the performance gap clear among power grid enterprises across regions. According to the empirical analysis of data from 2016 to 2020, China's power grid industry has reached a mature stage of development, with continual progress as well as significant differences in operational efficiency across regions. Technological innovation is needed for further operational improvement while expanding the scale.Overall, this research provides a comprehensive methodology for evaluating and comparing energy system performance across different levels. The findings have practical implications for policymakers, designers, and public policymakers, providing targeted suggestions for performance evaluation, policy analysis, and decision support. By identifying areas for improvement and analyzing industry trends, this research contributes to the development of more efficient and sustainable energy systems.