Abstract
We studied the measurement and structural factors influencing China’s provincial total-factor energy efficiency (TFEE) under resource and environmental constraints, using spatial weight matrix analysis, spatial econometric model selection, a generalized spatial econometric model with unknown heteroscedasticity, and a directional distance function global Malmquist–Luenberger (GML) superefficient model. The findings of this empirical research are as follows. Resource and environmental constraints should be considered while measuring TFEE. The results obtained in such cases are more accurate reflections of the actual situation in China. Furthermore, spatial effects should be considered when analyzing the factors influencing provincial TFEE; otherwise, the estimates will be biased. The following conclusions were obtained from the results of the empirical analysis: China’s provincial TFEE continued to decline under resource and environmental constraints, and the trend is not optimistic, implying an undue reliance on coal resources, which reduce TFEE by a considerable extent. Moreover, China’s interprovincial TFEE is affected by a variety of structural factors.
Highlights
The need for energy efficiency has been increasingly recognized from 1970 onward
The International Energy Agency (IEA), which was founded as a response to the oil crisis, released a report entitled “Redrawn Energy and Climate Map” in 2013, which pointed out that globally, energy from carbon grew by 1.4% in 2012, and will reach a record high of 31.6 billion tons by 2020
Before the data envelopment analysis (DEA) analysis, it should be ascertained whether the input and output variables are suitable for the analysis
Summary
The need for energy efficiency has been increasingly recognized from 1970 onward. The oil crisis of the 20th century has only underscored the urgency of the problem. This study considers resource and environmental constraints, and to do so, it uses a directional distance function global Malmquist–Luenberger (GML) superefficient model with unknown variances for the estimation of TFEE. Such a broad analysis of the factors influencing the spatial econometric model allows us to provide sound policy advice. The energy efficiency calculation in this study is conducted using a directional distance function GML superefficient model. Chen (2009) pointed out that the final estimation results and explanatory power of the spatial econometric model are closely related to whether the spatial weight matrix can be constructed and properly selected. The popular method of research in recent decades, MCMC, has shown its outstanding performance in the selection of spatial measurement models
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