Environmental Efficiency Evaluation of China’s Power Industry Based on the Two-Stage Network Slack-Based Measure Model

Abstract

How to achieve the continuous improvement of the environmental performance level of the power industry within the requirements of clean and low-carbon energy development is the fundamental requirement and inevitable choice for the construction of ecological civilization and sustainable development. From the perspective of environmental protection, based on the Data Envelopment Analysis (DEA) method and the internal mechanism of power system production and supply, the power industry environmental efficiency evaluation index system was constructed, and the two-stage Network Slack-based Measure (NSBM) model considering undesired output was used to calculate China’s 30 provinces and municipalities from 1998 to 2019. The environmental efficiency is divided into two links: power generation efficiency and transmission and distribution efficiency. The study found that, within the research interval, the overall environmental efficiency of China’s 30 provinces is low, and the differences between provinces and cities are large, but they have gradually developed in a better direction after 2015. The power generation efficiency of the first link in most provinces and municipalities is higher than the transmission and distribution efficiency of the second link, and the low transmission and distribution efficiency is an important reason for the low comprehensive level of environmental efficiency. The overall evolution trend of environmental efficiency in the six regions of China is roughly the same, but the regional differences are obvious, showing a trend of “high in the southeast and low in the northwest”. The economic and natural resource differences in different provinces and cities in each region have led to varying degrees of redundancy in five aspects, including investment in power assets, installed power generation capacity, and length of transmission lines, which seriously affect the environmental efficiency of the power industry. This research attempts to open the “black box” of the environmental efficiency conversion process of the power industry, which can provide directions and strategic suggestions for the improvement of the efficiency of the power industry in China.

Empirical study on the technical efficiency and total factor productivity of power industry: Evidence from Chinese provinces

“…To make a thorough inquiry of the regional disparities in power efficiency, we divide China into six major regional power networks based on the classification standards of regional power networks, i.e., Northeast, North China, Central China, East China, South China, and Northwest. The regional efficiency level of MTE and ETE from high to low is East (Central) China, Central (East) China, South China, North China, Northeast and Northwest, and the conclusion has been confirmed by other studies [38]. It shows that the ranking of MTE hardly changes after the inclusion of environment constraints, as shown in Table 3 and Figure 2.…”

Temporal–Spatial Evolution, Influencing Factors, and Driving Mechanisms of Environmental Regulation Performance Disparities: Evidence from China

“…In an empirical study of the environmental performance of urban agglomerations in the Beijing-Tianjin-Hebei region from 2003 to 2015, Xue et al found fluctuating changes in performance differences between regions but a trend in expansion in the last two years [31]. Moreover, a study of the power industry's environmental efficiency showed no significant contraction trend between regions [32]. In contrast, Wang and Wang examined the environmental regulation and tourism eco-efficiency of urban agglomerations in the Yangtze River Delta region using the entropy power method and the super-efficiency slacks-based measure (SBM) model.…”

Municipal Energy Efficiency in Portugal: An Analysis of Electricity and Natural Gas Consumption

“…In addition to analysing the efficiency of energy consumption, the doctrine also analyses the efficiency levels of distribution channels. Using the DEA model, Wei et al (2021) sought to estimate the efficiency levels in energy production, transmission, and distribution of 30 municipalities in China between 1998 and 2019. The study concludes that average efficiency is low and the disparities between municipalities are considerable.…”