Evaluating the Investment Efficiency of China’s Provincial Power Grid Enterprises under New Electricity Market Reform: Empirical Evidence Based on Three-Stage DEA Model

Abstract

The new round of electricity market reform in 2015 completely changed the profit pattern of power grid enterprises (PGEs) in China, and directly affected their investment plans. Under the new electricity market reform (NEMR), the government regulatory authority made higher requirements for the investment efficiency of PGEs, and the investment effectiveness hence became the core criterion for investment plans. Therefore, the PGEs are now attaching great importance to the investment efficiency. According to their geographical differences, this paper divides the Chinese provincial PGEs into three groups, namely eastern, central and western region enterprises. Based on the NEMR, we developed an evaluation system of investment efficiency for the above-mentioned enterprises. Moreover, this paper selects GDP per capita, electricity consumption in industry, and electrification rate as external environment variables, and conducts an empirical research on the investment efficiency of 31 provincial PGEs in China in 2017. The analysis reveals that three external environment variables have considerable impacts on the investment efficiency. Though the increase of GDP per capita and electricity consumption in industry are not conducive to improving investment efficiency, the advancement of electrification plays a positive role in its improvement. And from the real efficiency results, Tianjin, Liaoning, Jiangsu, and Fujian have relatively higher investment efficiency, while Henan, Shandong, and Shanghai exhibit lower investment efficiency. By comparing the investment efficiency of PGEs in the first and third stage, conclusions can be drawn that in the first stage the investment efficiency of PGEs was overestimated, and the inefficient investments prevailed some provincial PGEs, which caused by low scale efficiency.

Research on Innovation Efficiency of Listed Companies in Development Zone Based on the Three-Stage DEA-Tobit Model: A Case Study of Hubei Province

“…After we exclude environmental factors and random interference factors, the efficiency has been improved, indicating that the innovation efficiency of listed companies is adversely affected by the environment. Consistent with the conclusion of the impact of external environment on enterprise efficiency in [10], environmental factors have a significant impact on enterprise innovation efficiency [11,34]; that is, government-supported to high-tech industrial technology innovation policies and government systems need to increase their support and enhance the trade-off of configuration efficiency [34,35] to ensure maximum innovation efficiency (3) e scale of the enterprise, the company's operating capacity, and state-owned shares have a significant positive impact on innovation efficiency. e number of years of the establishment has a significant negative impact on innovation efficiency.…”

Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model

“…Due to many advantages, such as not having to preset specific functions and not having to normalize variable units, non-parametric methods have become the mainstream method for evaluating production efficiency in the world [ 11 ]. The data envelopment analysis model (DEA) is a non-parametric production efficiency evaluation method that estimates the effective production frontier based on a set of input–output observations [ 12 ]. Although DEA is a relatively mature and modern tool to calculate efficiency, it has some shortcomings.…”

“…The data envelopment analysis model (DEA) is a non-parametric production efficiency evaluation method that estimates the effective production frontier based on a set of input-output observations [12]. Although DEA is a relatively mature and modern tool to calculate efficiency, it has some shortcomings.…”

“…Although DEA is a relatively mature and modern tool to calculate efficiency, it has some shortcomings. The effect of errors on production efficiency cannot be explained by a one-stage DEA model, while external environmental effects cannot be eliminated by the two-stage DEA model [12][13][14]. In order to overcome the above shortcomings, combined with stochastic frontier analysis (SFA) technology, Fried et al proposed three-stage DEA model [15].…”

Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market

“…Nowadays, China's power market system is in the accelerating progress of changing from monopoly and regulation to competition [1,2]. The processes of generation, transmission, distribution, and sales in the power market are separated, and the behaviors of multiple participants are becoming more independent and complex [3][4][5]. At the same time, with the energy structure transition and the development of a smart grid in China [6,7], the uncertainty of a transmission network further increases, which generates many new operational scenarios [8][9][10].…”