Quantitative Evaluation and Characteristic Analysis of Resource Allocation Efficiency of the Energy Industry in the Yangtze River Economic Belt

Abstract

The resource allocation efficiency of the energy industry in the Yangtze River economic belt is related to the green and high-quality development of the region. This study constructed a quantitative model which modified from the traditional HK model to evaluate the efficiency of energy industry resource allocation. This paper makes a quantitative evaluation of the efficiency of inter-industrial and inter-regional resource allocation of the energy industry in the Yangtze River economic belt from 2000 to 2019. The results show that: the average annual loss of total factor productivity of the energy industry in the Yangtze River economic belt is as high as 25.11% due to the misallocation of resources among the energy industries, capital misallocation, labor misallocation, and intermediate input misallocation were 12.32%, 7.08%, and 5.08%, respectively. The misallocation of resources among the energy industries of the provinces in upper, middle, and lower reaches is gradually increasing in turn. The average annual loss of total factor productivity (TFP) in the energy industry of the Yangtze River economic belt is 6.5% due to the misallocation of resources between regions. Capital misallocation, labor misallocation, and intermediate input misallocation were 2.48%, 3.40%, and 0.63%, respectively. In the upper, middle, and lower reaches of the Yangtze River, the labor force, capital, and intermediate input of each province’s energy industry were misallocated to a different extent. According to the trend of resource input in the energy industry of various provinces and cities, the middle and lower reaches of the Yangtze River also show different characteristics.

Digital Economy, Factor Allocation Efficiency of Dual-Economy and Urban-Rural Income Gap

“…According to the quantitative evaluation results obtained by the index system and calculation method of the digital economy development level in this paper, they are highly consistent with the current "China Digital Economy Development Index" released by CCID Think Tank and the "China Digital Economy Development Index (DEDI)" released by the China Academy of Information and Communications Technology, for example: in the "China Digital Economy Development Index" in 2019 released by CCID Think Tank, the top seven provinces and cities in the Digital Economy Development Index are completely consistent with the results of this article, and in the "China Digital Economy Development Index (DEDI)" in 2020 released by the China Academy of Information and Communications Technology, among the digital industrialization scale rankings, the top seven provinces and cities are also basically the same as this article, which shows that the index system and measurement methods of the inter-provincial digital economy development level constructed in this paper have strong rationality, which can objectively reflect the level and basic pattern of digital economy development in various provinces in China, which lays a good foundation for the follow-up research of this paper. This article refers to the model construction ideas of Brandt et al [35], Jin [36], and Guo and Zhang [37] and, on the basis of their basic theoretical framework, further drew on the model design of Dong et al [38]; the original assumption of constant return on the scale of the production function is further relaxed, that is the return on the scale of the department and the overall production function was no longer assumed. Suppose that the economy is composed of two major industrial sectors (i.e., the agricultural sector and the nonagricultural sector; the same below), and the output of the agricultural sector and that of the nonagricultural sector cooperate with each other to obtain the total output Y, that is Y is the CES production function of Y i (the annual output of industry i), which is expressed as:…”