Study of the spatial divergence features and motivating factors of energy green consumption levels in "2+26" cities

Abstract

The level of energy green consumption is an essential factor influencing China's economy's high-quality development. In this paper, we select relevant panel data from 2010 to 2019 for 28 cities in Beijing-Tianjin-Hebei and surrounding areas, and use the DPSIR model and spatial econometric model to analyze the spatial divergence characteristics and driving factors of the energy green consumption index based on the construction of the energy green consumption level evaluation index system and index measurement. The results of this research are as follows: (1) The energy green consumption index has a distribution pattern of "high in the east and low in the west, high in the north, and low in the south," and the driving force (D) and response force (I) are the key factors influencing the spatial variation of the energy green consumption index. (2) The measurement results show that GDP per capita and built-up green coverage have a considerable positive effect on the energy green consumption index, whereas non-domestic energy structure has a significant inhibitory effect. (3) In terms of spatial spillover effects, greening coverage of built-up areas, industrial structure, and domestic energy structure, all have positive effects on neighboring regions' energy green consumption indexes, with positive spatial spillover effects, whereas GDP per capita, urbanization level, and non-domestic energy structure, all have negative spatial spillover effects. In light of the issues at hand, this paper suggests making good efforts to create and put into practice laws and policy norms, cultivate green energy-saving markets, vigorously promote the use and promotion of energy-saving technologies, and lower energy consumption in secondary industry and domestic consumption. The study's findings can help advance the modernization of the region's industrial structure and the growth of a green economy by providing the local government with a reference point for timely policy adjustments.

Keywords: DPSIR model; Energy green consumption index; Influence factors; Promotion path; Spatial Durbin model; Spatial dispersion.