Spatiotemporal Dynamic Distribution, Regional Differences and Spatial Convergence Mechanisms of Carbon Emission Intensity: Evidence from the Urban Agglomerations in the Yellow River Basin

Abstract

Low-carbon transition is of great importance in promoting the high-quality and sustainable development of urban agglomerations in the Yellow River Basin (YRB). In this study, the spatial Markov chain and Dagum’s Gini coefficient are used to describe the distribution dynamics and regional differences in the carbon emission intensity (CEI) of urban agglomerations in the YRB from 2007 to 2017. Additionally, based on the spatial convergence model, this paper analyzed the impact of technological innovation, industrial structure optimization and upgrading, and the government’s attention to green development on the CEI’s convergence speed for different urban agglomerations. The research results show that: (1) The probability of adjacent type transfer, cross-stage transfer, and cross-space transfer of the CEI of urban agglomerations in the YRB is small, indicating that the overall spatiotemporal distribution type of CEI is relatively stable. (2) The CEI of urban agglomerations in the YRB has decreased significantly, but the spatial differences are still significant, with a trend of continuous increase, and regional differences mainly come from the differences between urban agglomerations. (3) Expanding innovation output, promoting the optimization and upgrading of industrial structure, and enhancing the government’s attention to green development has a significant positive effect on the convergence rate of the CEI of urban agglomerations in the YRB. This paper holds that implementing differentiated emission reduction measures and actively expanding regional collaborative mechanisms will play an important role in reducing the spatial differences in carbon emissions in urban agglomerations in the YRB, realizing the goals of peak carbon and carbon neutrality.

Regional differences and dynamic evolution of high-quality development in service industry: A case study of the Chengdu-Chongqing economic circle

“…Compared with the traditional methods mentioned above, Kernel density estimation, Dagum Gini coefficient and Markov chain can effectively deal with the problem of cross-overlap between samples [43]. It also reflects the dynamic evolution of the research object [44][45][46]. The reliability of this method has been demonstrated by numerous researchers in recent years [41,47,48].…”

Research on Spatial Effects of Regional Carbon Emission Intensity in China

“…Based on literature review and theoretical analysis, with reference to the studies of Zhang Cuiju et al [24,25,26], the research focuses on the impact of economic level, population density, industrial structure, energy structure, scientific research investment, urbanization level, and foreign investment intensity on carbon emission intensity. The following factors were selected for analysis, and the dependent variable was Y, the total carbon emission intensity, which is the ratio of carbon emissions to regional gross domestic product.…”

Spatio-temporal evolution of sports tourism industry integration: A empirical analysis of Xinjiang in China

“…In addition, kernel density estimation can visually display evolution process of data distribution through three-dimensional evolution graphics. Zhang et al (2022) used kernel density estimation to describe the dynamic evolution of carbon emission intensity in China’s main strategic regions [ 35 ]. To strengthen regional cooperation, we should quantitatively analyze the degree of difference in industrial integration quality in various tourism industrial belts.…”