Convergence analysis in carbon intensity is a critical tool to decide the CO2 emission reduction targets. Accurately estimating convergence behavior on a finer scale is generally more effective and practical to address spatial-temporal heterogeneity. However, little research has focused on convergence in carbon intensity across the prefecture-level cities in China. Here, we tested the convergence hypothesis in carbon intensity across 264 prefecture-level cities in China from 1992 to 2013 using convergence analysis, cross-section regressions, and dynamic spatial panel econometric techniques. We also compared different time periods and regions to explore the spatial-temporal heterogeneity of convergence behavior. Findings reveal converging CO2 intensities across cities and significant spatial effects in this convergence process. In addition, carbon intensity convergence rates for 1992â€“2013 show an overall decline over time. Furthermore, the analysis of spatial dynamic panel data shows significant conditional Î²-convergence after controlling for economic growth, population density, urbanization, and finance. We further provide significant evidence that population density, urbanization, and finance had significant negative effects on carbon intensity, while GDP per capita significantly facilitated carbon intensity. By controlling dynamic temporal effects, we find larger long-term, compared to short-term, effects of control variables on carbon intensity. Finally, carbon intensity convergence differs in regional heterogeneity, suggesting the necessity of designing different carbon intensity reduction polices.
Keywords Carbon intensity; Convergence; Emissions reduction policy; Spatial econometric model; China