What drives the change in China's provincial industrial carbon unlocking efficiency? Evidence from a geographically and temporally weighted regression model.

In this paper, we propose the concept of carbon unlocking efficiency based on carbon lock-in. Supported by the "Techno-Institutional Complex" theory, we measure the industrial carbon unlocking efficiency (ICUE) of 30 Chinese provinces and analyze its spatial and temporal jump probabilities through spatial Markov chains, and finally identify and discuss the influencing factors through the GTWR model. We found that the ICUE of each province in China follows a decreasing distribution from east to central to west, with Shanghai, Beijing, and Guangdong having the highest ICUEs among all provinces and cities; although the overall ICUE converges to a higher level in the long run, there is still a certain predatory effect of developed regions on less developed regions in the short term, and the intensification of market competition may adversely affect the growth of ICUE in the lagging regions.

Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China.

Under the influence of complex urbanization, improving the carbon emission efficiency (CEE) plays an important role in the construction of low-carbon cities in China. Based on the panel data of 283 prefectural-level cities in China from 2005 to 2017, this study evaluated the CEE by the US-SBM model, and explored the spatial agglomeration evolution characteristics of CEE from static and dynamic perspectives by integrating ESDA and Spatial Markov Chains. Then, the spatial heterogeneity of the impacts of multi-dimensional urbanization on CEE were analyzed by using the Geographically and Temporally Weighted Regression (GTWR).