Spatiotemporal Characteristics and Factors Driving Exploration of Industrial Carbon-Emission Intensity: A Case Study of Guangdong Province, China

Research on spatiotemporal characteristics and influencing factors of industrial carbon emissions intensity is crucial to the efforts reducing emissions. This paper measures (CI) by energy consumption in Guangdong from 2012 2020 evaluates regional differences CI. In addition, we apply extended STIRPAT (stochastic impacts regression population, affluence technology) GTWR (geographically temporally weighted regression) models reveal influence driving CI spatial–temporal perspectives, based economic panel data night-time light (NTL) 21 cities Guangdong. To show robustness results, introduce ordinary least squares (OLS) model, geographically (GWR) model (TWR) compared with find that outperforms these models. The results are as follows: (1) shows an overall downward trend presents a pattern being low middle high both sides space. (2) emission mainly affected six main factors: development level, population scale, intensity, urbanization structure structure. Among them, occupies significant position poses positive impact sector.