4.6 Article

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

Journal

SUSTAINABILITY
Volume 14, Issue 22, Pages -

Publisher

MDPI
DOI: 10.3390/su142215064

Keywords

industrial carbon emission intensity; NTL; driving factors; STIRPAT; GTWR; Guangdong

Funding

  1. Science and Technology Planning Project of Xiamen City [3502Z20191021]
  2. Science and Technology Planning Project of Fujian Province, China [2022H0044]
  3. Strategic Priority Research Program of the Chinese Academy of Sciences [XDA23030203]

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

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