Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017
Kang Gao and
Applied Energy, 2022, vol. 307, issue C, No S0306261921015117
City-level accounting of industrial green productivity (IGP) is necessary to identify the different circumstances of industrial green transformation and development across Chinese cities. However, the lack of energy consumption data in the urban industrial sector leads to the assessment of China’s IGP at the macro provincial level, which is not conducive to further formulating systematic measures to improve IGP. Hence, based on the multi-source nighttime light (NTL) data fitted and corrected by DSMP/OLS and NPP/VIIRS images, this study simulates the total energy consumption of urban industrial sector and establishes an IGP analysis database combined with socio-economic data. Subsequently, the spatiotemporal pattern and drivers of IGP are systematically investigated by means of the Dagum Gini coefficient, spatial autocorrelation model, Markov transition probability matrix, and spatial econometric model. Four important findings arise from the analyses. Stylized facts show that an overall increase in IGP is accompanied by the exacerbation in spatial inequality, the year 2006 is a turning point from frequent fluctuations to steady growth. Specifically, interregional disparities have gradually become the main source of overall disparities in IGP across eight comprehensive economic zones, hypervariable density is the main source of overall disparities across three major urban agglomerations. Spatially, Global Moran’s I of IGP uninterruptedly increases from 0.043 in 2008 to 0.155 in 2017, showing a positive spatial correlation of high-high agglomeration and low-low agglomeration. Additionally, IGP has a strong spatial locking effect with the unchanged probability of 78.81%, 72.18%, 75.94%, 83.67%, and there is the coexistence of win–win and beggar-thy-neighbor phenomenon to a large extent. Results further reveal that IGP has a positively spatial spillover effect, economic development level, human capital, industrial agglomeration, and environmental regulations all work for driving IGP growth in local and neighboring areas. Based on the findings, policy recommendations for the improvement of IGP are provided.
Keywords: Industrial green productivity; Energy consumption simulation; Spatiotemporal pattern; Spatial drivers; Nighttime light data (search for similar items in EconPapers)
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