Investigating the spatio-temporal influences of urbanization and other socioeconomic factors on city-level industrial NOx emissions: A case study in China.

Cities play an essential role in industrial nitrogen oxides (NO x) control actions. Few studies have considered the spatiotemporal heterogeneity of the key influence factors that affect industrial NO x emissions at the city level. This work evaluates the impacts of urbanization level on industrial NO x emissions during 2017–2019 at the prefecture-city in China. We construct an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework based on the Geographically Weighted Regression (GWR), and Geographically and Temporally Weighted Regression (GTWR) model. The urbanization level was measured based on nighttime light (NTL) data. The results suggest that, first, the GTWR model has a better goodness of fit. Urbanization has a significant spatiotemporal heterogeneous effect on industrial NO x emissions. The median coefficient estimations of urbanization are −0.34 (95%CI: −1.38, 1.47), demonstrating a positive correlation in eastern China, yet negative in the western region. Other socioeconomic factors such as industrial electricity consumption, the share of secondary industry, population density, and research and development (R&D) expenditures have a significantly positive influence on industrial NO x emissions, while gross domestic production (GDP) has a negative impact. Our findings reveal that, due to the variances in urbanization levels and socio-economic factors, NO x control policies and regional emission reduction strategies in China implemented in a tailor-made form (such as "One-City-One-Policy") will be more effective. • NO x emissions of the industrial sector at the city level are investigated. • GTWR model examines the spatiotemporal heterogeneity effects of urbanization. • Nighttime light data acts as a proxy of urbanization in extended STIRPAT model. • Impacts of urbanization on NO x emissions differ from east to west China. • Other socioeconomic factors studied have a positive influence except for GDP. [ABSTRACT FROM AUTHOR]