Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO 2 Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China.

The emission of greenhouse gases, especially CO₂, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO₂ emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO₂ emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO₂ emissions data. The results show that the energy CO₂ emissions in BTH present a distribution pattern of "East greater than West", with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO₂ emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO₂ emissions. Among them, the growth of population is the main reason for the increase of CO₂ at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities. [ABSTRACT FROM AUTHOR]