What Are the Driving Forces of Urban CO2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels

Abstract

With the advancement of society and the economy, environmental problems have increasingly emerged, in particular, problems with urban CO2 emissions. Exploring the driving forces of urban CO2 emissions is necessary to gain a better understanding of the spatial patterns, processes, and mechanisms of environmental problems. Thus, the purpose of this study was to quantify the driving forces of urban CO2 emissions from 2000 to 2015 in China, including explicit consideration of a comparative analysis between national and urban agglomeration levels. Urban CO2 emissions with a 1-km spatial resolution were extracted for built-up areas based on the anthropogenic carbon dioxide (ODIAC) fossil fuel emission dataset. Six factors, namely precipitation, slope, temperature, population density, normalized difference vegetation index (NDVI), and gross domestic product (GDP), were selected to investigate the driving forces of urban CO2 emissions in China. Then, a probit model was applied to examine the effects of potential factors on urban CO2 emissions. The results revealed that the population, GDP, and NDVI were all positive driving forces, but that temperature and precipitation had negative effects on urban CO2 emissions at the national level. In the middle and south Liaoning urban agglomeration (MSL), the slope, population density, NDVI, and GDP were significant influencing factors. In the Pearl River Delta urban agglomeration (PRD), six factors had significant impacts on urban CO2 emissions, all of which were positive except for slope, which was a negative factor. Due to China’s hierarchical administrative levels, the model results suggest that regardless of which level is adopted, the impacts of the driving factors on urban CO2 emissions are quite different at the national compared to the urban agglomeration level. The degrees of influence of most factors at the national level were lower than those of factors at the urban agglomeration level. Based on an analysis of the forces driving urban CO2 emissions, we propose that it is necessary that the environment play a guiding role while regions formulate policies which are suitable for emission reductions according to their distinct characteristics.

Downscaling Building Energy Consumption Carbon Emissions by Machine Learning

“…son with the results of the PLS regression models, GST was shown to have a greater impact on BECCE intensity in region Ⅰ at 1 km 2 spatial resolution, which is consistent with the finding where there were differences on the importance of the driving factors on BECCE due to China's different spatial classification [58], further indicating that its effect in the microenvironment should not be ignored in relation to building energy conservation. able to extreme climate conditions in small spatial areas.…”

“…For estimation of fine-scale BECCE intensity, GDP and POP, representing the socioeconomic conditions and energy consumption membership respectively, had the greatest influence (Figure 8). In comparison with the results of the PLS regression models, GST was shown to have a greater impact on BECCE intensity in region I at 1 km 2 spatial resolution, which is consistent with the finding where there were differences on the importance of the driving factors on BECCE due to China's different spatial classification [58], further indicating that its effect in the microenvironment should not be ignored in relation to building energy conservation.…”

The relationship between Liaoning urban agglomeration based on Gravity Model

“…With the development of social economy, environmental problems are becoming more and more serious, especially urban carbon dioxide emissions. In order to quantify the driving force of carbon dioxide emissions in China's cities, it is necessary to consider the comparative analysis between the level of the state and the urban agglomeration( [8]). The research mentioned above lacks the analysis and definition of the current urban agglomeration of Liaoning Province.…”

What forces drive the rapid development of mariculture in China: Factor‐driven or total factor productivity‐driven?

“…The rapid growth of mariculture in China and its strong development momentum lead the researchers to explore the driving force of its growth. The term 'driving force' generally refers to the force that drives movement and development (Wang et al, 2019). In modern management, driving force is divided into material impetus, spiritual impetus and information impetus.…”

“…The term ‘driving force’ generally refers to the force that drives movement and development (Wang et al, 2019). In modern management, driving force is divided into material impetus, spiritual impetus and information impetus.…”