Analysis of Influencing Factors and Trend Forecast of CO2 Emission in Chengdu-Chongqing Urban Agglomeration

Abstract

Urban agglomeration is a primary source of global energy consumption and CO2 emissions. It is employed as a major means of modern economic and social activities. Analysis of the temporal and spatial characteristics of CO2 emissions in urban agglomerations and prediction of the future trends of CO2 emissions in urban agglomerations will help in the implementation of CO2 reduction policies within region-wide areas. So, based on that, this study contains four aspects. Firstly, it calculates the energy CO2 emissions of China’s Chengdu-Chongqing urban agglomeration. Secondly, it analyzes the time and space changes in the area by using ArcGIS. Then, the STIRPAT model is used to investigate the factors influencing CO2 emissions, and the elasticity coefficient of the influencing factors is estimated using the ridge regression method, and the important influencing factors are screened on the basis of the estimated results, which are then used as input features for prediction. Finally, a combined prediction model based on the improved GM (1, N) and SVR models is constructed, and then the optimal solution is found through the particle swarm optimization algorithm. It sets up different CO2 emission scenarios to predict the energy CO2 emission of the region and its cities. The results show that, first, the CO2 emissions of the Chengdu-Chongqing urban agglomeration have accumulated year by year, but by 2030, as predicted, it will not reach its peak. The spatial layout of CO2 emissions in this region is not expected to undergo major changes by 2030. Second, population, GDP, gas and electricity consumption, and industrial structure have served as important factors affecting energy CO2 emissions in the region. Third, on the basis of the prediction results for different scenarios, the CO2 emissions in the baseline scenario are low in the short term, but the CO2 emissions in the low-carbon scenario are low in the long run. This study also puts forward some policy recommendations on how to reduce CO2 emissions.

Decoupling analysis and peak prediction of carbon emission in less developed provinces: A case study of Sichuan province, China

“…We select social development and energy emission reduction variables as factors affecting carbon emissions. The main factors affecting carbon emission in the Sichuan province are population, 43 industrial structures, and energy consumption structure 44 . Please refer to Table 1 for the description of the variables.…”

“…The main factors affecting carbon emission in the Sichuan province are population, 43 industrial structures, and energy consumption structure. 44 Please refer to Table 1 for the description of the variables. The values of the variables are shown in Appendix Table S5.…”

Unveiling the spatial-temporal variation of urban land use efficiency of Yangtze River Economic Belt in China under carbon emission constraints

“…(3) Undesirable output indicators: carbon dioxide is a direct manifestation of the negative externalities of urban economic activities on the environment , so this paper measures the total land use carbon emissions of each city by adding up the carbon emissions generated from electricity, gas and LPG, IPCC2006 emission coefficient is used to calculate the CO 2 emissions of direct energy such as natural gas and liquefied petroleum gas. For the CO 2 consumed by electricity, due to its complexity, referring to the calculation method of power CO 2 emissions of (Wu and Guo, 2016) and (Zeng et al, 2022), that is, the power grid of each region uses the emission coefficient of the corresponding region, and the CO 2 emissions generated by electricity use in the YREB can be calculated according to the baseline emission coefficient of the power grid of the region over the years and the electricity consumption of each region. The calculation formula of CO 2 emissions is shown in the follow equation:…”

Spatial Driven Effects of Multi-Dimensional Urbanization on Carbon Emissions: A Case Study in Chengdu-Chongqing Urban Agglomeration

“…However, research on carbon emissions in the CUA has been scarce. To the best of our knowledge, only Zeng et al (2022) analyzed the spatio-temporal evolution of carbon emissions in the CUA at the city level and explored the impact of energy intensity, economic development level, and population size [23].…”