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It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation. [ABSTRACT FROM AUTHOR]

In order to realize low-carbon and high-quality development, this study took the carbon emissions of each district and county in the Chengdu–Chongqing region from 2005 to 2017 as the research object and used the spatial autocorrelation model to analyze the spatial and temporal evolution characteristics of carbon emissions in the counties of the Chengdu–Chongqing region, so as to fill in the research blank of carbon emissions in the counties of the Chengdu–Chongqing region. Then, the geographical detector model is used to explore the interaction among influencing factors of carbon emissions and reveal the time changes and regional differences of influencing factors, so as to improve the lack of spatial and temporal heterogeneity of influencing factors of carbon emissions by geographical detector. The results show the following: (i) The overall carbon emissions of counties show a year-on-year growth trend with the main urban areas of Chengdu and Chongqing as the core, but the growth rate slows down after 2010. (ii) The carbon emissions showed a significant positive spatial autocorrelation, and the neighboring counties showed a spatial clustering characteristic of "high-high" or "low-low", and the clustering status tended to be enhanced. (iii) The carbon emissions are strongly influenced by industrial structure, economic development, investment level, financial situation, urbanization rate and social consumption, and their interactions are all enhanced, but the influence mostly tends to rise first and then fall. (iv) County carbon emissions can be divided into four types of geographical types, such as population size influencing type, urbanization rate influencing type, economic development influencing type and industrial structure influencing type. Therefore, a variety of factors need to be considered comprehensively, a multi-pronged approach, and a comprehensive policy to realize low-carbon transformation in the Chengdu–Chongqing region. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]