Abstract
The role of urban residential buildings (URBs) in the carbon reduction goal of China is becoming increasingly important because of the rising energy consumption and carbon emission of such buildings in the region. Considering the increasing spatial interaction of the carbon emission of URBs (URBCE) in the region, this study investigates the influence of climate and economic factors on the URBCE in North and South China. First, the URBCE is calculated by using a decomposition energy balance table based on the carbon emission coefficient of electric and thermal power, thereby improving the estimation of the basic data of URBCE. Second, the influence of economic and climatic factors on the URBCE intensity in 30 provinces of China is explored by using a spatial econometric model. Results show that the URBCE intensity in China had a spatial autocorrelation from 2000 to 2016. Climatic and economic factors have great differences in the degree and direction of influencing the URBCE intensity in the country. Formulating emission reduction policies for climate or economic zones is more scientific and effective than developing national policies. Among these factors, urbanization rate, climate, and GDP per capita have a significant positive impact on the URBCE intensity in the region, whereas other factors have varying degrees of negative impact. In addition, climate, consumption level, and building area have significant spatial spillover effects on URBCE intensity, whereas other factors do not pass the significance test. Relevant conclusions should be given special attention by policymakers.
Highlights
An International Energy Agency survey (IEA, 2019) found that the residential building sector is responsible for more than 20% of the final energy consumption worldwide
The present study fully considers all the aforementioned carbon emission factors and constructs a carbon emission calculation model for urban residential buildings (URBs) in the northern and southern regions
Ij i i=1 j=1 i=1 where n is the total number of regions (n = 30); yi, y j,y are the carbon emission intensities of regions i and j, respectively; y is the average carbon intensity (LCI) of the residential buildings in all regions; and Wij is the spatial weight matrix, which reflects the proximity of spatial elements
Summary
An International Energy Agency survey (IEA, 2019) found that the residential building sector is responsible for more than 20% of the final energy consumption worldwide. With the continuous advancement of the urbanization (UR) process in China [5], urban population and urban residential building (URB) areas are growing, and the energy demand and URBCE will be increased further [6,7] This sector will contribute to over 50% of energy savings needed to reach the goal to peak carbon emissions ahead of 2030 [8].
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