Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method.

Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions.

Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model.

Carbon mitigation in the urban residential building sector is critical for China to achieve its carbon peak and carbon neutral commitment. However, how urbanization affects urban residential building carbon emissions is still unclear. This study adopts the panel threshold regression model to explore the dynamic influence mechanism of the urbanization on urban residential building CO2 emissions based on the evidence from China's 30 provincial regions during 2000-2015. Results indicate that urbanization contributes positively to the increase of urban residential building CO2 emissions, while such degree of influence varies across different stages of income and energy structure. As for per capita income, the promoting effect of the urbanization on urban residential building CO2 emissions is enhanced with the growth of per capita income. And the degree of such increasing effect becomes greater when per capita income exceeds its threshold value. Regarding the energy mix, the driving effect of urbanization on urban residential building CO2 emissions is also strengthened when the energy mix crosses its threshold value, showing a "stepwise growth" feature. This study reveals the nonlinear influence mechanism of urbanization on urban building CO2 emissions, and this is helpful in boosting the related theoretical and practical exploration on the impact of urbanization on the environment. Based on our findings, an environmentally-friendly consumption pattern should be promoted and more penetration of cleaner energies should be improved in urban households, which will be effective to alleviate the increase of residential carbon emissions.

What Is the Amount of China's Building Floor Space from 1996 to 2014?

The amount of building floor space (BFS) plays a key role in the energy and material demand prediction. Unfortunately, BFS estimation has faced the challenge of ineffective and inadequate approaches, and thus reliable data concerning China's BFS is unavailable. This study proposes a new estimation method for China's BFS and then estimates historical BFS by type in China from 1996 to 2014. The results show that total Chinese BFS grew from 28.1 billion m2 in 1996 to 61.3 billion m2 in 2014, increasing more than twice, with an annual growth rate of 4.4% from 1996 to 2014. During 1996-2014, urban residential BFS witnessed the highest annual increase rate (9.3%), while the growth rate for commercial and rural residential BFS was lower: 4.4% and 1.6%, respectively. By comparing with available statistics data, this study finds the model deviations are well below 5%, which indicates the reliability of the proposed method and robustness of the results. The proposed method not only can address the deficiencies of statistic yearbook and overcome the shortages of previous estimation approaches but also can derive more accurate and reliable data. This study lays a sound basis for the following study on building stock and building energy efficiency work.

Dilution effect of the building area on energy intensity in urban residential buildings.

Urban residential buildings make large contributions to energy consumption. Energy consumption per square meter is most widely used to measure energy efficiency in urban residential buildings. This study aims to explore whether it is an appropriate indicator. An extended STIRPAT model was used based on the survey data from 867 households. Here we present that building area per household has a dilution effect on energy consumption per square meter. Neglecting this dilution effect leads to a significant overestimation of the effectiveness of building energy savings standards. Further analysis suggests that the peak of energy consumption per square meter in China's urban residential buildings occurred in 2012 when accounting for the dilution effect, which is 11 years later than it would have occurred without considering the dilution effect. Overall, overlooking the dilution effect may lead to misleading judgments of crucial energy-saving policy tools, as well as the ongoing trend of residential energy consumption in China.

Estimating urban residential building-related energy consumption and energy intensity in China based on improved building stock turnover model.

Accurate estimation of urban residential building-related energy consumption (URBEC) and energy intensity per unit floor area at the national level has significant implications for the analysis of carbon emission peaks. However, reliable data on China's building floor space (BFS) are lacking, resulting in unclear energy intensity levels. This study proposes a China BFS estimation method (CBFSEM) based on improved building stock turnover model. Using CBFSEM, it estimates the BFS of historic urban dwelling stock, the demolished and newly built dwelling from 2000 to 2015. It then estimates the corresponding energy consumption and intensity based on the obtained urban residential BFS data. Results showed that total URBEC in China increased dramatically from 217.1 Mtce in 2000 to 417.2 Mtce in 2015 with an average annual growth rate of 4.45%. China's total dwelling stock almost doubled, from 10.6 billion m2 in 2000 to 27.4 billion m2 in 2015 with an annual growth rate of 6.56%. The operational energy consumption accounted for approximately 70% of total URBEC and the building material production energy intensity was the highest in total URBEC, >60 kgce/m2. A comparison with the China Population Census showed that the deviations were well below 8%, which indicated the reliability of the CBFSEM and the estimated results. In general, this study fills the gap in available data and addresses the shortage of estimation methods for BFS and energy intensity. It also provides the government with technical support and scientific evidence to promote building energy efficiency.