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The building sector, which accounts for over 20% of China's total energy-related carbon emissions, has great potential to reduce emissions and is critical to achieving China's emissions peak and carbon neutrality targets. However, the lack of data on operational carbon emissions and end-use carbon emissions in the building sector at the city level has become a major barrier to the development of building energy conservation policies and carbon peaking action plans. This study uses a combination of "top-down" and "bottom-up" methods to account for the operational carbon emissions of buildings in 321 cities in China from 2015 to 2020. The energy consumption in buildings is further broken down into six end uses: central heating, distributed heating, cooking and water heating (C&W), lighting, cooling, appliances and others (A&O). The dataset can serve as a reference to support city-level policies on peak building emissions and is of great value for the improvement of the carbon emissions statistical accounting system. [ABSTRACT FROM AUTHOR]

To explore the influence of tertiary industry development on carbon emissions from public buildings, we analyzed the spatial and temporal evolution of carbon emissions from public buildings in China during 2010–2020 using standard deviation ellipse and spatial autocorrelation analyses. The spatial spillover effects of carbon emissions from public buildings under the tertiary industry in each region were further examined using an empirical model. We found that (1) carbon emissions from public buildings in China were concentrated in Henan, with a northeast-southwest distribution. The overall spatial agglomeration effect of carbon emissions from public buildings showed an inverted U-shaped pattern of development, with "low-low aggregation" as the primary agglomeration mode in the local effect; (2) the structural transformation of the tertiary industry had a significant spatial effect on carbon emissions from public buildings, and the structural transformation of the tertiary industry in this region and neighboring regions may limit the increase in carbon emissions from public buildings in this region; (3) carbon emissions and the intensity of carbon emissions from public buildings decreased with the structural transformation of the tertiary industry. The mediating effect of the structural transformation of the tertiary industry on carbon emissions from public buildings was significant. These findings reveal the correlation between China's tertiary industry development and carbon emissions from public buildings, providing a foundation for the implementation of approaches to reduce carbon emissions from public buildings. • Standard deviation ellipses and spatial Durbin model were used. • We explored the mechanisms influencing carbon emissions from public buildings by the tertiary industry. • Carbon emissions from public buildings were centered in Henan, with significant spatial autocorrelation. • Industrial transformation in the region and in neighboring regions could curb carbon emissions in the region. • Industrial transformation could reduce the carbon emission by increasing the added value of the tertiary industry. [ABSTRACT FROM AUTHOR]

As the pillar industry of the national economy, the construction industry contributes to economic growth while consuming large amounts of energy and generating carbon emissions. This study adopted the generalized Divisia index method (GDIM) to decompose carbon emissions in the construction industry (CECI) of China and its 30 provinces. A decoupling effort index based on factor decomposition was constructed to analyze the drivers of changes in the CECI and decoupling states during 2000–2020. The results were as follows. (1) The output scale was the main driver for increasing CECI, whereas the carbon intensities of area and output were the largest contributors to decreasing CECI. (2) The national construction industry showed expansive negative decoupling during the 10th–13th FYPs (Five-Year Plans). The decoupling states of provinces showed significant spatial and temporal differences, with most provinces showing negative decoupling and the number of decoupled provinces peaking in the 12th FYP. (3) The scale effect was the key inhibiting factor in the decoupling process of most provinces, while the intensity effect was the main promoting factor, and the efficiency effect was weak. The inhibitory effect of scale gradually weakened, while the promoting effect of intensity increased. Overall, this study expands on current research related to the driving factors of CECI and provides valuable insights into the low-carbon development of the construction industry in other emerging countries. • A GDIM-based decoupling analysis was applied to the construction industry. • The output scale and area scale were the key factors that increased carbon emissions. • Most provincial construction industries showed negative decoupling. • Efforts to promote decoupling should focus more on intensity and efficiency effects. [ABSTRACT FROM AUTHOR]

Revealing the long-term development trajectory and peak scenarios of the urban residential building sector is crucial for formulating targeted emission reduction strategies and contributing to achieving the "Dual carbon" target. Given that, significant variables affecting carbon emissions from urban residential buildings (URBCE) at both macro and end-use building levels are identified in this study. Furthermore, an innovative dynamic simulation model is developed by integrating the Monte Carlo simulation model with the Kaya identity model, taking into account the uncertainties of the variables. Subsequently, the long-term evolution trajectories, potential emission peaks, and peak time of the URBCE close to 2060 in China's hot summer and cold winter (HSCW) zone are simulated. Results indicate that URBCE is expected to reach its peak of 272.43 Mt CO 2 in 2040 under the baseline scenario. Through the dynamic simulation, the probable peak time of energy consumption in the HSCW zone is 2044 (±1.85), with a peak of 145.42 (±6.91) Mtce, and the possible peak time of carbon emissions is probably 2039 (±1.60), with a peak of 271.93 (±13.40) Mt CO 2. According to dynamic sensitivity analysis, carbon emission factor, per capita residential space, and heating energy intensity can promote the peak size and timing. These findings lay a robust foundation for the formulation of regionally specific carbon reduction targets and initiatives. Dynamic simulation results of peaking time and peak value of URBEC and URBCE. [Display omitted] [ABSTRACT FROM AUTHOR]