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Reliable and objective data regarding building stock is essential for predicting and analyzing energy demand and carbon emission. However, China's building stock data is lacking. This study proposes a set of China building floor space estimation method (CBFSM) based on the improved building stock turnover model. Then it measures China's building stocks by vintage and type from 2000 to 2015, as well as building energy intensity (national level and provincial level) and energy-efficient buildings. Results showed that total building stocks increased significantly, rising from 35.2 billion m2 in 2000 to 63.6 billion m2 in 2015, with the average growth rate 4.0%. The deviations were well below 10% by comparing with China Population Census, which validated the reliability of CBFSM and the results. As for energy intensity, urban dwellings and rural dwellings showed relatively stable and increasing trend respectively. The commercial building energy intensity saw a downward trend during “12th Five Year Plan” period. This indicated the effectiveness of building energy efficiency work for commercial buildings since 2005.38.6 billion m2 residential dwellings and 5.7 billion m2 commercial buildings still need to be retrofitted in future. CBFSM can overcome shortages in previous studies. It can also provide Chinese government with technical support and data evidence to promote the building energy efficiency work.

This study uses the total-factor energy productivity change index (TFEPCH) to investigate the changes in energy productivity of construction industry for 30 provincial regions in China from 2006 to 2015, adopting the improved Luenberger productivity index combined with the directional distance function. In addition to traditional economic output indicator, this study introduces building floor space under construction as a physical output indicator for energy productivity evaluation. The TFEPCH was decomposed into energy technical efficiency change and energy technical progress shift. Results indicate that, first, energy productivity of China’s construction industry decreased by 7.1% annually during 2006–2015. Energy technical regress, rather than energy technical efficiency, contributed most to the overall decline in energy productivity of China’s construction industry. Second, energy productivity in the central region of China decreased dramatically, by a cumulative sum of approximately 77.1%, since 2006, while energy productivity in the eastern and western regions decreased by over 54.3 and 65.3%, respectively. Only two of the 30 provinces considered—Hebei and Shandong—improved their energy productivity during 2006–2015. The findings presented here provide a basis for decision-making and references for administrative departments to set differentiated energy efficiency goals and develop relevant measures. Additionally, the findings are highly significant for energy and resource allocation of Chinese construction industry in different regions.

China's energy consumption in the building sector (BEC) is not counted as a separate type of energy consumption, but divided and mixed in other sectors in China's statistical system. This led to the lack of historical data on China's BEC. Moreover, previous researches' shortages such as unsystematic research on BEC, various estimation methods with complex calculation process, and difficulties in data acquisition resulted in “heterogeneous” of current BEC in China. Aiming to these deficiencies, this study proposes a set of China building energy consumption calculation method (CBECM) by splitting out the building related energy consumption mixed in other sectors in the composition of China Statistical Yearbook-Energy Balance Sheet. Then, China's BEC from 2000 to 2014 are estimated using CBECM and compared with other studies. Results show that, from 2000 to 2014, China's BEC increased 1.7 times, rising from 301 to 814 million tons of standard coal consumed, with the BEC percentage of total energy consumption stayed relatively stable between 17.7% and 20.3%. By comparison, we find that our results are reliable and the CBECM has the following advantages over other methods: data source is authoritative, calculation process is concise, and it is easy to obtain time series data on BEC etc. The CBECM is particularly suitable for the provincial government to calculate the local BEC, even in the circumstance with statistical yearbook available only.

In accordance with China's commitments to reduce greenhouse gas emissions under the Paris Agreement, it is important to establish a fair and effective carbon emission allocation mechanism; this would help to achieve China's 2030 "carbon peak" target. Here, the fixed cost allocation model (FCAM) was used to allocate carbon emission quotas to public buildings in China in 2030, based on the principles of equity and efficiency. By comparing these provincial allocation results with those obtained using zero-sum data envelope analysis (ZSG-DEA), optimal carbon emission quotas were proposed for the public building sectors of thirty Chinese provinces. The results revealed large differences, with the public buildings of Guangdong having the highest quota (11.39%) and those of Xinjiang having the lowest (0.08%). Additionally, Heilongjiang, Inner Mongolia, and Liaoning were revealed to be under high pressure with regard to the reduction of emissions from their public buildings; Heilongjiang was predicted to require the highest reduction, exhibiting a need to reduce 34 million tons of emissions. These findings will help provide new ideas for relevant policy makers to formulate fair and effective carbon allowance allocation plans. • We propose a carbon emission allocation plan for public buildings in China in 2030. • Proposal of optimal carbon emission allowances using fixed cost allocation model. • Provinces with higher input-output efficiency, such as Guangdong, Jiangsu have higher allocations. • Proposes a low-carbon development path for China in 2030 by region. [ABSTRACT FROM AUTHOR]

Soaring building heating energy consumption (BHEC), along with rising income, is threatening carbon abatement efforts. However, the influencing mechanism of income on BHEC remains unclear. This study establishes a theoretical model of the impact of income on BHEC from three perspectives: building physical characteristics, building use characteristics and energy-consuming equipment characteristics. Then, we adopt a stepwise hierarchical regression method to examine the direct and intermediary effects between income and BHEC using microdata from 662 households in Chongqing, China. The results show that household income, residential building floor space, the amount of heating equipment, building energy efficiency standards, and the heating method significantly impact BHEC. Moreover, the direct effect of income on BHEC is up to 84%. The amount of heating equipment and residential building floor space are both strong intermediaries between income and BHEC, contributing 18.8% and 15.8%, respectively. The heating method is a weak intermediary, only contributing approximately 1% of the impact. Overall, this study can promote the development of the related theory of BHEC and provide a reference for the government to implement more effective energy-efficient policies on the local scale and even on the national and global scales. • A splitting model-based calculation method is developed to estimate heating energy consumption • A theoretical model is established to examine the impact of income on heating energy use • The impact of income on heating energy consumption is examined with micro-evidence • Income affects heating energy use through two paths of direct and indirect effects • The number of heating equipment and floor space are both strong mediating variables [ABSTRACT FROM AUTHOR]

Abstract 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. Graphical abstract Unlabelled Image Highlights • Proposed an urban residential building-related energy consumption (URBEC) and energy intensity estimation framework • Proposed a China building floor space estimation method (CBFSEM) • Estimated China's dwelling stocks, demolished and newly built residential BFS from 2000 to 2015 • Estimated urban residential building-related energy consumption and intensity of the building sector • Validated the results of study by comparison; the deviations are well below 8% [ABSTRACT FROM AUTHOR]

This study measures the "real" total-factor energy efficiency (TFEE) of the construction industry with data from 30 Chinese provinces (excluding Tibet) from 2006 to 2015 using the DEA model combined with the TFEE algorithm. The floor space under construction is considered to be an output indicator in addition to the traditional economic output under the TFEE framework. Next, the spatial distribution of the TFEE of construction industry among those provinces is detected and possible reasons are analyzed. Results indicate that, most of Chinese provincial regions are energy inefficient in construction industry during the research period. Conversely, the three provinces of Beijing, Hainan and Zhejiang have optimal efficiency, which constitutes the efficiency frontier of energy consumption throughout China. There exists divergence among 30 provinces and three regions as for TFEE, but the divergence decreases and manifests the convergence feature. Moreover, among the three main regions, central and eastern regions' TFEE of the construction industry are higher than the figure for the western region. By comparison, we found that the TFEE evaluation considering floor space under construction is more accurate and rational than without considering the floor space. On the basis of these findings, some policy implications are provided for improving Chinese energy efficiency, especially for less developed regions. [ABSTRACT FROM AUTHOR]

Carbon mitigation effectiveness in the building sector profoundly determines the achievement of the "Dual carbon" target. However, the timetable and roadmap for the building sector to achieve such a target are still lacking. This study constructs a STIRPAT-PLS model framework to clarify the impact of the key factors from various dimensions on building carbon emissions across sub-sectors. Then, a dynamic scenario simulation model is established and applied in China to explore the dynamic evolutionary paths, and the timetable and roadmap are ultimately formulated. The results show that building sector will probably peak at 2.99 (± 0.09) Gt CO 2 in 2036 (± 1). Rural residential buildings will peak earliest in 2026 (± 1), followed by urban residential and commercial buildings: in 2036 (± 2) and 2038 (± 1). Achieving carbon neutrality will require the building sector to peak its carbon emissions in 2026 with 2.16–2.40 Gt CO 2. The remaining 0.56–0.81 Gt CO 2 by 2060 will be offset by negative carbon technologies. Dynamic sensitivity analysis shows that building floor space per capita and energy intensity significantly promote the emission peaks, and population and building floor space per capita markedly impact the peaking times. This study can promote the development of the theory and model for carbon emissions prediction. It can also offer a scientific basis for governments to set effective carbon-reduction targets and paths. [Display omitted] • Constructing a STIRPAT-PLS model to clarify the impact of critical factors on building carbon emissions. • Establishing a dynamic scenario simulation model by combining the Monte Carlo simulation and scenario analysis. • Building sector will probably peak at 2.99 (± 0.09) Gt CO 2 in 2036 (± 1). • Achieving carbon neutrality requires the building sector to peak its carbon emissions in 2026 with 2.16–2.40 Gt CO 2. • Building floor space per capita and energy intensity significantly promote the emission peaks. [ABSTRACT FROM AUTHOR]

Urbanization has an important impact on building energy consumption. Unfortunately, whether the later-urbanized provincial regions are more energy-efficient than those early-urbanized regions in the building sector is still unclear. This study innovatively adopts the difference-in-differences (DID) model to examine the event effect of urbanization on building energy intensity with China's provincial evidence from 2000–2015. Moreover, its influence mechanism is identified with the mediating effect model. Results demonstrate that: (1) The building energy intensity is higher in provincial regions where the urbanization rate reached 45% earlier, indicating that the later-urbanized regions are more energy-efficient. (2) Considering heterogeneity, the event of urbanization rate reaching 45% contributes positively to building energy intensity in the eastern and central regions, but the effect in the western region is insignificant. This can further validate the later-mover advantage of the urbanization. (3) Regarding influence mechanism, urbanization can drive building energy intensity by increasing the proportion of the tertiary industry and population density or reducing the proportion of building coal consumption and the plot ratio of the urban area. This study can facilitate the related theoretical and practical exploration of the event effect of urbanization on energy consumption, which is conducive to formulating differentiated energy-conservation strategies. [Display omitted] • The DID model is applied to explore the event effect of urbanization on the building energy intensity. • The effect of urbanization on building energy intensity is explored considering regional heterogeneity. • The influence mechanism of urbanization on building energy consumption is investigated. • Later-urbanized regions are more energy-efficient in the building sector. • Urbanization can improve building energy intensity by potential mediating variables. [ABSTRACT FROM AUTHOR]

Carbon-reduction effect of the urban residential building sector is crucial to the carbon neutrality target. This study aims to explore the interaction mechanism among influencing factors and simulate the future evolutionary trajectories of urban residential building carbon emissions (URBCE). An integrated dynamic emission assessment (IDEA) model is innovatively established by coupling the system dynamics (SD) model and a bottom-up end-use decomposition model. Combining with the scenario analysis approach, this IDEA model is applied in China's urban residential building sector from 2000 to 2060. Results show that under the baseline scenario, the URBCE will fail to peak before 2030 but will peak at 1.19 Bt CO 2 in 2037. In comparison, it will peak at 0.79 Bt CO 2 in 2025 and decline to 0.17 Bt CO 2 in 2060 under carbon-neutral scenario, which can be neutralized by negative carbon technologies. Different end-uses and climate zones show marked discrepancies in emission-reduction potential, with the contribution of heating and appliances being over 50%. Sensitivity analyses shows that the low-carbon awareness, electrification rate, proportion of clean energy generation and technological progress contribute positively to the early peak of URBCE. This study provides a deeper understanding of China's potential peaking paths and can assist policy-makers in better evaluating emission paths for other nations and regions. [Display omitted] • Develops an IDEA model by combining system dynamics model and LEAP model. • Explores the interaction and feedback mechanism among different levels of factors. • Simulates the URBCE by combining the IDEA model and scenario analysis approach. • Urban residential buildings will peak at 1.19 Bt CO 2 in 2037 under the BAU scenario. • Different end-uses and climate zones show marked discrepancies in emission-reduction potential. [ABSTRACT FROM AUTHOR]