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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]

• A system dynamics model involving multiple agents in green housing market is constructed. • The dynamic and complex effects of multi-level factors on green housing purchase decisions are examined. • Policy interventions and supply-side factors play different roles in promoting public acceptance of green housing. • The study contributes to systematic thinking and promotes the cooperation of multiple stakeholders. Green housing (GH) is recognized as the key approach to address environmental issues and enhance human life quality. As a result of limited market demand, the promotion of GH faces challenges in China. Numerous research identified the driving factors that affect the GH purchase decisions, yet the dynamic interactions of multi-level factors has not been explored in depth. This research proposed a system dynamics model to explore the interrelationships between GH providers, residents, governments, social environment and GH product attributes, and investigate the dynamic impact mechanism of residents' GH purchase decisions. The results indicate that residents' GH purchase decisions are influenced intricately by a range of factors at multiple levels which interact dynamically. Specifically, publicity and guidance can effectively enhance residents' perceived benefits, administrative regulations can effectively reduce residents' perceived risks, while economic incentives have insignificant impact on residents' perception. Moreover, the cooperation among stakeholders involved in the provisions of GHs is the prerequisites for achieving green performance in whole life cycle. It is noted that combining policy interventions and improvement of GH provision will be more effective than offering either of those alone. This study provides a systematic and dynamic new perspective to deeply explain the dependent structure of multi-level factors affecting residents' GH purchase decisions. The findings have enlightening implications for policymakers and business decision-makers to jointly promote GH development. [ABSTRACT FROM AUTHOR]

Equitable and effective allocation of provincial carbon allowances is the key to achieving carbon peaking and carbon neutrality targets and addressing the prominent issue of resource and environmental constraints, while reasonable coupling of equity and efficiency is its prerequisite. The study first assessed the effectiveness of the mainstream allocation methods when applied to the building sector. Then, Monte Carlo simulation is used to properly couple the principles of equity and efficiency in the process of carbon allowance allocation in the building sector, and on this basis, a novel Allocation method of Regional Carbon Allowance in Building sector (ARCAB) is constructed to provide an inter-provincial allocation scheme of building carbon emission allowances in 2030 for China. The results show that the mainstream allocation methods are not applicable to the building sector, and their allocation results often appear the phenomenon of "low-hanging fruit" or "high-hanging fruit". In addition, the coupling degree of equity-efficiency is 12% in China's 2030 allocation scheme, indicating that provincial governments should formulate more stringent carbon reduction policies to achieve this goal. The proposed ARCAB and our findings can assist decision-makers to formulate reasonable carbon allowances and differentiated emission-reduction strategies and will be of national and global significance. • Propose the criteria to judge the effectiveness of building carbon allowances. • Mainstream carbon allowance allocation methods do not apply to the building sector. • Propose an Allocation method of Regional Carbon Allowance in Building sector (ARCAB). • Focus on carbon reduction in commercial buildings in China. [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]

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. [ABSTRACT FROM AUTHOR]

Cities play an essential role in industrial nitrogen oxides (NO x) control actions. Few studies have considered the spatiotemporal heterogeneity of the key influence factors that affect industrial NO x emissions at the city level. This work evaluates the impacts of urbanization level on industrial NO x emissions during 2017–2019 at the prefecture-city in China. We construct an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework based on the Geographically Weighted Regression (GWR), and Geographically and Temporally Weighted Regression (GTWR) model. The urbanization level was measured based on nighttime light (NTL) data. The results suggest that, first, the GTWR model has a better goodness of fit. Urbanization has a significant spatiotemporal heterogeneous effect on industrial NO x emissions. The median coefficient estimations of urbanization are −0.34 (95%CI: −1.38, 1.47), demonstrating a positive correlation in eastern China, yet negative in the western region. Other socioeconomic factors such as industrial electricity consumption, the share of secondary industry, population density, and research and development (R&D) expenditures have a significantly positive influence on industrial NO x emissions, while gross domestic production (GDP) has a negative impact. Our findings reveal that, due to the variances in urbanization levels and socio-economic factors, NO x control policies and regional emission reduction strategies in China implemented in a tailor-made form (such as "One-City-One-Policy") will be more effective. • NO x emissions of the industrial sector at the city level are investigated. • GTWR model examines the spatiotemporal heterogeneity effects of urbanization. • Nighttime light data acts as a proxy of urbanization in extended STIRPAT model. • Impacts of urbanization on NO x emissions differ from east to west China. • Other socioeconomic factors studied have a positive influence except for GDP. [ABSTRACT FROM AUTHOR]

China's goal to peak CO 2 emissions by 2030—in accordance with the Paris agreement—is being jeopardized by the increasing carbon emissions from the building sector. Allocating CO 2 emission reduction (CER) responsibilities for the building sector to the provinces fairly and efficiently remains challenging. In this study, we first quantify the government leading targets regarding CER responsibilities, while clarifying the extent to which China considers the principles of equity and efficiency while allocating provincial CER responsibilities. Subsequently, based on the carbon emission efficiency, CER potential, and abatement cost of China's building sector, we establish a more equitable and effective model for sharing the CER responsibilities in the building sector at the provincial level. Using data from the building sector in 30 provinces in China between 2005 and 2019, we define and compare the CER responsibilities that the building sector in each province should bear with the CER responsibility-sharing scheme under multiple perspectives. Results show that the Chinese government has not fully considered the principles of equity and efficiency in setting the CER targets for each province in the past 15 years. Our model for sharing the CER responsibility in the local building sector appears more reasonable and adequate: The marginal abatement cost (i.e., economic loss of abatement) is about 7% less than those of other schemes; additionally, it guarantees the fairness of development power. Overall, this study furthers the CER responsibility-sharing theory and constitutes a reference for implementing more effective energy saving and CER policies. • China's reduction targets has not fully considered the principles of equity and efficiency. • Our model considers both the principles of equity and efficiency. • Explored fairness and efficiency of emission reduction under different principles. • The share of marginal abatement costs is kept below 10% in this model. • Model can be used for emission reduction policy making. [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]

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]

• Establishes a comprehensive framework to explore the multiple-effects exerted by urbanization on future building carbon emissions. • Constructs a carbon emission dynamic scenario simulation model by combining the extended Kaya identity and Monte Carlo simulation. • The building sector will reach its peak at 3.09 (±0.36) Bt CO 2 in 2037 (±4). • Rural residential building sector will be the first to reach its energy and emission peak. • Economic urbanization and space urbanization exert major promoting effects on the peaking time and emission peaks of building carbon emissions. The accelerating urbanization has posed great challenges to China's carbon-mitigation efforts and global climate change response. However, how does the urbanization process impact the peak of future carbon emissions from buildings is still unclear. This study first establishes a comprehensive urbanization framework from three dimensions. Then, a dynamic scenario simulation model is innovatively constructed by combining the extended Kaya identity and Monte Carlo simulation approach to explore future dynamic evolution trajectory, possible peaks and peaking time of China's building carbon emissions from 2000 to 2050, considering the uncertainties of factors. Finally, dynamic sensitivity analysis is performed to investigate the contributions of different dimensions of the urbanization. Results show that under the business-as-usual scenario, the rural residential building sector will be the first to reach its energy peak in 2027. Dynamic scenario simulation shows that the building sector will peak at 3.09 (±0.36) Bt CO 2 in 2037 (±4). Specifically, the urban residential building sector will peak at 1.27 (±0.18) Bt CO 2 in 2040 (±4), while the rural residential building sector will peak at 0.51 (±0.03) Bt CO 2 in 2021 (±4) and the commercial building sector will peak in 2038 (±4) at 1.41 (±0.30) Bt CO 2. Dynamic sensitivity analysis indicates that rural–urban migration will exert promoting effect on the peaking time and emission peaks of building carbon emissions through the concomitant effects of the urbanization process, i.e., the economic urbanization and space urbanization. Overall, this study can provide a better insight into the impact of urbanization on carbon emission peak and can support governments and decision-makers to formulate effective carbon-mitigation strategies. [ABSTRACT FROM AUTHOR]

Decoupling residential building carbon emissions from residential income has great significance for carbon mitigation and even global climate change. However, the nature of the decoupling relationship between them is still unclear. This study adopts the Tapio decoupling model to explore the decoupling relationship among residential building carbon emissions (TC), per capita carbon emissions (PC), residential carbon intensity (FC) and per capita income (PCI) across 30 Chinese provinces from 2000 to 2015. Then, the Logarithmic Mean Divisa Index (LMDI) model is used to decompose the decoupling of residential building carbon intensity and PCI at the provincial level to determine the main factors determining this relationship. Results show that the main decoupling trend between FC and PCI in 30 provinces transformed from weak decoupling state to strong decoupling state during the whole period, while there is still extensive room for TC and PC to decouple with PCI. PCI has the major inhibiting effect on the decoupling between FC and PCI , while residential energy intensity (EI) has the primarily promoting effect on the decoupling. These findings provide valuable references for international policymakers in formulating precisely targeted energy-saving and carbon-reducing policies to coordinate the relationship between income and carbon emissions, and the results also can be applied to other economies and, to a greater extent, other pollutants. Decomposition of the decoupling between residential building carbon intensity and income level in 30 Chinese provinces during different periods [(a) 2000–2005, (b) 2006–2010, (c) 2011–2015, and (d) 2000–2015]. Unlabelled Image • Explores the decoupling state between residential building CO 2 and residential income at the provincial level. • China has made great progress in decoupling residential building CO 2 and residential income. • Residential energy intensity plays the primary promoting effect on the decoupling status. • Per capita income plays the inhibiting effect on the decoupling state. • The understanding of influencing factors provides references for carbon abatement. [ABSTRACT FROM AUTHOR]

• Estimates China's provincial urban building carbon emissions from 2000 to 2015 with China Energy Statistical Yearbook—Energy Balance Sheet- based splitting method (CESY-EBSM). • Establishes a comprehensive framework to systematically explore the multiple effects exerted by urbanization on urban building carbon emissions. • Uses panel data model to explore urbanization effects from quantitative and structural dimensions. • The evaluation indicators are from three perspectives: population, economy and space. • Considers the impact of climate factors on urban building carbon emissions. Urbanization is a significant factor for soaring building carbon emissions. However, few studies have explored how the urbanization influences urban building carbon emissions. This study attempts to establish a comprehensive framework for urbanization and systematically explore the multiple effects exerted by urbanization on urban building carbon emissions from both quantity and structure dimensions based on the STIRPAT model, using the panel data of China's 30 provinces from 2000 to 2015. Urbanization is measured from three perspectives: population, economy and space. Results show that the urban population and urban building floor space contribute negatively to the carbon emissions in the urban building sector, while the added value of the tertiary industry positively influences urban building carbon emissions in the quantity dimension. In the structure dimension, the percentage of the urban population on the total population exerts a positive impact on the increase of urban building carbon emissions, but the percentage of the tertiary industry and the plot ratio of the urban buildings play a role in cutting down urban building carbon emissions. This study provides deeper insights for the concomitant effects of urbanization on carbon emissions and can be conducive to the realization of energy conservation and carbon emission targets. [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]

The industrial sector has significantly contributed to global climate change and urban air pollution while driving economic development. This paper takes the synergistic CO2 and SO2 emission reduction in China's industrial sector as an entry point quantifies the synergistic CO2 and SO2 emission reduction effect using the Kaya extension model and analyzes the synergistic emission reduction expansion mechanism in depth. It is found that the synergistic effect of CO2 and SO2 in the industrial sector is prevalent in most provinces in China. Every 10,000 tons of CO2 emission reduction leads to 1600 tons of SO2 emission reduction. Regional heterogeneity also clarifies that there are differences in synergistic emission reduction among different provinces, with most regions having a significant positive effect of synergistic emission reduction, while a small number of regions have an inverse effect, and direct SO2 reduction measures should be considered separately. The interaction effect analysis found that energy efficiency improvement in the industrial sector is the critical path to synergistic effect enhancement. At the same time, R&D expenditure and fixed asset investment have a weak effect on synergistic emission reduction. The interaction of the three factors can synergize the emission reduction effect by more than twice. The study's findings can be used as a reference for China's industrial sector to achieve the goal of reducing pollution and carbon emissions and for the practice of regional synergistic emission reduction, as well as for the formulation of international synergistic emission reduction policies. [ABSTRACT FROM AUTHOR]

Revealing the regional differences and drivers of provincial urban residential building sector carbon emissions (URBCE) is critical for regional collaborative emission reduction in achieving the "Dural carbon" targets. This study tries to innovatively construct a framework for the exploration of the spatiotemporal patterns and driving mechanism of Chinese URBCE in 30 provinces during 2000–2019, combining kernel density estimation, spatial autocorrelation analysis and temporal-spatial LMDI model. Results indicate that: (1) A vast majority of regions have undergone a sharp growth in URBCE, with the regional gap of URBCE gradually widening. (2) Besides, the spatial pattern of regional URBCE is basically stable, with High-High distributed in the Northeast and North China, and Low-Low concentrated in the Southwest. (3) Urban residential building floor space per capita, population, and urbanization are promotive driving forces, while carbon intensity and energy intensity are increasingly inhibiting. This study can provide policymakers a better understanding of URBCE and help them develop regionally-specific emission reduction strategies. Additionally, it offers fresh perspective on how to research the spatiotemporal disparities of carbon emissions in other sectors and nations, and could be of international significance. [Display omitted] • Establishes a framework to explore spatiotemporal patterns of provincial URBCE. • Investigates the spatial characteristics and dynamic evolution of regional URBCE. • Adopts temporal-spatial LMDI model to uncover the driving mechanism on URBCE. • A vast majority of regions have seen a sharp growth of URBCE during 2000–2019. • Floor space per capita, population, and urbanization are promotive drivers to the URBCE. [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]

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., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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 m 2 in 1996 to 61.3 billion m 2 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.