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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 the construction of urban infrastructure projects, the ecology, economy, and society of surrounding areas will suffer from negative influences, which will eventually be transformed into economic loss, thus social costs are formed. The current bid evaluation mechanism has not yet taken social costs into account, while the present bid evaluation model for construction projects has ignored experts' group character and fuzziness. Based on the review of literature, this paper attempts to apply the intuitionistic fuzzy group decision model to urban infrastructure projects considering social costs, and to establish the bid evaluation indicator system for urban infrastructure projects considering social costs. This paper introduces the intuitionistic fuzzy entropy, the relative entropy, the intuitionistic fuzzy cross entropy, the intuitionistic fuzzy hybrid averaging operator, and the intuitionistic fuzzy weighted averaging operator to solve respectively attribute (indicator) weights, expert weights, and the information aggregation existing in the bid evaluation of projects, as well as the optimization of the bid evaluation model of construction projects. Finally, the validity and scientific character of this model is investigated through a numerical example. Key words: social costs, urban infrastructure, bid evaluation mechanism, entropy, relative entropy, cross entropy, IFHA and IFWA operators. Lors de la construction des projets d'infrastructure urbaine, les aspects ecologie, economie et societe des zones environnantes souffrent des influences negatives, qui seront eventuellement transformees en perte economique, generant ainsi des couts sociaux. Le mecanisme actuel d'evaluation des soumissions ne tient pas encore compte des couts sociaux alors que le modele d'evaluation des soumissions pour les projets de construction a ignore le caractere du groupe d'experts et l'indice de flou. En se basant sur la revue de litterature, cet article tente d'appliquer le modele de decision intuitionniste de groupe flou aux projets d'infrastructure urbaine, en tenant compte des couts sociaux, et d'etablir un systeme des indicateurs d'evaluation des soumissions pour les projets d'infrastructure urbaine tenant compte des couts sociaux. Cet article introduit l'entropie intuitionniste floue, l'entropie relative, la contre-entropie intuitionniste floue, l'operateur d'etablissement de moyenne hybride intuitionniste floue et l'operateur d'etablissement de moyenne ponderee intuitionniste floue afin de determiner les poids des attributs (indicateurs), les poids des experts et la synthese de l'information existante dans l'evaluation des soumissions des projets, ainsi que l'optimisation du modele d'evaluation des soumissions des projets de construction. Finalement, un exemple numerique examine la validite et l'aspect scientifique de ce modele. [Traduit par la Redaction] Mots-cles : couts sociaux, infrastructure urbaine, mecanisme d'evaluation des soumissions, entropie, entropie relative, contreentropie, operateurs IFHA et IFWA., Introduction Throughout the completion of construction projects, the ecology, economy and society of surrounding areas may suffer from negative influences, which will eventually transform into economic loss, thus social costs [...]

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]

• 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]