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Abstract 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. Graphical abstract The building stock in China's different provinces and climate zones in 2015. Image Highlights • Proposed a China building floor space estimation method (CBFSM) based on improved building stock turnover model. • Estimated China's building stocks by type and vintage from 2000 to 2015 using CBFSM. • Calculated the building energy intensity at the national level and provincial level. • Estimated the size of the energy efficient buildings in China. • Validated the results of study by comparing with the data from China Population Census; the deviations are well below 10%. [ABSTRACT FROM AUTHOR]

Abstract With the surging urbanization process and rapid economic growth in the service industry, carbon emissions released by the commercial building sector have become critical for China's national action towards meeting the "Post Paris" mitigation target. This paper is the first to confirm whether carbon intensity in the commercial building sector decouples from the economic development in service industry via the decomposition and decoupling methods at both the scales of the nation and the top five urban agglomerations [Jing-Jin-Ji (JJJ), Yangtze-River-Delta (YRD), Pearl-River-Delta (PRD), Yangtze-River-Middle-Reach (YRMR), and Cheng-Yu (CY) urban agglomerations] in China during the 2000–2015 period. The key findings indicate the following. The nationwide decoupling status was weak from 2001 to 2005 and then strong from 2006 to 2015. At the scale of urban agglomeration, only four states were observed in the decoupling status, and the order of the decoupling statuses across the different urban agglomerations is as follows: JJJ > CY > YRD > PRD > YRMR (2001–2005), YRD > JJJ > PRD > CY > YRMR (2006–2010), and YRD > YRMR > JJJ > CY > PRD (2011–2015). The carbon Kuznets curves of the commercial building sector successfully reveal the causes of the different decoupling paths across different urban agglomerations. The growing decoupling effects can be attributed to the rapid development of energy conservation projects in the commercial building sector over the past decade. Overall, it is expected that our contribution covers the research gap of decoupling carbon intensity in the commercial building sector from economic growth in the service industry, and our efforts constitute a significant contribution to the analysis of the carbon emission peak in the Chinese commercial building sector. Graphical abstract Decoupling elasticities of carbon intensity in the China's commercial building sector at the scales of the nation and urban agglomerations from 2001 to 2015. Image 1000 Highlights • Presenting the decoupling analysis integrating decomposition and decoupling methods. • Decoupling analysis of commercial buildings and the service industry was performed. • The case areas were China's top five urban agglomerations during the 2000–2015 period. • The decoupling levels of the five urban agglomerations were ranked and analyzed. • A pathway to achieve more significant decoupling effects was discussed and proposed. [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]

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]

The rapid development of urbanization in China inevitably leads to a sharp increase in building energy consumption, and Passive House (PH) have received much attention in China in recent years. In this paper, the annual energy demand (AED) and summer discomfort hours (SUH) of PH are used as optimization objectives to establish a set of design solutions for multi-objective optimization (MTO) and economic analysis of a PH in Shandong Province, China. It is found that (1) the algorithm based on nearest neighbor component analysis (NCA) feature extraction applies to the sensitivity analysis of multivariate nonlinear high-dimensional data, and the NCA-GA-RBF neural network technique is proposed, which has significantly improved the goodness of fit and generalization ability for AED and SUH. (2) The NSGA-II genetic algorithm is used for the optimization search, and the results show that the overall improvement rates of AED and SUH are 25.5% and 21.6%, respectively, among the nine solutions after comprehensive evaluation; (3) Finally, the optimal scheme of energy saving and the optimal scheme of the economy are selected for economic evaluation. By comparing the economics of the two options and the improvement rates of AEDs and SUHs, the guidelines for design decision-makers are provided. In summary, the design scheme of this paper is forward-looking and reference-oriented and provides some reference suggestions for the development of PH in China. • The NCA algorithm is suitable for multivariate nonlinear sensitivity analysis. • NCA-GA-RBF has stronger and more stable generalization ability and accuracy. • In this paper, climate is the main factor affecting thermal parameters. • Global costs have a stronger economic interpretation. [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]

Under global warming and severe environmental problems, the Paris Agreement urges China to accelerate the pace of the low-carbon economy. Improving the energy efficiency of the construction sector may be an important way to reduce CO 2 emissions. As a reasonable energy policy instrument, a carbon tax can effectively improve energy efficiency and reduce CO 2 emissions. In this study, a dynamic computable general equilibrium model is developed to explore the possible impacts of different carbon tax conditions on the energy consumption of the construction sector and the macroeconomy of China. Results show that the appropriate carbon tax is 60 RMB/t, which cannot only achieve the emission reduction target but also minimize the negative impact on the macroeconomy. However, the proper level of the carbon tax varies by building stage. Under the proper carbon tax level, the peak of carbon emissions could be reached in 2025. The main purpose of this paper is to provide a theoretical basis for the government in formulating different energy policies. • A computable general equilibrium model is established to explore the proper carbon tax for the construction sector. • The appropriate carbon tax rate for the construction sector is approximately 60 RMB/t. • Total amount of carbon emissions from the construction sector will peak by 2025 at the appropriate carbon tax level. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

National building energy savings (NBES) plays an essential role in policymaking of China's building energy–efficiency (BEE) work. Numerous factors, such as technological progress and users' behavior, affect NBES while most of them are unquantifiable. One missing possibility along this direction is that there is currently no method to calculate China's NBES by summarizing all driving factors. To arrive at a solution, we proposed a concept of comparable building energy consumption per unit area and a method of China's NBES calculation based on an extended version of IPAT model (I = PAT, I = Human Impact, P = Population, A = Affluence, T = Technology) and LMDI decomposition (Logarithmic Mean Divisia Index, LMDI). Calculation revealed that China's NBES in “The 10th Five–Year Plan” period (2001–2005), “The 11th Five–Year Plan” period (2006–2010), and the first four years of “The 12th Five–Year Plan” period (2011–2014) was 165, 158, and 127 million tce, respectively. Based on these calculation results, we checked NBES data then successfully proved the validity of this calculation method. Furthermore, after comparing the calculated NBES with the official planned NBES, we found that China surpassed its BEE targets. In the third stage of China's BEE work (2006–2015), the implementation of BEE policies obtained good results. [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]