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The construction industry plays a pivotal role in China's achievement of its "dual carbon" goals. This study conducts a decomposition analysis of the carbon emissions from the construction industry (CECI) at both national and provincial levels for the period 2010–2020 and employs the ARIMA model to predict the short-term peak trends at the provincial level. The findings are as follows. (1) Inner Mongolia, Shandong, Sichuan, and Chongqing exhibit an N-shaped trend in CECI, while the northeast region shows an inverted U-shaped trend. (2) Labor productivity and energy intensity are identified as the largest and smallest drivers of national CECI growth, respectively, with the driving force of the study's identified factors fluctuating between 1% and 60%. (3) Energy intensity significantly contributes to the growth of CECI in Tianjin and Zhejiang, while it aids in reducing CECI in western provinces. The "rebound effect" of building energy efficiency is particularly pronounced in provinces with strong resource endowments, such as Ningxia. (4) Between 2021 and 2025, CECI is predicted to decrease in the northern and economically developed provinces, while it is expected to increase in central and western provinces, with Heilongjiang, Shandong, Guangdong, Shanghai, and Shaanxi potentially reaching their peaks within the forecast period. The paper concludes with several recommendations. [ABSTRACT FROM AUTHOR]

Interprovincial disparities in carbon emissions from the construction industry (CECI) are an important challenge for future emissions reductions. Based on the CECI data of 30 provinces in China from 2010 to 2020, the interprovincial equity and decoupling of CECI were examined in this study. The conclusions are as follows: (1): The main CECI growth regions were the eastern Bohai Rim region and the Pearl River Delta region. Heilongjiang basically achieved a CECI carbon peak in 2016. (2) The three northeastern provinces and western provinces were the main high–high and low–low aggregation areas, respectively. The greatest degree of inequality was found in the western provinces. The inequality between the eastern and western areas was the largest, while the inequality between the central and western areas was the smallest. The inequality in CECI in the north–south region decreased year by year. (3) The decoupling status of Shandong and some western provinces has significantly worsened, while the decoupling status of Sichuan, Yunnan, and some eastern developed provinces has been improving. These conclusions will contribute to the improvement of regional emission reduction strategies. [ABSTRACT FROM AUTHOR]

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]

Understanding the mechanisms that influence land-use carbon emissions (LCE) and accurately predicting their performance is crucial for county-level cities to assess their urban development schemes and formulate low-carbon strategies. However, previous studies have not thoroughly investigated the mechanisms and dynamic changes in LCE intensity (LCE per unit area) across various land-use types. In this study, we propose an approach based on a coupled STIRPAT-SD model to analyze the comprehensive mechanisms behind five land-use types and further forecast their LCE intensities and total carbon emissions. Subsequently, we apply this method to Changxing, a representative county in Zhejiang Province of China with high carbon intensity, using 15 years of historical data to systematically examine driving factors, future trends, and policy impacts on its LCE performance from 2005 to 2035. The results reveal substantial variations in values and dynamic characteristics of LCE intensities across the five land-use types. By simulating evolving trends under different scenarios, our findings demonstrate that the Low Carbon Development (LCD) scenario is most suitable for Changxing as it can potentially reduce carbon intensity per unit GDP by approximately 37% by 2035 compared to levels in 2020. This study proposes two applications of the coupled model method: firstly, assessing scheme-based carbon emissions at the plot level using LCE intensity values; secondly, formulating effective carbon reduction strategies at the county level through scenario simulation. The integrated modeling approach presented in this research comprehensively incorporates various influencing factors and includes dynamic changes applied to different land-use types, thereby aiding in the formulation of multi-scale carbon reduction strategies. • A quantitative analysis of carbon emission drivers for five land use types was conducted. • The coupled STIRPAT-SD model investigated complex mechanisms and dynamic changes of LCE. • Scheme-based carbon emissions can be assessed using predicted LCE intensity values. • Effective carbon reduction strategies can be formulated through scenario simulation. [ABSTRACT FROM AUTHOR]

City size expansion in China creates substantial economic circulation, which impacts CO2 emissions. Since CO2 production primarily comes from human activities, CO2 emissions are mainly in cities. To achieve China's carbon neutrality and provide specific implementation guidance for future carbon-reduction policies, it is worth assessing China's pressure on carbon reduction in the urban aspect. Highly developed social productivity and a market economy lead to a dramatic increase in the interconnection between cities, and the spatial distribution of CO2 emissions emerges in a spatial association. Therefore, it is of great significance to investigate the interaction of CO2 emissions with spatial effects. Taking the Yangtze River Delta urban agglomeration (YRDUA) as the research target area, this paper utilizes city-size indices to construct spatial-association networks of CO2 emissions for the first time. It employs social network analysis to explore the structures of whole networks, clusters, and city nodes. The main results show that: (1) the spatial associations of CO2 emissions in the YRDUA's cities have become tighter over time. (2) The networks of CO2 emissions in the YRDUA's cities have noticeable spatial-spillover effects, and the interaction of CO2 emissions between cities is dominant. (3) Nanjing is the paramount "bridge" node in the networks. (4) Nanjing, Hangzhou, Wuxi, Shanghai, Changzhou, Suzhou, Nantong, and Hefei will be the decisive cities for efficient CO2 emission control in the future. Overall, this paper reveals the role of carbon reduction in the YRDUA's cities and proposes suggestions for establishing a transboundary energy-saving mechanism to improve the efficiency of energy conservation and emission reduction. [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]

All human beings contribute to climate change; however, their contributions are not equal. Using China as an example, the core objective of this study is to explore the city-level carbon inequality of residential buildings from 2015 to 2020 combined Theil index and Gini coefficient. The outcomes indicate that: (1) Total residential buildings' carbon emissions in 321 Chinese cities increased from 987.81 million tons (Mt) in 2015 to 1161.70 Mt. in 2020; the leading five cities that own the largest carbon emissions are Beijing, Tianjin, Shanghai, Shenyang, and Harbin in 2020. (2) Among eight major economic zones of China, the highest residential buildings' carbon emission per capita and residents' disposable income per capita are the Northeast Economic Zone and Eastern Coastal Economic Zone, respectively. (3) Both the Theil index and Gini coefficient show that the overall carbon inequality is higher than income inequality; the Theil index decomposition indicates that city-level carbon inequality has declined; the key forces of carbon emission per capita and disposable income per capita inequality are inter-regional inequality (contribution rate: 55%–63%) and intra-regional inequality (contribution rate: 59%–63%), respectively. (4) Moreover, regarding income earners during 2015–2020 period, the top 10% of 321 cities occupy about 33% in the total national residential buildings' carbon emissions; the medium 40% of cities account for about 42% in total carbon emissions; in contrast, the bottom 50% of cities represent only about 25% of the total carbon emissions. These discoveries emphasize the necessity for the Chinese government to pay more attention to city-level residential building carbon emissions to track advances toward residents' sustainable lifestyles. • The city-level features of carbon emissions and resident incomes were analyzed. • Carbon and income inequality via Theil index and Gini coefficient were compared. • Carbon emission per capita inequality's core force via Theil index was identified. • Carbon inequality's disparities in economic zones and climate zones were discussed. [ABSTRACT FROM AUTHOR]

The speedy raise of residential buildings' carbon emissions is a hindrance to achieving China's 2030 carbon peak goal. This study constructs an assessment framework for comprehensive consideration of 30 Chinese provinces' socioeconomic circumstances, energy demand, and emissions reduction technology to meet the consistent coupling degree of equity and efficiency (CDEE). This study is the first to propose an allocation scheme for equilibrating provincial carbon increments for rural and urban residential buildings in 2030 under carbon peaking constraints. The relevant results are fourfold. (1) Residential building's floor area per capita and energy carbon emissions coefficients are the soliddest drivers to facilitate and inhibit the raise of carbon emissions during 2010–2020. (2) Through dynamic Monte Carlo simulation from 2021 to 2030, we demonstrate that provinces with the most gamey carbon emissions in urban and rural areas include Shandong, at 121.52 (± 5.50) Mt. and Hebei, at 61.34 (± 3.08) Mt. in 2030, respectively. (3) A CDEE of 52.3% (biased equity) in urban areas and 34.5% (biased efficiency) in rural areas indicates equilibrated allocation of provincial carbon increment. (4) In the final 2030 allocation scheme, the greatest carbon mitigation pressures are in Beijing (11.34 Mt) and Heilongjiang (3.23 Mt), and the provinces with the largest carbon increment in urban areas include Hebei, Henan, and Guangdong, while the largest carbon increments in rural areas are in Hebei, Henan, and Guangdong. Overall, this study furnishes a targeted and valuable decision making reference for the government to determine provincial carbon peak goals for Chinese residential buildings. Theoretical framework of this study and provincial-level allocation outcomes of residential buildings' carbon emissions in China. [Display omitted] • A carbon emission forecasting model combining static and dynamic scenarios was built. • The coupling degree of equity and efficiency at the provincial scale was measured. • An incremental allocation scheme for provincial carbon emissions was ascertained. • The causes leading the difference of the provincial carbon increments were discussed. [ABSTRACT FROM AUTHOR]

Assessing the historical peak situation at the provincial level is critical to better predict the future evolution of residential building carbon emissions. However, the provincial carbon peak status of this sector is still unclear. To this end, this study proposes a carbon peak situation integrated assessment model (CPSIAM), which is utilized to comprehensively evaluate the provincial peak situation of total carbon emissions, carbon emissions per capita and carbon emissions per floor space of urban and rural residential buildings and figure out carbon peaking progress in each province. Then, the driving mechanism of differentiated carbon peak status of provincial residential building carbon emissions are examined using the panel STIRPAT model and group regression model. Results show that as for the three residential building carbon emission indicators, Heilongjiang and Shanghai have both reached their full peaks as of 2020, however the former is in passive decline and the latter is in an active peak. Population, population structure, and floor space per capita are the main variables causing the differentiated peak status. This study not only offers a general methodological system for assessing the regional carbon peak situation, but also empowers policymakers to develop effective and differentiated emission-reduction measures based on their knowledge of peaked regions. [Display omitted] • A carbon peak situation integrated assessment model (CPSIAM) is proposed. • Evaluate the provincial carbon peak situation of residential building sector. • Figure out each province's carbon peaking progress. • The peak order is carbon emissions per floor space, per capita, and overall. • Energy intensity is the main variables causing differentiated peak status. [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]

The decarbonization of China's central heating (CH) is important for reaching China's carbon peak and carbon neutrality goal. However, unclear CO 2 emissions from CH of each city and their disparity hinder the formulation of targeted decarbonization strategies. To solve these issues, this study quantifies the CO 2 emissions from CH in 137 cities during the period of 2006–2019 and introduces the Kaya identity and Theil index to investigate the disparity in the per capita CO 2 emissions from CH. The results indicate that 1) China's CH CO 2 emissions increased to 435.53 MtCO 2 in 2019. Residential and public and commercial (P&C) buildings accounted for 74.18% and 25.82%, respectively. 2) The cities with the largest CH CO 2 emissions of all (22.58 MtCO2), residential (16.33 MtCO 2), and P&C buildings (6.86 MtCO 2) were Harbin, Shenyang and Harbin, respectively. 3) The Theil index for all buildings decreased to 0.44 in 2019, and P&C buildings had the largest disparity. 4) The central heating penetration rate was the major positive contributor to the disparity of residential and P&C buildings, contributing more than 40%. In summary, at the city level, this study offers a comprehensive investigation of the CO 2 emissions from CH and benefits the formulation of specific decarbonization strategies. [Display omitted] • The CO 2 emissions from China's central heating (CH) are quantified at the city level. • The Theil index was employed to determine the disparity of per capita CO 2 emissions from CH. • Residential buildings have a larger disparity than public and commercial (P&C) buildings. • The CH penetration rate was the major positive contributor to the disparity. • Eliminating the disparity can reduce CO 2 emissions of China's CH by 6.92% -77.74%. [ABSTRACT FROM AUTHOR]

As the energy demand of public building (PB) in China continues to increase, it is meaningful for forecasting the future trend of carbon emissions (CE) of PB. This study analyzes the drivers of CE that affect the operational stage of PB in China by the Generalized Divisia Index Method (GDIM), which considers the effect of the output value and structural transformation of the tertiary industry and makes a scenario prediction of the future carbon-neutral trajectory of CE in PB under the Shared Socioeconomic Pathway (SSP). The results show that from 2010 to 2020, the added value of the tertiary industry (AVTI) is the strongest positive driver of CE from PB (31.912% of the total contribution), which correspond to a cumulative contribution of 212.3 million tons (MtCO 2) in 11 years. Regulating the CE per unit of the AVTI is the key indicator in reducing CE from PB, with a cumulative reduction of 130.1 MtCO 2. The results of the five scenarios under the SSP framework differ significantly, with SSP1 peaking first in 2026 at 884.5 MtCO 2 (876.2–893.9 MtCO 2) and SSP5 peaking latest in 2049 at 1372.3 MtCO 2 (1362-1389.7 MtCO 2). SSP1 yielded the smallest projection in 2060 with 189.8 MtCO 2 (185.4–194.3 MtCO 2). The findings could help public buildings to implement carbon reduction and provide more scientific and rational decision support for possible paths to achieve carbon neutrality in China. • Projection of carbon emission changes in China's public building operation phase to 2060. • The GDIM which considers the added value of the tertiary industry and industrial transformation factors are employed. • Added value of tertiary industry is essential to the change of carbon emissions. • Carbon emissions of public buildings peak first in 2026 and are lowest in 2060 under the SSP1 scenario. [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]

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]

Due to large energy consumption and carbon emissions (ECCE) in the building sector, there is huge potential for carbon emission reduction, and this will strongly influence peak carbon emissions and carbon neutrality in the future. To get a better sense of the current research situation and future trends and to provide a valuable reference and guidance for subsequent research, this study presents a summary of carbon peak and carbon neutrality (CPCN) in buildings using a bibliometric approach. Three areas are addressed in the review through the analysis of 364 articles published from 1990–2021: (1) Which countries, institutions, and individuals have conducted extensive and in-depth research on CPCN in buildings, and what is the status quo of their collaboration and contributions? (2) What subjects and topics have aroused wide interest and enthusiasm among scholars, and what are their time trajectories? (3) What journals and authors have grabbed the attention of many scholars, and what are the research directions related to them? Moreover, we propose future research directions. Filling these gaps will enrich the research body of CPCN and overcome current limitations by developing more methods and exploring other practical applications. [ABSTRACT FROM AUTHOR]

A timely analysis for carbon emission reduction in buildings is an effective global response to the crisis of climate change. The logarithmic mean Divisia index (LMDI) decomposition analysis approach has been extensively used to assess the carbon emission reduction potential of the buildings sector. In order to simplify the calculation process and to expand its application scope, a new open-source Python tool (PyLMDI) developed in this article is used to compute the results of LMDI decomposition analysis, including multiplicative and additive decomposition. Users can quickly obtain the decomposition result by initializing the input data through a simple class data structure. In addition, the carbon emissions from commercial buildings are used as a numerical example to demonstrate the function of PyLMDI. In summary, PyLMDI is a potential calculation tool for index decomposition analysis that can provide calculation guidance for carbon emission reduction in the buildings sector. The data and codes for the numerical example are also included. [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]

Highlights: A new method to calculate the URBCE is proposed for explore the influencing factors of carbon emission. The spatial econometric model solved the bias estimation caused by spatial correlation. The effect of regional factors on carbon emission is determined by an econometric model. Regional emission reduction policies are more scientific and effective than national ones. The role of urban residential buildings (URBs) in the carbon reduction goal of China is becoming increasingly important because of the rising energy consumption and carbon emission of such buildings in the region. Considering the increasing spatial interaction of the carbon emission of URBs (URBCE) in the region, this study investigates the influence of climate and economic factors on the URBCE in North and South China. First, the URBCE is calculated by using a decomposition energy balance table based on the carbon emission coefficient of electric and thermal power, thereby improving the estimation of the basic data of URBCE. Second, the influence of economic and climatic factors on the URBCE intensity in 30 provinces of China is explored by using a spatial econometric model. Results show that the URBCE intensity in China had a spatial autocorrelation from 2000 to 2016. Climatic and economic factors have great differences in the degree and direction of influencing the URBCE intensity in the country. Formulating emission reduction policies for climate or economic zones is more scientific and effective than developing national policies. Among these factors, urbanization rate, climate, and GDP per capita have a significant positive impact on the URBCE intensity in the region, whereas other factors have varying degrees of negative impact. In addition, climate, consumption level, and building area have significant spatial spillover effects on URBCE intensity, whereas other factors do not pass the significance test. Relevant conclusions should be given special attention by policymakers. [ABSTRACT FROM AUTHOR]

Scientific assessment of the historical carbon peak situation of provincial buildings in China is the premise and basis for understanding the country's development trends and formulating carbon peak goals. The population size, urbanization stages, economic development levels, natural resources endowment, and energy structure characteristics vary significantly for the different provinces in China, resulting in significant differences in the peaking situation of building carbon emissions (BCE). The differences require more attention given the current environmental status. Based on the judgment function of carbon peaking conditions and the statistical Mann-Kendall (MK) trend test method, this study evaluates the historical peak situation of building carbon emissions at the provincial level in China. The peaking sequence of BCE, building carbon emissions per capita (BCEP), and carbon emissions per unit floor area (BCEA) were analyzed, and the driving factors that cause different carbon peak situations were discussed. Further, with reference to the experience of the United States, a peak strategy for building carbon emissions in China was proposed. The research results showed that BCE in Beijing and Yunnan have peaked, and the three provinces of Shanghai, Sichuan, and Hubei have plateaued. The most important factors that cause different peaking situations for BCE are the floor area per capita and carbon emissions per unit of energy consumption. In addition, the peak order of building carbon emissions was BCEA, BCEP, and BCE. A strategy that should be adopted in the promotion of buildings' carbon peak in China is to formulate phased peak goals for BCE, BCEP, and BCEA at a national level and differentiated echelon peak goals at a provincial level considering interprovincial differences. This study provides a scientific basis and decision-making reference for formulating a path to buildings' carbon peak at a provincial level in China. [Display omitted] • An assessment method for the peak situation of building carbon emissions is constructed. • Beijing, Yunnan have peaked, and Shanghai, Sichuan, Hubei have plateaued. • Carbon emissions per floor area reaches the peak earlier than building carbon emissions. • Carbon emissions per unit of energy consumption determines the different peak situations. • Peak strategy needs to be formulated at both national and provincial levels. [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]

• 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]

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]

[Display omitted] • Establishes an integrated dynamic emission assessment model by coupling end-use energy model and system dynamics model. • Explores the dynamic interaction mechanism between macrolevel and microlevel factors as well as technical options. • Commercial buildings will probably hit the emission peak at 1.28Gt CO 2 in 2037 under the BAU scenario. • Lighting and cooling will contribute over 70% to the carbon emission growth. • Technological progress, clean energy, and electrification rate play significant roles in boosting emission peak and peaking time. Carbon-mitigation in the commercial building sector is critical to carbon peaking and carbon neutral commitment. However, there has been little scientific focus on long-term evolutionary trajectories and peak path in this sector. This study innovatively develops an integrated dynamic emission assessment model (IDEAM) by combining the system dynamics model with the bottom-up end-use energy model. Moreover, the IDEAM is combined with the scenario analysis approach to model the dynamic evolution of Chinese commercial building carbon emissions toward 2060. The results show that commercial building carbon emissions will peak at 1.28 Gigatons (Gt) of CO 2 in 2037 under the baseline scenario and will advance toward 2029 with an emissions peak of 0.98 Gt CO 2 under the low-carbon scenario. Cooling and lighting are the two end-uses that contribute most to the growth of carbon emissions at over 70%. These two end-uses indicate different carbon-abatement potentials across climate zones. Sensitivity analysis reveals that promoting technological progress, increasing the share of clean energy, and improving low-carbon awareness are major ways to facilitate the early realization of carbon peaks and carbon neutrality. This study provides a deeper understanding of possible emission pathways and could assist policy-makers in devising scientific carbon mitigation plans. [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. [Display omitted] • Adopting the SNA method to explore the spatial network structure of China's building carbon emissions. • Using the QAP method to determine the factors affecting the spatial correlation network of building carbon emissions. • The relevance and stability of the spatial associations gradually strengthened. • Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang occupy a central position in the network. • Spatial adjacency, economic development, energy intensity and industrial structure significantly affected spatial network. [ABSTRACT FROM AUTHOR]

(a–f) Changes of carbon reductions of commercial building operations at the provincial level and (g) the difference with the nation-based results (2001–2016). [Display omitted] • Provincial carbon intensity of commercial building operations grew 2.88 % yr−1 in 2001–2016. • Northeast and north China mitigated the most in carbon intensity and emissions, respectively. • Southwest China showed the highest ratio of carbon reductions to carbon emissions at 29.53%. • The total reduction of China's commercial building operation was 1.38 GtCO 2 in 2001–2016. • Annual provincial reductions peaked in 2013, one year earlier than the national result. The growing energy consumption in commercial building operations has slowed the pace of carbon reduction in the building sector, which has hindered China's movement towards peaking emissions by 2030 and carbon neutrality by mid-century. Considering the technical, socio-economic, climatic, and behavioral factors, this study evaluates the carbon reduction changes of commercial building operations in China's 30 provinces during the period 2001–2016. An assessment framework of the reduction intensity, amount, and efficiency has been built by carbon intensity decomposition. It shows that (1) the average provincial carbon intensity of commercial building operations kept rising by 2.88% per year from 2000 to 2016; (2) provinces of northeast and north China, respectively, took the lead in reduction intensity and amount at 17.09 kg of carbon dioxide per square meter and 73.79 megatonnes of carbon dioxide (MtCO 2) from 2001 to 2016. Meanwhile, the greatest efficiency of carbon reduction was seen in provinces of southwest China with the highest reductions-emissions ratio being 29.53%; (3) compared to the national level results, the annual carbon reductions based on provincial units hit their peak one year earlier at 167.35 MtCO 2 in 2013, although similar emission drivers have been observed. Moreover, effective practices by local governments have been summarized for energy efficiency promotion in commercial building operations. Overall, this study makes a reference for other economies to assess the historical carbon reduction in the building operation, which provides policymakers with insights into balanced and targeted emission reduction strategies towards a low-carbon transition in commercial building sector. [ABSTRACT FROM AUTHOR]

Residential central heating (RCH) is an important part of residents' well-being policy. However, it is accompanied by extensive energy consumption and carbon emissions. To identify and design efficient future clean heating strategies, we quantified the carbon emissions from RCH in China from 2000 to 2017. Notably, our study is the first to analyze the driving factors of RCH carbon emissions using decomposition analysis and introduce the climate parameter into the Kaya identity. The following results were obtained. 1) The RCH carbon emissions reached the flection point in 2016 (321.75 MtCO 2). 2) Area-related energy intensity is the key driving factor to mitigate carbon emissions of RCH; the area-related carbon intensity in 2017 was one-third less than that in 2000. Conversely, per capita residential heating area and urban population increased carbon emissions from RCH. 3) At the provincial level, the recommended emission mitigation strategies were different. For example, Heilongjiang and Jilin should focus on decreasing the emission factors, whereas Shandong and Xinjiang need to focus on decreasing the area-related energy intensity. The results of this study can help governments to formulate more rational and feasible policies for the emission road map, and the targeted advice also offers a reference for future studies. [ABSTRACT FROM AUTHOR]