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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]

Clarifying the critical driving factors and the driving mechanism for building carbon emissions is fundamental for carbon peak and carbon neutrality prediction. This study comprehensively identifies 23 representative factors using the literature research and questionnaire survey method. Then, by coupling the decision-making trial and evaluation laboratory (DEMATEL) method and the interpretive structural model (ISM), an integrated DEMATEL-ISM model is developed. The proposed model is further employed to quantify the causal relationships among the factors and establish a multi-level recursive structure model to determine the action mechanism of those drivers. The results show that the 11 cause factors, 12 effect factors and the three most important factors including the economic development level, building end-use energy consumption and end-use energy intensity are identified based on the calculated cause and center degree. Based on the obtained hierarchy, the 23 factors are divided into six levels, among which the population factors, the economic development, the urbanization level and the share of the tertiary sector are the root factors leading to the building carbon emissions. In comparison, policy factors are the most critical inhibiting factors. This study can not only promote the theory and model development for the driving mechanism of carbon emissions, but also help decision-makers develop scientific carbon reduction strategies. • Establishes an integrated DEMATEL-ISM model by coupling the DEMATEL method with the ISM. • Identifies the representative factors affecting the building carbon emissions from the systematic perspective. • Explores the significance of the factors and their intricate interactive mechanism. • Population factors, socio-economic development, urbanization level and the tertiary sector share are the fundamental factors. • Policy factors are the most critical inhibiting factors. [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]

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]

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]

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]

[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]

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]

Modern projects are normally characterized by huge investments, long construction periods, and complex technology. Therefore, the selection of an appropriate contractor for smooth project delivery is challenging. Previous studies attempted to develop suitable frameworks for contractor selection. However, correlation among indicators, the subjectivity of indicator weights, and heterogeneity among experts' professional capabilities for selecting contractors were not successfully removed from the decision-making process. Typical partial least square (PLS) path modeling can solve these problems. However, it can only solve problems with the same direction correlation among the indicators (e.g., the correlation coefficients of the indicators are all positive). For indicators with different direction correlations, path modeling is helpless. Aiming to overcome this limitation, this research introduces a group decision model based on two-stage PLSs path modeling. This decision model can eliminate correlation among indicators and the impact of subjectivity and heterogeneity among experts on the reliability of weighting schemes; more importantly, it can effectively solve the problem of having correlation indicators with different directions. Through a literature review, we first established an indicator system of contractor selection on large scale construction projects. Second, a two-stage PLS path modeling combined with the maximization of deviations principle was proposed as an aggregation approach for performance evaluation. Finally, a comparison was made between the two-stage and typical PLS path modeling methods through a case study, which was conducted to validate the reliability of the new approach. [ABSTRACT FROM AUTHOR]

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. [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]

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]

Green construction is to conserve the resources and reduce the negative impact of construction activities on the environment to the maximum extent, with scientific management and technological progress under the premise of ensuring the quality, safety and other basic requirements. Recent years, accompanied by the rapid development of construction industry, a considerable proportion of energy and resources, particularly high-carbon materials, is consumed in the construction activities, emitting mass greenhouse gases, which have a damaging impact on the environment. This is especially for the mega public projects. Therefore, under the green and sustainable perspective, actions to control carbon dioxide (CO 2 ) emissions in the construction industry are imperative. In this case, if CO 2 emissions reduction can be considered beforehand during the bid evaluation stage, contractors will be guided by bid evaluation indicators and therefore improve their construction schemes, enabling the construction phase to be green, energy saving and sustainable. This is because bid evaluation indicators can serve as guiding effect on the contractors. Given that CO 2 emissions are rarely given sufficient consideration in traditional bid evaluation, this study conducts a systematic analysis to identify major sources of CO 2 in construction phase and attempts to develop a conceptual computational model in terms of CO 2 emission. Then, a linguistic group decision-making framework for bid evaluation in mega public projects considering CO 2 emissions reduction is developed. In this framework, entropy, relative entropy, the standard deviation method and weighted aggregation operators are applied to determine the indicator weights, the expert weights and the information aggregation in bid evaluation decision-making. Finally, a scenario comparison between the proposed linguistic group decision-making bid evaluation framework and the traditional framework is made through a case study, and the scientific basis and reliability of this new framework are validated. The establishment of this framework can enrich the decision-making methods in construction management field. And it can provide meaningful reference for owners to choose the most qualified contractors and spur contractors to improve their construction schemes, enabling construction phase to be green, energy saving and sustainable. [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]

[Display omitted] • The presented model can simulate future material and energy demand and carbon emissions. • The model builds upon real individual buildings from GIS data. • Insulation contributes most to carbon reduction for space heating. • Greening electricity mix plays a key role in residential decarbonization. • Nearly 80% of electricity could be met if photovoltaic systems are installed on half of roofs. The building sector plays a key role in energy transition and carbon reduction while capturing the dynamic characteristics (e.g. materials, energy performance, and environmental impact) of building stock is a great challenge during the gradual process. This study presents a bottom-up dynamic building stock model that links dynamic material flow analysis with building energy modeling. The environmental impact of material and energy requirements is assessed by considering future electricity mix. The model is applied to evaluate the pathways to the climate-neutral energy supply of residential building stock in the Netherlands by 2050. Results show that space heating demand decreases by about 2/3 by 2050, while the energy for hot water increases to 92% of space heating demand. 80% of public grid electricity for appliances and lighting can be potentially substituted if rooftop photovoltaic (PV) systems are installed on 50% of renovated buildings and all the new buildings. Greenhouse gas (GHG) emissions of operational energy are reduced by approximately 60–90%, depending on the electricity mix. Annual GHG emissions from material production are not as important as those related to operational energy. Insulation materials account for a large proportion of the carbon footprint of material production. The model has a high spatial and temporal resolution and can be linked with local energy source availability (e.g. buildings or neighborhoods) to provide more accurate support for policymaking. [ABSTRACT FROM AUTHOR]