Search

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

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]

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]

• 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]

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

Urbanization has an important impact on building energy consumption. Unfortunately, whether the later-urbanized provincial regions are more energy-efficient than those early-urbanized regions in the building sector is still unclear. This study innovatively adopts the difference-in-differences (DID) model to examine the event effect of urbanization on building energy intensity with China's provincial evidence from 2000–2015. Moreover, its influence mechanism is identified with the mediating effect model. Results demonstrate that: (1) The building energy intensity is higher in provincial regions where the urbanization rate reached 45% earlier, indicating that the later-urbanized regions are more energy-efficient. (2) Considering heterogeneity, the event of urbanization rate reaching 45% contributes positively to building energy intensity in the eastern and central regions, but the effect in the western region is insignificant. This can further validate the later-mover advantage of the urbanization. (3) Regarding influence mechanism, urbanization can drive building energy intensity by increasing the proportion of the tertiary industry and population density or reducing the proportion of building coal consumption and the plot ratio of the urban area. This study can facilitate the related theoretical and practical exploration of the event effect of urbanization on energy consumption, which is conducive to formulating differentiated energy-conservation strategies. [Display omitted] • The DID model is applied to explore the event effect of urbanization on the building energy intensity. • The effect of urbanization on building energy intensity is explored considering regional heterogeneity. • The influence mechanism of urbanization on building energy consumption is investigated. • Later-urbanized regions are more energy-efficient in the building sector. • Urbanization can improve building energy intensity by potential mediating variables. [ABSTRACT FROM AUTHOR]