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With the acceleration of urbanization, building energy consumption carbon emissions (BECCE) were increasing, weightily influencing global warming and the social-economic developments in cities. The detailed exploration of the most effective distance of urban three-dimensional (3D) compact form on BECCE guides urban space optimization. In this study, 135 People's Banks of China (PBC) were taken as samples. Based on the banks building features, socioeconomic conditions, macroclimate, and urban 3D compact form at different buffers, the most effective distance on the BECCE was determined. According to Partial least-squares regression (PLSR), the results show that the most effective distance of the urban compact form on the BECCE was 150 m. The study divided the normalized 3D compactness index (NVCI) values from 135 buildings for the distance of 150 m into five categories and used the geographic detector to recognize statistically obvious differences between the subregions. The natural breakpoint method was used to categorize the study area into five classes, specifically: low, medium-low, medium, medium-high, and high compact form. Based on the geographic detector method result, the compact form of medium, medium high, and high compact form was optimized to low, medium low. The optimization can effectively reduce the BECCE due to changing the compact form of the building by 65.2% or 65.7%. The study combined mathematical statistics with spatial analysis approaches to identify the most effective distance for reducing energy consumption. Our results will contribute toward considerable reductions in the BECCE. HIGHLIGHT: Innovatively used mathematical statistics to determine the most effective distance of urban compact forms. Demonstrated the influence of on BECCE to urban compact forms. Determined the most effective distance was 150 m. Verified the most effective distance was 150 m in different building climate zones. Optimized the urban 3D compact form within 150 m to reduce the BECCE. [ABSTRACT FROM AUTHOR]

Carbon mitigation in the building sector is crucial for China to fulfill its commitments towards achieving a carbon peak and carbon neutrality. However, the impact of societal development and ecological indicators on building carbon emissions remains unclear. This study employs the panel smooth transition regression model to investigate the complex implications of societal development comprehensive indicators, characterized by harmonious development, decoupling, and technological advances, on buildings' total carbon emissions, based on the evidence from China's 30 provinces for the period between 2007 and 2020. Additionally, the robustness of the model confirms that the conclusion is still valid. The empirical results indicate a strongly non-linear relationship between societal development comprehensive indicators and building carbon emissions. Both the harmonious development and technological advances exhibit two transition functions, and decoupling features a single transition function. Harmonious development is more sensitive to the impact of building carbon emissions, while technological advances have tremendous emission reduction potential. From the time dimension, fluctuation trends and ranges are different. From the spatial dimension, the inhibiting and promoting effects on each province have regional heterogeneity. Our results entail suggestions for reduced building total carbon emissions and practical strategies for regional climate resilience and efficiency in mitigating climate change. [ABSTRACT FROM AUTHOR]

New and refurbished non-domestic buildings are failing to live up to their anticipated performance. Shortfalls show in excess energy consumption, high carbon dioxide emissions and other failings in quantitative and qualitative performance metrics. This paper describes the component parts of the performance gap using evidence from building performance evaluations. It introduces a way of visualising the consequences of decisions and actions that are known to compromise performance outcomes using a performance curve methodology (the S-curve) which plots performance, and the root causes of underperformance, from project inception to initial operation and beyond. The paper tests the hypothesis with two case studies. It also covers the initial development of a prototype visualisation tool designed to enable live projects to track emerging operational energy and emissions against a high energy and emissions trajectory created from empirical evidence. The tool aims to help practitioners identify key risk factors that could compromise building performance and mitigate these risks at different stages of procurement. Practical application: The Operational Energy and Carbon (OpEC) visualisation tool is designed for wide industrial application, on all sizes of a non-domestic building project, large and small. It aims to visualise the likely outturn energy performance of a project by calculating the penalties for shortcomings in project delivery. The penalties are visualised as weighted trajectories of energy and carbon dioxide emissions. The prototype tool aims to fill a gap between the capabilities of powerful energy modelling tools used in design and the capacity of non-specialist stakeholders to understand the emerging energy characteristics of a project as it moves through procurement, design, construction, and delivery. [ABSTRACT FROM AUTHOR]

The energy performance of appliances, buildings and groups of buildings is commonly assessed in terms of equivalent carbon emissions or primary energy, usually following procedures defined by regulations. The most appropriate treatment of energy (usually heat) that is rejected as waste from one process, but which may be a useful input to another process, is not obvious and has proved to be problematic. This note describes and illustrates how the situation can be handled by adapting a procedure that is already in use for somewhat similar purposes. The direct users of the approach are likely to be developers and system designers, but since the procedures are often set by regulation, the most important potential users are those responsible for the regulations The principal features of the procedure are that • The use of waste energy reduces the primary energy (or carbon emissions) attributed to the "donor" process: by allocating some of it to the eventual user: an equitable principle. • The total primary energy (or carbon emissions) of the output streams equals the input to the transformation process: a fundamental requirement • The PEF values of outputs are never negative: required for physical plausibility • If there is only one useful output, all the primary energy is assigned to it: for consistency with the fundamental assumptions of the policy metric • The PEF assigned to the recipient user is lower than that of the alternative supply (if its PEF is zero, so is the that of the waste heat) The principal challenge is the need to define a "counterfactual" alternative source of energy for the recipient's energy needs: options for this this discussed in the note Practical application : The question of the most appropriate convention for the assignment of primary energy and carbon emissions to the application of energy flows that have traditionally considered to be waste has gained importance in recent years, especially in the context of energy distribution systems that transfer energy between buildings. It seems reasonable that the "donor" of the waste energy should be incentivised by a reduction of the primary energy consumption or carbon emissions that is attributed to them (with the reduction balanced by a compensating attribution to the user of the "waste" energy). This is not reflected by the currently widespread convention that waste energy can always be considered to have a carbon intensity and primary energy factor (PEF) of zero. This Technical Note sets out a procedure to achieve this. It is suggested that its application would result in a more equitable and consistent attribution of primary energy and carbon emissions and therefore more robust design of systems that reuse "waste" energy. [ABSTRACT FROM AUTHOR]

Nearly-zero energy buildings (NZEB) would effectively improve building energy efficiency and promote building electrification. By using a carbon emission model integrated into a bottom-up mid-to-long term energy consumption model, this study analyzes the contribution of NZEB standards to carbon emission targets in the urban area of China by 2060. Three scenarios are set, namely BAU, steady development (S1), and high-speed development (S2). For BAU, the total carbon emissions will reach a peak of 1.94 Gt CO2 by 2040. In S1 scenario, total building carbon emissions will reach the peak of 1.72 Gt CO2 by 2030. In S2 scenario, the carbon emissions will reach a peak by 2025 with 1.64 Gt CO2. Under S1 scenario, which features consistency with NZEB market development and periodic improvement of building energy-efficiency standards, the carbon emission peak in 2030 will be accomplished. To achieve carbon neutrality by 2060, the upgrading of building energy standards to NZEB will contribute 50.1%, while zero-carbon electricity contribution is 49.9%. It is concluded that 2025, 2030, and 2035 could be set as mandatory enforcement years for ultra-low energy buildings, NZEB and zero energy building (ZEB), respectively.

As a major province of energy consumption and carbon emission, Jiangsu Province is also a major province of the construction industry, which is a key region and potential area for carbon emission reduction in China. The research and prediction of carbon emission in the construction industry is of great significance for the development of low-carbon policies in the construction industry of other cities. The purpose of this paper is to study the influencing factors of the whole life cycle carbon emissions of buildings in Jiangsu Province, and to predict the carbon emissions of buildings in Jiangsu Province based on the main influencing factors. This paper uses the energy balance sheet splitting method, STIRPAT model, gray correlation method and GA-BP neural network model to study and predict the carbon emissions of construction industry in Jiangsu Province. The research results show that the resident population, urbanization rate, steel production, average distance of road transportation, and labor productivity of construction enterprises have a catalytic effect on construction carbon emissions; GDP per capita and added value of tertiary industry have a suppressive effect; construction carbon emissions reached the historical peak in 2012; the prediction results show that the future construction carbon emissions in Jiangsu province generally show a decreasing trend. The research results of this paper provide a possibility to refine the study of construction carbon emission, and also provide a basis and guidance for subsequent research on construction carbon emission. [ABSTRACT FROM AUTHOR]

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai's building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity). [ABSTRACT FROM AUTHOR]

The average carbon intensity (gCO2e/kWh) of electricity provided by the UK National Grid is decreasing and becoming more time variable. This paper reviews the impact on energy calculations of using various levels of data resolution (half hourly, daily, monthly and annual) and of moving to region specific data. This analysis is in two parts, one focused on the potential impact on Part L assessments and the other on reported carbon emissions for existing buildings. Analysis demonstrated that an increase in calculated emissions of up to 12% is possible when using an emissions calculation methodology employing higher resolution grid carbon intensity data. Regional analysis indicated an even larger calculation discrepancy, with some regions annual emissions increasing by a factor of ten as compared to other regions. This paper proposes a path forward for the industry to improve the accuracy of analysis by using better data sources. The proposed change in calculation methodology is analogous to moving from using an annual average external temperature to using a CIBSE weather profile for a specific city or using a future weather file. Practical application: This paper aims to quantify the inaccuracy of a calculation methodology in common use in the industry and key to building regulations (specifically Building Regulations Part L – Conservation of Fuel and Power) – translating electricity consumption into carbon emissions. It proposes an alternative methodology which improves the accuracy of the calculation based on improved data inputs. [ABSTRACT FROM AUTHOR]

Prefabricated steel structures are being promoted in China as an important measure to reduce building carbon emissions (CEs). Currently, high-quality carbon emission factors and calculation methods that identify the sources of CEs from construction process are lacking. This study proposed a process-based method for calculating on-site construction CEs and empirically demonstrated its feasibility using a case of a multistory steel structure with typical column-beam systems. Six construction processes comprising 38 subprocesses were first categorized, then the energy and materials consumed for each subprocess were determined to obtain the first-hand CE parameters. The CEs from hoisting the first- and second-section columns were 10.52 kgCO 2 /column and 14.41 kgCO 2 /column, respectively, and the CEs from their installation were in the range 38.16–69.18 kgCO 2 /column. The CEs from hoisting the primary and secondary beams using a car crane were 7.73 kgCO 2 /beam and 4.52 kgCO 2 /beam, respectively, with lower CEs of 2.40 kgCO 2 /beam and 2.22 kgCO 2 /beam, respectively when a tower crane was used for hoisting. The 14 connection types used to install the primary beams and the 11 connection types used to install the secondary beams had CEs of 13.84–125.96 kgCO 2 /beam and 10.39–43.49 kgCO 2 /beam, respectively. The total on-site construction CE was subsequently calculated by summing the CEs from each process. This method was able to track the CE sources for each construction process and their contributions to identify those with high CE intensities. In addition, the case study was evaluated to provide specific feedback on the current national standard for calculating CEs from building construction. • Proposed method for calculating steel construction carbon emissions. • Conducted on-site case study to determine process-specific carbon emissions. • Considered fuel, electricity, and material consumptions. • Evaluated the results to critique the current national calculation standard. • Gave suggestions to improve calculation for on-site construction carbon emissions. [ABSTRACT FROM AUTHOR]

• The GWR perform better on estimating building carbon emissions using NTL indexes. • The carbon intensity and ANLI are more reliable on establishing estimation model. • City-level scale is the most suitable scope for applying NTL indexes. • The restriction of applicable scope relying on more spatial parameters. As one of the fundamental sectors to measure the carbon emission levels in a certain region, building carbon emission plays an important role in determining low-carbon development plans. Most of the carbon emission estimation research mainly focuses on the establishment of bottom-up GHG inventory and the implication of policy-driven approaches, there are still many theoretical gaps in the usage of remote sensing data to predict building carbon emission. This paper presents a comprehensive study to discuss the performance of different regression models using various open nighttime light (NTL) data sources. The Noord Brabant province was employed as a case study to verify the feasibility of using different estimation models at various spatial scales (city-level, district-level, and neighborhood-level). Among all regression models, the geographically weighted regression (GWR) has been proven to better reflect the relationship between building carbon emissions and the NTL index. For practical applications, the carbon intensity (CI) and annual nighttime light index (ANLI) are a pair of optimal sets to establish a reliable estimation model. It exhibits higher utility value at the city-level due to the fewer interferences caused by non-building lighting sources. The results of this comparative study provide a new reference to support the establishment of carbon inventory. By illustrating the differences among various estimation models, the applicable scope of using open remote sensing data to estimate building carbon emissions can be further defined. The conclusion may provide more detailed instructions during the process of developing low-carbon cities. [ABSTRACT FROM AUTHOR]

The related concepts of Primary Energy and Primary Energy Factors (which describe how much Primary energy is contained in each unit of delivered energy) are used for international comparisons of national energy use and have become increasingly important regulatory and statistical metrics, especially in relation to the European Energy Performance of Buildings Directive. As concepts they contain a mixture of technical, political and economic dimensions, so it is perhaps unsurprising that reported values from different organisations and countries do not seem to be calculated in the same way. This review aims to clarify the origins of such differences by identifying, summarising and commenting on alternative conventions that are or can be applied. In particular, it aims to provide a starting point for the development of a transparent means of reporting the procedures and conventions that are employed. Such a framework would provide a basis for understanding the reasons for differences. The review does not recommend specific conventions or procedures – preferences will vary, depending on a number of factors.Practical application: Primary Energy Factors are central to international comparisons of national energy consumption and to several aspects of European energy policy. In the context of buildings, they are especially important to the newly revised Energy Performance of Buildings Directive which requires primary energy to be the primary metric, rather than, for example, carbon emissions. It is known that different countries use different methodologies and that this can have substantial repercussions. The methodologies used are rarely reported and range of possible options has not previously been set out. This paper sets out to do this in order so that choices can be made with greater transparency and clarity. [ABSTRACT FROM AUTHOR]

Abstract The carbon emission pattern of the built environment is closely associated with its morphological and functional structures. Based on the online volunteered geographic information and some publicly available official data sources, this study intends to provide a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs), to better forecast the LCZ carbon emission patterns and assist district wide energy management. The whole research is devised into four sequential sections: First, the statistics of energy use intensity of different building uses (including residential and non-residential buildings) are retrieved from official data sources using a down-scaled approach; then a random forest machine learning method is applied to automatically identify building uses based on the training samples; next, a GIS method is developed to delineate the LCZs in Shanghai utilizing calculated urban form and land cover parameters; finally, the building carbon emission values are linked to the LCZs to determine the emission coefficient of different LCZ categories in Shanghai. [ABSTRACT FROM AUTHOR]

Due to a considerable imbalance in the levels of social and economic development and urbanization in different regions of China, factors such as population, floor area, and building energy consumption have gradually undergone regional structural changes, and further show a specific center of gravity migration trend. The impact of this center of gravity migration on building carbon emissions deserves further exploration. Based on center of gravity model and comprehensive carbon emission reduction effect analysis model, this study depicts the center of gravity migration trajectory of China's population, floor area, building energy consumption, and elucidates the reduction effect of the center of gravity migration on building carbon emissions. Finally, the theoretical and essential reasons for the building carbon emission reduction effect are further discussed. The results showed that the center of gravity of China's population, floor area, building energy consumption, and building carbon emissions moved from north to south, with that for building energy consumption being most obvious (moved south by 1.22°). A total reduction of 32.7 million tCO 2 in building carbon emissions was facilitated by the southward migration of the three centers of gravity. This occurred essentially because the southward migration of the center of gravity led to relatively cleaner building energy consumption across the country, which in turn weakened the overall energy consumption intensity and carbon emission factor. This study expands and deepens the theoretical mechanism of building carbon emission reduction, and provides a decision-making basis for government policymakers to balance regional differences to formulate differentiated building carbon emission reduction strategies. [Display omitted] • Population, floor area, and building energy consumption centers of gravity gradually shifted from north to south. • Southward migration was particularly evident for the building energy consumption center of gravity. • Southward migration of the three centers of gravity could facilitate reductions in building carbon emissions. • Building carbon emission reduction is essentially caused by the weakening of energy intensity and carbon emission factor. [ABSTRACT FROM AUTHOR]

In September 2020, China proposed the achievement of the emission reduction targets of "carbon peak" and "carbon neutral" by 2030 and 2060, respectively. As an important area of energy consumption in addition to industry and transportation, the construction industry has great energy-saving potential and is gradually becoming the key to achieving China's energy-saving and emission-reduction goals. Energy data is an important basic support for measuring carbon emissions, analyzing energy-saving potential, and formulating energy-saving targets. In order to solve the he lack of data on China's carbon emissions, this paper uses lamplight remote sensing image data in the study. Combined with China's eastern, central, and western regions of building carbon emissions data and the establishment of a partition of China building carbon emissions calculation model, panel data found building carbon emissions and smooth lamp brightness values between the balanced relations. After that, using the building carbon emissions models of the three regions, the building carbon emissions of 30 provinces, 360 cities, and 2778 counties in China were measured, and the changing trends and temporal and spatial directions of building carbon emissions at three spatial scales were analyzed. The results showed that although the total carbon emissions of civil buildings in China has been increasing year by year, its average annual growth rate is gradually slowing down. In addition, the temporal and spatial development directions of carbon emissions from buildings of different spatial scales are basically the same, and they all show a trend of shifting to the east. [ABSTRACT FROM AUTHOR]

Life cycle assessment of building carbon emissions has drawn great attention recently, due to the policy of controlling the greenhouse effect. Various approaches including process-based method, input-output analysis, and hybrid method have been applied for the assessment. Hence, with the purpose of achieving a comprehensive comparison of these approaches, the present study made a comparative analysis in light of the uncertainties in parameter, scenario, and modeling. Based on the life cycle assessment of two residential buildings, both deterministic and stochastic analyses were conducted to quantify the differences and relevant uncertainties in the emissions by different assessment methods. The results indicated that the choice of methods had significant influences on the calculated emissions, which ranged from 3.3 to 3.6 tCO 2e per floor area for the two buildings. With consideration of the parameter uncertainty, pure input-output analysis could lead to considerable errors, and hybrid methods were recommended in the emission assessment. Furthermore, based on the analysis of defined scenarios, scenario and model uncertainties were evaluated and compared for different assessment methods. Overall, the findings and suggestions could be helpful for understanding the differences and uncertainties in the results of carbon emission assessment, and therefore benefit the quantification of building emissions. • Comparative study of life cycle approaches for building emission assessment. • Deterministic and stochastic analyses are combined to assess the uncertainty. • Scenario analyses indicate the characteristic of emissions by different methods. • Suggestions proposed for the comparison and uncertainty of life cycle approaches. [ABSTRACT FROM AUTHOR]