Building Carbon Emissions Research Articles

A Review of Building Carbon Emission Accounting Methods under Low-Carbon Building Background

With the continuous development of the global economy, carbon dioxide and other greenhouse gas emissions are persistently increasing, making global warming an indisputable fact. As a high-energy consuming industry, the building industry has gradually emerged as the primary source of greenhouse gas emissions during urban expansion. Consequently, countries are exploring sustainable development pathways for low-carbon buildings to minimize the detrimental impact caused by the construction industry. This paper summarizes the current status of low-carbon building development and, through literature analysis concerning carbon standard systems and carbon emission accountings, discusses the challenges and possible improvements for the future. Establishing a quantitative evaluation tool for carbon emissions and elucidating accounting methods in the construction field is fundamental and a prerequisite for comprehensively studying low-carbon buildings throughout their life cycle. The challenges of low-carbon building development are as follows: (1) lack of a set of carbon emission measurement standards that can be commonly used internationally, (2) lack of a deep and systematic study of the theory of carbon emission accountings and (3) difficulty in recognizing carbon emission boundaries and related data for existing carbon emission accounting methods. This paper provides a comprehensive analysis of the current progress in low-carbon building development, along with an examination and optimization of the application of carbon emission accounting methodologies within construction to address the challenges.

Evolution and Influencing Factors of Spatial Correlation Network of Construction Carbon Emission in China from the Perspective of Whole Life Cycle

Based on the whole life cycle perspective, the carbon emissions of the provincial construction industry in China from 2011 to 2019 were calculated from the production, construction, operation, and demolition stages of building materials. A spatial correlation network matrix of the carbon emissions in the construction industry was constructed by using the modified gravity model, and the structural characteristics of the correlation network were described by introducing social network analysis. Through the quadratic assignment program, the spatial correlation matrix of carbon emissions in the construction industry and its influencing factors were regressed and analyzed. The conclusions were as follows:① the spatial correlation network of carbon emissions in China's construction industry clearly existed. The network density and network correlation numbers were gradually rising, and the network tightness and stability were gradually improving. ② Shanghai, Tianjin, Beijing, and Jiangsu had a higher degree centrality and closeness centrality, which are the core and dominant positions of the spatial correlation network of carbon emissions in the construction industry. Zhejiang replaced Shanghai in the top four from 2013 to 2018, and the betweenness centrality of each province had unbalanced characteristics. ③ Beijing, Tianjin, Jiangsu, Inner Mongolia, Shanghai, and Shandong were "net beneficiaries" blocks, receiving the carbon emissions from other regions. Four provinces, Guangdong, Chongqing, Fujian, and Shandong, belonged to the "broker" sector, achieving a dynamic balance between the production and consumption sides of building carbon emissions. The remaining 20 provinces played a "net spillovers" role, actively sending carbon emissions from the construction industry to other provinces. The correlation between blocks was much greater than the correlation relationship within the blocks. ④ Industrial structure, urban population, spatial adjacency, consumption level, and construction industry process structure had a significant influence on the spatial correlation of carbon emissions in the construction industry. The greater the inter-provincial differences in industrial structure, urban population, spatial adjacency, and consumption level, the greater the similarity of inter-provincial construction industry process structure, and the stronger the spatial correlation and spatial spillover of the construction industry carbon emissions. Finally, according to the evolution characteristics and influencing factors of the spatial correlation network of building carbon emissions, relevant countermeasures and suggestions were provided for the collaborative carbon reduction development of the construction industry region.

Low-carbon-oriented commercial district urban form optimization and impact assessment analysis

Commercial buildings have become one of the main sources of carbon emissions. However, the current research on urban form optimization has lacked analyses of the low-carbon indicators of buildings. With the Beijing commercial district as an example, a low-carbon-oriented urban form optimization path is established in this study, and a new method of urban form optimization is developed from a low-carbon perspective. With the help of the parametric platform and multiobjective optimization algorithm, a dynamic optimization model of urban morphology based on parametric modeling, numerical simulation and algorithm optimization is developed, and the morphological characteristics of low-carbon commercial blocks in Beijing are assessed with a Pareto-optimal solution set. Through principal component regression analysis, the correlations between urban form factors and low-carbon evaluation indicators are discussed. The study shows that the multiobjective optimization algorithm effectively optimizes the low-carbon indicators of buildings at the block scale. The urban forms obtained from the Pareto solution set display several commonalities reflected from three perspectives: building type, vertical dimension and building orientation. It is found that the optimal block layout form reduces the building carbon emission intensity and the building outer skin radiation difference, and the renewable energy-based carbon reduction is large. The urban form factors that have a significant impact on the carbon emission intensity of buildings include the maintenance coefficients, the average area to perimeter ratio, and the sky view factor, with respective impact coefficients of 0.301, −0.309, and −0.319.

Embodied carbon emissions of buildings: Taking a step towards net zero buildings

As global concerns about climate change and environmental sustainability escalate, the construction industry is shifting significantly towards eco-resilient and sustainable building practices. This paper comprehensively explores integrating Building Information Modelling (BIM) with eco-resilient principles to create a framework for designing and constructing sustainable buildings. This research introduced a practical approach for evaluating the embodied carbon from building construction, drawing insights from a residential building in Yangon City, Myanmar. The method developed in this study aims to assess the carbon footprints associated with distinct stages of the construction process, including raw material production and material transportation. Additionally, the proposed method compares the material embodied carbon, transportation embodied carbon, and total cost in the residential building, considering two cases of traditional and low-carbon materials. The results indicate that material embodied carbon contributes merely 84% to the overall, while material transportation makes up the remaining 16% for both cases. Utilising low-carbon materials yields remarkable reductions, with a 40% decrease in material embodied carbon and a 39% decrease in transportation carbon footprint compared to conventional materials. However, adopting low-carbon materials incurs a modest increase of approximately 6.7% in total cost. This study underscores the imperative of integrating low-carbon materials into the design of future passive buildings, advancing the pursuit of a net-zero strategy. This research underlines the potential for BIM-driven eco-resilient practices in mitigating carbon emissions and the need for continued innovation and collaboration in sustainable building design and construction.

The practical study of steel-wood composite column in light and low-carbon building

Steel improves the safety performance of buildings, but also greatly increases the carbon emissions of buildings, which is not conducive to environmental development. Based on the consideration of reducing building carbon emissions, this study proposes to replace the pure steel load-bearing column in the low-rise building with a new load-bearing column in the form of steel-wood combination, and establishes a theoretical and numerical model to explore its reasonable combination size to verify the safety of the structure in practical applications. And the carbon emission assessment and analysis of the whole life cycle. It is found that the steel and wood structure can replace the pure steel structure of the corresponding size in the low-rise building, which greatly reduces the carbon emissions of the building while achieving structural safety.

Renewable Energy Application and Carbon Redution Study on the Campus Teaching Building

This work conducts carbon simulation and accounting of one public teaching building in South China. The calculation results show that the carbon emission of public teaching building is 2586.5 Tons of CO2 per year. Based on the simulation, Solar Photovoltaic (PV) system can achieve carbon reduction of 595.5 TCO2 per year, the total power of the PV system reaches 1201kWp, the average power generation of the system is 1025MWh/year, and the system efficiency is 68.1%. The PV system can reduce the carbon emissions of public teaching buildings by 23%. The utilization rate of the roof space of public teaching buildings reaches 64%. Based on the simulation results to analyse the models of colleges and universities participating in the carbon trading market, three models are proposed: unified integration into the national carbon trading market system, incomplete integration into the carbon trading market, and the establishment of a university carbon trading market

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model.

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

Carbon emission and maintenance cost of commercial buildings: Quantification, analysis and benchmarking

Minimizing the carbon emissions of buildings entails effective resources deployment to maintain the buildings and their facilities. Through an environmental-economics lens, a mixed methods research was conducted on 27 commercial buildings, from which reliable and longitudinal data were collected. Considering the different mixes of premises (office, retail and car park) in the buildings and their variability in carbon emission intensity and maintenance cost intensity, a normalization method was developed based on appropriate scaling factors of the premises. Correlation analyses revealed significant correlations between building age and builder's work maintenance cost, as well as between building area and carbon emissions. The finding that the carbon emission intensities of the buildings decreased with capital project costs implies that capital projects, especially energy retrofits, can significantly reduce carbon emissions. Using the benchmarking charts constructed, the buildings - with or without capital projects implemented - were compared in terms of carbon emissions and maintenance costs. Besides contributing insights into future research, the study results hold significance for stakeholders, including policy makers, building owners and facilities managers, in optimizing maintenance resources to attain a cleaner built environment.

Reducing embodied carbon emissions of buildings – a key consideration to meet the net zero target

The prevalence of modern techniques and industrialized materials has resulted in environmental contamination. Therefore, prioritizing the use of sustainable materials with minimal CO2 emissions should be the fundamental principle guiding future developments in construction projects. Rammed earth construction, a traditional method in Latin America, is valued for its sustainable, environmentally friendly properties. The research examines integrating Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in Colombia, a region with limited exploration of BIM-LCA interoperability on earth-based materials. This study applies LCA (Cradle-to-Gate) to assess material use, and transportation, aiming to reduce construction carbon emissions. This study compares the traditional and Sustainable building environmental impacts through the BIM (using Autodesk Revit) and carbon footpring. Findings show that BIM-based models with traditional materials have significantly higher carbon footprints (171.93 kg CO2 eq per square meter) than sustainable models (62.25 kg CO2 eq per square meter). This research highlights the importance of low carbon materials for the carbon reduction to meet net zero target in construction industry.

Life cycle assessment of curtain wall facades: A screening study on end-of-life scenarios

By 2030, Australia is committed to reducing 43 % of its greenhouse gas emissions below 2005 record, and achieving net zero emissions by 2050. Embodied carbon emissions in materials used in Australia's building and construction sector are the next frontier to significantly reduce carbon emissions. Façades are among the highest contributors to embodied carbon emissions in commercial buildings. However, there are limited studies on the life cycle assessment examining the embodied impacts of different types of curtain walls. Further, the majority focus on the upfront carbon, oversimplifying the end-of-life (EoL) possibilities, often assuming demolition as the only scenario. This study explores the life cycle assessment of three façade systems from cradle-to-grave analysing the trade-off between the impacts of the service life and end-of-life stages to identify the associated environmental benefits. A total of 27 scenarios based on different façade service lives of 20, 40 and 100 years and different EoL scenarios, including landfill, recycling and reuse are examined. The assessed variables include the global warming potential (GWP), total use of renewable primary energy resources (PERT), total use of non-renewable primary energy resources (PENRT) and net freshwater use (FW). This study highlighted the importance of considering the net benefits beyond the boundary. Compared to recycling, reuse provides more net benefits to GWP, PERT and PENRT. By extending the façade lifespan, the uncertainty of the impact indicators associated with different EoL decreases. Moreover, the life cycle benefits of selecting a façade with higher operational savings increases, given the same EoL scenario.

City-level building operation and end-use carbon emissions dataset from China for 2015–2020

sThe 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.

Carbon emission measurement of the envelope of a university teaching building in Hefei City

Abstract In response to the challenges posed by global warming, China has formulated dual carbon goals. In this context, the construction industry, a key carbon emitter, is vital for implementing energy-saving and emission-reducing strategies. This study zeroes in on the pivotal role of building envelope structures in carbon emissions, with a focus on a university building in Hefei. Commencing with onsite measurements to gather initial data, the study leverages simulation software to conduct an in-depth analysis. Employing orthogonal experimental design and variance analysis, it meticulously assesses the carbon emissions associated with different materials used in exterior walls, roofs and windows, considering both the material production and building operation phases. The research scrutinizes the impact of these materials on carbon emissions, with a special focus on the performance of seven distinct building retrofit schemes. Key findings of the study underscore that the type and thickness of exterior wall materials substantially influence carbon emissions during the production phase. Conversely, the choice of window materials emerges as more critical in reducing emissions during the building operation phase. The implementation of the various retrofit schemes demonstrates a tangible reduction in overall building carbon emissions. Specifically, these schemes yield a yearly reduction in carbon emissions of 2.96–3.62 tons during operation and a substantial decrease of 30.36–165.97 tons in the production phase, compared to the original structure of the case study building. These insights not only offer practical and viable strategies for the construction industry’s low-carbon development but also provide theoretical underpinnings and references for future building designs and retrofits.

Considering critical building materials for embodied carbon emissions in buildings: A machine learning-based prediction model and tool

Construction activities discharge considerable carbon emissions, causing serious environmental problems and gaining increasing attention. For the large-scale construction area, high emission intensity, and significant carbon reduction potential, embodied carbon emissions of buildings worth special studying. However, previous studies are usually post-evaluation and ignore the influences of project, construction and management. This paper focuses on critical building materials and adopts machine learning methods to realize carbon prediction at design stage. The activity data, including critical building materials, water, and energy consumption, is analyzed and 30 influencing factors at the project, construction, and management levels are identified. Three algorithms (artificial neural network, support vector regression and extreme gradient boosting) are used to develop machine learning models. The proposed methodology is applied to 70 projects in the Yangtze River Delta region of China. Results show that the established models achieved high interpretability (R2 >0.7) and small average error (5.33%), well proving theirs feasibility. Furthermore, an automated tool is developed to assist practitioners to predict the critical materials consumption and embodied carbon emissions conveniently. The proposed operable model and practical tool can efficiently support effective adjustments and improvement to reduce carbon in construction.

Embodied residential building carbon emissions reduction in Nepal using linear optimization modeling

The study of embodied residential carbon emissions has attracted significant attention in sustainability research amidst growing awareness of climate change. As Nepal experiences economic growth and urbanization, urban areas are witnessing a significant surge in carbon emissions, in contrast to past decades. Despite national level initiatives aimed at achieving net zero emissions by 2045, there is a noticeable gap in actionable approaches at provincial, ecological, and city levels regarding the tracking and mitigation of carbon emissions. This research combines building component data from the National Statistics Office (NSO) of Nepal and emissions data from literature in order to model three distinct emissions forecasting scenarios: (1) the current optimized, (2) a 50 % reduction emissions, and (3) a 75 % reduction in emissions. Based on these estimations, Bagmati province is found to exhibit the highest levels of embodied emissions, while Karnali province is found to have the lowest emissions. This can aid in the formulation of region-specific policy frameworks aimed at reducing emissions in high-emission areas. Additionally, the study highlights that urban emissions outweigh rural emissions by a factor of more than two, suggesting a need for policymakers to concentrate their efforts on urban areas.

Energy consumption and carbon emission of conventional and green buildings using building information modelling (BIM)

PurposeUsing building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.Design/methodology/approachDigital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.FindingsBy comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.Practical implicationsThe study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.Social implicationsOccupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.Originality/valueThis investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.

Assessment of operational carbon emissions for residential buildings comparing different machine learning approaches: A study of 34 cities in China

As a major contributor to the energy consumption and carbon emissions of the whole society, carbon reduction from residential building operation holds significant importance for achieving the carbon peak and neutrality goals. Although research on carbon emissions of the building sector on a global and regional scale has been conducted, it is challenging to identify overall patterns representing the operational carbon emissions of residential buildings in cities. This study utilized operational energy use to analyze and characterize the relevant carbon emissions of residential buildings in 34 representative cities across China over a decade from 2012 to 2021. The average emissions were assessed as 1.0 tCO2e per capita which were relatively higher in cold regions. Based on a machine learning algorithm, regression analysis was conducted to predict the operational carbon emissions considering the features including population, economy, geography, urbanization renewable energy, and building electrification. Five models based on polynomial regression, support vector regression, random forest, gradient boosting decision tree, and extreme gradient boosting were trained. It was found that the extreme gradient boosting model exhibited the best performance for predicting operational carbon emissions per capita with R2 value of 0.948 for the testing dataset. Further analysis identified building climate zone, population, urbanization rate, and building electrification rate as the most influential features on the emissions. The predictive approach for estimating operational carbon emissions in cities can provide valuable insight into carbon reduction of the building sector.

Process-based evaluation of carbon emissions from the on-site construction of prefabricated steel structures: A case study of a multistory data center in China

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 kgCO2/column and 14.41 kgCO2/column, respectively, and the CEs from their installation were in the range 38.16–69.18 kgCO2/column. The CEs from hoisting the primary and secondary beams using a car crane were 7.73 kgCO2/beam and 4.52 kgCO2/beam, respectively, with lower CEs of 2.40 kgCO2/beam and 2.22 kgCO2/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 kgCO2/beam and 10.39–43.49 kgCO2/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.

The Net Zero Energy Building Definition Framework: An Overview Pathway to Enhancing Sustainable Development and Mitigating Climate Change in Morocco

Morocco's commitment to the Marrakech and Paris Agreements' sustainability goals necessitates the implementation of Net Zero Energy Building (NZEB) initiatives to mitigate climate change and promote sustainable development for the built environment industry. Utilizing Morocco's abundant solar resources, NZEBs aim to minimize energy use and carbon emissions in buildings, aligning with global climate objectives and enhancing economic stability through reduced energy costs. This approach aids Morocco in adapting to climate change and achieving international financial and environmental targets. This paper explores Morocco's energy strategy and the regulatory framework for Nearly Zero Energy Buildings, providing a NZEB definition, assessment metrics, and performance indicators, while addressing the challenges posed by existing laws on energy-efficient buildings.

Energy use and its contributors in hotel buildings: A systematic review and meta-analysis.

Energy use is the major source of carbon emissions in hotel buildings. Past studies presented contributors to energy use, most related to hotels' physical and economics characteristics. In search of effective variables affecting energy use in hotels, this systematic review and meta-analysis aims to synthesize empirical evidence. A descriptive picture of 28 previous studies, the arguments for the direction of effects in each variable, and a quantitative synthesis of the mean effect sizes were presented. Among 18 selected contributors from past studies, 15 were statistically significant (0.05 level). The analyses also revealed that the operationalization of the energy variable is important in evaluating the relationship with a contributor. Studies considering Energy Use Intensity (EUI) indicators presented weaker correlations with gross floor area (GFA) and number of guestrooms than those considering energy raw data, for example. The occupancy rate resulted in a non-significant outcome, but this result could be related to differences among the hotels categories, as identified in the subgroup and meta-regression analyses. Future research could help develop and investigate theories to sustain or deny the relationships found here, in addition to the assessment of the outcomes in other regions, bringing more variables related to sustainable management.

Low-carbon optimization in commercial buildings through data mining

The government of China commits to achieve peak carbon dioxide emissions by 2030. According to the United Nations Environment Programme, nearly 40% of energy-related carbon dioxide emissions are attributable to the building sector while building operations account for over 70% of carbon emissions. How to quantitatively analyze the factors related to operational carbon emissions under the circumstance of rapid growth of data volume is the key problem to be solved. This article explored the main factor affecting carbon emissions of commercial office buildings based on data mining and analyzed all carbon sources using the variation and deviation method in the city of Beijing, China. It is found that electricity, trains, airplanes, and hotels have a great impact on the building’s operation stage carbon emission, and targeted carbon emission reduction policies and measures can be made to reduce the sample’s carbon emission.