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Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building's length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption. [ABSTRACT FROM AUTHOR]

The direct release of energy from the groundwater under the building of the Sobieski Family Palace in Lublin, Poland, and the obtainment of heat and cooling energy for Heating, Ventilation, and Air Conditioning systems (HVAC) provide an opportunity to reduce electricity and heat consumption and to limit CO2 emissions by 15–50%. The upgrade to the Sobieski Family Palace and the addition of new educational and administrative functions require state-of-the-art, energy-saving, and environmentally friendly solutions such as Fuel Cells and Hydrogen for Heating, Ventilation, and Air Conditioning (FCH HVAC) systems. As part of the program "Research for high-quality air in architecture and urban studies," carried out since 2018 at the Institute of Architecture Planning, Department of Contemporary Architecture, Faculty of Civil Engineering and Architecture of the Lublin University of Technology, in 2021, further research was carried out in order to prepare for the potential implementation in 2022 of the project entitled "Development and Upgrade of the Former Sobieski Family Palace in Lublin. The main body of the building—educational and administrative function," whose chief designer was Architect J. Wrana, Lublin University of Technology, 2021. The objective of this paper is to identify technologies and solutions specifically designed for HVAC systems in upgraded and renovated historic buildings. This paper is also a call for cooperation among institutions, scientists, higher education institutions, as well as an expression of appreciation for the immense energy stored in groundwater. This energy not only has the lowest carbon footprint but also is the only generally accessible large storage source from which we were unable to obtain ecologically pure energy before the introduction of FCH technology. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

Maximizing the carbon reduction in substations with minimum cost investments can be achieved by taking advantage of the potential of substations in terms of the envelope and renewable energy, which is significant in promoting carbon reduction in substations. Therefore, firstly, the relationship between building cost–energy consumption–carbon emissions is explored, and then the global optimal calculation model of substation envelope–renewable energy is established, with the lowest life-cycle carbon emission of the substation as the optimization goal. Finally, the validity of the model is verified based on a case study of a typical 110 kV outdoor substation. The model calculation results show that, without considering the cost constraint, Harbin has the highest maximum carbon reduction of 180,350 kg, which is 25.15% and 13.74% higher than the maximum carbon reduction in Shanghai and Haikou, respectively. Furthermore, based on the comparison of the cost and benefits of each carbon reduction technology, a prioritization of various carbon reduction technologies is given for each climate zone. The model established in this paper can provide the optimal configuration of substation carbon reduction technologies with different incremental cost constraints, and provide a reference for the low-carbon design of substations. [ABSTRACT FROM AUTHOR]

The recent trend is to transform existing buildings into smart, green, or passive buildings by adopting sustainable technologies to achieve increased life comfort and production efficiency through reducing carbon emissions, energy consumption, and operating costs. Thus, existing buildings can be rehabilitated in order to have the lowest possible energy consumption. One of the highest demands on the energy consumption of buildings is the heating system used during the winter months. In this paper, we propose a management and control system for electrical and energy heating consumption, based on a BMS (building management system) that is used for the rehabilitation of the students' dormitories on a university campus. This includes a cogeneration plant that can be controlled in order to produce electrical and heating energy, depending on the requirements needed to heat the building in the cold season. The system reduced the consumption of energy for heating by 13% and of electricity by 32%. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest energy user and carbon emitters globally. To increase the utilization rate of renewable energy and reduce carbon dioxide emissions, the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied. In this study, the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software, combined with energy conversion efficiency and data released by the National Bureau of Statistics, and the total energy demand and total energy supply of public institutions were predicted using the load density method. Based on the coupling mechanism of the MARKAL model, the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined. Through the study of the number of public institutions in various regions of China, energy consumption characteristics, construction area, and other related data, the reverse energy flow method is creatively proposed, and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established. The results show that the central region has the highest utilization rate of renewable energy in the public sector, reaching 36.18%. The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%, and it was 12.82% in cold zones. By 2025, the proportion of renewable energy resources in China is expected to reach 29.2%. The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China. [ABSTRACT FROM AUTHOR]

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents' awareness of energy conservation. [ABSTRACT FROM AUTHOR]

Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions. [ABSTRACT FROM AUTHOR]

In order to achieve China's energy conservation and emission reduction goal of peaking carbon dioxide emissions around 2030, it is of great significance. An important means of building energy conservation and emission reduction is the fine management of building energy consumption, which is based on the accurate prediction of building energy consumption, so as to support the optimal management of building operation and achieve the goal of energy conservation and emission reduction. This paper puts forward the evaluation indexes of the results of the building energy consumption prediction model, uses MAPE and RMSE indexes to evaluate the accuracy of the prediction results of the model, and uses the prediction time and input parameter dimensions to evaluate the time cost of the prediction model. Then, using the three building energy consumption prediction models based on machine learning algorithm established above, the prediction of energy consumption of four types of public buildings in different seasons is completed, and the prediction results are evaluated and analyzed. According to the prediction results and the requirements of related work on the accuracy of building energy consumption prediction model, the adaptation relationship between different types of buildings and different machine learning algorithm prediction models is summarized. [ABSTRACT FROM AUTHOR]

As a pillar industry with high energy consumption and low efficiency, the building industry of China has produced consistently high carbon emission levels in recent years. The important goals in the coordinated development of this industry include the large-scale development of green buildings, the use of energy technologies to reduce carbon emissions, and an effective reduction of carbon intensity. Prefabricated buildings have become popular in this industry due to their low energy consumption, emission, and pollution and environment-friendly nature. This paper examines those factors that influence the carbon emissions from the construction of prefabricated buildings across three phases, namely, production in plants, logistics transportation, and assembly construction, builds an evaluation index system for studying the carbon emissions in the materialization phase of prefabricated buildings, and employs the hierarchical fuzzy comprehensive evaluation method to construct an evaluation model. Results show that the overall energy consumption of prefabricated buildings is lower than that of traditional concrete pouring-type buildings. The hierarchical fuzzy comprehensive evaluation model is scientific and reasonable when used to measure the comprehensive benefits of carbon emissions from prefabricated buildings. By taking a project in Zhengzhou City, Henan Province as an example, the comprehensive evaluation results show that the carbon emissions of this project are at moderate levels. The carbon emissions from prefabricated buildings can be reduced by expanding the market scale of the building industry, adjusting its use of building materials, and setting up special funds for these buildings. The findings of this work provide a certain reference value for analysing the differences between prefabricated and traditional buildings in terms of their carbon emissions in the materialization phase, for evaluating how the carbon emissions of the former can be reduced, and for formulating and executing building emission reduction plans. [ABSTRACT FROM AUTHOR]

In order to calculate the carbon emissions in the construction process to achieve low-carbon buildings and low-carbon construction, the author puts forward the calculation and evaluation of building thermal energy consumption and carbon emissions based on building information modeling (BIM) technology. The author first proposed the important value and application of BIM technology in energy consumption evaluation of green buildings, taking a gymnasium as an example, a carbon emission accounting system for building construction and installation process is established based on BIM technology, and the carbon emissions in building construction and installation process are calculated and analyzed. The results show that the carbon emission during the construction and installation of a gymnasium is 766300 tons, of which the carbon emission caused by building materials is 737200 tons, the carbon emission caused by mechanical equipment is 4500 tons, and that caused by office and living is 34500 tons, accounting for 94.90%, 0.59%, and 4.51%, respectively. In conclusion through data analysis, determine the largest carbon emission source in the construction process, and then propose targeted carbon emission reduction measures in the construction process of the construction industry. [ABSTRACT FROM AUTHOR]

The Australian policy approach, based on the NABERS energy performance benchmarking scheme for commercial offices, is promoted as a 'success story' and is of interest to governments internationally. This paper explores the evidence for the impacts of the Australian approach on energy use and carbon emissions, identifies the key elements of its conception, design and implementation, and investigates the role of government. It uses a mixture of literature review, re-analysis of quantitative data and analysis of 30 original stakeholder interviews. The literature suggests that NABERS' appeal to multiple benefits of energy efficiency and its place in the wider policy mix should help deliver positive results. Analysis of publicly available data has highlighted data gaps. However, evidence suggests significant energy savings have been made, although the attribution to NABERS alone, given the policy mix, is uncertain. The interviews show high levels of agreement that the policy mix has transformed large commercial office buildings. They also highlight the wide range of actors mobilised to deliver this change and the central role of well-designed government intervention and support. The Australian experience is rightly of interest to international governments, but they must recognise that replicating its success requires attention to detail and long-term commitment. [ABSTRACT FROM AUTHOR]

Compared to general public and residential buildings, large public buildings are often difficult to construct and have a long construction period, creating greater construction energy consumption and carbon emissions on the one hand, while generating a large amount and many types of difficult-to-track process data on the other. As such, it is difficult to measure carbon emissions and analyze various influencing factors. By realizing the simple calculation of energy consumption and carbon emissions, as well as discerning the degree of influence of various factors based on the results of influencing factors research, it is of considerable practical significance to propose energy savings and emission reductions in a targeted manner. In view of the above, this work aimed to establish a more practical calculation method to measure energy consumption and carbon emissions in the construction of large public buildings, as well as to identify the multiple influencing factors related to energy consumption and carbon emissions during the construction process. To demonstrate the practicality of our approach, quantitative calculations are carried out for a new terminal building in a certain place and from the perspective of sustainable urban construction; thus, the driving factors of the traditional STIRPAT model are extended to seven. Based on the calculation results, a modified STIRPAT model is used to analyze the comparative study of impact factors, such as population and construction machinery performance, on energy consumption and carbon emission intensity. The results show the following: (1) The energy consumption value per square meter of this terminal building is 3.43 kgce/m2, and the average carbon emission per square meter is about 13.88 kgCO2/m2, which is much larger than the national average of 6.96 kgCO2/m2, and (2) the type of energy used in the construction process has the greatest degree of influence on energy consumption and carbon emission, and the local GDP, population factor, construction machinery performance specifications, and shift usage also show a positive correlation with the growth of total energy consumption and carbon emissions. Moreover, while the government's continuous investment in energy conservation and environmental protection has reduced the total energy consumption and carbon emissions in construction, there is still considerable room for improvement. Finally, according to the results, we provide theoretical references and constructive suggestions for the low-carbon construction of large public buildings in the construction stage. Thus, the results of our study will allow policy makers to formulate appropriate policies. [ABSTRACT FROM AUTHOR]

Owing to the advantages of high construction efficiency, prefabricated residential buildings have been of increasing interest in recent years. Against the background of global heating, designing low-carbon facades for prefabricated residential buildings has become a focus. The main challenge for this research is in designing windows for prefabricated residential buildings that can lead to the best performance in carbon emissions. The purpose of this paper is to summarize window design advice for prefabricated residential building facades with low-carbon goals. This paper adopts the single control variable research method. Building energy consumption and carbon dioxide emissions under different conditions comprise the primary data used in the study. In the process of achieving the research aim, this study firstly extracts the window design elements of prefabricated residential facades. Secondly, objective function formulas are established and a basic model is built for obtaining data. Thirdly, data results are analyzed and window design advice is put forward under the condition of a low-carbon goal. This paper discusses that the optimal window-to-wall ratio (WWR) with a low-carbon orientation is around 0.15, and compares it innovatively with the optimal WWR under an energy-saving orientation at around 0.38. The research results of this paper can deepen the understanding of architectural low-carbon design and play a guiding role for architects. [ABSTRACT FROM AUTHOR]

Urban green infrastructures (UGI) have been suggested as a natural solution to tackle the problem of human thermal comfort as well as to reduce energy consumption in buildings under the pressures of rapid urbanization and global warming. However, the acceptance of UGI to mitigate the urban heat effect is not yet universal. The development of such an infrastructure is also not consistent across the regions, emphasizing the different objective parameters and methodologies. A systematic review has been conducted to analyze the published research work on UGI, targeting thermal comfort, in the past decade to identify the trends of UGI development around the world. The result shows that most of the studied locations were situated around the Mediterranean Sea region in a temperate climate, and most of the studied cities are within countries with a high gross domestic product, large urban area and urban population, primary energy consumption, and high greenhouse gas and carbon dioxide emissions. Extensive green roofs are the most popular type of UGI and mostly use Sedum plants. In the published studies, experimental setups are the most common methods by which to collect data. EnergyPlus is the most popular software used to conduct energy analysis for buildings, whereas ENVI-met is more commonly used for microclimate analysis. These results indicated that the direction of UGI studies is driven by climate characteristics and the socioeconomic factors of geographical location, which favor low construction cost and maintenance needs, with a minimal irrigation requirement for small-scale UGI projects. Understanding the trend of UGI approaches for thermal comfort allows researchers to standardize practices that help the decision-making process for future researchers while recognizing the limitations and potential of current UGI practices. It is recommended that future studies should include arid and equatorial climate regions, with more focus on large-scale projects including high-rise building environments to comprehensively evaluate the effectiveness of UGIs. [ABSTRACT FROM AUTHOR]

Operational building energy consumption accounts for 55% of global energy consumption. Most of this is attributed to residential buildings, as they make up the largest building type when compared to the total building stock worldwide. As the building envelope is a major contributor to building energy performance, especially the external walls, its optimisation is therefore imperative to reduce energy consumption and carbon emissions. This study set out to assess the effects of waste material additions to external walls and their effect on building energy performance. This research aimed to critically investigate the effect of rice husk ash (RHA) masonry blocks on building energy performance when compared to conventional masonry blocks in tropical climates. A mix of methods, including experimental investigation and simulation studies, were employed for this study. Three variations of RHA block samples were created for this investigation: RHA 5%, RHA 10%, and RHA 15%. Using prototype buildings from the study context, the building simulation results helped quantify the impact on building energy performance from the reuse of rice waste. The largest improvement to the building fabric was recorded with the RHA15% blocks, which resulted in a 9.9% and 11.3% reduction in solar heat gains through the external walls for the selected bungalow and duplex/storey building, respectively. This resulted in a 6.55% and 4.2% reduction in cooling loads and a 4.1% and 2.8% reduction in carbon emissions, respectively, for the bungalow and duplex/storey building. The findings of this research will prove valuable to householders, researchers, architects, and policymakers in their decision-making processes. The findings will also be useful in introducing new methods that can be adopted for similar studies, bridging the knowledge gap while promoting a circular economy through the reuse of landfilled waste. [ABSTRACT FROM AUTHOR]

Despite a gradual decline in rural population due to urbanization, as of 2022, approximately 35% of China's total population still resides in villages. Over a span of 40 years, carbon emissions from villages have significantly surged, with a sevenfold increase from energy consumption and a 46% rise from agriculture. Consequentially, the development of low-carbon villages is imperative. A comprehensive understanding of the primary sources of carbon emissions in villages is crucial for implementing practical and effective strategies towards low-carbon development. However, limited research has been conducted on quantifying carbon emissions and sinks for Chinese villages. This study aims to address this gap by proposing a methodology for assessing carbon emissions in villages, including the emissions of CO2, CH4 and N2O. Inspired by the IPCC standard methodology for greenhouse gas emissions at national levels and provincial greenhouse gas inventory guidelines customized for China's context incorporating localized characteristics, this approach has been applied to seven villages in Northern China based on field investigations. Employing a range of methods including field surveys, questionnaires, statistical records and big-data platforms, we collected the carbon emission activity levels of the seven villages using the most up-to-date carbon emission factors. Subsequently, the collected data and facts are quantitatively processed to generate results that are compared among the seven villages. These findings are also compared with those from other studies. The analysis indicates that the primary industries in these villages significantly influence the total carbon emissions. Moreover, the study reveals that energy consumption in buildings, agriculture, transportation and waste disposal are the most influential emission sources. These findings provide valuable insights into the carbon emission landscape of villages and can serve as a guide for implementing strategies and policies aimed at promoting low-carbon development in the rural areas of Northern China. [ABSTRACT FROM AUTHOR]

In response to extreme climate change, China has set a goal of reaching emission peak by 2030 and achieving carbon neutrality by 2050. Energy conservation and emission reduction of building heating in northern China are key to achieving this goal. Based on building area prediction and energy consumption intensity, this paper establishes a model for calculating heating energy consumption and carbon emissions in northern residential buildings at the macro level, which provides a basis for formulating policies related to heating and emission reduction in northern buildings. Based on the research method of scenario projection, combining the carbon emission subsets and future heating energy mix projections in northern China, the heating energy consumption in northern China decreases to 175, 149 and 135 Mtce in 2050 under the baseline, medium control and strict control scenarios, respectively. The heating energy consumption in the northern region should be controlled at least under the medium control scheme. Under this scenario, building heating carbon emissions in the north could be reduced to 450 MtCO2 by 2050, or 280 MtCO2 if more stringent abatement technologies are adopted. In order to achieve this goal, a combination of energy-saving technologies must be used. The use of biomass and solar technologies should be emphasized in rural heating, while envelope renovation makes the greatest contribution to heating energy savings, with envelope renovation contributing up to 92 Mtce to urban heating. [ABSTRACT FROM AUTHOR]

Since decoration is an essential part of buildings, the carbon emissions generated by decoration work should not be ignored. In recent years, prefabricated decoration has attracted much attention as efforts are made to pursue green, low-carbon, and waste-reducing buildings. However, research on carbon emissions assessment of prefabricated buildings has focused mainly on the structural aspect of prefabricated buildings, with few studies having considered prefabricated decoration. This study therefore focuses on assessing the carbon emissions of prefabricated decoration from the life cycle perspective of a case study residential building and explores the potential for reducing carbon emissions by decorating buildings with prefabricated components. The results show that using prefabricated decoration in the case study building reduced carbon emissions by 29.08% at the building material production stage compared to traditional decoration, and using an optimized design of prefabricated decoration, the building's energy consumption over its design life could reduce carbon emissions by 1046 kgCO2/m2. These findings demonstrate the benefits of prefabrication decoration for reducing carbon emissions. This study provides decoration companies with robust data and insights to guide future decisions and practices, helping to transform and achieve the carbon neutrality goal for the building decoration industry. [ABSTRACT FROM AUTHOR]

• A BIM-supported energy performance analysis for green building design is proposed. • This systematic analytical framework integrates explainable machine learning and multi-objective optimization. • It can serve as a data-driven decision tool to guide green building development even under uncertainty. • The top important features that greatly affect the building energy performance can be quantitatively identified. • Building energy performance can be improved by 13.43% using the obtained optimal strategy. Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO 2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design. [ABSTRACT FROM AUTHOR]

To meet the goals established by the 2015 Paris Accord, scientists say that we will need to achieve zero emissions between 2030 and 2050, which will require a drastic departure from current practices in the building industry, as buildings are a primary contributor of carbon emissions and a major consumer of natural resources. However, building technology research is often driven by disciplinary 'experts' and end-users have little opportunity for involvement in the design process. As buildings become more technologically complex, users must rely on industry professionals with specialized knowledge to design, manufacture and manage these increasingly complex environments Instead, this paper provides an alternative attitude to approachable research methods that open up building technology innovation opportunities to untraditional innovators. When the user has control of the design/innovation process, it opens up a new realm of possibilities rich with lived experience. Understanding the movement of heat, or thermal transmittance , through materials and assemblies is critical in controlling energy consumption in buildings. Despite recent advances in computational technologies, physical testing is still the industry standard to determine a material's thermal properties. However, this process can require expensive equipment, a controlled environment for testing and deep technical knowledge to create a degree of accuracy that is not necessary for all phases of the innovation process and can even act as a barrier to the early stages of design. In this paper we propose the Approachable Hot Box (AHB), a low-cost, low-tech and accessible testing apparatus that can quickly and easily evaluate the impact of simple building materials and assemblies on ambient environmental conditions. By reducing unnecessary complexity for early-level design decisions, the AHB is consistent and reliable at an affordable price. Not only does the AHB rapidly and affordably test small material samples, it helps to articulate potentially complex concepts in a tangible and understandable way, which helps to lower the barrier to innovation. By creating a non-disciplinary, iterative design approach that uses conventional materials and accessible concepts, this work demonstrates that rapid experimentation can be used as a tool to study building assemblies or systems by non-traditional innovators. [ABSTRACT FROM AUTHOR]

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing. • A carbon-embedded multilateral energy interaction mechanism in a PIES is discussed. • A time-varying carbon emission measurement model is established. • An energy-carbon integrated pricing strategy based on the dynamic carbon factor and virtual carbon emission flow is designed. • The impacts of energy-carbon integrated pricing on the scheduling of the PIES and carbon emission are investigated. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption of buildings, an air source heat pump assisted rooftop photovoltaic-thermal integration system is designed. The installation area of photovoltaic modules and collectors will not only affect the power side, but also affect the thermal side. Therefore, the basic architecture of the photovoltaic photothermal integration system is first established, and then the improved whale algorithm is used to optimize the photovoltaic photothermal integration system with the daily operating cost as the optimization goal. At the same time, the influence of the installation area of the photovoltaic photothermal module on the comprehensive performance of the system is analyzed, and the environmental and economic benefits of the photovoltaic photothermal system are analyzed. The results of the example show that the roof of the building has significant benefits in environmental protection and investment recovery period when the photovoltaic photothermal system with the optimal area ratio is installed on the roof of the building. The solar photovoltaic power generation system can reduce carbon dioxide emissions by 147.11 t within 25 years, and the solar collector system can save 170.5 thousand yuan in 1 year. It has achieved the purpose of saving energy, reducing carbon dioxide emissions and protecting the environment. [ABSTRACT FROM AUTHOR]

The building sector releases 36% of global CO 2 emissions, with 66% of emissions occurring during the operation stage of the life cycle of a building. While current research focuses on using Building Information Modelling (BIM) for energy management of a building, there is little research on the visualizing building carbon emission data in BIM to support decision makings during operation phase. This paper proposes an approach for gathering, analyzing and visualizing building carbon emissions data by integrating BIM and carbon estimation models, to assist the building operation management teams in discovering carbon emissions problems and reducing total carbon emission. Data requirements, carbon emission estimation algorithms, integration mechanism with BIM are investigated in this paper. A case is used to demonstrate the proposed approach. The approach described in this paper provides the inhabitants important graphical representation of data to determine a buildings sustainability performance, and can allow policy makers and building managers to make informed decisions and respond quickly to emergency situations. [ABSTRACT FROM AUTHOR]

Buildings are responsible for more than 40% of global energy usage and as much as 33% of global greenhouse gas emissions in both developed and developing countries. However, the study of carbon emissions over a building's life cycle based on Ecotect and building information modeling in developing areas of Nanjing has yet to be conducted. Therefore, the results and recommendations of this paper represent a contribution to the literature on this topic. The scientific value of this paper is that Ecotect and building information modeling can be helpful in simplifying the estimation of carbon emissions over a building's life cycle because they can provide a majority of the information and calculation tools necessary for performing an life cycle assessment (LCA), which may alleviate the problem of insufficient information when executing an LCA of a building. A sensitivity analysis was performed by changing several parameters to identify the parameters that have the largest impacts on the performance a building. A comparison of the life cycle carbon emissions for each stage showed that the operational stage is the largest contributor to carbon emissions. Approximately 85.4% of the total carbon emissions were generated during operation. The construction stage accounted for 12.6% of the total carbon emissions. Approximately 2% of the total carbon emissions occurred during the demolition stage. Compared to the total carbon emissions over the entire life cycle of the building, carbon sequestration by vegetation was minimal. Thus, these findings indicate that governments can achieve the greatest reductions in carbon emissions by targeting the operational phase of buildings. However, the opportunity for addressing non-operational phases should not be ignored because the average carbon emissions per working area per year of non-operational stages are far greater than those of the use phase. [ABSTRACT FROM AUTHOR]

The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model. [ABSTRACT FROM AUTHOR]

Traditional buildings constitute a large proportion of the building stock in many countries worldwide; around 40% of the UK's housing stock was built before 1940 and was primarily made with solid masonry walls. Only 11% of UK solid-walled dwellings had insulation installed, suggesting the high potential of the low-carbon retrofit of traditional buildings. However, there is evidence of the occurrence of unintended consequences, often associated with excess moisture. A method is presented for moisture risk management that includes the development of a process and a framework. These tools are then integrated into a novel framework for the combined energy and moisture performance retrofit of traditional buildings. An example of the framework's practical application is provided, with a focus on retrofit measures for solid-wall insulation. The proposed systematic approach demonstrates the interconnected nature of energy and moisture. It harmonises the principles needed to support organisations in the delivery of robust retrofit of traditional buildings through the integration of pre-retrofit building assessment and post-retrofit monitoring in the process. The risk-management process and framework presented can be valuable tools to support designers in providing robust and scalable retrofit measures and strategies. [ABSTRACT FROM AUTHOR]

It is well known that the Gulf Cooperation Council (GCC) of countries resides at or close to the top of the global table of CO2 emissions per capita and its economy relies heavily on its fossil fuels. This provides a context for green building programs that initially aim to create an understanding of emission pathways within the GCC and hence develop approaches to their reduction in the built environment. A set of criteria will allow specific analysis to be undertaken linked to the spatial dimensions of the sector under study. In this paper, approaches to modelling energy consumption and CO2 emissions are presented. As investment in the built environment continues, natural resources dwindle and the cost of energy increases, delivering low-energy buildings will become mandatory. In this study, a hybrid modelling approach (bottom-top & top-bottom) is presented. Energy benchmarks are developed for different buildings' uses and compared with international standards. The main goals are to establish design benchmarks and develop a modelling tool that contains specific information for all buildings types (existing and new), as well as planned and projected growths within the various city districts, then integrate this database within a geospatial information system that will allow us to answer a range of "what-if"-type questions about various intervention strategies, emissions savings, and acceptability of pre-defined course of actions in the city sector under consideration. The spatial carbon intensity may be adjusted over a certain period, (e.g., through local generation (microgeneration)) or due to an increasing proportion of lower carbon-energy in the generation mix and this can be related to the sector and city overall consumption. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [ABSTRACT FROM AUTHOR]

The building sector accounts for a significant proportion of global energy usage and carbon dioxide emissions. It is important to explore technological advances to curtail building energy usage to support the transition to a sustainable energy future. This study provides an overview of emerging and sustainable technologies and strategies that can assist in achieving building decarbonization. The main technologies reviewed include uncertainty-based design, renewable integration in buildings, thermal energy storage, heat pump technologies, thermal energy sharing, building retrofits, demand flexibility, data-driven modeling, improved control, and grid-buildings integrated control. The review results indicated that these emerging and sustainable technologies showed great potential in reducing building operating costs and carbon footprint. The synergy among these technologies is an important area that should be explored. An appropriate combination of these technologies can help achieve grid-responsive net-zero energy buildings, which is anticipated to be one of the best options to simultaneously reduce building emissions, energy consumption, and operating costs, as well as support dynamic supply conditions of the renewable energy-powered grids. However, to unlock the full potential of these technologies, collaborative efforts between different stakeholders are needed to facilitate their integration and deployment on a larger and wider scale. [ABSTRACT FROM AUTHOR]

Solar-powered air conditioners offer a high potential for energy-efficient cooling with a high economic feasibility. They can significantly reduce the energy consumption in the building sector, which is essential to meet the greater ambition of reducing greenhouse gas emissions by 80% in the EU by 2050. This paper presents a computational model development capable of simulating the behaviour of a photovoltaic-assisted heat pump in different locations and working conditions. In addition, this model has been used to optimise a solar on-grid air conditioning system. The generated model has been validated with experimental data obtained in a real facility for a whole summer of operation (more than 100 tested days) in a Mediterranean climate (Alicante, Spain). According to the simulation results, the average Energy Efficiency Ratio (EER) of the system is 16.0, 10.7 and 7.8 in Barcelona, Madrid and Seville, respectively. The optimisation analysis has proven that the severity of the climatic region increases the costs as well as the optimum PV power to drive the AC unit. The obtained values for the the PV power and the annualised cost are 400 W and 506.2 € for Barcelona, 900 W and 536.7 € for Madrid, and 1300 W and 564.7 € for Seville. The annualised cost and the CO 2 emission levels are higher for the conventional system (no PV panels) than for the solar on-grid system, regardless of the installed PV power. This difference can be up to 66.64 € (10.55%) and 112.94 kg CO 2 (64.83%) per summer season in the case of Seville. [ABSTRACT FROM AUTHOR]

Strategic selection of glazing, its window-to-wall ratio, and wall thickness of building reduce the energy consumption in the built environment. This paper presents the experimental results of solar optical properties of five glasses: clear, tinted bronze, tinted green, bronze reflective, and polymer dispersed liquid crystal glasses. Laterite room models were modeled with four different thicknesses and four different glasses using Design Builder, and thermal simulation tests were carried out using Energy Plus. The energy savings and carbon emission mitigation prospective of a building's glazing variety, window-to-wall ratio (WWR), and wall thickness were investigated. The results revealed that among the five window glasses studied, the polymer dispersed liquid crystal glazing window (PDLCGW) was found to be the most energy-efficient for low heat gain in laterite rooms. The laterite room with 0.23 m wall thickness and 40% PDLCGW WWR reduced 18.9% heat gain in comparison with the laterite room with 0.23 m wall thickness and 40% clear glass WWR. The laterite room of 0.23 m wall thickness with PDLCGW glazing of 40% WWR enhanced cooling cost savings up to USD 31.9 compared to the laterite room of 0.08 m wall thickness with 40% PDLCGW. The laterite room of 0.23 m wall thickness with PDLCGW glazing of 40% WWR also showed improved carbon mitigation of 516 kg of CO2/year compared to the 0.23 m wall thickness laterite room of 40% WWR with clear glass glazing. The results also showed that the laterite room with 0.23 m wall thickness and 100% clear glass WWR increased heat gain by 28.2% in comparison with the laterite room with 0.23 m wall thickness and 20% clear glass WWR. The results of this article are essential for the strategic design of buildings for energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

Abstract: The construction industry is characterized by high-energy consumption and intensive-carbon emissions. With the promotion of energy-saving technologies in buildings combined with concerns for global-warming, there has been a gradual shift in energy conservation requirements from the operation stage to the construction stage. This shift is primarily delivered through the use of improved materials and technologies implemented during the building construction stage. Building industrialization is an innovative construction approach that has experienced rapid up-take in China. The use of prefabricated components in building construction is now a key feature of the building industrialization process, and thus warrants greater attention in terms of its energy consumption and carbon emission. This paper utilizes a process-based method to assess carbon emissions during the prefabrication manufacturing process in offsite factories. A life cycle assessment is applied to a case study concerning a prefabricated concrete interior wall board. Carbon sources during the manufacturing process is identified and the carbon emissions are quantified using a factor method. The results show that carbon emissions from the interior prefabricated concrete wallboard of volume 0.609 m3 is 427 kg, with the vast majority of the emissions originating from building materials, at 96.2%. The carbon emissions from electricity consumption constitutes 3.65%, and that from workers make up only 0.16%. The findings of this study offer a carbon emission benchmark for building industrialization, which in turn serve as a solid data foundation for carbon assessment of prefabricated buildings in China. Highlights • Building industrialization is an innovative construction approach. • A process-based method is developed to assess carbon emissions of pre-components. • A life cycle assessment is applied to a case study. • Carbon sources during the manufacturing process is identified. [ABSTRACT FROM AUTHOR]

Highlights • Energy consumption in higher education institutions (HEIs) modelled. • For the first time, panel data used, with cross-sectional and time-series variations. • Over time and across HEIs, energy use increases as income and size increase. • Economies of scale as HEIs grow, but energy use still rising with sector growth. • Research universities more energy intensive. Abstract Among the various sustainability goals of higher education institutions (HEIs), reducing energy use and carbon emissions are particularly important. However, not much is known about energy demand from the higher education sector – especially since there is a lack of robust models of energy demand in this sector. This paper, the first to utilize a panel dataset and advanced panel econometric techniques in order to model energy use in higher education, investigates variations in energy use between HEIs (cross-sectional analysis), and also changes in energy use over time (temporal analysis), using the UK as a case study. We argue that panel dataset and methods are more useful for understanding growth (and reduction) in energy use within the HE sector than the methods used within previous cross-sectional studies. Results show that, over time and also across the sector, energy consumption in the HEIs increases with increases in income and floor space, but at a slower rate. As HEIs grow overall (in terms of income, floor space, student and staff number) over time, they become more 'energy efficient' (using less energy per unit of area, population or income), indicating economies of scale in the temporal dimension. Results also show that after controlling for income and size, research intensive HEIs consume more energy. We also find a small but statistically significant effect of energy prices on energy consumption, as might be expected. Simulation using the model parameters for an example scenario suggests that energy consumption will continue to increase unless there is a significant change in the policies driving income growth and spatial expansion in the HE sector in the UK. [ABSTRACT FROM AUTHOR]

Carbon emissions contribute substantially to climate change. Many researchers believe that the construction industry can contribute significantly to carbon dioxide reduction through cleaner and more sustainable building production and construction, e.g. low energy building design and the use of low carbon materials. Nonetheless, the environmental impacts associated with construction process should not be underestimated. Despite that, as construction process is complex and involves many participants, the calculation of carbon emissions during the construction process is never an easy task. Divergence and limitation are found in both the system boundary and carbon sourcing method. This research aims to resolve the problem and facilitate construction managers to identify carbon emissions so as to optimize them through better construction arrangement. In this paper, the system boundary of the construction stage is first defined and the related activities being included in the construction process are identified. A methodology of establishing the sources of carbon emissions during the construction process based on a quota-based method of China is proposed. The calculation model for carbon emissions is established according to the traditional emission factor approach with some modifications. The carbon emitted during the construction process of a case teaching building is calculated using the proposed quota-based carbon tracing (QCT) model. The QCT model not only provides a systematic framework for the tracing of carbon emissions, it could also help construction managers identify low carbon construction methods and logistics schedule in order to maximize the carbon reduction opportunity during the construction stage. [ABSTRACT FROM AUTHOR]

More than 50% of the world's population lives in cities. Its buildings consume more than a third of the energy and generate 40% of the emissions. This makes cities in general and their buildings in particular priority points of attention for policymakers and utilities. This paper uses population density as a variable to know its influence on energy consumption and emissions produced in buildings. Furthermore, to show its effect more clearly, the influence of the climate was eliminated. The usual energy consumption in buildings is thermal and electrical. The study was carried out at the city level, both per inhabitant and per household. The area actually occupied by the city was considered. The proposed method was applied to the case of Spanish cities with more than 50,000 inhabitants. The results show that the higher the population density, the higher the energy consumption per inhabitant and household in buildings. The consumption of thermal energy is elastic, while that of electrical energy is inelastic, varying more than 100% between extreme groups. Regarding CO2 emissions, the higher the population density, the higher the emissions. Emissions of electrical origin barely vary by 2% and are greater than those of thermal origin. In addition, the proportion of emissions of electrical origin, with respect to the total, decreases with increasing population density from 74% to 55%. This research aims to help policymakers and utilities to take the appropriate measures that favor the use of renewable energies and reduce CO2 emissions. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

In the context of low-carbon construction, reducing carbon dioxide emissions from town construction land is the key to mitigating the problems caused by global warming. The influence of spatial form on carbon emissions has been generally recognized, but its influence at the level of town construction land is less explored. Therefore, in this study, in order to investigate the relationship between the spatial form of town construction land and carbon emissions, the relationship between them was analyzed, taking Macheng town of Bengbu city as the research object, selecting spatial form elements and quantifying them, and characterizing and accounting for the carbon emissions from the town construction land by each building's energy consumption. The study demonstrates that the spatial form elements such as building area and building storeys are important factors affecting the carbon emissions of residential land. Likewise, the building area, building shape coefficient, and floor area ratio are crucial factors impacting the carbon emissions of public lands. This research offers spatial form optimization strategies from a carbon reduction perspective by delving into the inherent relationship between spatial form and carbon emissions in town construction land. Consequently, it provides valuable scientific guidance for quantifying spatial planning and formulating carbon reduction strategies within a low-carbon framework. [ABSTRACT FROM AUTHOR]

More than half of the world population live in urban settlements which are responsible for a large share of energy consumption and, consequently, carbon emissions. The transition towards a more sustainable urban environment requires a change in paradigm in terms of how we design and manage our cities. Urban areas require innovative technologies and strategies to reduce energy consumption and carbon emissions, and to be included in comprehensive plans encompassing all technical, social and economic dimensions which characterise cities. This involves the transformation of urban contexts, with a focus on local and urban-level mitigation measures, such as the construction of positive energy buildings, deployment of renewable energy, promotion of a sustainable mobility, creation of resilient urban infrastructure, implementation of circular economy and recycling practices, etc. The present article provides a perspective on the sustainable energy transition in cities, focusing on the building and transportation sectors. Furthermore, insights on supporting mechanisms and innovative management strategies are presented. [ABSTRACT FROM AUTHOR]

For decades, among other industries, the construction sector has accounted for high energy consumption and emissions. As the energy crisis and climate change have become a growing concern, mitigating energy usage is a significant issue. The operational and end of life phases are all included in the building life cycle stages. Although the operation stage accounts for more energy consumption with higher carbon emissions, the embodied stage occurs in a time-intensive manner. In this paper, an attempt has been made to review the existing methods, aiming to lower the consumption of energy and carbon emission in the construction buildings through optimizing the construction processes, especially with the lean construction approach. First, the energy consumption and emissions for primary construction materials and processes are introduced. It is followed by a review of the structural optimization and lean techniques that seek to improve the construction processes. Then, the influence of these methods on the reduction of energy consumption is discussed. Based on these methods, a general algorithm is proposed with the purpose of improving the construction processes' performance. It includes structural optimization and lean and life cycle assessments, which are expected to influence the possible reduction of energy consumption and carbon emissions during the execution of construction works. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is considered to be the most systematic methodology that is widely used in the area of energy analysis. However, embodied energy (EE) and carbon (EC) analysis requires significant time and effort to ensure the reliability of the LCA results. Therefore, a more comprehensive model of conducting the energy analysis is required to provide more realistic EE and EC analysis in turn. Hybrid LCA has been identified as the best model in improving the completeness of EE and EC inventory data. However, such a benefit was not empirically verified extensively, especially in the Malaysian construction industry. This paper demonstrates an extended application of hybrid LCA to Malaysian building design systems, and further investigates the completeness of the model. Finally, the potential for EE and EC reduction through the allocation of low EE and EC intensities of alternative materials, products or components has been evaluated. The results revealed that the hybrid LCA improved the completeness of the EE and EC inventory data compared with other models. By using low EE and EC intensity materials, products or components, a total EE and EC reduction of 43% and 41%, respectively was achieved. The results showed that the proposed methodology can assist designers practically during the early stage of the design process in the Malaysian construction industry. [ABSTRACT FROM AUTHOR]

• Implementation and benchmarking of five advanced control methods. • Comparison of black-, gray-, and white-box MPC with RL and approximate MPC. • Introduction of qualitative KPIs to assess the applicability. • Application in a one-year simulation to evaluate adaptability. Advanced building control strategies like model predictive control and reinforcement learning can consider forecasts for weather, occupancy, and energy prices. Combined with system and domain knowledge, this makes them a promising approach to reduce buildings' energy consumption and CO 2 emissions. For this reason, model predictive control and reinforcement learning have recently gained more popularity in the scientific literature. Nevertheless, publications often lack comparability among different control algorithms. The studies in the literature mainly focus on the comparison of an advanced algorithm with a conventional alternative. At the same time, use cases and key performance indicators vary strongly. This paper extensively evaluates six advanced control algorithms based on quantitative and qualitative key performance indicators. The considered control algorithms are a state-of-the-art model-free reinforcement learning algorithm (Soft-Actor-Critic), three model predictive controllers based on white-box, gray-box, and black-box modeling, approximate model predictive control, and a well-designed rule-based controller for fair benchmarking. The controllers are applied to an exemplary multi–input–multi–output building energy system and evaluated using a one-year simulation to cover seasonal effects. The considered building energy system is an office room supplied with heat and cold by an air handling unit and a concrete core activation. We consider the violation of air temperature constraints as thermal discomfort, the yearly energy consumption, and the computational effort as quantitative key performance indicators. Compared to the well-tuned rule-based controller, all advanced controllers decrease thermal discomfort. The black-box model predictive controller achieves the highest energy savings with 8.4 %, followed by the white-box model predictive controller with 7.4 % and the gray-box controller with 7.2 %. The reinforcement learning algorithm reduces energy consumption by 7.1 % and the approximate model predictive controller by 4.8 %. Next to these quantitative key performance indicators, we introduce qualitative criteria like adaptability, interpretability, and required know-how. Furthermore, we discuss the shortcomings and potential improvements of each controller. [ABSTRACT FROM AUTHOR]

University buildings have high energy requirements due to their size, numerous users, and activities, which considerably contribute to environmental contamination. Implementing energy-saving solutions in these structures has a favorable influence on the economics and the conservation of energy resources. A higher education building's energy behavior can be simulated using software to identify the optimal strategies that result in energy savings. In this research, Autodesk Revit, Autodesk Insight, and Green Building Studio are among the programs utilized to examine the energy efficiency of the university building in four European cities. Following the development of several energy-saving scenarios for the building, the offered solutions are evaluated based on their annual energy consumption, energy costs, and CO 2   emissions. Finally, multicriteria analysis techniques such as the AHP and PROMETHEE are applied to choose the best scenario for each instance. The study's findings indicate that the ASHRAE Terminal Package Heat Pump scenario performed well in all of the cities examined, reducing yearly energy usage by 43.75% in Wien and annual energy costs by 47.31% in Mallorca. In comparison, the scenario utilizing a high-efficiency VAV system with a gas boiler and chiller came in last in all situations, resulting in a decrease of 12.67% in Mallorca's annual energy usage and a reduction of 17.57% in Palermo's annual energy expenses. [ABSTRACT FROM AUTHOR]

In the tourism industry, hotels and homestay facilities account for considerable amounts of energy consumption and CO 2 emissions. Unlike other types of buildings, occupancy has significant fluctuations over time, while mostly being ignored or using a simple assumption based on energy use intensity or setting the occupancy levels. Due to the lack of occupancy rate information, great uncertainties exist in hotel operations, which leads to erroneous estimates of energy consumption in existing hotel buildings. This paper examines hotel operation practices in China with real occupancy rate data which is critical for reducing the uncertainty of estimating energy consumption patterns. Specifically, this study proposes a two-stage comprehensive and quantitative performance assessment approach for the homestay hotel building considering the system's energy and economic performance simultaneously. Regarding energy performance, there are two key facets to consider: renewable energy penetration and grid impact performance. Additionally, the economic performance (annual income and extra annual investment) of various energy portfolios is evaluated using break-even analysis. The impacts of large fluctuation in occupancy rate on electrical loads are considered and assessed in typical homestay hotels. The proposed assessment approach can comprehensively quantify the impacts of the photovoltaics (PV) areas and battery capacities on the system performance in terms of energy and economics. According to the results of the occupancy-based assessment, the ideal PV area and battery capacity are 400 m2 and 16 kWh, respectively, as opposed to the optimal energy portfolios (PV area 600 m2 and battery capacity 24 kWh) determined from conventional assessment results. [ABSTRACT FROM AUTHOR]

In recent years, in the process of promoting prefabricated buildings, problems such as waste of resources and energy have been present, which have seriously hindered the realization of carbon emission reduction benefits of prefabricated buildings. Especially during the materialization stage of prefabricated buildings which involves the most engineering activities and the most extensive sources of carbon emissions, it is urgent to further meet the low-carbon development of the construction industry. This study takes the 4 substages of design and development, component production, transportation, and installation during the materialization stage as the point of penetration and identifies the carbon reduction impact pathways based on the 3E (Environment-Economy-Energy) system theory in 5 dimensions: government policy, management mode, technology level, economy input, and energy structure. The data are collected through the questionnaire survey, and structural equation modeling (SEM) is utilized to examine the hypothesis and impact dimensions of the study. The results confirm that the management mode has the strongest effects on carbon emission reduction, followed by government policy, economy input, and technology level, and the energy structure has the weakest effects. This study presents the key carbon reduction pathways during the materialization stage of prefabricated buildings and provides recommendations for different participants to optimize policy guidance, strengthen management supervision, accelerate technology research and development, increase economy input, and optimize energy structure, with a view to achieving low-carbon governance capacity, management mode, technology system, capital, and energy utilization, and also enriches the theory in the field of prefabricated buildings carbon emission reduction, which can better achieve low-carbon development of prefabricated buildings. [ABSTRACT FROM AUTHOR]

• The outdoor environment indexes are integrated by simulation and empirical method. • Quantitatively study the interaction between green space and water bodies. • The proper layout of landscape planning can reduce the building carbon emissions. The rapid urban construction has made urban residential buildings become the main source of greenhouse gas emissions. So far, the existing studies on the impact of urban micro-climate on building energy carbon emissions have shown that green space (GS) and water bodies (WB) play positive roles in regulating outdoor thermal environment and reducing building energy consumption. Accordingly, this paper quantitatively explores the influence of the interaction between GS and WB on reducing building carbon emissions. First, the building's micro-climate is simulated by Computational Fluid Dynamics (CFD) to obtain outdoor thermal environment indexes, which are used as inputting data to calculate carbon emissions by software HDY-SMAD. Then, we utilize Geographic Information System (GIS) and Remote Sensing (RS) techniques to explore the natural surroundings of buildings, such as GS and WB distribution within the administrative scope of Dujiangyan. Next, the linear regression method is applied to analyze the relationship between GS, WB, and residential building carbon emissions, in which the residential carbon emissions data is obtained by social survey. Finally, the simulation results are verified by empirical research method. The findings demonstrate that the GS and WB have area, accessibility and seasonal differences in regulating local thermal environment and reducing building carbon emissions, and the overall regulation ability of WB for micro-climate is more significant than that of GS. In addition, the ratio area between GS and WB has the most significant influence on reducing building carbon emissions when it is 3:1 in winter, but this optimal ratio is 1:2 in summer. These conclusions can provide benchmark information for landscape planning and building energy conservation in the future. [ABSTRACT FROM AUTHOR]

• Review identified 29 unintended impacts post residential energy retrofits. • Moisture and dampness are common issues reported in post retrofitted housing. • The indoor environment is significantly affected by airtightness and over-insulation. • Pre retrofit assessment is essential to avoid hygrothermal failure. Improving energy efficiency of existing buildings is currently among the most diverse and extensive mitigation opportunities to reduce energy consumption and CO 2 emissions worldwide. However, the implementation of energy-saving measures has caused unintended impacts, often correlated with dampness and mold growth connected to poor hygrothermal behavior in residential buildings. The focus of this paper is research on the impacts of energy efficiency measures (EEMs) in regard to the hygrothermal behavior resulting from the interaction of building's envelope, indoor environment, and occupants. The results show that dampness and mold growth are by no means exclusive to neglected houses, since the occurrence of these pathologies actually depends upon a complex set of conditions, including indoor and outdoor conditions, occupancy, maintenance, ventilation, mechanical systems, and quality of the envelope. We found that building envelope post-retrofit may suffer from higher levels of moisture and dampness, higher condensations risks, and a faster structural degradation caused by higher humidity levels. We also found that measuring hygrothermal behavior may play a role in more accurately predicting both overall energy consumption and occupant comfort. While hygrothermal behavior may be problematic, we found evidence that retrofits may moderately improve thermal comfort. [ABSTRACT FROM AUTHOR]