Residential Buildings In China Research Articles

Sustainable-Driven Renovation of Existing Residential Buildings in China: A Systematic Exploration Based on Review and Solution Approaches

Under the backdrop of China’s national strategy to achieve carbon neutrality by 2060, efforts are underway across governmental, corporate, societal, and individual sectors to actively explore energy-saving renovations in existing buildings. Given that residential buildings constitute a significant proportion of the total energy consumption throughout the lifecycle of buildings in China, sustainable renovation of residential structures can contribute significantly to implementing China’s carbon emission reduction policies. While there exists a plethora of technological means in the market aimed at improving the energy performance of residential buildings, there still needs to be a more systematic discussion on the framework for sustainable renovation of existing Chinese residential buildings, with knowledge dissemination still needing to be more cohesive. In this context, this paper provides a comprehensive review of the field, utilizing bibliometric methods. Through a systematic review of selected peer-reviewed literature from the Web of Science and Scopus databases, the study focuses on the sustainable renovation of existing Chinese residential buildings, categorizing the process into three main stages: sustainable renovation, building performance simulation and suitability assessment. The paper also reviews the research methods adopted by previous researchers in the renovation, simulation and assessment stages, considering various optimization algorithms, variables, objectives, and software tools. Subsequently, the paper synthesizes a research framework comprising these three stages combined for different research objectives, aiming to assist policymakers, designers, and researchers in gaining a comprehensive understanding of the implementation status of sustainable renovation in existing Chinese residential buildings, identifying barriers to implementation, and formulating more efficient renovation policies and strategies for the future.

Gas Engine CCHP System Optimization: An Energy, Exergy, Economic, and Environment Analysis and Optimization based on Developed Northern Goshawk Optimization Algorithm

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Predictive models of embodied carbon emissions in building design phases: Machine learning approaches based on residential buildings in China

As one of the major architectural forms, residential building construction consumes considerable resources, and it has been regarded as a crucial factor for the implementation of carbon peaking and neutrality targets. Estimation of carbon emissions is essential for making informed decisions regarding building design and carbon reduction measures. Some studies have established predictive models for building operational carbon emissions. However, there are relatively few studies concerning the prediction of embodied carbon emissions. This study established a dataset containing 850 residential building samples. Based on the trade-offs and combinations of different explanatory variables, including building features and material use, this study developed two-phase models. These models, incorporating six scenarios, predicted embodied carbon intensities using twelve machine learning algorithms. For the planning design phase, an extremely randomized tree model using the building height, structural form, seismic fortification intensity, delivery form, geographical region, and material cost as inputs was favored considering R2 as 0.821 on the testing dataset. For the preliminary design phase, an extreme gradient boosting model using both building features and material intensities provided superior performance with R2 and MAPE values of 0.917 and 0.038, respectively. The optimal models and relevant feature importance analysis can be helpful for efficiently predicting embodied carbon emissions, and therefore, facilitate low-carbon designs.

Performance Analysis of Application of BIM Technology and Two-Way Progressive Optimization Algorithm in Energy-Saving Design of Residential Buildings Based on BIM Technology

The current residential energy-saving design is lagging behind, and lack of systematic design methods and scientific technical means seriously restricts process of improving quality of residential buildings in China. With development and application of digital information technology in construction field, BIM technology has brought new opportunities construction industry, and building performance data has become information basis for scientific promotion of energy conservation. This paper uses BIM technology as a platform to establish an information base of common building materials and components in Chongqing through literature research, field studies, summarization, and application practice, and to help architects make wise decisions at early design stage by using "one-click" scheme comparison and design evaluation to avoid a large number of potential energy consumption problems at a later stage, It greatly improves energy efficiency and design quality of buildings.

Enhancing domestic hot water energy efficiency in High-Rise residential buildings through interconnected and sharing mode

Residential buildings in China are consuming over 20 % of their operating energy on domestic hot water (DHW), a percentage that has been increasing in recent years. The use of energy-efficient water heaters is a direct solution to reduce DHW energy consumption, but high costs and installation requirements often deter households from adopting them. This issue is particularly prevalent in high-rise residential buildings, resulting in a low adoption rate of energy-efficient water heaters in China's high-rise residential sector. To address this challenge, this study proposes a DHW interconnected and sharing mode as a novel approach to enhance DHW energy efficiency in high-rise residential buildings. A dynamic performance calculation model based on the China Energy Efficiency Label (CEEL) for various types of water heaters was developed to evaluate the energy efficiency of this mode. The total energy consumption, carbon emissions, and energy costs of the building were assessed under both decentralized mode and interconnected and sharing mode. The findings indicate that implementing the interconnected and sharing mode can significantly decrease the annual total energy consumption, carbon emissions, and energy costs of DHW. Among the two operational strategies examined (economic optimal and environmental optimal), the economic optimal strategy is more suitable for China's current energy landscape. Under the economic optimal strategy, the annual total energy consumption, carbon emissions, and energy costs of DHW in the target building are reduced by 41.58 %, 27.97 %, and 34.87%, respectively.

Statistical characteristics and scenario analysis of embodied carbon emissions of multi-story residential buildings in China

Residential buildings are major contributors to carbon emissions from the building and construction industry. Although previous studies have comprehensively investigated the methods for embodied carbon assessment and proposed measures for carbon reduction, it remains challenging to screen the features and influencing factors of embodied carbon intensities from a statistical perspective. This study explored the characteristics of embodied carbon emissions based on 438 multi-story residential building samples. The influences of building features, such as the number of floors, number of basements, structural form, seismic design requirement, delivery type, and building area, were analyzed using clustering and significance tests. The average embodied carbon intensity was evaluated as 467.6 kg CO2e/m2 and relevant coefficient of variation was approximately 20 %. Further discussion on building material weight, cost, and embodied carbon intensities indicated that a simplified system boundary considering 95 % thresholds of both weight and cost can provide satisfactory results for embodied carbon assessment with relative errors below 3 %. Moreover, three linear models were proposed for predicting embodied carbon intensities. The results showed that the carbon intensities can be predicted based on five types of primary materials with good accuracy. The proposed system boundary, predictive models, and identified characteristics have practical applications for assessing, estimating, and reducing embodied carbon. Accordingly, it is possible to facilitate low-carbon design of multi-story residential buildings.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

A Systematic Review on the Studies of Thermal Comfort in Urban Residential Buildings in China

There have been fruitful publications on thermal comfort of urban residential buildings in China. However, there is a lack of reviews on this topic to perform a comprehensive analysis and find opportunities to meet occupants’ thermal comfort needs while improving building energy efficiencies. This paper addresses this issue by presenting a systematic review on the advancements in research on thermal comfort in urban residential buildings in China. Firstly, two common thermal comfort research approaches, i.e., field studies and laboratory studies, are discussed. Secondly, eleven main thermal comfort evaluation indicators are summarized. Finally, this paper analyzes the thermal comfort survey data from different researchers, discusses the impacts of adaptive behaviors on human thermal comfort, and provides recommendations for future research on urban residential thermal comfort. It was found that people have higher and higher requirements for their indoor thermal environment as time goes by, especially in the winter; the thermoneutral temperature is higher in warmer climate regions in the summer but lower in the winter than in colder climate regions; the thermoneutral temperature tends to increase with the indoor air temperature due to an adaptation to the indoor thermal environment. The outcomes of this paper provide valuable information on thermal comfort behaviors of urban residents in different climate zones in China, which can serve as a resource for the academic community conducting future research on thermal comfort and assist policymakers in enhancing building energy efficiencies without compromising the occupants’ comfort.

Reducing Carbon Emissions from Prefabricated Decoration: A Case Study of Residential Buildings in China

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.

Photovoltaic technology in rural residential buildings in China: a comprehensive review on applications, development and its benefits

ABSTRACT China’s rural residential photovoltaic system has been greatly developed in recent years. However, most existing researches, are difficult to reflect the real development situation of the whole system. This study systematically sorts out the results in related fields through the literature review, aiming to clarify the current situation of applications and benefits of photovoltaic technology. The results show that currently the photovoltaic power generation technology is relatively mature and widely applied, and passive photovoltaic technology can play a greater role in reducing energy consumption in rural residential buildings in China. However, the development of photothermal and photovoltaic combined with other energy technologies, as well as the integration between photovoltaic technology and buildings are relatively lagging behind. Besides, the life cycle analysis and evaluation of photovoltaic system has not been established. In view of opportunities and challenges, this study prospects two future development directions. Firstly, the system coupling of photovoltaic technology should be strengthened, especially between the joint supply subsystems, between the active and passive technologies, and between photovoltaic technology and buildings. Secondly, the rationality of photovoltaic technology evaluation should be ensured, particularly to cover the three aspects of economy, ecology and society on the whole life cycle dimension.

Natural Ventilation Potential of Residential Buildings in China Considering the Combined Effect of Indoor and Outdoor Air Pollution

With its rapid economic development, China has had to confront the serious issues of high energy consumption and air pollution. Natural ventilation is regarded as an effective method to reduce building energy consumption, but it is largely influenced by indoor and outdoor air pollution. However, most of the previous studies estimating natural ventilation potential (NVP) in China do not consider air pollution. This research estimated the NVP for residential buildings in major cities from four climate regions in China (Guangzhou, Chengdu, Shanghai, Beijing, and Shenyang) while considering the combined effect of indoor and outdoor air pollution. We compared the yearly NVP in three different scenarios, namely without considering air pollution, only considering outdoor air pollution, and considering both outdoor and indoor air pollution. The results show that Guangzhou had the highest yearly NVP, followed by Shanghai, Beijing, Shenyang, and Chengdu. The impact of air pollution could reduce the annual NVP in China by 78–95%. In addition, the main factors causing a low NVP differed between the four cities. The key factors for Chengdu and Guangzhou were natural ventilation flow rate and indoor air pollution, respectively. Beijing and Shenyang were mostly influenced by outdoor air pollution. Shanghai had two main factors with similar influence degrees, namely outdoor air pollution and indoor air pollution. The findings of this study will guide architects and policymakers in better forming natural ventilation strategies.

Evaluating Energy Efficiency Potential in Residential Buildings in China’s Hot Summer and Cold Winter Zones

Evaluating Energy Efficiency Potential in Residential Buildings in China’s Hot Summer and Cold Winter Zones

Analysis of Structural System Selection and Seismic Design for Modern Residential Buildings in China

This article aims to research and review the selection of structural systems and seismic design for modern residential buildings in China. Through a comprehensive analysis of the current research status and practical experience, it explores the application and suitable scenarios of different structural systems in seismic design. The goal of this paper is to provide guidance for architects, engineers, and researchers and promote the development of structural system selection and seismic design for modern residential buildings in China. Conclusions are drawn and future research and practice are discussed through comparison and analysis. This article will discuss the selection of structural systems in building construction, ensuring the quality of construction projects, and guaranteeing the stability and safety of the structures.

Equilibrating provincial carbon increments for residential buildings in China under carbon peaking constraints

The speedy raise of residential buildings' carbon emissions is a hindrance to achieving China's 2030 carbon peak goal. This study constructs an assessment framework for comprehensive consideration of 30 Chinese provinces' socioeconomic circumstances, energy demand, and emissions reduction technology to meet the consistent coupling degree of equity and efficiency (CDEE). This study is the first to propose an allocation scheme for equilibrating provincial carbon increments for rural and urban residential buildings in 2030 under carbon peaking constraints. The relevant results are fourfold. (1) Residential building's floor area per capita and energy carbon emissions coefficients are the soliddest drivers to facilitate and inhibit the raise of carbon emissions during 2010–2020. (2) Through dynamic Monte Carlo simulation from 2021 to 2030, we demonstrate that provinces with the most gamey carbon emissions in urban and rural areas include Shandong, at 121.52 (± 5.50) Mt. and Hebei, at 61.34 (± 3.08) Mt. in 2030, respectively. (3) A CDEE of 52.3% (biased equity) in urban areas and 34.5% (biased efficiency) in rural areas indicates equilibrated allocation of provincial carbon increment. (4) In the final 2030 allocation scheme, the greatest carbon mitigation pressures are in Beijing (11.34 Mt) and Heilongjiang (3.23 Mt), and the provinces with the largest carbon increment in urban areas include Hebei, Henan, and Guangdong, while the largest carbon increments in rural areas are in Hebei, Henan, and Guangdong. Overall, this study furnishes a targeted and valuable decision making reference for the government to determine provincial carbon peak goals for Chinese residential buildings.

Shake table test on a five-story residential masonry building strengthened by engineered cementitious composites based on minimum disturbance

Multi-storey masonry structures are popular structural systems for residential buildings in China in the 1970 s, which need strengthening urgently due to poor seismic performance. To save construction time and achieve minimum disturbance, a single-sided engineered cementitious composites (ECC) strengthened method for masonry structures was proposed. To investigate the effectiveness of the strengthening method, two 1/4-scaled 5-storey masonry structures, including a reference unreinforced structure (US) model and a corresponding retrofitted structure (RS) model, were designed and tested by shake table tests. The comparisons between the US model and the RS model were analyzed in terms of the natural frequency, acceleration amplification factor, acceleration response, and storey drift response. The results show that the strengthening method can improve the seismic performance of the masonry structure. The connection method can ensure that the existing masonry walls and ECC layers work together. The initial natural frequency of the RS model is 1.3 times that of the US model. The maximum storey drift of the US model is 3.0 times that of the RS model at PGA = 0.550 g. To ensure the strengthening efficiency, the acceptable structural aspect ratio range for the strengthening method was suggested, and an improved strengthening method was proposed.

The ANN Architecture Analysis: A Case Study on Daylight, Visual, and Outdoor Thermal Metrics of Residential Buildings in China

Selecting an appropriate ANN model is crucial for speeding up the process of building performance simulation during the design phase of residential building layouts, particularly when evaluating three or more green performance metrics simultaneously. In this study, daylight, visual, and outdoor thermal metrics were selected as main green performance. To find the suitable ANN model, sensitivity analysis was used to obtain a set of proper parameters applied to the ANN structure. To train the ANN model with a higher predicting accuracy, this paper tested four different scenarios of ANN parameter setups to find some general guidelines about how to set up an ANN model to predict DF, sunlight hours, QuVue and UTCI. The results showed that an ANN model with a combined output variable demonstrated better average prediction accuracy than ANN models with a separated output variable. Having two times the number of training samplings compared to the number of input variables can lead to a high accuracy of prediction. The ideal number of neurons in the hidden layer was approximately 1.5 times the number of input variables. These findings of how to improve the ANN model may provide guidance for modeling an ANN for building performance.

Revealing the influence mechanism of the service lifespan of residential buildings in China

The total amount of resources and energy consumed by long-lifespan residential buildings (RBs) is far less than that of short-lifespan RBs with repeated demolitions and constructions. This study aims to expand revealing of comprehensive influencing of the service lifespan of RBs beyond the previous approach that focused only on the one aspect of physical conditions with RBs. This study proposed these latent variables (i.e., physical, functional, economic, environmental and social lifespans), which made a better understanding of the service lifespan of RBs. To analyze the influence mechanism of the service lifespan of RBs, an empirical RB's lifespan dataset was established and tested utilizing a two-stage structural equation modeling (SEM) method. The results indicated that: (1) functional lifespan (total effects = 2.15) and environmental lifespan (total effects = 1.46) have an obvious positive impact on the service lifespan of RBs; (2) the influencing factors including structure design, land finance, employment of migrant workers, planning forward, and technical measures have a significant impact in corresponding lifespan dimension; and (3) improving construction technology and guaranteeing land value are regarded as the fundamental extending measure for the service lifespan of RBs. This study complements the connotation of RB service lifespan to a certain extent and further improves the sustainable development theory. Policy implications derived from the results were discussed at the end of the study.

Towards COP27: Decarbonization patterns of residential building in China and India

As the two largest emerging emitters with the highest growth in operational carbon emissions from residential buildings, the historical emission patterns and decarbonization efforts of China and India warrant further exploration. This study aims to be the first to present a carbon intensity model considering end-use performances, assessing the operational decarbonization progress of residential building in India and China over the past two decades using the newest decomposing structural decomposition approach. Results indicate (1) the annual operational carbon intensity increased by 1.4% and 2.5% in China and India, respectively, between 2000 and 2020. Household expenditure-related energy intensity and emission factors were crucial in decarbonizing residential buildings. (2) Building electrification played a significant role in decarbonizing space cooling (−87.7 in China and − 130.2 kg of carbon dioxide (kgCO2) per household in India) and appliances (∼ −169.7 in China and ∼ −43.4 kgCO2 per household in India). (3) China and India collectively decarbonized 1498.3 and 399.7 M-tons of CO2 in residential building operations, respectively. In terms of decarbonization intensity, India (164.8 kgCO2 per household) nearly caught up with China (182.5 kgCO2 per household) in 2020 and is expected to surpass China in the upcoming years, given the country's robust annual growth rate of 7.3%. Overall, this study provides an effective data-driven tool for investigating the building decarbonization potential in China and India, and offers valuable insights for other emerging economies seeking to decarbonize residential buildings in the forthcoming COP2811COP28 is the abbreviation of 2023 United Nations Climate Change Conference. age.

Comparing the Standards of Life Cycle Carbon Assessment of Buildings: An Analysis of the Pros and Cons

Buildings play a substantial role in carbon emissions and contribute to approximately 37% of global carbon emissions. In order to control and reduce the carbon emissions of buildings, a report of life cycle carbon assessment (LCCA) has been mandated for all the new buildings in China since 1 April 2022. As a technical support for carbon accounting efforts in China, it is important to adhere to the national standards for conducting an LCCA report. In this context, the GB/T51366-Standard for Building Carbon Emission Calculation is the designated national standard that should be followed. However, GB/T51366 has several deficiencies, including incomplete life cycle processes, impractical calculation methods, the unrepresentativeness of default emission factors, and so forth. Therefore, it is essential to critically analyze the pros and cons of employing an LCCA methodology adhering to GB/T51366. To fulfill the research aim, this study develops a computational toolkit based on GB/T51366. We propose two data collection methods and conduct a case study of a residential building in China. GB/T51366 was also used as the baseline scenario and compared with the European standard EN15978. The results show that GB/T51366 is less comprehensive than EN15978, leading to a 2.9% reduction in the total life cycle emissions. Notably, up to 26.7% difference was observed in the comparison of the emission factors of the main construction materials. Based on the research outcomes, it is suggested to improve the national standard in terms of the scope and data availability, as well as to promote the harmonization of existing national LCCA standard of buildings with international standards.

Characteristics of embodied carbon emissions for high-rise building construction: A statistical study on 403 residential buildings in China

High-rise residential buildings are prevalent in Chinese cities and have considerable impacts on the sustainable construction industry. Although individual case studies have assessed the embodied carbon emissions, there is still a need to explore the general characteristics and driving factors. This study established a dataset containing 403 buildings and assessed the statistics and distribution of relevant embodied carbon intensities using a cradle-to-site system boundary. An average embodied carbon intensity of 424.1 kgCO2e/m2 was obtained, with 2/3 of the carbon emissions attributed to structural materials including steel, concrete, and prefabricated members. Based on contribution analyses and significance tests, the influences of driving factors encompassing building height, structural form, seismic design grade, delivery type, and building investment were examined, and carbon reduction strategies were discussed. The results indicated a potential 28.8% reduction in the embodied carbon intensity considering a cascaded strategy of substituting low-carbon materials, optimizing material use, and using local materials.