Commercial Buildings In China Research Articles

Effect of building energy efficiency standards on carbon emission efficiency in commercial buildings

The building sector plays a crucial role in the worldwide shift toward achieving net-zero emissions. Building energy efficiency standards (BEESs) are highly effective policies for reducing carbon emissions. Therefore, exploring the provincial variations in carbon emission efficiency (CEE) in the building sector and identifying the effect of BEESs on CEE is crucial. This study focuses on commercial buildings in China and applies a difference in differences model to evaluate the impact of BEESs on the CEE of commercial buildings. The slacks-based measure–data envelopment analysis model is employed to assess the CEE of commercial buildings in 30 Chinese provinces from 2000 to 2019. Furthermore, heterogeneous tests are used to explore how climate characteristics and economic conditions affect the efficiency of BEESs. The results indicate that BEESs positively influence the CEE of commercial buildings. Specifically, a 1% increase in the intensity of BEESs causes a 0.148 4% increase in the CEE of commercial buildings. Moreover, the impact of BEESs is particularly pronounced in the southern and western provinces. This study provides valuable scientific evidence for governments to enhance BEESs implementation.

Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in China

Energy systems for flexibility in buildings are hybrid, primarily including rooftop photovoltaics (PV), cooling storage, and battery. Considering their techno-economic patterns, this research establishes an optimization model to determine the optimal technology portfolio and financial advantages of PV-battery-cooling storage systems for commercial buildings in China. The analysis of all cases indicates that cooling storage outperforms batteries in economic benefits, suggesting the prioritization of cooling storage installation. Once the optimal cooling storage rate is exceeded, it is advisable to proceed with batteries. Meanwhile, PV integration significantly enhances the system efficiency and promotes battery utilization. For example, a 40% PV penetration combined with a 0.006 $/(a·kWhe) energy storage investment results in an impressive 27.3% cost reduction in a Beijing mall, while the optimal cooling storage rate decreases from 55% to 40%. Furthermore, the study emphasizes the impact of tariff patterns and electricity demand on the economic feasibility of hybrid energy systems. The museum's substantial annual cooling requirements and nighttime loads make cooling storage favorable, with PV less suitable than the mall and office. Notably, cities like Beijing, Shanghai, Chongqing, and Guangzhou exhibit considerable peak-to-valley tariff differences, yielding higher economic benefits ranging from 23% to 27%. Finally, as battery costs decline and electricity price becomes more volatile, the battery would gradually replace cooling storage, especially when battery cost drops from 150 $/kWh to 70 $/kWh.

Feature extraction and an interpretable hierarchical model for annual hourly electricity consumption profile of commercial buildings in China

Understanding and modeling building electricity consumption is paramount for demand-side decarbonization. Using a data-driven approach, this study proposes a hierarchical model to generate annual hourly electricity consumption profiles for commercial buildings, to facilitate energy system design and renewable energy integration. Electricity consumption profiles and the corresponding weather data from 55 commercial buildings across all five climate zones in China were collected to address the scarcity of relevant open datasets. Furthermore, periodicity and correlation analyses of the collected dataset indicate that the annual electricity consumption profiles can be decomposed into daily fluctuations primarily influenced by climate change and intra-day fluctuations, which are predominantly affected by building schedules. Based on these features, suitable algorithms were selected to address the issues independently in these two temporal paradigms, forming a hierarchical model. A regression tree was used to reveal the relationships between daily consumption and weather data as well as the climate zone label. Additionally, k-means clustering was employed to extract typical intra-day consumption patterns, followed by the deployment of a classification tree to predict intra-day pattern labels under specific conditions. In the cross-validation, the proposed hierarchical model achieved a high coefficient of determination (R2) of 0.941, which further improved to 0.949 after incorporating residual regression, demonstrating its high accuracy. Compared to the single-step gradient boosting decision tree (GBDT) model (R2 = 0.951), the proposed hierarchical model exhibited comparable accuracy and offered better interpretability and lower overall complexity, making it a favorable choice for engineering applications. This study provides insights into the development of a data-driven model for building electricity consumption profiles.

Exploring the design risks affecting operation performance of green commercial buildings in China

Despite the number of new green commercial buildings (GCBs) increases rapidly over the last decade, its operation performance has not been improved simultaneously. Risk exploration is considered as an essential way to improve project performance. Design risks, which could have significant effects on the operation performance of GCBs, should be identified. However, little research has been done on the design risk factors of GCBs with interfere of system vulnerability. Therefore, the objective of this study is to detect the impact of the design risks on the operation performance of GCBs moderated by system vulnerability in the context of China. Through systematic literature review, four categories of design risks were initially identified (design technical risks, design management risks, design behavior risks and green certification risks) and three types of system vulnerability of GCBs were distinguished (exposure, resilience and sensitivity). Structural equation modeling was employed to test the theoretical hypothesis with 481 valid questionnaires. The research findings revealed that all four categories of design risks have significant negative impacts on the operational performance of GCBs with different influence levels and behavior risk is the most influential kind of risks. Additionally, the three sets of system vulnerability play positive moderating roles for the connection between the design risks and the operational performance of GCBs. The higher the system vulnerability, the greater the negative impact of the design risks on the operational performance. The positive moderating effect of resilience is the highest, meaning that the negative impact of the design risks on the operation performance of GCBs is much more significant in case of resilience factors. The research results could offer effective and valuable information not only for designers and owners to adjust strategies to improve the operation performance of GCBs, but also for related government departments to update green building assessment systems and set up regulations for improving the actual performance of green buildings.

Risk allocation for energy performance contract from the perspective of incomplete contract: a study of commercial buildings in China

PurposeThis article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.Design/methodology/approachThe article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.FindingsThis study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.Originality/valueExisting research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

Green Retrofitting Simulation for Sustainable Commercial Buildings in China Using a Proposed Multi-Agent Evolutionary Game

Green retrofit is regarded as an effective environmental measure to reduce greenhouse gas emissions in high energy-consuming commercial buildings. However, the current retrofitting rate of complex structures is lower than the expected rate. This study proposed a method of stimulating the interaction of multiple agents (government, developers, and occupants) involved in the green renovation of China’s commercial buildings. To this end, the evolutionary game theory was applied to determine the multiple interaction mechanism of the behaviors of the agents, after which the key factors affecting the contrasting behavior of developers and occupants were demonstrated, and a sensitivity analysis was performed to distinguish detailed set parameters. The major results observed are as follows: (1) occupants are less sensitive to varied conditions owing to their vulnerable economic scale, meaning that a more friendly policy environment is essential to facilitate their support; (2) government financial support, such as subsidies or compensation costs, can strongly induce more positive behavior in developers to promote green retrofit; and (3) life-cycle awareness of developers should be improved as a reasonable energy-saving performance can act as a key motivating factor to support green renovation. This research provided a comparative perspective to that of a public–private partnership model.

Operational Carbon Change in Commercial Buildings under the Carbon Neutral Goal: A LASSO–WOA Approach

The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter. The key findings show the following: (1) The major driving forces of carbon emissions from commercial buildings in China were found to be the population size and energy intensity of carbon emissions, and their elastic coefficients were 0.6346 and 0.2487, respectively. (2) The peak emissions of the commercial building sector were 1264.81 MtCO2, and the peak year was estimated to be 2030. Overall, this study analyzed the historical emission reduction levels and prospective peaks of carbon emissions from China’s commercial buildings from a new perspective. The research results are helpful for governments and decision makers to formulate effective emission reduction policies and can also provide references for the low-carbon development of other countries and regions.

Historical carbon abatement in the commercial building operation: China versus the US

Building is the “last mile” sector in carbon neutrality transition, and commercial buildings have the largest decarbonization potential using current strategies and technologies. This study focused on China and the United States (US) to assess carbon-dioxide (CO2) abatement in commercial building operations at different emission scales and investigate the carbon abatement efficiency of the two countries from 2001 to 2018. The results show that: (i) Economic efficiency and energy intensity are key to reducing CO2 intensity in Chinese and American commercial buildings, respectively; (ii) Generally, CO2 abatement efficiency in China was 1.1–1.9 times that of the US, although CO2 abatement in China and the US in 2001–2018 was close [China: 1451.89 (±549.05) mega-tons of CO2, US: 1929.84 (±757.36) mega-tons of CO2]; (iii) Ridge regression tested the robustness of CO2 abatement results of the assessment model successfully. Furthermore, the study mapped paths for energy efficiency improvement in commercial buildings in China and the US to explore the strategy that best decarbonizes buildings. Overall, this study covers the research gap on a carbon abatement assessment tool for different economies or regions to compare the historical carbon abatement features on building operation.

Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China

Estimation of construction waste generation (CWG) at the field scale is a crucial but challenging task for effective construction waste management (CWM). Extant field-scale CWG modeling approaches have faced difficulties in obtaining accurate results due to a lack of detailed CWG data, and most of them fail to consider the complex relationship among predictive variables. This study attempts to tackle this issue by proposing a novel CWG modeling approach that integrates improved on-site measurement (IOM) and a support vector machine (SVM)-based prediction model. To achieve this goal, 206 ongoing commercial construction sites were investigated to obtain the predictor values and waste generation rates (WGRs) of five types of waste (i.e., inorganic nonmetallic waste, organic waste, metal waste, composite waste, and hazardous waste) generated at three construction stages (i.e., the understructure stage, superstructure stage, and finishing stage). The data were introduced to the SVM to develop the relationships between predictive variables and WGRs. An actual commercial building under construction was used to demonstrate the applicability of the proposed approach. The results showed that the superiority of the IOM can be used as a basis to implement robust CWG data collection. In addition, the SVM-based WGR prediction model (SWPM) can obtain more accurate prediction results (R2 = 86.87%) than the back-propagation neural network (R2 = 75.14%) and multiple linear regression (R2 = 61.93%).

Impacts of Environment and Individual Factors on Human Premovement Time in Underground Commercial Buildings in China: A Virtual Reality–Based Study

The premovement time has been considered a critical factor of safety in building fire evacuations because the casualty of fire disasters is strongly associated with delayed evacuation. In the present study, an immersive virtual environment is employed to explore the effect of fire visibility, fire alarm voices, and individual characteristics (gender, occupation/major, and experience of fire safety training) on individuals’ fire evacuation premovement time in underground commercial buildings. A regression analysis of relevant time distribution is then provided. According to the results, the distribution of occupants’ premovement time varies in different underground commercial buildings. Overall fitting error is small in the Burr distribution and Weber distribution of a common regression model. Additionally, it proves that fire visibility exerts significant influence on occupants’ premovement time while other factors are not significantly influential in the case of predictable fire. Excluding their experience of fire prevention and safety training, factors such as fire alarm content, individual’s gender, and occupation (major) have a significant effect on premovement time when fires are not predictable. Moreover, the obtained findings could contribute to improving the current evaluation procedures of underground commercial building evacuation and relevant policies of fire prevention and safety.

A Quantitative Process-Based Inventory Study on Material Embodied Carbon Emissions of Residential, Office, and Commercial Buildings in China

Studies on building carbon emissions focus mainly on the materialization phase of life cycle, as carbon emissions in this stage is intensive and high. This paper proposes a simplified model to calculate embodied carbon emissions in building design stage by conducting a process-based inventory analysis of carbon emissions from materials used in 129 residential buildings, 41 office buildings, and 21 commercial buildings during materialization phase. The results indicate that average carbon emissions per unit area from building materials used in residential buildings, office buildings, and commercial buildings are 514.66 kgCO2e/m2, 533.69 kgCO2e/m2 and 494.19 kgCO2e/m2, respectively. Besides, ten kinds of building materials (namely, steel, commercial concrete, wall building materials, mortar, copper core cables, architectural ceramics, PVC pipes, thermal insulation materials, doors and windows, and water paint) constitute 99% of total carbon emissions in all three types of buildings. These materials are major carbon emissions sources in materialization phase. Thus, embodied carbon emissions can be significantly reduced by limiting the amount of these materials in architectural design as well as by using environmental friendly materials.

Effect Evaluation of Chiller-side Strategies in Demand Response of Chinese Commercial Buildings

Air-conditioning systems in commercial buildings play a vital role in the demand respond (DR) of cities. At present, many researches are focused on zone temperature reset strategies which are used frequently in the commercial buildings of the United States. However, this strategy requires the support of the energy management and control system (EMCS), which is not suitable for most commercial buildings in China. This paper focuses on the DR effect of the chiller-side control strategies in Chinese commercial buildings. Taking an office building as the research object, the coupling thermal inertial model of the building and the air conditioning system was built; based on the coupling model, the demand reduction effects of the typical chiller-side DR control strategies, including intermittent shutdown, chiller quantity reduction and chilled water temperature increase, were simulated; and the characteristics and applicability of these strategies were pointed out.

Optimal design of photovoltaic shading systems for multi-story buildings

This study provides new insights into the comprehensive energy and economic performances of Photovoltaic Shading Systems (PVSS) in multi-story buildings. A numerical shading model was developed to evaluate the shading effect from an upper PVSS row on its subjacent row. Simulation models based on EnergyPlus were developed to analyze the Net Electricity Consumption (NEC) of PVSS with different tilt angles and widths in different climates. Benefit per Capacity (BC) and the Cost of Benefit (CB) indicators were used to analyze the economic performances of PVSS. Finally, the optimum PVSS tilt angles and widths in different cities were obtained. Harbin, Beijing, Changsha, Kunming, and Guangzhou, were selected as representative cities for different geographical and climatic conditions. The results indicate that the optimum tilt angles for PVSS installed in Harbin, Beijing, Changsha, Kunming and Guangzhou are 55°, 50°, 40°, 40° and 30°, respectively. Optimum PVSS width for all five cities is 1.156 m (7 columns of standard solar cells). PVSS installed, using the optimal design scheme, in multi-story buildings have better energy-saving potentials than either rooftop photovoltaic systems or traditional power supply modes for commercial buildings in China.

Big Data Mining for Assessing Calibration of Building Energy Models

Residential and commercial buildings in China, India, the United States (US), United Kingdom (UK), and Italy consume 39-45% of each nation's primary energy. Building energy models can be used to automatically optimize the return-on-investment for retrofits to improve a building’s energy efficiency. However, with an average of 3,000 building descriptors necessary to accurately simulate a single building, there is a market need to reduce the transaction cost for creating a more robust model for simulating every building in a city and accurately estimate savings prior to capital expenditures. We used two of the world’s fastest supercomputers, assembled unique datasets, and developed innovative algorithms for big data mining to assess different methods to create an accurate building energy model. The team has leveraged a total of eight high performance computing resources and 218 servers to analyze the best methods, metrics, and algorithms for creating an accurate building energy model beyond current industry standards necessary for private-sector financing. The project developed the world’s fastest buildings simulator, has completed over 8 million simulations totaling over 300TB, and mined this data with over 130,000 parallel artificial intelligence algorithms. This was used to quantify accuracy of AI-developed calibration algorithms with results that surpass industry standard guidelines and can identify individual building parameters to between 15% and 32% of their actual value.

Energy consumption quota management of Wanda commercial buildings in China

There is limited research of commercial buildings’ energy use data conducted based on practical analysis in China nowadays. Some energy consumption quota tools like Energy Star in U.S or VDI 3807 in Germany have limitation in China's building sector. This study introduces an innovative methodology of applying energy use quota model and empirical management to commercial buildings, which was in accordance of more than one hundred opened shopping centers of a real estate group in China. On the basis of statistical benchmarking, a new concept of “Modified coefficient”, which considers weather, occupancy, business layout, operation schedule and HVAC efficiency, is originally introduced in this paper. Our study shows that the average energy use quota increases from north to south. The average energy use quota of sample buildings is 159 kWh/(m2.a) of severe cold climate zone, 179 kWh/(m2.a) of cold zone, 188 kWh/(m2.a) of hot summer and cold winter zone, and 200 kWh/(m2.a) of hot summer and warm winter zone. The energy use quota model has been validated in the property management for year 2016, providing a new method of commercial building energy management to the industry. As a key result, there is 180 million energy saving potential based on energy quota management in 2016, equals to 6.2% saving rate of actual energy use in 2015.

Improving The Energy Performance of Large Commercial Buildings in China Using Effective Energy Management

In many Chinese cities, such as Beijing and Shanghai, the energy use in large commercial buildings is significantly higher than that in the other building sectors. Monthly electricity consumption data from six large commercial buildings in Shanghai and Beijing in 2006 and 2007 were analyzed by exploring the energy consumption with temperature variations throughout the year. Intrinsic heating and cooling electricity requirements and baseline functional electricity consumptions were identified. Different patterns of energy consumption in buildings emerged and it was possible to separate the intrinsic energy requirements for heating and cooling from other energy uses. In most buildings, it was found that the dominant energy use was for requirements other than heating and cooling and this information provides an important insight into those areas where the most cost effective energy conservation strategies should be targeted. Thus, in many cases improving the thermal performance of the building by technical means will have less impact than reducing the requirements of functional energy use within the building. Such reductions may be achieved either by the deployment of more efficient appliances or through the use of effective energy management.

Commercial building energy use in six cities in Southern China

With China’s continuing economic growth, the percentage of government offices and large commercial buildings has increased tremendously; thus, the impact of their energy usage has grown drastically. In this survey, a database with more than 400 buildings was created and analyzed. We researched energy consumption by region, building type, building size and vintage, and we determined the total energy use and performed end use breakdowns of typical buildings in six cities in southern China. The statistical analysis shows that, on average, the annual building electricity use ranged from 50 to 100kWh/m2 for office buildings, 120 to 250kWh/m2 for shopping malls and hotels, and below 40kWh/m2 for education facilities. Building size has no direct correlation with building energy intensity. Although modern commercial buildings built in the 1990s and 2000s did not use more energy on average than buildings built previously, the highest electricity intensive modern buildings used much more energy than those built prior to 1990. Commercial buildings in China used less energy than buildings in equivalent weather locations in the US and about the same amount of energy as buildings in India. However, commercial buildings in China provide comparatively less thermal comfort than buildings in comparable US climates.

Analysis of outdoor temperature ranges for the operation of different ventilating modes in commercial buildings of China

Heat gains in commercial buildings in China are rather large compared to those of other public buildings, for occupant densities are usually high in such buildings. To match the requirements for both indoor environment and building energy conservation, ventilation systems, either mechanical ventilation, i.e., ventilating cooling (VC) or air-conditioning cooling (AC) systems, play important roles in maintaining good indoor air quality and thermal comfort. As the time durations for cooling and heating of AC systems in such buildings are different from other types of public buildings, temperature ranges for the application of different ventilation strategies all year round were obtained by using the local meteorological records in the present study. The results show that the time period in a whole year can be divided into five ranges according to the four outdoor critical temperatures for the operation of VC and AC systems with a heat recovery ventilator (HRV). The critical temperatures are mainly influenced by local climatic characteristics, occupant density, and pressure drop of the ventilation system, etc. Furthermore, energy effectiveness by employing different ventilation modes in commercial buildings with large internal gains was discussed and compared for four cities located in four different climatic zones of China.

Using Insulation in China’s Buildings: Potential for Significant Energy Savings and Carbon Emission Reductions

Most old residential and commercial buildings in China are not insulated or not to new standards. This is true even in regions with very cold winters or regions with very hot summers. The insulation in new buildings is very poor relative to North American standards. However, insulating the exterior walls, attic roofs, and foundation walls to new standards of buildings in China would reduce the need for heating and air conditioning, and thus would save roughly 200 million tons of coal each year. This in turn would lead to a reduction in CO2 emissions of about 510 million tons per year, which is equal to about 8.4% of China’s total carbon emissions in 2006. The use of insulation in buildings will help homeowners save money, help China save energy and help the environment all at the same time. Insulation will provide a continued payback to the homeowner, not only in dollars and cents, but also by providing a more enjoyable and comfortable living environment for many years.

Overcoming barriers to implementation of carbon reduction strategies in large commercial buildings in China

With an economic growth in GDP of around 10% per annum in recent years, energy consumption in the building sector in China now accounts for 25% of the total energy use in the whole nation. In large buildings in Beijing and Shanghai the consumption rate, at approximately 190 kWh/m 2 per annum, is around five times the energy use in residential buildings in those cities. Addressing this ever increasing energy consumption and the consequential green house gas (GHG) emissions must be a priority to achieve low carbon sustainability in China. As part of the Kyoto Protocol the Clean Development Mechanism (CDM) has been used to assist in the finance of GHG reduction projects in developing countries in the context of low carbon sustainable development. However, hitherto the majority of CDM projects of an energy related nature have focussed almost exclusively on renewable energy projects or those which enhance the efficiency of industrial processes. To date the opportunities for using the CDM approach in reducing emissions in large buildings have been largely overlooked in China and other countries. Part of the reason is the lack of an adequate established baseline methodology against which the impact of any proposed CDM project can be judged. This paper explores the barriers to the implementation of carbon reduction strategies in large commercial buildings using China as a particular example.