• Energy Research

Abstract The rapidly growing construction industry has accelerated water and energy scarcity in China, threatening its sustainable development. This study integrates multi-regional input-output (MRIO) and data envelopment analysis (DEA) to investigate the water-energy nexus in the construction industry at the provincial level through the entire industrial supply chain. Results show that the construction industry accounts for 8.97% and 27.20% of virtual water and embodied energy in China, respectively. The western area experiences the most energy- and water-intensive construction processes given its backward economy and outdated technological development. The northern area faces great challenges with regard to energy intensity improvements, whereas the central regions suffer from large pressure relating to inefficient water use. The manufacture of non-metallic mineral products, smelting, and the pressing of metals are the largest suppliers of virtual water and embodied energy. The efficiency assessment results demonstrate that Jiangsu and Zhejiang are two DEA-effective regions. China has achieved a relatively high level of scale efficiency but suffers from backward technology.

Uncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.

Abstract The building sector is responsible for a major share of energy consumption, with the most energy being consumed during the operation stage of buildings. Energy-efficiency retrofit (EER) policies have been promoted by numerous countries. However, the effectiveness of these incentive policies has been insufficient, a main reason being the agency problem between the government and building owners. In addition, most policies ignored the diversity of buildings and building owners, resulting in a lack of reaction from owners. To address this problem, this study proposed an agent-based model for policy making on EER. The model defined the government and owners as agents and their decision-making behaviors were modeled with principal-agent theory. A platform based on the proposed model was then developed and the incentive policy was optimized under different circumstances. To verify the effectiveness of the proposed model, three policy scenarios were compared on the platform, which are the policy by the proposed model, the incentive policy in Shanghai and Shenzhen, China. The results showed that the incentive policy based on the proposed model has the best performance on energy savings, returns on investment, and leverage effects. A sensitivity analysis indicated that the government should pay more attention to energy price.

Abstract As a major contributor to the increasing adverse environmental impact worldwide, China’s construction industry faces major challenges in energy conservation. Improving energy utilization efficiency is an effective method for mitigating energy-intensive problems induced by this industry. Accordingly, this study combined a multiregional input–output model with data envelopment analysis to assess the energy efficiency of China’s construction industry at the provincial level. Results show that the majority of the provincial construction sectors in China are energy inefficient and the performance of energy efficiency is highly related to the level of regional economic development. Scale efficiency is comparatively high in the regional construction sectors, but technical aspects are lagging behind. Moreover, cross-regional energy allocation demonstrates higher efficiency than that of the energy structure. The inefficient utilization of unclean and clean energy can be attributed to the lack of required instruments that can facilitate clean production processes in the construction field. To solve these problems, this study presents corresponding governmental measures and market-driven strategies from the institutional, technical and management aspects. The findings of this study can provide a holistic understanding of the current efficiency status of the construction industry and help decision makers stratify regional energy conservation targets at the industrial level.

Abstract Although sustainable development in the construction industry has attracted much attention, relevant studies on the regional scale analysis of industrial energy performance are still rare in China, especially for Guangdong province, which is currently on the frontier and fast track of national low-carbon development. In response, this study integrates a multi-regional input-output method with field-based operational data to quantify the total embodied energy consumption and energy transfers from the construction industry and assess the life cycle energy use of residential and office buildings in Guangdong Province. The results show that the embodied energy consumption of the provincial construction industry is localization dominant and fossil fuel oriented, which accounts for approximately 18.6% of the total regional energy consumption. The geographical connection and resource characteristic are the two factors that influence the interregional energy transmissions induced by construction activities in Guangdong province. The result of uncertainty analysis indicates that the mean value of energy intensities simulated by Monte Carlo simulation is highly consistent with the deterministic results. It is crucial to improve the accuracy of the input-output analysis by providing sufficient economic information. At last, a number of recommendations are given through the technology, product, and management aspects at the industrial and building levels. The local government and construction department can benefit from implementing such environment-friendly, technology innovative and multi-disciplinary solutions in the full-process management and improvement of energy reduction of buildings.

PurposeThe objective of this study is to evaluate the overall technical efficiency, labor efficiency, capital efficiency and equipment efficiency of 30 Chinese construction sectors to foster sustainable economic growth in the construction industry.Design/methodology/approachThis study employed the super-efficiency data envelopment analysis (SE-DEA) and artificial neural network model (ANN) to evaluate the industrial performance and improvement potential of the Chinese regional construction sectors from 2000 to 2017.FindingsResults showed that the overall technical and capital efficiencies displayed relatively stable patterns. Equipment efficiency presented a relatively huge fluctuation during the sample period. Meanwhile, labor, capital and equipment efficiencies could potentially improve in the next five years. A spatial examination of efficiencies implied that the economic level was still a major factor in determining the efficiency performance of the regional construction industry. Beijing, Shanghai and Zhejiang were consistently the leading regions with the best performance in all efficiencies. Shandong and Hubei were critical regions with respect to their large reduction potential of labor, capital and equipment.Research limitations/implicationsThe study focused on the regional efficiency performance of the construction industry; however, it failed to further deeply discover the mechanism that captured the regional inefficiency. In addition, sample datasets used to predict might induce the accuracy of prediction results. Qualitative policy implications failed to regress the efficiency performance of the industrial policy variables. These limitations will be discussed in our further researches.Practical implicationsEnhancing the overall performance of the Chinese construction industry should focus on regions located in the western areas. In comparison with labor and capital efficiencies, equipment efficiency should be given priority by eliminating outdated equipment and developing high technology in the construction industry. In addition, the setting of the national reduction responsibility system should be stratified to account for regional variations.Originality/valueThe findings of this study can provide a systematic understanding for the current and future industry performance of the Chinese construction industry, which would help decision makers to customize appropriate strategies to improve the overall industrial performance with the consideration of regional differences.

Abstract To achieve China′s mandatory energy conservation and emission reduction targets, it is necessary to examine the driving factors in the energy increase with the due consideration of regional disparities. This study develops a region-based structural decomposition analysis method to capture the spatial heterogeneity of driving factors between eastern, central, and western China from a temporal perspective. The results show that there is a clear declining trend both in the amount and growth rate of energy increase linked to the continuous decrease in the rate of national GDP growth. Historically, final demand was the largest driver of increased energy use during the whole period under investigation whilst the change in energy intensity and structural change are identified as the biggest contributors to reductions from 2007 to 2010 and from 2010 to 2012, respectively. From a spatial perspective, energy demands in eastern regions have grown most, followed by the western and central regions. The impacts of all driving factors were more local-dominant. The changes in production structure and final demand volumes generated the most significant spillover effects. The findings of this study enhance the understanding of dynamic evolution in energy-driven factors at the regional level, which is beneficial to making well-directed energy conservation policies by considering regional specificity.

Carbon emission quantifications in China are not consistent, with many standards and methods having been used over the years. This study identified the non-consideration of China-specific technology and databases as a factor limiting comprehensive quantification. The study aimed to comprehensively quantify regional direct CO2 emission in the industry using a hybrid of economic and environmental data. We retrieved nineteen (19) sets of fossil fuel and electricity data from provincial energy yearbooks between 1997 and 2015 for the study. To generate regression models for each of the six regional construction industries in China, the study further integrated the results with three sets of econometric data: total annual construction output, cement, and steel product yearly consumption data. The study identified the North China region as the main source of direct CO2 emission with over 30%, while Southeast China contributed the least. While there is a gradual shift to other energy sources, the study identified coal and crude oil to remain as the main energy sources in the industry. Cement and steel data exhibited a significant predictive relationship with CO2 emissions in five regional construction industries. The study identified the need to have policies tailored to technological improvements to enhance renewable energy generation and usage in the industry. The models developed in this study could be used to generate initial quantifications of carbon emissions in construction industries with similar carbon-emitting characteristics for carbon tracking, and energy policies for decision making. However, the three economic indicators used in the study could be extended to generate more robust models in future research.