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Green construction technologies (GCTs) are important drivers of sustainable development in the construction industry. Despite a wide range of GCTs being available on the Chinese construction market, they are not yet widely popular. This study aims to evaluate the critical barriers hampering large-scale GCT adoption in China. Through a literature review, 21 barriers were identified and listed in the questionnaire survey, and 225 valid responses from 21 provinces in China were collected. The Mann–Whitney U test was conducted to verify whether different stakeholder groups perceive these barriers differently. Moreover, a comparative analysis of barriers to GCT, GBT (green building technologies), and GC (green construction) adoption was conducted. Results of statistical analyses showed that the top five barriers inhibiting GCT adoption are “lack of government incentives”, “extra costs associated with GCTs”, “dependence on traditional construction technology”, “a shortage of technological training for project staff”, and “conflicts of interest among stakeholders in GCT adoption”. Moreover, the top five factors preventing the adoption of GCTs differ from those of GBTs and GCs. This study not only provides valuable resources for stakeholders to better understand the critical factors preventing GCT adoption, but also could help policy makers to effectively promote GCT adoption.
Yujing Wang; Dan Chong; Xun Liu. Evaluating the Critical Barriers to Green Construction Technologies Adoption in China. Sustainability 2021, 13, 6510 .
AMA StyleYujing Wang, Dan Chong, Xun Liu. Evaluating the Critical Barriers to Green Construction Technologies Adoption in China. Sustainability. 2021; 13 (12):6510.
Chicago/Turabian StyleYujing Wang; Dan Chong; Xun Liu. 2021. "Evaluating the Critical Barriers to Green Construction Technologies Adoption in China." Sustainability 13, no. 12: 6510.
The relationship among cities is getting closer, so are housing prices. Based on the sale price of stocking houses in thirty-five large and medium-sized cities in China from 2010 to 2021, this study established the modified gravity model and used the method of social network analysis to explore the spatial linkage of urban housing prices. The results show that: (1) from the overall network structure, the integration degree of housing price network in China is still at a low stage, and the influence of housing price is polarized; (2) from the individual network structure, Beijing, Shanghai, Shenzhen, Nanjing, Hangzhou, and Hefei have a higher degree of centrality. Chengdu, Xining, Kunming, Urumqi, and Lanzhou stay in an isolation position every year; (3) from the results of cohesive subgroup analysis, different cities play different roles in the block each year and have different influences on other cities. (4) Emergencies, such as outbreaks of COVID-19, also have an impact on the housing price network. Structural divergence among urban housing prices has become more pronounced, and the diversity of house price network has been somewhat reduced. Based on the above findings, this paper puts forward some recommendations for the healthy development of housing market from the perspective of housing price network.
Guancen Wu; Jing Li; Dan Chong; Xing Niu. Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model. Sustainability 2021, 13, 4071 .
AMA StyleGuancen Wu, Jing Li, Dan Chong, Xing Niu. Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model. Sustainability. 2021; 13 (7):4071.
Chicago/Turabian StyleGuancen Wu; Jing Li; Dan Chong; Xing Niu. 2021. "Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model." Sustainability 13, no. 7: 4071.
With the rapid development of urbanization, more and more people are willing to improve their living conditions, thus substantial attention has been paid to residential renovation in China. As a result, large quantities of renovation waste are generated annually which seriously challenge sustainable urban development. To effectively manage renovation waste, accurate prediction of waste generation rates is a prerequisite. However, in the literature, few attempts have been made for predicting renovation waste as renovation activities vary significantly in different cases. This study offers an approach to estimate the amount of renovation waste based on the vacancy rate and renovation waste generation rates at a city level. The grey system theory was applied to predict the amount of renovation waste in Shenzhen. Results showed that the amount of renovation waste would reach 135,620 tons in 2023. The research findings can provide supportive information to relevant stakeholders for developing a renovation waste management framework.
Zhikun Ding; Mengjie Shi; Chen Lu; Zezhou Wu; Dan Chong; Wenyan Gong. Predicting Renovation Waste Generation Based on Grey System Theory: A Case Study of Shenzhen. Sustainability 2019, 11, 4326 .
AMA StyleZhikun Ding, Mengjie Shi, Chen Lu, Zezhou Wu, Dan Chong, Wenyan Gong. Predicting Renovation Waste Generation Based on Grey System Theory: A Case Study of Shenzhen. Sustainability. 2019; 11 (16):4326.
Chicago/Turabian StyleZhikun Ding; Mengjie Shi; Chen Lu; Zezhou Wu; Dan Chong; Wenyan Gong. 2019. "Predicting Renovation Waste Generation Based on Grey System Theory: A Case Study of Shenzhen." Sustainability 11, no. 16: 4326.