How affordable housing becomes more sustainable? A stakeholder study

Gan, Xiaolong; Zuo, Jian; Wu, Peng; Jun, Wang; Chang, Ruidong; Wen, Tao

doi:10.1016/j.jclepro.2017.06.048

Cited by 120 publications

(108 citation statements)

References 33 publications

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“…SEM is divided into two parts: the measurement model and the structural model. The measurement model describes the relationships between the latent variables and the observed variables (1,2), and the structural model describes the relationships among the latent variables (3). SEM can be constructed and analyzed with SPSS24.0 and AMOS22.0 software.…”

Section: Methodsmentioning

confidence: 99%

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Factors affecting prefabricated construction promotion in China: A structural equation modeling approach

et al. 2020

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Prefabricated construction (PC) has attracted wide spread attention as a model of sustainable development for the construction industry of the future. Although the PC has many advantages, it is still at an initial stage in China. Based on the current conditions in China, this study focuses on the interrelationships of factors affecting PC promotion. Firstly, through a comprehensive review of relevant literatures and expert recommendations, 5 factors were identified: policy factor, technical factor, management factor, market factor and cost factor. Next, the data were collected through a questionnaire survey, and the questionnaire data were processed using SPSS 24.0 and AMOS 22.0. The overall relationships of each factor were quantitatively analyzed with Structural Equation Modeling (SEM). The results show that the policy factor plays a dominant role, while the management factor and market factors are also significant. This study also provides decision makers with relevant information about the factors involved, which will be helpful in devising appropriate strategies for the wider adoption of PC. OPEN ACCESS Citation: Jiang W, Huang Z, Peng Y, Fang Y, Cao Y (2020) Factors affecting prefabricated construction promotion in China: A structural equation modeling approach. PLoS ONE 15(1): e0227787. https://doi.

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Section: Methodsmentioning

confidence: 99%

“…With the economic growth and urbanization, Chinese urbanization process will reach 60% by 2020 [1,2]. The "National New Town Planning" issued by the government indicates that 30 billion square meters of buildings will be built by 2020.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting prefabricated construction promotion in China: A structural equation modeling approach

et al. 2020

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“…Based on the results and discussion, we argue that the increasing provision of low-income housing is an avenue for both climate mitigation and development goals to be met. By identifying which energy services are pursued by households, and which corresponding set of appliances should be targeted, low-carbon measures such as adoption of energy efficient technologies can be locked-in (Winkler et al 2002, Zaid and Graham 2011, Cabeza et al 2014, Charoenkit and Kumar 2014, Gan et al 2017, Hernandez-Roman et al 2017, Zaid and Graham 2017, Saldaña-Márquez et al 2018. This could be especially effective if implemented alongside existing policies, such as schemes to deliver low-income housing.…”

Section: Policy Implications and Conclusion For Climate Mitigation Imentioning

confidence: 99%

Energy demand transitions and climate mitigation in low-income urban households in India

Khosla

Sircar

Bhardwaj

2019

Environ. Res. Lett.

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Growing household energy demand, particularly in developing countries starting from a low base of consumption, is an important driver of current and future greenhouse gas emissions. Yet, our understanding of transitioning residential energy demand in developing country contexts is limited. This paper discusses changing energy service demands in urban low-income households in India, an emerging economy where the largest future growth in energy demand globally is projected to occur, and where 12 million new low-income homes for the urban poor are to be built by the government between 2015 and 2022. Based on mixed quantitative and qualitative methods comprising of surveys, interviews and focus group discussions, we analyze two inter-related questions: how does the demand for energy services change as the ability of low-income households to consume increases; and how do energy related behaviors influence household electricity consumption? We analyze the data collected to rank households according to their ability to consume and to identify the types of, and progression in, energy services acquired. The appliances and associated services pursued are lights, fans, televisions, and refrigerators, with varied energy efficiencies. Analogously, we quantify the influence of behavior in determining electricity consumption, and show that the inclusion of socio-demographic and behavioral factors explain a significant proportion (51%) of the variance in household electricity consumption, along with the role of material factors such as building physical characteristics and appliance stocks. We complement the statistical analysis with qualitative fieldwork and discuss changing energy related behaviors as the ability of households to consume increases. We conclude with recommendations for climate actions that are compatible with development in the growing lowincome housing stock.

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“…FSE can be used to represent experiential knowledge of practitioners from a nonnormal distribution (Lam, Wang, Lee, & Tsang, 2007). By using FSE, the uncertainty and the participants' perceived influence on the criticality of the PSI could be reduced (Gan et al, 2017). Therefore, in this study, this method is used to build the SI for TPPP projects.…”

Section: Fuzzy Synthetic Evaluationmentioning

confidence: 99%

Developing a project sustainability index for sustainable development in transnational public–private partnership projects

Chen

Osei‐Kyei

et al. 2019

Sustainable Development

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During the last couple of years, sustainability has become topical in transnational public–private partnership (TPPP) projects primarily because of the enormous impact TPPP projects have on the environment, economic, and well‐being of people. This paper aims to develop a model that could be used to evaluate the sustainability levels of TPPP projects. The proposed model consists of four categories of social responsibility factors; these include economic, environment, social, and people‐oriented factors. Of the four categories, social factors obtained the highest coefficient (i.e., 0.255) in the TPPP sustainability index equation. The substantive model will enable practitioners to quantify the sustainability levels of TPPP projects prior to their implementations. Moreover, the outputs of this study inform private investors of the key areas to focus on to enhance sustainable development through TPPP project implementations.

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How affordable housing becomes more sustainable? A stakeholder study

Cited by 120 publications

References 33 publications

Factors affecting prefabricated construction promotion in China: A structural equation modeling approach

Factors affecting prefabricated construction promotion in China: A structural equation modeling approach

Energy demand transitions and climate mitigation in low-income urban households in India

Developing a project sustainability index for sustainable development in transnational public–private partnership projects

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