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The purpose of this paper is to understand the distributional impact of house price increases on consumption in the context of the energy transition.

This study draws from two micro cross-sectional datasets, the English Housing Survey (EHS) and the Living Costs and Food Survey (LCFS) to study the Marginal Propensity to Consume (MPC) out of changes in house prices. By employing pseudo-panel regressions, the paper examines the impact of house price changes on consumption among diverse household types.

This paper finds varying consumption responses to house price changes across age and tenure groups. Older homeowners tend to increase consumption when house prices rise. In contrast, middle-aged individuals, often renters or mortgage holders, reduce consumption in response to price increases. The youngest age group also experiences increased consumption but to a lesser degree than the oldest group. Energy-efficient homes are related to lower consumption across all tenure levels. However, when interacted with house prices and age, the estimates are positive, pointing to an unequal accrual of property premiums depending on housing market positions.

The main limitations stem from data constraints. First, using a pseudo-panel approach hinders control for unobservable selection bias. Additionally, while robust under cross-validation and specifications tests, the energy efficiency variable imputation results in a low number of energy-efficient homes. Due to heterogeneous responses to rising house prices, this paper contends that an energy transition model that subsidises homeowners’ renovation is likely to produce a negative impact on consumption among younger and middle-aged households.

This paper contributes to the MPC literature by incorporating energy efficiency as a key variable. It draws from recent data to obtain new estimates. By highlighting shifts in consumption patterns the paper contributes to a well-established body of literature with renewed policy relevance regarding housing retrofit.

This study aims to assess carbon emissions in urban aged residential buildings in Qingdao, Shandong Province, constructed prior to 2000, and to evaluate retrofitting and rebuilding strategies for potential carbon reduction.

Field investigations and literature reviews were conducted to identify key factors influencing carbon emissions, such as shape coefficient, window-to-wall ratio and envelope structure. A combination of generalization and mathematical statistical methods was used to classify buildings based on construction year, form, structural type and energy-saving goals. Cluster analysis was employed to extract six typical building models.

Results demonstrate that building form complexity positively correlates with carbon emissions per unit area, while longer lifespans reduce emission intensity. Retrofitting exhibits shorter carbon payback periods (1.62–3.92 years) than rebuilding (18.7–49.94 years), indicating superior environmental performance. Pre-1986 buildings are advised for demolition/rebuilding due to limited retrofit benefits. For 1986–1995 buildings, retrofitting is recommended if structurally viable. Post-1996 buildings favor retrofitting over new construction for its shorter payback and lower emissions, enhancing long-term carbon reduction.

This study contributes to the understanding of carbon emissions in urban aged residential buildings by considering various factors and providing specific recommendations for retrofitting and rebuilding strategies tailored to different construction periods. Additionally, it highlights the importance of building form complexity and remaining lifespan in determining carbon emissions, offering insights for sustainable urban development and carbon reduction initiatives.

This article explores the challenges and opportunities in adaptive reuse projects for historic buildings, focusing on the interrelationship between energy efficiency and heritage conservation.

The study utilises a mixed-method approach, including semi-structured interviews and energy retrofit surveys, conducted in seven adaptively reused historical buildings in the Bey neighbourhood, Gaziantep, Türkiye. Thematic analysis is used for interviews, and survey results were discussed together within the framework of CSN EN16883.

The adaptive reuse of historic buildings can result in economic, social, cultural and environmental benefits for local communities. However, for this to occur, careful consideration must be given when selecting the new function, ensuring that it aligns with the buildings' environmental performance potential and the community’s needs. Considering the CSN EN16883 Guidelines for improving the energy performance of historic buildings, when the retrofits made by the users are assessed, the general approach is to preserve the heritage value of the building rather than making it energy efficient.

This study will add to a cross-cultural understanding of the complex relationship between adaptive reuse, energy efficiency and heritage conservation by looking at the local context of Gaziantep. No similar qualitative study addresses this issue in this region.

Net zero energy buildings (NZEBs) play a crucial role in mitigating the environmental impact of the construction industry. However, this concept in Ghana is still in the infancy stage, and the level of embracement in the construction industry is uncertain which further poses challenges to its adoption. This can be attributed to the lack of awareness of NZEB among construction professionals. Hence, understanding the awareness among construction professionals is essential for promoting sustainable building practices and reducing the carbon footprint of buildings. Therefore, this study investigates the level of awareness of NZEBs among construction professionals in the Ghanaian construction industry (GCI).

The study adopted a quantitative research method where questionnaire survey was used to obtain data from sixty-six (66) construction professionals in the GCI through snowball sampling technique. The collected data were analysed using frequencies, mean scores, one-sample t-test and cross-tabulation.

The study revealed that thirty (30) construction professionals out of the sixty-six (66) had a moderate level of awareness of NZEBs, and 14 professionals had a low level of awareness. Thirteen had a high level of awareness. Three of the profesionals were extremely unaware, while six had a very high level of awareness. The study’s findings highlight the need to create awareness of NZEBs and their practices among construction professionals and employees in Ghana.

NZEB is an under-explored area in the Ghanaian context and therefore, this study uniquely highlights the nascent awareness of NZEBs among Ghanaian construction professionals, unlike previous studies in more developed contexts. It underscores the critical need for targeted awareness programs essential for reducing the carbon footprint and advancing the adoption of NZEBs in the GCI.

Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases.

This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio.

Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction.

The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing.

This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.

This paper aims to analyze the impact of sustainability measures taken during the design and construction phases, by examining two categories of sustainability: energy efficiency and reducing carbon emissions and material selection and waste management. These aspects are examined from the perspectives of long-term building performance and maintenance practices, as well as user/tenant satisfaction.

This study includes a literature review related to the two topics under consideration, followed by a comparative case study analysis of four projects to determine practical validity. All case studies in this paper used a semi-structured survey with various project stakeholders, which helped the authors identify measures taken as well as obstacles and challenges during the process.

According to the four case studies, adequate attention should be paid to the two areas of interest during a project’s design and construction phases. Including case studies from around the world (four case studies from three different countries) offers insights into effective sustainability practices in building design and construction, providing instances of successful implementation and emphasizing the obstacles and potential when incorporating sustainability into the design and construction phases.

The findings also show that design and construction participants and companies should reduce waste generation and carbon emissions. In addition, they should make decisions on material selection to enhance projects’ sustainability and to contribute to creating a habitable planet for the future.

The influence of the design and construction phases on long-term project sustainability is of major importance and concern to users, owners, designers, contractors and facility managers. This study illustrates the necessity of including sustainability measures in the design and construction phases, highlighting the importance of sustainability in building design and construction through effective implementation techniques and interdisciplinary teamwork to realize sustainable goals.

Taking instances from extant findings from the literature, the study aims to examine the community perception toward renewable energy (RE) off-grid (mini-grid/microgrid) intervention, the underlying rationales for engagement of communities in RE off-grid projects, the different alternatives/models to engage communities in various phases of RE off-grid project deployment.

The study has followed the structured literature review to explore the identified research question of the study.

Based on findings from the review, the framework for effective community engagement in RE mini-grid projects is suggested. Furthermore, the study also draws suggestions and implications for future research and practice.

Based on such understanding the present study offers the framework which suggests the steps for the engagement of the communities in the off-grid projects. The key steps are managing the perception of the community (including generation of awareness among the community), planning for the benefits of the community, linkage the sustainable development goals (SDG), planning for the inclusion of the community and measuring performance (in the line of social and economic criteria and SDG).

This study finds the gap in the literature on the nexus of community, off-grid energy projects and SDG. Following the findings from the scholars in this field, a few gaps in the policy and practice have been highlighted which could be useful for practitioners and policymakers in this area.

The purpose of this study is to explore the complex interplay between technology, personal norms and emotional factors in shaping the sustainable housing choices of millennials in emerging economies. It integrates the model of goal-directed behavior, technology acceptance model and norm activation model, incorporating both self-interest and prosocial motivations. Key adaptations involve replacing perceived behavioral control with financial self-efficacy and substituting hedonic motivation for anticipated positive emotions. Moreover, it introduces location as a practical anchor.

A quantitative, cross-sectional and descriptive research design was used in this study. Data were gathered from a sample of 610 millennial residential real estate investors across Indian smart cities. A multistage stratified sampling technique was used to ensure a representative sample. For data analysis, partial least squares structural equation modeling was used. The analysis focused on hypothesis testing to examine the relationships between the constructs of interest. Bootstrap t-values and effect sizes were used to assess the significance and magnitude of these relationships, respectively.

One of the key findings of this study was the establishment of significant positive relationships between awareness of consequences, ascription of responsibility and personal norms with behavioral intentions. This underscored the importance of personal ethical considerations in shaping intentions. Perceived usefulness and ease of use were found to significantly influence attitudes positively, highlighting the relevance of these factors in forming favorable attitudes toward behaviors. Attitude, subjective norms, financial self-efficacy and location played significant positive roles. However, negative anticipated emotions decreased desire. This illustrated the complex role emotions play in motivational processes. The study also revealed that subjective norms did not significantly contribute to shaping personal norms. This indicated a potential decoupling of societal expectations from personal ethical obligations in the decision-making process.

This study offers actionable insights for both policymakers and real estate developers. For policymakers, the findings highlight the need to craft initiatives that go beyond mere awareness, instead fostering a deep sense of personal responsibility and environmental stewardship among potential homebuyers. For real estate developers, the emphasis on financial self-efficacy and location suggests a strategy shift toward designing sustainable homes that not only meet environmental standards but also align with buyers’ financial confidence and geographic preferences. Together, these strategies can drive a more widespread adoption of sustainable housing, making sustainability a tangible and appealing choice for millennials.

To the best of the authors’ knowledge, this empirical research study was one of the first studies that contributed to the literature by integrating the model of goal-directed behavior, technology acceptance model and norm activation model. This study thus offered a nuanced understanding of the interplay between normative influences, usability perceptions, ethical considerations and emotions in the context of behavioral intentions.

Due to the increasing frequency of extreme weather and densifying urban landscapes, residences are susceptible to heat-related discomfort, especially those in a naturally ventilated built environment in tropical climates. Indoor thermal comfort is thus paramount to building sustainability and improving occupants' health and well-being. However, to assess indoor thermal comfort considering the urban context, it is conventional to use questionnaire surveys and monitoring units, which are both case-centric and time-intensive. This study presents a dynamic computational thermal comfort modeling framework that can determine indoor thermal comfort at an urban scale to bridge this gap.

The framework culminates in developing a deep learning model for predicting the accurate hourly indoor temperature of urban building stock by the coupling urban scale capabilities of environment modeling with single-building dynamic thermal simulations.

Using the framework, a surrogate model is created and verified for Dharavi, India's informal urban settlement. The results indicated that the developed surrogate model could predict the building's indoor temperature in several complex new urban scenarios with different building orientations, layouts, building-to-building distances and surrounding building heights, using five different random urban representative scenarios as the training set. The prediction accuracy was reliable, as evidenced by the mean bias error (MBE) and coefficient of (CV) root mean squared error (MSE) falling between 0 and 5%. The findings also showed that if the urban context is ignored, estimates of annual discomfort hours may be inaccurate by as much as 70%.

The developed computational framework could help regulators and policymakers engage in more informed and quantitative decision-making and direct efforts to enhance the thermal comfort of low-income dwellings and informal settlements.

Up to this point, majority of literature that has been presented has concentrated on building a body of knowledge about urban-based modeling from an energy management standpoint. In contrast, this study suggests a dynamic computational thermal comfort modeling framework that takes into account the urban context of the neighborhood while examining the indoor thermal comfort of the residential building stock.

Using and promoting green technologies in residential buildings might be a far more practical strategy for developing a sustainable built environment. The primary goal of this study is to examine homeowners' knowledge and awareness of the different green technologies and concepts that can be adopted to improve the quality of their homes.

The study employed a sequential mix technique methodology in order to accomplish its goal. A total of 156 respondents were chosen for a survey within the research areas using a simple random sample approach, while interviewees were chosen using a purposive sampling approach methodology. Descriptive and inferential statistics as well as content analysis were used to analyze the quantitative and qualitative data, respectively.

The findings indicate that homeowners have moderate knowledge of green technologies. It was also evident that print and electronic media are excellent at capturing and reaching a diverse range of homeowners interested in learning about sustainable development issues. Furthermore, the top three green technologies that most homeowners are aware of are using local materials over imported materials, grey water reclaiming and reuse technology and solar water heating technology. Subsequently, the homeowners in the three communities have differing opinions about the majority (82%) of the green technologies examined.

The findings will serve as a useful guide to assist practitioners and policymakers in implementing appropriate methods to integrate green technologies into housing projects and subsequently encourage their adoption.