Despite the potential of digital transformation to enhance resource efficiency and waste reduction, numerous barriers hinder its adoption. This study examines the critical barriers to digital technology adoption for circular economy implementation in the construction industry in developing countries, using Ghana as a case study. A structured quantitative approach was employed, integrating mean score ranking, exploratory factor analysis, and fuzzy synthetic evaluation to assess the severity of identified barriers. Data were collected from construction professionals through structured surveys, and statistical analyses were performed using SPSS, Excel, and RStudio to determine the criticality of the barriers. The fuzzy synthetic evaluation revealed that financial and adoption constraints emerged as the most critical barrier group, followed closely by institutional and knowledge barriers, while technological and market limitations and regulatory and organizational challenges also exhibited significant impediments. In response, this study develops a strategic framework comprising targeted solutions such as financial incentives, capacity building, regulatory reforms, and technological infrastructure development. This framework addresses not only the barriers but also the associated risks, including financial uncertainty, data security threats, and regulatory gaps. This study contributes to the theoretical understanding of digital technology adoption in CE practices and offers practical recommendations for policymakers, industry stakeholders, and academics seeking to foster sustainable construction practices in the construction industry.

The rapid increase in global urbanization, along with the growth of the construction industry, highlights the urgent need for effective management of construction and demolition (C&D) waste. Intelligent technologies offer a viable solution to this critical challenge. However, there remains a significant challenge in integrating these technologies into a cohesive framework. This study conducts a quantitative analysis of 214 papers from 2000 to 2023, highlighting the extensive use of artificial intelligence (AI) and building information modeling (BIM), along with geographic information systems (GIS) and big data (BD). A further qualitative analysis of 73 selected papers investigates the use of seven different intelligent technologies in the context of C&D waste management (CDWM). To overcome current limitations in knowledge, future research should concentrate on (1) the comprehensive integration of technology, (2) inclusive studies throughout all lifecycle phases of CDWM, and (3) the continued examination of new technologies, such as blockchain. Based on these insights, this study suggests a strategic framework for the effective implementation of intelligent technologies in CDWM. This framework aims to assist professionals in merging various technologies, undertaking lifecycle-wide research, and narrowing the divide between existing and new technologies. It also lays a solid foundation for future academic work to examine specific intelligent technologies, conduct comparative studies, and refine strategic decisions. Regular updates on technological developments are essential for stakeholders to consistently enhance CDWM standards.

Aligning public demand with government supply of clean air aids in efficient air pollution control and enhancement of public happiness. However, comparative empirical analyses of public and government attention to air quality changes are still sparse due to data and methodological constraints. Here, we adopt multi-source data fusion approaches to assess the impacts of air pollution exposure on public and government attention. Specifically, remote and social sensing data, alongside keywords extracted from textual data, are utilized to quantify air pollution exposure and corresponding public and government attention levels in 273 Chinese cities from 2011 to 2019, and a two-stage least squares regression model is employed to tackle reverse causality issues underlying the exposure-response relationship. Our findings reveal that, on average, a unit increase in PM_2.5 levels would result in a 17.7% growth in public attention and a 12.7% rise in government attention, respectively, suggesting that demand-driven public attention tends to be more sensitive to air quality changes than policy-driven government attention. Results for the spatial-temporal heterogeneity further demonstrate that public attention varies across time and space, whereas government attention remains relatively consistent. Additionally, we have identified 116 cities exhibiting disparities between the public and government responses to air quality changes, calling for environmental policy refinements to better serve the needs of residents. This study emphasizes the necessity of public engagement in environmental governance and offers rich policy implications for air pollution control in China.