Purpose: The purpose of this research is to address the existing gap in the study of construction and demolition waste (C&DW) by focusing on its impact on human health throughout the entire life cycle. And this research provides a comprehensive assessment model that incorporates the release of gaseous pollutants and particulate matter during the whole life cycle of C&DW, thereby contributing to a more holistic understanding of its impact on human health.Design/methodology/approach: The research was conducted in two stages. Firstly, the quantitative model framework of pollutants emitted by C&DW was established. Three types of pollutants were considered, namely nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and inhalable particulate matter (PM10). Second, disability-adjusted life year (DALY) and willingness to pay (WTP) assessments were used to provide a monetary quantified health impact for pollutants released by C&DW.Findings: The results show that the WTP value of PM10 is the highest among all pollutants and 8.68E+07 dollars/a, while the WTP value in the disposal stage accounts for the largest proportion compared to the generation and transportation stage. These findings emphasize the importance of PM10 and C&DW treatment stage for pollutant treatment.Originality/value: The results of this study are of great significance for the management department to optimize the construction management scheme to reduce the total amount of pollutants produced by C&DW and its harm to human health. Meanwhile, this study fills the gap in existing research on the impact assessment of C&DW on human health throughout the whole life cycle, and provides reference and basis for future research and policy formulation.

The digital transformation of Chinese construction enterprises is crucial for achieving sustainable and high-quality development in the construction industry. However, there is still a lack of in-depth research on the impact mechanism of digital transformation in construction enterprises. The purpose of this study is to explore the multiple influencing factors and complex causal relationships of digital transformation in construction enterprises and promote the deep integration of digitalization and construction enterprises. To this end, based on the dual-effect perspective (net effect perspective of a single influencing factor and configuration effect perspective of multiple influencing factors), using the “technology–organization–environment” framework (TOE framework) to construct a research model of influencing factors for digital transformation in construction enterprises. A sample of 236 construction enterprise managers was surveyed, and partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) methods were used to empirically analyze the dual effects of influencing factors for digital transformation in construction enterprises. The results show that: (1) from the net effect perspective, there are seven factors that significantly impact digital transformation in construction enterprises; (2) from the configuration effect perspective, there are three paths that can achieve high-level digital transformation in construction enterprises, and one path that leads to low-level digital transformation; (3) from the dual-effect perspective, top management support and policy support are key factors for digital transformation in Chinese construction enterprises. The research results enrich the relevant research on digital transformation in construction enterprises and provide a reference basis for promoting digital transformation in construction enterprises.

With the rapid development of the manufacturing industry, demand forecasting has been important. In view of this, considering the influence of environmental complexity and diversity, this study aims to find a more accurate method to forecast manufacturing industry demand. On this basis, this paper utilizes a deep learning model for training and makes a comparative study through other models. The results show that: (1) the performance of deep learning is better than other methods; by comparing the results, the reliability of this study is verified. (2) Although the prediction based on the historical data of manufacturing demand alone is successful, the accuracy of the prediction results is significantly lower than when taking into account multiple factors. According to these results, we put forward the development strategy of the manufacturing industry in Guangdong. This will help promote the sustainable development of the manufacturing industry.

This study aims to analyze the development trend of the manufacturing industry transformation and upgrading in the Guangdong–Hong Kong–Macao Greater Bay Area (2008–2018). On the basis of synergetics, the order parameter method of factor analysis is used to study these factors. The results show that: (1) There are five slow variable factors, such as intelligent manufacturing industry, technological innovation, scale agglomeration, market demand, and fixed asset investment, which are important power sources of the transformation and upgrading of the manufacturing industry in Greater Bay Area. The development of these factors is relatively mature, and they cooperate with each other. (2) Similar to a fast variable of manufacturing development ecology, green development is an important coordinating factor in removing bottlenecks. Finally, suggestions for the development of the transformation and upgrading of the manufacturing industry are put forward.