In pursuing low-carbon solutions in the construction sector, it is critically essential to unravel the intricate nexus between population aging and carbon emission efficiency (CEE). This research investigates this complex association utilizing provincial panel data from 2004 to 2020. Through the application of the super-efficient Slack-Based Measure and Malmquist–Luenberger (SBM-ML) model, the study quantifies the construction industry’s CEE, followed by using a fixed-effect model to assess population aging’s impact on CEE in the construction industry. Our study reveals a distinct “U”-shaped pattern, illustrating that population aging initially depresses but eventually amplifies CEE in the construction industry. We also observe marked regional disparities. For instance, the construction sectors in the eastern and northern regions show a rising trend, counteracting the initial decline in CEE due to aging. However, the central western and southern regions need more pronounced aging progression to experience comparable CEE improvement. These findings underscore the critical role of regional contexts and demographic transitions in shaping the CEE in the construction industry, providing valuable insights for crafting sustainable policies aiming at balanced population mobility and carbon neutrality.

Based on the panel data of 30 provinces (except Tibet) in China from 2012 to 2019, this paper uses the ML index method and spatial econometric model to evaluate the impact and spillover effect of innovation investment on environmental total factor productivity. The study found the following conclusions. (1) Environmental total factor productivity showed a fluctuating upward trend as a whole, and the driving force of environmental total factor productivity growth mainly came from technological progress. (2) There is global spatial correlation and positive spatial spillover effect in regional environmental total factor productivity. There is a certain spatial correlation in provincial environmental total factor productivity, and the improvement of environmental total factor productivity in this region will promote the improvement of environmental total factor productivity in surrounding areas. (3) Innovation investment, economic development, and foreign investment level play a significant role in promoting the improvement of environmental total factor productivity, and energy structure and human capital level have a negative impact on the improvement of environmental total factor productivity. (4) Innovation investment, economic development, human capital, and foreign investment have positive spillover effects on the improvement of environmental total factor productivity in the surrounding areas, while the energy structure shows negative spillover effects.

Digital transformation (DT) is a core driver of supply chain sustainable development (SCSD). From the perspective of supply chain spillovers, this study empirically examines the impact of customer enterprises’ DT on the ESG performance of supply chain partners, using a sample of Chinese A-share listed firms and their disclosed suppliers and customers during 2012–2023. The results show that customer enterprises’ DT exerts a significant positive spillover effect on the ESG performance of supply chain partners, namely an enabling effect on SCSD, and this effect presents an asymmetric characteristic that is more prominent when customers’ DT level exceeds that of their partners. Mechanism tests reveal that this positive spillover is realized through two paths: supply chain coordinated development and supply chain innovation development. Heterogeneity analysis further finds that the above effect is more significant among non-heavy polluting enterprises, state-owned enterprises and enterprises in eastern China. This study enriches the theoretical research on DT and SCSD, and provides empirical evidence and practical guidance for enterprises to formulate DT strategies and optimize supply chain coordination mechanisms, thus contributing to the improvement of overall SCSD level.

The application of Building Information Modeling (BIM) in construction projects can significantly improve project efficiency, accuracy, and collaboration among stakeholders. However, construction professionals, particularly design engineers, exhibit behavioral resistance to BIM implementation, which has hindered the achievement of expected benefits. To explore the behavioral resistance to BIM implementation, this study integrates the status quo Bias (SQB) theory and the innovation diffusion theory and proposes a factor model for predicting the behavioral resistance of architectural engineering design engineers to BIM implementation. The model was empirically tested through partial least squares structural equation modeling (PLS-SEM) on survey data collected from design engineers in BIM-based construction projects in China. The results indicate the following: (1) resistance to change (β = 0.383), BIM compatibility, and BIM user satisfaction play prominent but independent roles in predicting behavioral resistance to BIM implementation, and (2) resistance to change is motivated by inertia (β = 0.473), self-efficacy, and perceived distributive equity. The proposed framework provides a more nuanced account of resistance to BIM implementation. Specifically, deep-seated cognitive biases and innovation-specific perceptions independently and jointly shape behavioral resistance, a distinction often overlooked in prior research. Practically, this research provides recommendations for effectively addressing resistance behaviors within construction project settings.

Building Information Modeling (BIM) is an innovative and effective solution to transform the Architecture, Engineering, and Construction (AEC) sector, offering advantages that extend across the entire lifecycle of project management. Nonetheless, several obstacles hinder the comprehensive implementation of BIM. As a result of these obstacles, construction enterprises opt to delegate the development and utilization of BIM models to specialized outsourcing providers that focus on BIM services. Since limited research focused on examining the formation mechanisms behind BIM outsourcing process, this paper elucidates the mechanisms that underpin BIM outsourcing behavior by applying Ajzen’s theory of planned behavior (TPB) and integrating transition costs along with institutional pressures theory. The behavioral model underwent empirical validation through the application of partial least squares structural equation modeling (PLS-SEM) on survey data collected from construction engineers working for construction enterprises in China. The results indicated that (1) BIM outsourcing degree is motivated by an organization’s BIM outsourcing intention and BIM application capability; (2) behavioral attitudes, especially external production cost advantage, contributes the most toward achieving a high BIM outsourcing intention, followed by normative pressures; (3) transition cost contributes the most toward achieving a low BIM outsourcing intention. This research expands the theoretical framework of the TPB and provides insight into BIM outsourcing behavior in construction enterprises.