Various industries see digital transformation (DT) as the pillar to coping with intensified competition, energy crises, and climate change. As a critical sector for DT, the construction industry’s project-oriented paradigm and immature industrialized production method limit the research on emerging digital technology and ignore the theoretical mechanism. Through the lens of system adaptability, this study proposes a multifaceted model to examine the DT effectiveness and unveil the driving mechanism. (1) An extensive literature review, action research, and the nominal group technique identified 21 determinants, which were categorized into a technological–organizational–environmental (TOE) framework to analyze the construction industry’s DT determinants from multiple dimensions. (2) This research utilizes data from 272 respondents collected through field research, with a survey designed to measure the relationships among variables. (3) Structural equation modeling (SEM) through Analysis of Moment Structures (AMOSs) has been used to analyze the hypotheses and analyze the impact of determinants from various dimensions on DT and examine their influence pathways. The results indicate that determinants in the technological, organizational, and environmental dimensions positively affect DT’s success in the construction industry. The influence of the technological dimension is the strongest, and the organizational dimension is the weakest. The research findings offer valuable recommendations and insights for stakeholders in the construction industry, highlighting the importance of considering these three dimensions to enhance the overall effectiveness of DT when driving industry transformation and upgrading. Additionally, this study uses the TOE framework to reveal determinants from multiple dimensions. It combines SEM to explore the pathways of their effects, offering key theoretical insights for the body of knowledge.

The coordinated development of artificial intelligence (AI) and high-quality energy development (HED) is essential for advancing digital transformation and energy transition. However, existing research primarily explores the unidirectional impact of AI on HED, neglecting their bidirectional relationship. Drawing on provincial data from China during 2012–2022, this study examines the coupling coordination between AI and HED (AHCC) and its spatiotemporal differentiation and driving factors. Results show that although both AI and HED advanced steadily, AI started from a lower base and remained below HED, and their spatial distributions were mismatched. The national average AHCC improved from mild imbalance to marginal imbalance, but large regional disparities persisted. Only several provinces—six in the east and two in the west—entered coordination stages, with most remaining in imbalance. Intensifying spatial autocorrelation of AHCC indicates that strong or weak regions are increasingly locked into self-reinforcing trajectories, making balanced regional development difficult. Regression results indicate that technological innovation, economic development level, and industrial structure upgrading significantly promote AHCC, whereas environmental regulation, urbanization level, and government intervention inhibit it. These effects exhibit pronounced spatiotemporal heterogeneity. This study enriches the theoretical understanding of AHCC and provides empirical evidence to inform coordinated digital and energy transition policies.