Predicting green technology innovation in the construction field from a technology convergence perspective: A two-stage predictive approach based on interpretable machine learning

Abstract

The construction industry, as a major global energy consumer and carbon emitter, plays a crucial role in achieving global sustainability. A key strategy for the green transformation of this industry—without compromising development—involves fostering green technology innovation. Nevertheless, existing studies exhibit a notable gap in identifying and evaluating potential green technology innovation opportunities within the construction field, leading to a scarcity of decision-making information for governments and innovation entities during the research and development stage. Recognizing this, our study proposes a two-stage technology opportunity prediction approach based on interpretable machine learning from the perspective of technology convergence. Diverging from previous methods, it not only predicts the probability of technology opportunity occurrence but also forecasts the technical impact of convergence opportunities. By analysing 600,442 patent documents in the green and construction fields, we identify 305 high-potential technology convergence opportunities. Our results reveal that technologies such as carbon capture and storage, pollution alarms, solar energy, forestry techniques, wind energy, energy-saving methods, and waste materials for water treatment have significant potential for convergence with construction technologies. Additionally, we analyse the influencing factors behind these convergence innovations, finding that technical similarity and proximity play crucial roles. These findings provide robust decision support for governments and industry stakeholders in formulating scientifically grounded green technology innovation strategies, thereby accelerating the green transformation of the construction industry and contributing to the goal of sustainable development.