Lingkai Kong

The overconsumption of natural resources and the production of waste are causing severe degradation of our environment. Generic technologies (GTs) of eco-friendly materials could alleviate environmental pollution, solve resource and environment-related conflicts, and promote society’s sustainable development. The identification of GTs is the first step towards GT innovation and establishing a supply of eco-friendly materials; thus, how to accurately identify GTs is an important challenge for governments and enterprises. In this paper, a new method that integrates latent Dirichlet allocation (LDA), term frequency–inverse document frequency (TF–IDF), social network analysis (SNA), and a hidden Markov model (HMM) is proposed for GT identification. The LDA model was employed to extract hidden information of eco-friendly materials. In addition, scientific linkage, betweenness centrality, technology co-occurrence rate, the number of patents, and the number of valid countries or territories designated by the patent were selected to analyse the technology topic characteristics. Then, the fundamentality, pervasiveness, and benefit characteristics of GTs were combined to identify GTs of eco-friendly materials. On this basis, HMM was employed to predict the evolution trend of GTs. The results show that sixteen technologies are GTs of eco-friendly materials. Furthermore, degradable composite materials and cellulose extraction methods will become the focus of research in the future. These studies can provide a new method for the identification of GTs of eco-friendly materials and help nations or enterprises to make effective decisions to develop GTs, minimizing the burden on the environment.

Green building technologies (GBTs) are becoming increasingly complex and are being rapidly updated. These factors lead to an inherent demand for GBT innovation cooperation. This article considers the common policy instruments of the Chinese government and builds a GBT innovation cooperation network by joint patent applications. Then, evolutionary game models are constructed based on the GBT innovation cooperation network. Numerical simulation is employed to analyze the impact of different policy intervention intensities on the decision-making behavior of enterprises and network structures. The results show that government intervention is reasonable and legitimate for GBT innovation cooperation. Subsidies, preferential tax policy, and intellectual property protection are the main policy instruments of the Chinese government. When subsidies and preferential tax policy exceed certain thresholds, network efficiency and network cooperation density can be improved. In addition, the simulation results show that green building intellectual property protection in China has difficulty stimulating the enthusiasm of GBT innovation enterprises. GBT innovation cooperation network efficiency and network cooperation density evolution trends are basically the same. This study also provides insights into government policy formulation and adds to the increasing literature on the topic of GBT innovation cooperation. It is one of the few studies to explore the impact of government policy instruments on GBT innovation cooperation.