Green innovation is an important capability for an enterprise’s sustainable development. Evaluating the green innovation efficiency of China’s industrial enterprises can greatly affect its improvement. Considering the existing research results of green innovation efficiency evaluation, this paper added the impact of environmental factors in the technology industrialization stage. Various negative environmental externality indicators produced by industrial enterprise activities were incorporated into the undesired output, and an index system and model were constructed for evaluating the green innovation efficiency of China’s industrial enterprises. Combined with the actual data, a three-stage chain network SBM (slack-based measure) model was used to dynamically evaluate and analyze the industrial enterprises in 29 Chinese provinces. The results show that the overall efficiency of green innovation in China’s industrial enterprises has an upward trend, and the rise fluctuates during the technological development, achievement transformation, and industrialization stages. In the three major regions of China, industrial enterprises in the eastern area have the highest green innovation efficiency, and the industrial enterprises in the west and central regions have lower efficiencies. Based on the evaluation results, the corresponding countermeasures are proposed.

The industrial revolution has brought a leap in productivity; however, some severe ecological environment and resource problems are coming out with the development of industrialization. To achieve sustainable development of social economy, green technology innovation emerges in response to the proper time and conditions. In this context, the correct approach to measure the efficiency of green technology innovation in China’s industrial enterprises is an important research topic. Based on existing studies, this paper divides innovation activities into two stages based on the innovation value chain and constructs a two-stage evaluation index system including all kinds of undesirable outputs transferred from negative externalities of the ecological environment. To evaluate green technology innovation efficiency, SBM and EBM network DEA models are applied to conduct empirical analysis from the perspective of time and space. This paper also calculates the efficiency values based on the SBM model and the EBM model, respectively, and compares the differences between them. The results show that the efficiency value of the achievement transformation stage is significantly higher than that of technology development stage; there are noticeable gaps in the efficiency of green technology innovation between different regions of China. Besides, the eastern region has a better performance in green technology innovation than the western region and Qinghai has the best green technology innovation performance. Combining the empirical results, the corresponding policy recommendations are put forward.

This study aims to investigate the classification of technological innovation meta-theories based on classical texts, as well as the relationships between various classifications. Both qualitative and quantitative methods are employed. From the perspective of technological innovation, using scientometric methods, 105 pieces of classic texts from the 1930s–2010s are extracted from the references of 3862 pieces of high-quality literature from the 1900s–2020s. As a result, based on a combination of qualitative data analysis and topic model analysis, we developed a typology with eight meta-theories of technological innovation, including performance-based, resource-based, knowledge-based, capability-based, network-based, technological-innovation-system, dual-innovation, and dynamic-sustainability views. Then we analyzed 1) the evolution, reification, and confusion relationships between different meta-theories; 2) the causes of technological innovation's concept jungle; and 3) an integrated framework of technological innovation meta-theories. This study analyzed the benefits of the meta-theoretical analysis on the future study of technological innovation. Additionally, the results of this study can help to measure technological innovation, construct new theories, and improve the efficiency of the connection between the practical problems of innovation and potentially useful theoretical frameworks.

The impact of social network position on innovation has been widely confirmed in past studies. However, research on the time-lag structure of the impact is still insufficient. Within the time window 2010 to 2017, this study constructs a two-mode social network between Chinese listed companies and other participants. To analyze the lag structure of the effect of social network position on innovation, this study uses a panel negative binomial regression model transformed by the Almon polynomial. The results show that a firm does need an advantageous past social network position for innovation. Previous local and global centrality in a social network has a different influence on innovation. For the local centrality indices, degree centrality has a positive impact in the short-term, but has a negative impact in the long-term; the impact of betweenness centrality is not significant in the short-term and is negative in the long run. For the global centrality indices, closeness centrality has a positive influence that decreases with the increase of the time-lag. At the same time, using the method of necessary condition analysis (NCA), this study calculates the bottleneck for a given innovation level. Finally, based on these research conclusions, the theoretical implications and management practice implications are summarized.

The mechanism of the impact of inter-firm social networks on innovation capabilities has attracted much research from both theoretical and empirical perspectives. However, as a special emerged and developing complex production system, how the scenario factors affect the relationship between these variables has not yet been analyzed. This study identified several scenario factors which can affect the firm’s technological innovation capabilities. Take the manufacturing scenario in China as an example, combined with the need for firms’ ambidexterity innovation and green innovation capability, a multi-objective simulation model is constructed. Past empirical analysis results on the relationship between inter-firm social network factors and innovation capabilities are used in the model. In addition, a numerical analysis was conducted using data from the Chinese auto manufacturing industry. The results of the simulation model led to several optimization strategies for firms that are in a dilemma of development in the manufacturing scenario.

This paper investigates whether the COVID-19 (coronavirus disease 2019) pandemic affects the green inventions of firms, universities, and firm–university collaborations (FUCs) differently. Our identification used provincial-level monthly data from China. Results from the difference-in-differences (DID) model showed that the COVID-19 pandemic has prompted the output of three types of green invention patents. After the parallel-trend test, placebo test, and triple-difference estimation, our conclusion has good robustness. However, the COVID-19 pandemic also influences the role of other policies, such as the SO₂-emissions-trading pilot policy for universities’ green inventions. There has been a slight change in the effect of dual carbon targets on green inventions since the start of the pandemic. The positive effect of the COVID-19 pandemic has been weaker for provinces where the pandemic has been more severe than in other provinces. The results of this study are compared with the results and empirical evidence of other related studies and the theoretical logic of COVID-19 crisis-promoted green inventions are discussed.