The impact of patentees assessment based on the heterogeneous patent innovation network.

As a vital factor in technological innovation, patentee plays a significant role in the process of scientific and technological innovation, researching patentee has attracted the attention of experts and scholars. Previously, scholars have mainly quantified patent indicators or constructed homogeneous information networks to analyze patentees, but these methods cannot objectively measure the impact of patentees. Therefore, this study proposes a novel approach to assessing patentee impact based on a heterogeneous information network. The proposed method distinguishes the weight of different types of nodes using a weighted mechanism and extracts three types of fine-grained characteristics of network nodes. This approach results in the construction of a heterogeneous patent innovation network and the development of a new patentee impact assessment algorithm called CWAPN. Using Chinese green patents in the field of energy conservation and environmental protection as an example, experimental results show that the CWAPN algorithm can effectively assess the impact of patentees. Thereby identifying patentees who have made outstanding contributions to sustainable development in China.

Role of environmental ownership and associated parameters to assess green patents in technologies with environmental scanning system as a controlling factor.

There is a general conception that environmental firms are more adapted to green solutions, and environmental patents are just lagging. The existing literature has paid particular attention to identifying obstacles and situational factors associated with established firms going green and has concentrated on how and why established businesses are becoming more financially viable and ecologically sustainable. In changing environment, manufacturing companies are direct contributors to environmental impacts. Increased awareness of consumers about the environment puts a handful amount of pressure on manufacturing companies to care about the environment. It also asserts unseen pressure on the financial performance of the companies. Therefore, it is time to go for green patenting of such firms while satisfying the eco-innovation and environmental scanning process. Moreover, Environmental ownership and its associated parameters keenly monitor this aspect. This paper evaluates the performance of the support vector machine (SVM/SVR) approach for estimating patents in environment-related technologies (PERT) in China from 1995 to 2021. For this work, six independent variables related to environmental ownership and environment-related technologies were selected, which include medium and high-tech exports (MHTE), green patents applicants (GPA), listed domestic companies (LDC), human capital index (HCI), self-employment (SE), and manufacturing value added in GDP (MVA). Data for dependent and independent variables were gathered from the World Bank (WB) official data bank portal. To make an initial understanding of the data basic statistical summary was computed in R programming to see the mean, minimum and maximum values in the data set. A correlation matrix plot showed the association between dependent and independent variables. SVM/SVR with radial basis function (RBF) regression was applied to see the impact of contributing parameters that influence PERT. For PERT, the model generated 0.95 R2 (RMSE = 92.43). The results of the SVR showed that the association among environmental parameters is strong. With a value of 4.82, the strongest coefficient in the SVR model is PAR. This work is novel and will benefit the manufacturing sector, analysts, policymakers, environmentalists as how green patenting can boost the eco innovation and environmental ownership and scanning system with advance technologies and practices.

Does the Digital Economy Increase Green TFP in Cities?

COVID-19 accelerated the growth of the digital economy and digital transformation across the globe. Meanwhile, it also created a higher demand for productivity in the real economy. Hence, the correlation between the digital economy and green productivity is worth studying as COVID-19 prevention becomes the norm. The digital economy overcomes the limitations imposed by traditional factors of production on economic growth and empowers innovative R&D and resource allocation in all aspects. This study delved into the digital economy by focusing on its green value at different levels of development. The study gathered the green-productivity indices and the principal components of the digital economy for each prefecture-level city in China from 2011 to 2019 and meticulously portrayed their trends in spatial and temporal figures. Meanwhile, regression models were used to verify the mechanism through which digital-economy development influences the changes in green productivity. The results showed that: (1) a higher level of digital economy helps to increase urban green total-factor productivity (GTFP) and that the conclusions of this paper still held after potential endogeneity problems were solved through the instrumental-variables approach; (2) the digital economy will drive an increase in urban GTFP by upgrading firms' production technologies and that digital-economy development encourages green patent applications from firms; and (3) as the digital economy develops, it will also drive urban GTFP increases by removing polluting enterprises from the market and that the higher the level of digital-economy development, the greater the number and probability of polluting enterprises exiting the market. In view of this study's results, the government should increase the importance of the digital economy, strengthen the role of the digital economy in promoting urban green development, and provide more guidance on regional green development with the help of the digital economy.