Role of environmental ownership and associated parameters to assess green patents in technologies with environmental...

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.