Abstract
In order to solve the problem of environmental governance investment planning in the transportation industry, a cost prediction model is proposed under technological constraints, where the input output indictors emphasizes the flexibility of prediction and its characters are asymmetric, while the constructs of prediction model focuses on the standardization and its characters are symmetrical. The basic principle of the cost prediction model is based on an extended belief rule-based (EBRB) system to model the input-output relationship in investment planning, and a parameter learning model to improve the accuracy of the EBRB system. Additionally, the technological innovation factors are also embedded in the cost prediction model to investigate the influence of technology-related outcomes on investment planning. Finally, based on the data of environmental governance in China’s transportation industry from 2003 to 2016, the cost of transportation industry environmental management in China’s thirty provinces from 2017 to 2033 is predicted under the constraints of technological innovation. Results show that: (1) the accuracy of the proposed cost prediction model is higher than some existing cost prediction methods; (2) the predicted environmental governance costs have a significant regional difference; (3) the upgrading of technological innovation is conducive to saving the future environmental governance costs of the transportation industry in some provinces. In addition to the above results, the present study provides model supports and policy references for government decision makers in transportation industry-related environmental cost planning.
Highlights
The transportation industry is one of the three major carbon emission industries in China [1].With the development of economies, the pollution emissions of the transportation industry increased rapidly
To overcome the limitations of the existing studies, this paper proposes an environmental governance cost prediction model based on an extended belief rule-based (EBRB) system and technological constraints, in which the EBRB system was proposed by Liu et al [14] and has demonstrated its excellent prediction performance on many real problems [15]
Adding to the data of input and output indicators of be of the basic parameters used to generate an EBRB system can determined by splitting the range of lower and upper bounds in each indicator when the determined by lower and bounds each indicator the number of utility valuessplitting in input the andrange outputofindicators areupper assumed to beinfive, namely verywhen low (VL), number of utility values in input and output indicators are assumed to be five, namely very low (VL), low (L), medium (M), high (H) and very high (VH)
Summary
The transportation industry is one of the three major carbon emission industries in China [1]. Existing studies on environmental cost mainly focus on the relationship between management cost and carbon emissions, such as the input-output performance analysis of carbon emissions based on data envelopment analysis (DEA) [5], the cost saving effect of carbon emission trading mechanism [3] and the environmental cost difference under different emission reduction measures [6]. Based on the policy objectives of the 13th five year plan and the technological innovation differences among different provinces, the environmental governance costs of the transportation industry in China from 2017 to 2033 are regarded as the targets to be predicted using the proposed model. In the comparison of existing methods, some commonly used cost prediction methods are introduced to compare accuracy with the proposed model and its effectiveness is verified by considering the constraints of technological innovation in the transportation industry, which provides a certain reference for the government to make effective environmental cost budget
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