Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints

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.

Research and Development Talents Training in China Universities—Based on the Consideration of Education Management Cost Planning

“…, x Mt ); y t is the actual output of the input data x t ; T is the total number of historical data used to train the BRB expert system; Equation (2b,c) are constraints on the belief degree; Equation (2d,e) are constraints on the antecedent attribute weights and the rule weights, respectively; and Equation (2f-i) are the constraint on the utility values of the referential values used for antecedent attributes and the consequents used for consequent attribute. Note that the global parameter learning model can be solved by using the MATLAB optimization toolbox [15], clonal selection algorithm [16], and differential evolution algorithm [17].…”

An improved fuzzy rule-based system using evidential reasoning and subtractive clustering for environmental investment prediction

“…The representative studies include: in the study of environmental governance cost prediction [47], which introduced tree structure to represent the mathematical function of each output by combining possible operators in non-leaf nodes and all inputs in leaf nodes and the results showed that the predicted costs based on tree structure are closer to actual costs over time series forecasting-based models. Meanwhile, another concise but effective expression, namely IF-THEN rule, is also used to represent the input-output relationship between environmental pollution and environmental investment, in which extended belief rule is one of IF-THEN rules and has been successfully used in investment prediction modeling, i.e., extended belief rule-based system (EBRBS)-based model was introduced to predict environmental governance costs [43] and transportation industry governance costs [40]. The results of both two studies demonstrated that the EBRBS-based model has a high accuracy in investment prediction for labor, capital, and energy better than time series forecasting-based models.…”

Comparative Analysis of Long-Distance Transportation with the Example of Sea and Rail Transport

“…The assignment of weights to criteria in the analysis depends on several factors. The first is the amount of transported cargo and its type, which depends on the weight ratio for the delivery time and the cost of delivery [34]. If the ordering party wishes to transport many non-perishable goods, the delivery time is not the most important criterion in the company's planned strategy.…”