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

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## Abstract

**:**

## 1. Introduction

## 2. A Review of Traditional EBRB System

_{i,j}and consequent D

_{n}in the kth rule; ${\delta}_{i}(0<{\delta}_{i}\le 1)$ and ${\theta}_{k}(0<{\theta}_{k}\le 1)$ are the weight of the ith antecedent attribute and the kth rule. Furthermore, the extended belief rules of an EBRB system are generated from a set of input-output data pairs without any iterative optimization algorithm, the specific steps in the case of having M input indicators U

_{i}(i = 1, …, M) and one output indicator D for the cost prediction in the transportation industry can be expressed as follows:

**Step 1**: To initialize the basic parameters of an EBRB system. The basic parameters, including the utility value u(A

_{i,j}) of all referential values, the utility value u(D

_{n}) of all consequents D

_{n}(n = 1, …, N), and the weight of all input attributes ${\delta}_{i}$ (i = 1, …, M), should be determined by using the domain knowledge of experts.

**Step 2**: To calculate the belief degree of all indicators. Firstly, T training data <

**x**,

_{k}**y**> (k = 1, …, T) are used to generate extended belief rules, where x

_{k}_{k}= (x

_{k}

_{,1}, …, x

_{k,M}) denotes the input vector of the kth training data, x

_{k,i}represents the input data corresponding to the ith input indicator in the kth input indicator vector, and y

_{k}represents the kth output data. On the basis of the utility-based transformation method, these T input-output data pairs can be transformed into the belief degree of input and output indicators in an extended belief rule. The calculation formula of belief degree in the ith input indicator is as:

**Step 3**: To calculate the rule weights of the EBRB system. Firstly, according to the belief degrees of input and output indicators obtained from Step 2, the similarity of rule antecedent (SRA) and consequent (SRC) are calculated as follows:

## 3. Environmental Governance Cost Prediction by EBRB System for the Transportation Industry

#### 3.1. Parameter Learning to Optimize Basic Parameters of EBRB System

- (1)
- For the indicator weight of the ith input indicator, the constraint conditions are as follows:$$0<{\delta}_{i}\le 1;i=1,\dots ,M$$
- (2)
- For the utility values of the ith input indicator, the constraint conditions are as follows:$$u({A}_{i,j})\le u({A}_{i,j+1});i=1,\dots ,M;j=1,\dots ,{J}_{i}-1$$$$u({A}_{i,1})=l{b}_{i};i=1,\dots ,M$$$$u({A}_{i,{J}_{i}})=u{b}_{i};i=1,\dots ,M$$
_{i}and ub_{i}is the lower and upper bounds of ith input indicator. - (3)
- For the utility values of the output indicator, the constraint conditions are as follows:$$u({D}_{n})\le u({D}_{n+1});n=1,\dots ,N-1$$$$u({D}_{1})=lb$$$$u({D}_{N})=ub$$

**x**,

_{t}**y**> (t = 1, …, T) and the inference result of the EBRB system for each data is $f({x}_{t})$, the objective function is defined as follows:

_{t}_{i,j}; $u{({D}_{n}^{})}^{s,c}$ is the utility value of D

_{n}; ${p}_{k}^{s,c}$ denotes the kth basic parameter; K denotes the total number of basic parameters. When ub

_{k}and lb

_{k}are the upper and lower bounds of kth parameter ${p}_{k}^{s,c}$, the initial values of basic parameters can be obtained:

#### 3.2. Cost Prediction Using the Improved EBRB System with Technological Constraints

**Step 1**: To calculate technological innovation factors for the EBRB system. Suppose that P technological innovation indicators are used to calculate technological innovation factors and their historical data are z

_{k,p}(k = 1, …, T; p = 1, …, P). Due to the incommensurability among these P indicators, the historical data of these indicators need to be normalized to eliminate dimensional units using

_{k,p}is the positive value which is larger the better; ${z}_{k,p}\in {\mathbf{\Omega}}_{Negative}$ denotes that z

_{k,p}is the negative value which is larger the better.

_{k}

_{,}by:

**Step 2**: To consider technological constraint in the EBRB system. When the technological innovation factors TIF

_{k}(k = 1, …, T) are considered in the EBRB system, an extended dataset can be generated by combining the original data <

**x**, y

_{k}_{k}> (k = 1, …, T) and the technological innovation factors TIF

_{k}, namely <

**x**, TIF

_{k}_{k}, y

_{k}>. On the basis of the new dataset and taking the technological innovation factor as a new input indicator of the EBRB system, an EBRB system with the consideration of technological constraint can be generated, according to Section 2 and Section 3.1.

**Step 3**: To calculate the activation weight of each rule. Suppose that a new input data vector including technological innovation factor, denotes as

**x**= (x

_{i}; i = 1, …, M + 1), is provided for the EBRB system, and the input data can be transformed into belief distribution $S({x}_{i})=\{({A}_{i,j},{\alpha}_{i,j});j=1,\dots ,{J}_{i}\}$ using Equations (3) and (4). Afterwards, the activation of the kth (k = 1, …, L) rule can be calculated as follows:

_{i,j}; ${d}^{k}({x}_{i},{U}_{i})$ denotes the distance between input data and rule, then the active weight can be calculated:

**Step 4**: To integrate all activated rules based on the evidence reasoning (ER) algorithm. According to the analytical ER algorithm, the rule which has activation weight w

_{k}> 0 should be integrated by:

_{n}); n = 1, …, N}, the predicted cost for the environmental governance in the transportation industry can be obtained by:

**Step 1**: To calculate the future input data of the EBRB system. Suppose that the input indicators of the EBRB system can be divided into two categories: positive and negative. The positive indicators are those indictors for maximization, such as benefit, whose values are always the larger the better. The negative indicators are those for minimization, such as cost, whose values are better when smaller. The values of the rth positive (r = 1, …, M

_{1}) attribute and the fth (f = 1, …, M

_{2}) negative attribute are denoted as ${\widehat{x}}_{r}^{(t)}$ and ${\widehat{x}}_{f}^{(t)}$ in the future the tth (t = 0, …, $\infty $) year. Additionally, suppose that a

_{r}is the target change ratio of the rth positive indicator and b

_{f}is the target change proportion of the fth negative indicator of the transportation industry in the future. Hence, the calculation of the future input data of the EBRB system can be obtained as follows:

**Step 2**: Future cost prediction with technological constraints. According to the future input data ${\widehat{x}}_{r}^{(t)}$ and ${\widehat{x}}_{f}^{(t)}$ of the transportation industry, the predicted environmental governance costs in the transportation industry can be obtained according to Equations (24) to (27).

## 4. Case Study of Cost Prediction in the Transportation Industry

#### 4.1. Data Resource and Variable Determination

_{2}) emissions of the transportation industry are taken as input indicators, and the labor input, capital investment and energy consumption of the transportation industry are taken as output indicators. The statistical analysis of specific input and output indicators is shown in Table 2.

_{2}emission industries in China, so taking carbon dioxide as an input indicator can better fit the actual environmental governance process of the transportation industry. In this paper, cost prediction is the goal of this study, so in the model construction, labor, capital and other indicators related to cost are taken as the output indicators of the prediction model, while pollution emissions and industrial added value are taken as input indicators.

#### 4.2. Model Development and Results Discussion of Cost Prediction with Technological Constraint

_{2}, which is 0.8220. The weight of TIF is lower than that of IAV and CO

_{2}, which is 0.7787. From the view of the utility values of input and output indicators, the utility values of all indicators have certain changes compared with the initial value shown in Table 4. For example, the initial utility values of IAV are {u(VL) = 310, u(L) = 478, u(M) = 647, u(H) = 815, u(VH) = 983} in Table 5 and the optimized utility value of IAV are {u(VL) = 310, u(L) = 529, u(M) = 714, u(H) = 814, u(VH) = 983} in Table 6.

#### 4.3. Future Environmental Governance Cost Prediction with Technology Constraint

_{2}emissions, it is clear that the promotion of technological innovation can reduce the cost input to a certain extent, and realize the effective utilization of resources.

## 5. Conclusions

- (1)
- In the term of constructing cost prediction model, the original EBRB system is improved through the parameter learning model and DE algorithm, so as to ensure the EBRB system has an accurate basic parameters to ensure the accuracy of the EBRB system, and avoid the influence of experts’ subjectivity on basic parameters. Moreover, by using the historical data of the transportation industry in 30 provinces of China as training and testing data, it demonstrates that the prediction error of the proposed cost prediction model is smaller than some exiting model, such as GM (1.1) and ANFIS.
- (2)
- According to the policy of China’s 13th five year plan, the future values of IAV, CO
_{2}and technology innovation level of the transportation industry from 2017 to 2033 are calculated by combining with the cost input and pollution situation of the transportation industry. Moreover, all these future values are used as the inputs of the proposed environmental governance cost prediction model to predict the cost of the transportation industry of each province from 2017 to 2033. The results show that the regional difference of cost forecast value is consistent with the regional difference of transportation industry and economic development in China, which demonstrates that the future cost input in Northwest China is low, while that in eastern coastal area is higher. - (3)
- With the aim of analyzing the impact of technological innovation on the cost input of the transportation industry, the technological innovation factor is calculated and used in the EBRB system to propose a cost prediction model with the consideration of technological constraints. From the research results, the impact of technological innovation on energy consumption and fixed asset investment is greater than that of labor cost. The reason is that technological innovation has a more significant effect on the renewal of fixed assets and equipment, the development of clean energy technology and energy conservation, and has a lower impact on labor force employment. In terms of the impact of technological innovation on the overall cost input, the future energy consumption cost and labor cost, without considering technological innovation, are higher than the cost of considering technological innovation.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 6.**Cost prediction in Beijing from 2017–2033; (

**a**) prediction of labor; (

**b**) prediction of capital; (

**c**) prediction of energy.

References | Input-Output Indicators |
---|---|

Chen et al., 2017 [4]; Ye et al., 2020 [12]; Ye et al., 2019 [17]; Long et al., 2018 [18] | GDP, CO_{2}, labor input, capital investment, energy consumption |

Zhang et al.,2020 [19]; Cheng et al., 2017 [20]; | CO_{2} |

Wang et al., 2020 [21] | Labor input, capital investment |

Svensson et al., 2005 [22] | Energy consumption |

Vanesa et al., 2018 [23] | CO_{2}, energy consumption |

Indicator | Input Indicator | Output Indicator | |||
---|---|---|---|---|---|

IAV | CO_{2} | Labor | Capital | Energy | |

Max | 3210 | 6727 | 85.4 | 3738 | 3139 |

Min | 28.12 | 18.96 | 2.82 | 31.88 | 10.64 |

Average | 706.7 | 1705 | 23.53 | 759.8 | 767.2 |

Std | 600 | 1233 | 14.03 | 656.7 | 564 |

Unit | 107 RMB | 104 Tons | 104 People | 107 RMB | 104 Tons |

Indicator | Student in University | Education Funding | New Product | New Patent |
---|---|---|---|---|

Max | 890 | 28,915,729 | 66,843 | 236,918 |

Min | 305 | 1,724,469 | 126 | 393 |

Average | 541 | 10,040,558 | 13,062 | 25,658 |

Std | 120 | 5,993,580 | 18,762 | 46,008 |

Unit | People | 104 RMB | Item | Item |

Indicator | IAV | CO_{2} | TIF |
---|---|---|---|

Max | 9363 | 6512 | 4.17 |

Min | 101 | 181 | 0.09 |

Average | 2161 | 1793 | 1.49 |

Std | 1658 | 1093 | 0.708 |

Unit | 107 RMB | 104 Tons | - |

Indicator Type | Indicator Name | Utility Values | Indicator Weight | ||||
---|---|---|---|---|---|---|---|

u(VL) | u(L) | u(M) | u(H) | u(VH) | ${\mathit{\delta}}_{\mathit{i}}$ | ||

Input indicator | IAV | 310 | 478 | 647 | 815 | 983 | 1 |

CO_{2} | 64 | 613 | 1163 | 1713 | 2262 | 1 | |

TIF | 1.32 | 1.58 | 1.85 | 2.12 | 2.38 | 1 | |

Output indicator | Energy | 18.84 | 276 | 534 | 792 | 1050 | - |

Indicator Type | Indicator Name | Utility Values | Indicator Weight | ||||
---|---|---|---|---|---|---|---|

u(VL) | u(L) | u(M) | u(H) | u(VH) | ${\mathit{\delta}}_{\mathit{i}}$ | ||

Input indicator | IAV | 310 | 529 | 714 | 814 | 983 | 0.8318 |

CO_{2} | 64 | 955 | 1437 | 1621 | 2262 | 0.8220 | |

TIF | 1.32 | 1.49 | 1.85 | 1.91 | 2.38 | 0.7787 | |

Output indicator | Energy | 18.84 | 215 | 454 | 783 | 1050 | - |

GM (1.1) | ANFIS | Proposed Model | |||||||
---|---|---|---|---|---|---|---|---|---|

Labor | Capital | Energy | Labor | Capital | Energy | Labor | Capital | Energy | |

MAE | 36 | 1689 | 2843 | 7.3 | 894 | 523 | 1.6 | 350 | 82 |

MAPE | 1.20 | 56.30 | 94.75 | 0.27 | 0.56 | 0.59 | 0.05 | 0.22 | 0.08 |

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**MDPI and ACS Style**

Wang, S.; Ye, F.-F.
Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints. *Symmetry* **2020**, *12*, 1352.
https://doi.org/10.3390/sym12081352

**AMA Style**

Wang S, Ye F-F.
Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints. *Symmetry*. 2020; 12(8):1352.
https://doi.org/10.3390/sym12081352

**Chicago/Turabian Style**

Wang, Suhui, and Fei-Fei Ye.
2020. "Environmental Governance Cost Prediction of Transportation Industry by Considering the Technological Constraints" *Symmetry* 12, no. 8: 1352.
https://doi.org/10.3390/sym12081352