Elsevier

Journal of Cleaner Production

Volume 212, 1 March 2019, Pages 548-566
Journal of Cleaner Production

A new environmental governance cost prediction method based on indicator synthesis and different risk coefficients

https://doi.org/10.1016/j.jclepro.2018.12.029Get rights and content

Highlights

  • Considering different importance and complete information of indicators.

  • Considering the influence of decision-maker's subjectivity on cost prediction.

  • Combining the genetic programming models with different risk coefficients.

  • Providing a case study of environmental governance cost prediction of China.

Abstract

Environment protection is important for the survival of residents, and the government must improve its governance model on environmental cost prediction methods to address the increasing level of environmental pollution. Therefore, a science-based investment scheme is of great significance. To improve the accuracy and effectiveness of environmental governance cost prediction method, it is important to consider the completeness of the indicators and their degree of contributions-both of which need to be studied further. Considering the influence of a decision-maker's subjectivity on an investment scheme, this paper proposes a prediction method accommodating the risk preferences of different decision-makers. The proposed method is based on the synthesis of evidential reasoning approach. An objective empowerment is carried out according to the standard deviation method of correlation coefficient to highlight the importance degree of different indicators. At the same time, to improve the practical usage of the synthetic cost prediction method, the future cost is predicted by combining the genetic programming models under different risk coefficients, namely, the risk preference, the risk neutrality, and the risk aversion. Finally, a case study involving environmental governance cost prediction of 29 provinces of China is presented. A comparison of the cost predictions and the actual value of different risk coefficients for the different methods are given to evaluate the effectiveness of the proposed method.

Introduction

As a result of the economic reform in China, the average annual growth rate measured by the gross domestic product GDP was approximately 9.5% during the period of 1978–2011 (Song et al., 2016). However, this high economic growth has also resulted in an increasing level of environmental pollution. The air quality of 254 cities in China exceeded the National Ambient Air Quality Standards during 2016. 71.5%, 58.3%, 17.5%, 3%, and 16.9% cities suffered from air pollution due to PM2.5, PM10, O3, SO2, and NO2 (MEP, 2017). Such a serious pollution problem has prompted the government to gradually increase the investments toward environmental pollution control, aiming to identify means for sustainable development.

Investment in environmental governance serves as an important mechanism to reduce the pollution, which goes hand in hand with more stringent environmental protection policies. As indicated by Annamalai et al., 2016, Dhinesh et al., 2016, Dhinesh et al., 2017, the discovery of more efficient protection techniques and alternative environmental measures must be financially supported by the government. Determining the relationship between the environmental input and output has been a contested issue amongst the sustainable development community. Along with economic development and increasing level of income, the citizens' demand for environmental quality is also increasing (Lui and Leamon, 2014, Dong et al., 2015). The relationship between the sets of input-output data is highly subjective. This is prone to a researcher's bias as shown in previous studies (Yu et al., 2016a, Liu et al., 2016, Chen et al., 2017a, Song et al., 2018). Therefore, for a cost prediction method to be effective, it must meet the following criteria: good ability to predict, insight into the inner working of a system, and unbiased results. Therefore, it is important to incorporate the environmental governance cost and the subjective intention of the environmental management into a prediction model that can effectively measure the input-output relationship of the inner environmental governance system.

In recent years, the government has pushed forward an environmental reform, and is consistently increasing the expenditure on environmental governance (Zhu et al., 2014, Zhao et al., 2015). As a result, the accuracy of the environmental governance cost prediction has become a key strategic issue for the policy makers. It can also be predicted that a large-scale fiscal expenditure will ease the issue of underfunding, and will reduce the on-going resource burden. The prediction of environmental governance cost is, therefore, a critical aspect of environmental protection. However, the following questions still have to be addressed: (1) what factors affect the environmental cost prediction? (2) How much expenditure is required for environmental protection? (3) How to examine the internal relation between the environmental input and the corresponding output? If the environmental governance cost can meet the demand of sustainable development, then it needs to reform, and adjust the environmental system to improve the accuracy and effectiveness of the government expenses on pollution control and economic development. However, in fact, the effects are often unpredictable in environment protection scenarios.

By analyzing previous cost prediction methods of environmental governance, three challenges must be overcome to develop a new prediction method. First, the relationship between the environmental governance cost and the environmental pollution must be established, as environmental governance is a highly complex process (Franke, 2018). Second, complete information of indicators must be considered, as fewer indicators would affect the performance of the cost prediction methods (Assunta et al., 2018). Third, the attitude of the decision makers in environmental governance must be considered, as the investment preference of the decision makers ultimately determines the scale of expenditure (Lev and Andrea, 2015).

Thus, a scientific investment scheme is the key to realize sustainable development of a society. To verify the internal relationships between the environmental protection and the environmental governance cost, this study uses ER method with CCSD method to synthesize the output indicators. The proposed method also uses GP system under different risk coefficients, namely the risk preference, the risk neutrality, and the risk aversion, to predict the environmental costs. The proposed method includes: (1) application of GP method for the first time to predict the cost of environmental governance in accordance with the target environmental economic benefits and environmental pollution levels. (2) Use of the weight calculation method and ER method to improve the cost prediction system, applying indicator importance difference assignment and environmental indicators synthesis. (3) Construction and training of the cost prediction system based on different risk coefficients to accommodate the subjectivity of the decision maker.

To demonstrate the efficiency of the new cost prediction method, environmental data from 2006 to 2015 from 29 provinces in China are analyzed. The experimental results reveal that the new method can accurately obtain the amount of environmental investment and reduce the uncertainties in cost input. In addition, the new cost prediction method is further verified by comparing with other previous studies.

The rest of this paper is organized as follows. Available literature is reviewed in Section 2. Section 3 provides the theoretical foundations of the proposed cost prediction method. Section 4 introduces the proposed cost prediction method under indicator synthesis and risk preference. Section 5 carries out the empirical study. Finally, the conclusions are given in Section 6.

Section snippets

Literature review and innovation

During the last decades, a number of studies have investigated means to effectively assess the environmental governance costs and the corresponding efficiency. This section reviews the literature regarding the methods that focus on the cost prediction in the environmental governance sector to delineate the research emphases. In addition, a literature review of various environmental input-output indicators applied to the measurement is also undertaken to provide background information on the

Theoretical basis of the proposed cost prediction method

Complete indicator information and input-output relationship are important for the accuracy of the cost prediction. In addition, previous studies often ignore the combination of objective data prediction and decision-maker's preference, where it is easy to cause errors in decision-making. Therefore, the objective environmental governance data with the decision maker's risk preferences are combined in this study to ensure the accuracy of environmental governance cost assessment.

New cost prediction method under indicator synthesis and risk preference

A new method for environmental governance cost prediction is developed in this section by applying the GP-based cost prediction system with indicator synthesis and different risk coefficient. First, the weight calculation and indicator synthesis based on the CCSD and ER methods are discussed in Sections 4.1 and 4.2. Second, the GP method is applied to construct the basic input-output model in Section 4.3. Finally, a description of the new environmental governance cost prediction method is given

Experimental studies

To verify the new environmental governance cost prediction method, the regional environmental governance data from 29 provinces in the mainland of China are utilized to illustrate the process of application. Then we compare the results with the cost prediction methods executed under different risk coefficients with actual cost values. Additionally, the comparative analysis of the different cost prediction methods is provided to verify the effectiveness of the proposed environmental governance

Conclusion and policy recommendations

In this study, the ER and CCSD methods are used to select the output indicators, and the input-output relationship is used to establish the GP method to predict the environmental governance cost under different risk coefficients. This ensures the integrity of the indicator information and the accuracy of the cost prediction. The results show that different risk coefficients have greater impact on the investment in environmental governance. Because the indicator information considered in this

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Nos. 61773123, 71371053, 71701050, 71801050 and 71501047), the Humanities and Social Science Foundation of the Ministry of Education under Grant (No. 14YJC630056), the Natural Science Foundation of Fujian Province, China (No. 2015J01248) and the Social Science Foundation of Fujian Province, China (No. FJ2018C014).

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