Evaluation on the effect of water environment treatment –A new exploration considering time based on the RCS

Abstract

The River Chief System (RCS), an innovative top-down and bottom-up water resource management system in China, is implemented to manage increasingly complex water environment issues. However, there has been a time lag in policy implementation. To accurately and scientifically assess the effect of RCS, a dynamic multiple-attribute decision-making method considering the time factor (DMADM) has been proposed. We have constructed the model consisting of 17 indicators from four aspects and determined the index weights and time weights by using the gray relation analysis method, the maximum entropy principle, and the subjective empowerment method. Finally, we applied the model to evaluate the water environment governance effect in the Taihu Basin from 2008 to 2020. The results have been ranked by possibility degree matrix, showing that: (1) The water environment in Taihu Basin maintains a steady improvement trend until 2014, except in Jiangsu Province;(2) The ranking result of the final comprehensive evaluation value is Shanghai ([0.334, 0.376]) ≻ Zhejiang ([0.316, 0.353]) ≻ Jiangsu ([0.305, 0.336]). Shanghai is far ahead with systematic pollution control measures, while Jiangsu lags due to the large fluctuation of pollutants (COD, NH3-N) in the wastewater. The study finds that the water environment management in Taihu Basin did improve over the past years, but failed to achieve the RCS governance goals at each stage. Enhancing coordination and cooperation and improving supervision mechanism for precise governance can better consolidate the results of RCS governance.

Appendices

Appendix A: The initial decision matrices R _k

Tables 11, 12, 13, 14, 15, 16, 17

Table 11 The initial decision matric R₁

Table 12 The initial decision matric R₂

Table 13 The initial decision matric R₃

Table 14 The initial decision matric R₄

Table 15 The initial decision matric R₅

Table 16 The initial decision matric R₆

Table 17 The initial decision matric R₇

Appendix B: The normalized decision matrices $\widetilde{{\mathrm{R}}_{\mathrm{k}}}$

Tables 18, 19, 20, 21, 22, 23, 24

Table 18 The normalized decision matric $\widetilde{{\mathrm{R}}_{1}}$

Table 19 The normalized decision matric $\widetilde{{\mathrm{R}}_{2}}$

Table 20 The normalized decision matric $\widetilde{{\mathrm{R}}_{3}}$

Table 21 The normalized decision matric $\widetilde{{\mathrm{R}}_{4}}$

Table 22 The normalized decision matric $\widetilde{{\mathrm{R}}_{5}}$

Table 23 The normalized decision matric $\widetilde{{\mathrm{R}}_{6}}$

Table 24 The normalized decision matric $\widetilde{{\mathrm{R}}_{7}}$

Appendix C ₁: The positive distance matrix ${\mathrm{D}}_{\mathrm{k}}$

Tables 25, 26, 27, 28, 29, 30, 31

Table 25 The positive distance matrix ${\mathrm{D}}_{1}$

Table 26 The positive distance matrix ${\mathrm{D}}_{2}$

Table 27 The positive distance matrix ${\mathrm{D}}_{3}$

Table 28 The positive distance matrix ${\mathrm{D}}_{4}$

Table 29 The positive distance matrix ${\mathrm{D}}_{5}$

Table 30 The positive distance matrix ${\mathrm{D}}_{6}$

Table 31 The positive distance matrix ${\mathrm{D}}_{7}$

Appendix C ₂: The negative distance matrix $\widetilde{{\mathrm{D}}_{\mathrm{k}}}$

Tables 32, 33, 34, 35, 36, 37, 38

Table 32 The negative distance matrix $\widetilde{{\mathrm{D}}_{1}}$

Table 33 The negative distance matrix $\widetilde{{\mathrm{D}}_{2}}$

Table 34 The negative distance matrix $\widetilde{{\mathrm{D}}_{3}}$

Table 35 The negative distance matrix $\widetilde{{\mathrm{D}}_{4}}$

Table 36 The negative distance matrix $\widetilde{{\mathrm{D}}_{5}}$

Table 37 The negative distance matrix $\widetilde{{\mathrm{D}}_{6}}$

Table 38 The negative distance matrix $\widetilde{{\mathrm{D}}_{7}}$

Appendix D: Evaluation index weights of water environment treatment effect in Taihu Lake

ωj/Year	2008	2010	2012	2014	2016	2018	2020
ω1	0.060	0.060	0.060	0.060	0.059	0.060	0.060
ω2	0.066	0.064	0.062	0.062	0.062	0.062	0.064
ω3	0.060	0.059	0.059	0.059	0.059	0.059	0.059
ω4	0.060	0.059	0.059	0.059	0.059	0.059	0.059
ω5	0.059	0.059	0.059	0.059	0.059	0.059	0.059
ω6	0.060	0.060	0.060	0.060	0.061	0.061	0.060
ω7	0.060	0.059	0.061	0.061	0.062	0.062	0.062
ω8	0.061	0.062	0.064	0.065	0.067	0.069	0.070
ω9	0.065	0.069	0.061	0.061	0.063	0.062	0.059
ω10	0.063	0.063	0.060	0.059	0.060	0.059	0.059
ω11	0.060	0.059	0.059	0.059	0.059	0.059	0.059
ω12	0.062	0.064	0.072	0.068	0.064	0.062	0.060
ω13	0.060	0.059	0.059	0.059	0.059	0.059	0.059
ω14	0.061	0.061	0.061	0.061	0.061	0.062	0.062
ω15	0.041	0.047	0.049	0.049	0.048	0.049	0.049
ω16	0.055	0.049	0.048	0.049	0.050	0.049	0.049
ω17	0.049	0.048	0.050	0.049	0.049	0.049	0.053