Ecological Well-Being Performance Evaluation of Chinese Major Node Cities along the Belt and Road
Abstract
:1. Introduction
2. Literature Review
3. Methods and Materials
3.1. Methods
3.1.1. Super-NSBM Model Considering Undesired Outputs
3.1.2. Malmquist–Luenberger Productivity Index
3.1.3. The Overall Research Method
3.2. Indicator Selection of the EWP
3.2.1. Indicator System Structure
3.2.2. The EWP Evaluation Index System
3.3. Study Area and Data Sources
4. Results
4.1. The EWP Scores and Its Two-Stage Efficiency
4.2. Analysis of the MLPI and Its Decomposition
5. Discussion
5.1. Static Analysis on the EWP
5.2. Dynamic Analysis of the EWP
5.3. Comprehensive Comparative Analysis Based on the EWP and HDI
6. Conclusions and Policy Recommendations
6.1. Conclusions
6.2. Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Decomposition of The EWP of Chinese Node Cities along the B&R during the Period of 2011–2018
City | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | S1 | S2 | |
Beijing | 1.16 | 1.02 | 0.99 | 0.56 | 1.15 | 1.01 | 1.15 | 1.04 | 1.16 | 1.09 | 1.13 | 1.06 | 1.06 | 1.12 | 1.19 | 1.33 |
Changchun | 0.75 | 1.00 | 0.67 | 0.97 | 0.75 | 1.00 | 1.07 | 1.05 | 0.87 | 0.91 | 1.09 | 1.04 | 1.09 | 1.27 | 1.09 | 1.04 |
Changsha | 1.17 | 1.25 | 1.17 | 1.27 | 1.16 | 1.21 | 1.14 | 1.16 | 1.12 | 1.24 | 1.14 | 1.10 | 1.07 | 1.24 | 1.13 | 1.08 |
Chengdu | 0.89 | 1.00 | 0.78 | 1.00 | 0.98 | 1.00 | 1.00 | 1.02 | 0.95 | 1.03 | 0.92 | 0.82 | 0.97 | 1.01 | 1.06 | 1.02 |
Dalian | 0.81 | 0.42 | 0.74 | 0.46 | 0.88 | 0.46 | 0.77 | 0.50 | 0.72 | 0.64 | 0.65 | 0.60 | 0.70 | 0.56 | 0.82 | 0.65 |
Fuzhou | 0.82 | 1.00 | 0.67 | 0.92 | 0.81 | 0.97 | 1.06 | 1.01 | 1.09 | 1.01 | 1.11 | 1.04 | 1.09 | 1.06 | 0.79 | 1.00 |
Guangzhou | 0.72 | 0.68 | 0.70 | 0.91 | 0.73 | 0.86 | 0.76 | 0.88 | 0.80 | 0.78 | 0.81 | 0.60 | 0.81 | 0.80 | 0.83 | 0.78 |
Guiyang | 0.87 | 1.07 | 0.79 | 1.17 | 0.85 | 1.04 | 0.77 | 1.00 | 0.78 | 1.00 | 0.68 | 1.00 | 0.63 | 0.79 | 0.67 | 0.92 |
Harbin | 1.01 | 1.08 | 0.72 | 1.00 | 0.99 | 1.27 | 1.01 | 1.10 | 1.05 | 1.26 | 1.03 | 1.24 | 1.03 | 1.12 | 1.05 | 1.42 |
Haikou | 1.00 | 1.26 | 1.02 | 1.26 | 0.99 | 1.39 | 0.99 | 2.49 | 0.93 | 2.02 | 1.07 | 1.13 | 1.02 | 1.21 | 0.99 | 1.67 |
Hefei | 0.99 | 1.09 | 0.94 | 1.07 | 1.00 | 1.03 | 1.02 | 1.05 | 0.99 | 1.04 | 0.96 | 1.06 | 0.88 | 1.00 | 0.78 | 1.00 |
Hohhot | 1.13 | 1.13 | 1.09 | 1.08 | 1.12 | 1.05 | 1.08 | 1.03 | 1.09 | 1.00 | 0.70 | 0.92 | 1.03 | 1.22 | 1.04 | 1.07 |
Kunming | 0.60 | 0.96 | 1.01 | 1.01 | 0.94 | 1.00 | 0.89 | 1.09 | 0.90 | 1.11 | 0.98 | 1.16 | 0.96 | 1.00 | 0.75 | 0.77 |
Lanzhou | 0.57 | 0.73 | 0.69 | 0.60 | 0.79 | 1.09 | 0.89 | 1.02 | 0.92 | 1.02 | 0.87 | 1.03 | 0.76 | 0.87 | 0.66 | 0.73 |
Nanchang | 0.96 | 1.14 | 1.03 | 1.14 | 0.97 | 1.07 | 0.88 | 1.12 | 0.90 | 1.09 | 0.92 | 1.09 | 1.01 | 1.17 | 1.00 | 1.18 |
Nanning | 0.62 | 0.91 | 0.61 | 0.93 | 0.80 | 1.02 | 0.64 | 0.97 | 0.76 | 1.00 | 0.88 | 1.00 | 0.95 | 1.13 | 0.97 | 1.06 |
Ningbo | 0.84 | 0.40 | 0.79 | 0.38 | 0.82 | 0.42 | 0.85 | 0.42 | 0.76 | 0.45 | 0.86 | 0.40 | 0.91 | 0.52 | 0.84 | 0.36 |
Qingdao | 0.79 | 0.93 | 0.72 | 0.80 | 0.91 | 0.72 | 0.87 | 0.71 | 0.84 | 0.66 | 1.00 | 0.69 | 1.12 | 1.20 | 1.11 | 1.20 |
Quanzhou | 0.74 | 0.86 | 1.14 | 1.02 | 1.16 | 1.07 | 1.15 | 1.07 | 1.18 | 1.21 | 1.19 | 1.14 | 0.85 | 1.16 | 1.14 | 1.05 |
Sanya | 1.25 | 2.29 | 1.23 | 3.66 | 1.25 | 3.74 | 1.27 | 4.93 | 1.25 | 4.25 | 1.22 | 3.25 | 1.30 | 4.72 | 1.27 | 4.07 |
Shantou | 0.97 | 1.06 | 0.95 | 1.01 | 0.78 | 1.04 | 0.92 | 1.06 | 0.96 | 1.04 | 0.89 | 1.31 | 1.19 | 1.46 | 0.80 | 1.09 |
Shanghai | 0.93 | 0.45 | 0.91 | 0.40 | 0.77 | 0.53 | 0.72 | 0.56 | 0.81 | 0.56 | 0.77 | 0.51 | 1.16 | 1.14 | 0.80 | 0.41 |
Shenzhen | 1.28 | 1.49 | 1.30 | 1.64 | 1.30 | 1.63 | 1.31 | 1.76 | 1.30 | 1.63 | 1.24 | 1.39 | 1.23 | 1.47 | 1.29 | 1.69 |
Shenyang | 0.70 | 0.82 | 0.74 | 0.52 | 0.78 | 0.64 | 0.73 | 0.65 | 0.70 | 0.69 | 0.65 | 0.65 | 0.65 | 0.76 | 0.67 | 0.76 |
Tianjin | 0.83 | 0.59 | 0.81 | 0.51 | 0.89 | 0.54 | 0.79 | 0.60 | 0.77 | 0.58 | 0.93 | 0.43 | 0.90 | 0.59 | 0.89 | 0.57 |
Urumqi | 0.70 | 0.38 | 0.57 | 0.29 | 0.78 | 0.37 | 0.70 | 0.46 | 0.77 | 0.42 | 0.65 | 0.38 | 0.66 | 0.45 | 0.74 | 0.36 |
Wuhan | 0.93 | 1.00 | 0.80 | 0.95 | 1.00 | 0.99 | 1.11 | 1.06 | 1.13 | 1.17 | 1.04 | 1.13 | 0.79 | 0.97 | 0.67 | 0.92 |
Xi’an | 0.98 | 1.11 | 0.88 | 1.13 | 1.06 | 1.23 | 0.92 | 1.19 | 0.98 | 1.28 | 0.99 | 1.95 | 0.98 | 1.69 | 1.10 | 1.44 |
Xining | 0.66 | 0.53 | 0.58 | 0.60 | 0.55 | 0.62 | 0.55 | 0.64 | 0.65 | 0.61 | 0.64 | 0.58 | 0.69 | 0.62 | 0.63 | 0.45 |
Xiamen | 0.70 | 0.75 | 0.70 | 0.79 | 0.74 | 0.75 | 0.67 | 0.60 | 0.72 | 0.60 | 0.76 | 0.61 | 0.70 | 0.67 | 0.79 | 0.96 |
Yantai | 0.78 | 1.00 | 0.73 | 1.00 | 0.81 | 0.92 | 0.79 | 1.00 | 0.89 | 1.00 | 0.85 | 0.84 | 1.06 | 1.14 | 1.17 | 1.15 |
Yinchuan | 0.63 | 0.48 | 0.71 | 0.63 | 1.32 | 20.78 | 0.68 | 0.63 | 0.71 | 0.61 | 0.68 | 0.61 | 0.66 | 0.45 | 0.65 | 0.44 |
Zhanjiang | 0.95 | 1.41 | 1.16 | 1.58 | 1.02 | 1.40 | 1.06 | 1.43 | 1.04 | 1.43 | 1.04 | 1.39 | 1.06 | 1.44 | 1.04 | 1.43 |
Zhengzhou | 1.04 | 1.04 | 1.07 | 1.10 | 1.02 | 1.20 | 1.07 | 1.24 | 1.05 | 1.15 | 1.05 | 1.21 | 1.01 | 1.37 | 1.01 | 1.28 |
Chongqing | 0.78 | 0.88 | 0.69 | 0.93 | 0.73 | 0.62 | 0.75 | 0.60 | 0.74 | 0.54 | 0.72 | 0.50 | 0.79 | 0.54 | 0.82 | 0.50 |
Zhoushan | 0.98 | 0.46 | 0.79 | 0.53 | 0.86 | 0.45 | 0.95 | 0.44 | 0.78 | 0.56 | 0.93 | 0.49 | 1.04 | 1.02 | 1.19 | 1.11 |
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Stage | Category | Dimension | Secondary Indicators | Unit |
---|---|---|---|---|
Input indicators | Resource inputs | Water consumption | Per capita water consumption | Ton |
Energy consumption | Per capita urban electricity consumption | Kw·h | ||
Land consumption | Per capita urban construction land area | m2 | ||
Intermediate indicators | Desirable outputs | Economic development | Per capita GDP | Yuan |
Undesirable outputs | Wastewater discharge | Per capita wastewater discharge | Ton | |
Exhaust gas emission | Per capita SO2 | kg | ||
Waste emission | Per capita Soot/dust | kg | ||
Output indicators | Well-being outputs | Education development | The number of college students enrolled per 104 persons | Person |
Health care development | The average life expectancy | Year |
Category | Number | Cities |
---|---|---|
Node cities of Silk Road Economic Belt | 10 | Beijing, Hohhot, Shenyang, Changchun, Harbin, Xi’an, Lanzhou, Xining, Yinchuan, Urumqi |
Node cities of Maritime Silk Road | 17 | Tianjin, Dalian, Shanghai, Ningbo, Zhoushan, Fuzhou, Xiamen, Quanzhou, Qingdao, Yantai, Guangzhou, Shenzhen, Shantou, Zhanjiang, Nanning, Haikou, Sanya |
Inland open node cities | 9 | Kunming, Hefei, Nanchang, Zhengzhou, Wuhan, Changsha, Chongqing, Chengdu, Guiyang |
Category | City | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2011–2018 |
---|---|---|---|---|---|---|---|---|---|---|
Node cities of Silk Road Economic Belt | Beijing | 1.010 | 0.710 | 1.000 | 1.020 | 1.040 | 1.030 | 1.060 | 1.140 | 1.001 |
Hohhot | 1.060 | 1.040 | 1.030 | 1.010 | 1.000 | 0.730 | 1.100 | 1.030 | 1.000 | |
Shenyang | 0.720 | 0.600 | 0.670 | 0.660 | 0.660 | 0.620 | 0.670 | 0.690 | 0.661 | |
Changchun | 0.810 | 0.760 | 0.820 | 1.020 | 0.850 | 1.020 | 1.120 | 1.030 | 0.929 | |
Harbin | 1.040 | 0.810 | 1.120 | 1.060 | 1.110 | 1.110 | 1.060 | 1.170 | 1.060 | |
Xi’an | 1.050 | 1.060 | 1.100 | 1.090 | 1.120 | 1.320 | 1.260 | 1.180 | 1.148 | |
Lanzhou | 0.690 | 0.670 | 1.040 | 1.010 | 1.010 | 1.020 | 0.800 | 0.710 | 0.869 | |
Xining | 0.580 | 0.580 | 0.590 | 0.600 | 0.620 | 0.600 | 0.620 | 0.530 | 0.590 | |
Yinchuan | 0.550 | 0.640 | 1.910 | 0.640 | 0.640 | 0.620 | 0.550 | 0.530 | 0.760 | |
Urumqi | 0.510 | 0.410 | 0.530 | 0.560 | 0.560 | 0.500 | 0.540 | 0.530 | 0.518 | |
Node cities ofMaritime Silk Road | Tianjin | 0.670 | 0.610 | 0.640 | 0.630 | 0.610 | 0.580 | 0.670 | 0.670 | 0.635 |
Dalian | 0.540 | 0.540 | 0.590 | 0.590 | 0.630 | 0.580 | 0.580 | 0.690 | 0.593 | |
Shanghai | 0.590 | 0.540 | 0.600 | 0.600 | 0.630 | 0.570 | 1.060 | 0.520 | 0.639 | |
Ningbo | 0.510 | 0.490 | 0.530 | 0.540 | 0.530 | 0.520 | 0.620 | 0.480 | 0.528 | |
Zhoushan | 0.610 | 0.590 | 0.560 | 0.580 | 0.610 | 0.610 | 1.010 | 1.050 | 0.703 | |
Fuzhou | 0.860 | 0.740 | 0.850 | 1.010 | 1.010 | 1.020 | 1.030 | 0.800 | 0.915 | |
Xiamen | 0.660 | 0.670 | 0.680 | 0.580 | 0.610 | 0.650 | 0.670 | 0.850 | 0.671 | |
Quanzhou | 0.720 | 1.010 | 1.040 | 1.030 | 1.090 | 1.060 | 1.070 | 1.020 | 1.005 | |
Qingdao | 0.790 | 0.710 | 0.750 | 0.720 | 0.690 | 0.770 | 1.090 | 1.090 | 0.826 | |
Yantai | 0.780 | 0.740 | 0.780 | 0.790 | 0.860 | 0.770 | 1.070 | 1.070 | 0.858 | |
Guangzhou | 0.640 | 0.710 | 0.710 | 0.730 | 0.720 | 0.660 | 0.750 | 0.740 | 0.708 | |
Shenzhen | 1.200 | 1.240 | 1.240 | 1.270 | 1.240 | 1.160 | 1.190 | 1.260 | 1.225 | |
Shantou | 1.030 | 1.000 | 1.020 | 1.030 | 1.020 | 1.140 | 1.190 | 1.040 | 1.059 | |
Zhanjiang | 1.170 | 1.220 | 1.170 | 1.180 | 1.180 | 1.160 | 1.180 | 1.180 | 1.180 | |
Nanning | 0.760 | 0.760 | 1.010 | 0.800 | 0.900 | 1.000 | 1.060 | 1.030 | 0.915 | |
Haikou | 1.110 | 1.120 | 1.160 | 1.430 | 1.340 | 1.110 | 1.130 | 1.250 | 1.206 | |
Sanya | 1.440 | 1.570 | 1.580 | 1.660 | 1.620 | 1.530 | 1.830 | 1.750 | 1.623 | |
Inland Open node cities | Kunming | 0.740 | 1.010 | 1.000 | 1.040 | 1.050 | 1.070 | 1.000 | 0.770 | 0.960 |
Hefei | 1.040 | 1.040 | 1.020 | 1.030 | 1.020 | 1.030 | 0.930 | 0.810 | 0.990 | |
Nanchang | 1.070 | 1.060 | 1.030 | 1.060 | 1.050 | 1.040 | 1.080 | 1.080 | 1.059 | |
Zhengzhou | 1.020 | 1.050 | 1.090 | 1.110 | 1.070 | 1.100 | 1.160 | 1.120 | 1.090 | |
Wuhan | 1.000 | 0.850 | 0.950 | 1.030 | 1.080 | 1.060 | 0.800 | 0.710 | 0.935 | |
Changsha | 1.110 | 1.120 | 1.100 | 1.080 | 1.110 | 1.050 | 1.110 | 1.040 | 1.090 | |
Chongqing | 0.820 | 0.750 | 0.650 | 0.650 | 0.610 | 0.580 | 0.610 | 0.610 | 0.660 | |
Chengdu | 0.930 | 0.850 | 0.960 | 1.010 | 1.010 | 0.890 | 1.000 | 1.010 | 0.958 | |
Guiyang | 1.070 | 1.080 | 1.020 | 0.840 | 0.840 | 0.760 | 0.700 | 0.740 | 0.881 | |
Average value | 0.858 | 0.843 | 0.931 | 0.908 | 0.909 | 0.890 | 0.955 | 0.915 | 0.901 |
City | MLPI | EC | TC | City | MLPI | EC | TC |
---|---|---|---|---|---|---|---|
Changsha | 1.025 | 1.039 | 1.063 | Shenzhen | 1.007 | 1.020 | 1.027 |
Changchun | 1.043 | 1.067 | 1.105 | Shenyang | 0.994 | 1.035 | 1.024 |
Changsha | 0.990 | 1.017 | 1.007 | Tianjin | 1.018 | 1.145 | 1.143 |
Chengdu | 1.020 | 1.038 | 1.048 | Urumqi | 1.013 | 1.083 | 1.082 |
Dalian | 1.028 | 1.079 | 1.109 | Wuhan | 0.969 | 1.053 | 1.005 |
Fuzhou | 1.021 | 1.058 | 1.057 | Xi’an | 1.012 | 1.019 | 1.031 |
Guangzhou | 1.035 | 1.083 | 1.090 | Xining | 0.994 | 1.017 | 1.007 |
Guiyang | 0.952 | 1.063 | 1.012 | Xiamen | 1.028 | 1.149 | 1.173 |
Harbin | 1.028 | 1.043 | 1.054 | Yantai | 1.009 | 1.022 | 1.032 |
Haikou | 1.013 | 1.018 | 1.028 | Yinchuan | 1.167 | 1.030 | 1.156 |
Hefei | 0.971 | 1.034 | 1.002 | Zhanjiang | 1.001 | 1.004 | 1.006 |
Hohhot | 0.991 | 1.019 | 1.010 | Zhengzhou | 1.007 | 1.024 | 1.031 |
Kunming | 1.031 | 1.026 | 1.042 | Chongqing | 0.976 | 1.039 | 1.006 |
Lanzhou | 1.045 | 1.057 | 1.091 | Zhoushan | 1.080 | 1.106 | 1.193 |
Nanchang | 1.002 | 0.999 | 1.000 | 2011–2012 | 1.065 | 1.008 | 1.068 |
Nanning | 1.077 | 0.965 | 1.038 | 2012–2013 | 1.076 | 1.140 | 0.955 |
Ningbo | 1.087 | 1.104 | 1.162 | 2013–2014 | 0.976 | 0.975 | 1.004 |
Qingdao | 1.023 | 1.062 | 1.080 | 2014–2015 | 1.066 | 1.015 | 1.053 |
Quanzhou | 0.999 | 1.051 | 1.051 | 2015–2016 | 1.068 | 0.972 | 1.107 |
Sanya | 0.954 | 1.017 | 0.967 | 2016–2017 | 1.054 | 1.038 | 1.017 |
Shantou | 1.005 | 1.148 | 1.148 | 2017–2018 | 1.088 | 0.964 | 1.133 |
Shanghai | 0.955 | 0.995 | 0.946 | Average value | 1.056 | 1.016 | 1.048 |
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Bian, J.; Lan, F.; Hui, Z.; Bai, J.; Wang, Y. Ecological Well-Being Performance Evaluation of Chinese Major Node Cities along the Belt and Road. Land 2022, 11, 1928. https://doi.org/10.3390/land11111928
Bian J, Lan F, Hui Z, Bai J, Wang Y. Ecological Well-Being Performance Evaluation of Chinese Major Node Cities along the Belt and Road. Land. 2022; 11(11):1928. https://doi.org/10.3390/land11111928
Chicago/Turabian StyleBian, Jing, Feng Lan, Zhao Hui, Jiamin Bai, and Yuanping Wang. 2022. "Ecological Well-Being Performance Evaluation of Chinese Major Node Cities along the Belt and Road" Land 11, no. 11: 1928. https://doi.org/10.3390/land11111928