Spatial Correlation and Convergence Analysis of Eco-Efficiency in China
Abstract
:1. Introduction
2. Models and Data
2.1. SBM Model
2.2. Construction and Decomposition of the Malmquist Index Model
2.3. Exploratory Spatial Data Analysis
2.4. Construction of Spatial Weighting Matrix
2.4.1. Queen Contiguity
2.4.2. Rook Contiguity
2.4.3. K-nearest Neighbors
2.4.4. Threshold Distance
2.4.5. Asymmetrical Spatial Weighting Matrix Based on the EETI-Distance Reciprocal Principle
3. Analysis of Spatial Correlation of Ecological Efficiency in Mainland China
3.1. Variables and Data Source
3.2. Comparison of Evaluation Results Based on Symmetric Spatial Weight Matrix
3.2.1. Global Spatial Autocorrelation Analysis of Eco-Efficiency in Mainland China
3.2.2. Local Spatial Autocorrelation Analysis of Eco-Efficiency in Mainland China
3.3. Analysis of Evaluation Results Based on Asymmetrical Spatial Weight Matrix
3.3.1. Global Spatial Autocorrelation Analysis of Eco-Efficiency in Mainland China Based on the EETI-Distance Reciprocal Principle
3.3.2. Local Spatial Autocorrelation Analysis of Eco-Efficiency in Mainland China Based on the EETI-Distance Reciprocal Principle
4. Analysis of the Convergence of Eco-Efficiency in Mainland China
4.1. Convergence Model
4.2. σ Convergence
4.3. Absolute β Convergence
4.4. Conditional β Convergence
5. Conclusions and Suggestions
Author Contributions
Funding
Conflicts of Interest
References
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Year | 2000 | 2002 | 2004 | 2006 | 2008 | 2010 | 2012 | 2014 | 2015 |
---|---|---|---|---|---|---|---|---|---|
Obs. | 31 | 31 | 31 | 31 | 31 | 31 | 31 | 31 | 31 |
Water footprint (in 100 million m3) | |||||||||
mean | 337.60 | 339.54 | 336.21 | 351.85 | 352.47 | 368.69 | 359.20 | 398.95 | 398.85 |
std. | 221.62 | 222.65 | 222.84 | 231.89 | 230.40 | 244.69 | 237.54 | 265.45 | 265.75 |
min | 13.92 | 14.66 | 12.90 | 16.98 | 17.82 | 18.84 | 17.57 | 24.60 | 25.17 |
max | 851.28 | 817.14 | 863.06 | 965.79 | 962.59 | 1081.67 | 1083.41 | 1182.21 | 116.28 |
Labor force (in 10 thousand persons) | |||||||||
mean | 2032.37 | 2054.55 | 2131.96 | 2290.42 | 2338.94 | 2466.54 | 2504.70 | 2658.39 | 2671.13 |
std. | 1419.37 | 1413.41 | 1460.19 | 1618.90 | 1619.29 | 1703.29 | 1778.02 | 1787.39 | 1794.14 |
min | 123.40 | 128.80 | 134.80 | 148.20 | 160.40 | 175.00 | 202.10 | 214.00 | 235.00 |
max | 5571.70 | 5522.00 | 5587.40 | 5960.00 | 5835.50 | 6041.60 | 6554.30 | 6607.00 | 6636.00 |
Fixed-asset investment (in 100 million RMB) a | |||||||||
mean | 4256.35 | 5958.27 | 8880.29 | 14,209.95 | 21,738.55 | 32,829.04 | 46,654.70 | 61,328.10 | 67,968.82 |
std. | 3199.16 | 4445.54 | 6781.26 | 10,657.41 | 15,518.34 | 22,747.40 | 31,142.32 | 39,595.44 | 435,533.41 |
min | 78.70 | 213.48 | 569.15 | 918.82 | 1324.68 | 2251.98 | 3136.48 | 4711.90 | 5217.04 |
max | 12,267.43 | 16,548.13 | 25,472.23 | 41,820.05 | 60,337.81 | 91,441.69 | 125,471.11 | 161,918.16 | 179,884.17 |
Cost of resource and environment (in 100 million RMB) | |||||||||
mean | 2290.03 | 2414.45 | 2537.97 | 2742.56 | 3015.37 | 3401.47 | 3903.69 | 4494.86 | 4521.03 |
std. | 2887.47 | 3064.18 | 3264.74 | 3507.48 | 3840.88 | 4315.43 | 4948.16 | 5797.25 | 5849.13 |
min | 67.32 | 81.76 | 78.97 | 82.97 | 91.40 | 95.15 | 108.80 | 233.98 | 134.32 |
max | 12,473.96 | 13,176.42 | 13,918.91 | 14,992.44 | 16,225.47 | 17,916.77 | 20,918.61 | 23,619.78 | 23,948.83 |
Construction land area (in km2) | |||||||||
mean | 116.80 | 99.11 | 101.78 | 104.41 | 106.54 | 112.26 | 117.98 | 122.67 | 124.49 |
std. | 74.67 | 58.32 | 59.64 | 61.13 | 61.88 | 64.90 | 68.06 | 70.50 | 71.33 |
min | 8.64 | 5.49 | 6.13 | 6.50 | 6.70 | 9.54 | 12.38 | 14.15 | 14.50 |
max | 292.59 | 230.25 | 238.36 | 246.30 | 251.10 | 261.22 | 271.34 | 279.21 | 282.01 |
GDP (in 100 million RMB) a | |||||||||
mean | 3135.78 | 3923.95 | 5591.79 | 7627.81 | 10,776.89 | 14,545.55 | 18,719.43 | 21,808.46 | 22,935.83 |
std. | 2462.39 | 3159.95 | 4604.09 | 6561.43 | 8736.92 | 11,358.44 | 13,846.73 | 16,320.80 | 17,481.28 |
min | 117.46 | 171.89 | 225.98 | 301.38 | 398.44 | 520.91 | 716.41 | 932.61 | 1041.40 |
max | 9662.23 | 13,612.10 | 19,443.50 | 27,103.18 | 35,567.59 | 46,677.51 | 55,728.78 | 65,973.84 | 70,972.52 |
Gray water footprint (in 100 million m3) | |||||||||
mean | 159.57 | 155.79 | 161.73 | 168.08 | 147.29 | 144.15 | 143.75 | 140.29 | 137.93 |
std. | 102.68 | 102.33 | 103.01 | 105.28 | 88.20 | 86.71 | 83.16 | 81.57 | 79.69 |
min | 31.16 | 24.99 | 25.12 | 19.29 | 15.54 | 11.48 | 12.37 | 8.02 | 4.98 |
max | 421.79 | 400.56 | 410.41 | 434.14 | 358.95 | 351.83 | 334.35 | 329.84 | 322.41 |
Environmental pollutants (kilo tons) | |||||||||
mean | 2159.50 | 1868.98 | 1941.12 | 1866.38 | 1479.47 | 1277.81 | 1123.54 | 1217.60 | 1113.84 |
std. | 1981.97 | 1754.05 | 1757.67 | 1429.80 | 1011.91 | 760.15 | 703.64 | 783.78 | 717.16 |
min | 51.73 | 40.00 | 45.18 | 45.20 | 40.06 | 45.01 | 10.80 | 18.14 | 22.50 |
max | 8658.11 | 8955.30 | 9373.22 | 7539.02 | 4834.86 | 3188.82 | 2,577,100.00 | 2987.59 | 2683.80 |
Weight Schemes | Statistics | 2000 | 2002 | 2004 | 2006 | 2008 | 2010 | 2012 | 2014 | 2015 |
---|---|---|---|---|---|---|---|---|---|---|
Scheme I Queen contiguity | GMI | 0.460 *** | 0.339 *** | 0.299 *** | 0.294 *** | 0.308 *** | 0.294 *** | 0.262 *** | 0.276 ** | 0.277 ** |
p-value | 0.001 | 0.004 | 0.005 | 0.008 | 0.009 | 0.010 | 0.010 | 0.014 | 0.011 | |
Scheme II Rook contiguity | GMI | 0.460 *** | 0.339 *** | 0.299 *** | 0.294 ** | 0.308 *** | 0.294 *** | 0.262 ** | 0.276 *** | 0.277 *** |
p-value | 0.001 | 0.005 | 0.005 | 0.011 | 0.006 | 0.008 | 0.012 | 0.009 | 0.010 | |
Scheme III 4-nearest neighbors | GMI | 0.313 *** | 0.226 ** | 0.134 * | 0.146 ** | 0.116 * | 0.083 | 0.041 | 0.043 | 0.039 |
p-value | 0.007 | 0.015 | 0.077 | 0.044 | 0.084 | 0.133 | 0.215 | 0.197 | 0.206 | |
Scheme IV Threshold contiguity | GMI | 0.114 ** | 0.092 ** | 0.055 | 0.022 | 0.012 | 0.012 | −0.001 | -0.006 | −0.008 |
p-value | 0.034 | 0.043 | 0.109 | 0.182 | 0.211 | 0.249 | 0.753 | 0.688 | 0.720 |
Weight Scheme | Statistics | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 |
Scheme V (EETI-distance reciprocal) | GMI | 0.108 *** | 0.096 *** | 0.091 *** | 0.089 *** | 0.044 ** | 0.083 *** | 0.052 ** | 0.090 *** |
Z-value | 3.639 | 3.340 | 3.198 | 3.162 | 1.989 | 2.998 | 2.205 | 3.185 | |
p-value | 0.000 | 0.000 | 0.001 | 0.001 | 0.023 | 0.001 | 0.014 | 0.001 | |
Statistic | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | |
GMI | 0.085 *** | 0.071 *** | 0.074 *** | 0.065 *** | 0.061 *** | 0.064 *** | 0.060 *** | 0.059 *** | |
Z-value | 3.045 | 2.678 | 2.778 | 2.543 | 2.439 | 2.504 | 2.418 | 2.381 | |
p-value | 0.001 | 0.004 | 0.003 | 0.006 | 0.007 | 0.006 | 0.008 | 0.009 |
Statistics | Regions | |||
---|---|---|---|---|
Mainland China | Eastern Region | Central Region | Western Region | |
Constant | 0.028 | 0.012 | 0.087 | 0.005 |
(0.810) | (0.714) | (1.201) | 0.223 | |
β | −0.150 *** | −0.219 ** | −0.102 | −0.026 |
(−3.017) | (−2.562) | (−1.093) | (−0.322) | |
R2 | 0.098 | 0.360 | 0.0700 | 0.098 |
Statistics | Regions | |||
---|---|---|---|---|
Mainland China | Eastern Region | Central Region | Western Region | |
Constant | 0.068 | 0.025 | 0.265 *** | −0.127 |
(0.643) | (0.071) | (2.943) | (−1.242) | |
β | −0.182 *** | −0.269 *** | −0.314 *** | −0.067 |
(−3.692) | (−3.272) | (−3.101) | (−0.824) | |
PESV | 1.04 × 10−7 | −4.77 × 10−5 | −0.624 *** | 3.60 × 10−7 |
(0.221) | (−0.734) | (−3.322) | (1.357) | |
PGDP | 3.64 × 10−6 *** | 1.25 × 10−5 *** | 0.076 | 1.14 × 10−6 |
(3.29) | (3.195) | (0.367) | (0.892) | |
FDI | 6.25 × 10−5 | 2.72 × 10−5 | 0.064 | 2.19 × 10−4 |
(1.613) | (0.483) | (1.638) | (1.156) | |
R&D | −2.86 × 10−4 *** | −4.59 × 10−4 *** | 0.443 *** | −4.18 × 10−4 |
(−2.872) | (−2.823) | (7.052) | (−1.116) | |
PTI | −0.202 | −0.287 | −0.811 | 0.225 |
(−0.796) | (−0.425) | (−1.416) | (0.876) | |
EETI | 0.002 | 0.006 *** | 0.080 *** | 0.005 |
(1.923) | (2.654) | (3.854) | (0.512) | |
RECGp | −0.001 | 0.820 | −1.218 *** | −0.001 |
(0.647) | (1.623) | (−3.547) | (−0.663) | |
R2 | 0.735 | 0.716 | 0.927 | 0.348 |
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Zheng, D.; Hao, S.; Sun, C.; Lyu, L. Spatial Correlation and Convergence Analysis of Eco-Efficiency in China. Sustainability 2019, 11, 2490. https://doi.org/10.3390/su11092490
Zheng D, Hao S, Sun C, Lyu L. Spatial Correlation and Convergence Analysis of Eco-Efficiency in China. Sustainability. 2019; 11(9):2490. https://doi.org/10.3390/su11092490
Chicago/Turabian StyleZheng, Defeng, Shuai Hao, Caizhi Sun, and Leting Lyu. 2019. "Spatial Correlation and Convergence Analysis of Eco-Efficiency in China" Sustainability 11, no. 9: 2490. https://doi.org/10.3390/su11092490