Distribution Characteristics, Regional Differences and Spatial Convergence of the Water-Energy-Land-Food Nexus: A Case Study of China
Abstract
:1. Introduction
2. Data and Methodology
2.1. Construction of Evaluation Index System
2.2. Research Methodology
2.2.1. WELF Nexus Integrated Evaluation Index
2.2.2. WELF Nexus Coupling Coordination Model
2.2.3. Dagum Gini Coefficient
2.2.4. Kernel Density Estimation
2.2.5. Global Moran’s I
2.2.6. Spatial β Convergence Model
2.3. Data Source
3. Results and Discussion
3.1. Analysis of Comprehensive Evaluation Index of WELF Nexus
3.2. Spatial-Temporal Characteristics of the Coupling Coordination of the WELF Nexus
3.2.1. Time-Series Change Characteristics
3.2.2. Spatial Distribution Characteristics
3.3. Analysis of Regional Differences and Their Decomposition in the Coupling Coordination of the WELF Nexus
3.3.1. National and Intra-Regional Differences
3.3.2. Inter-Regional Differences
3.3.3. Sources of Variation and Contribution Rates
3.4. Dynamic Evolution of Nuclear Density Distribution
3.5. Spatial Convergence Analysis of WELF Nexus Coupling Coordination
3.5.1. Source of Variation and Contribution Rate
3.5.2. Spatial Absolute β Convergence Analysis
3.5.3. Spatial Condition β Convergence Analysis
4. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Subsystems | Evaluation Indicators | Number | Measurement Method | Unit | Properties |
---|---|---|---|---|---|
Water | Water resources per capita | W1 | Statistics | m³/person | + |
Precipitation | W2 | Statistics | 108 m³ | + | |
Number of water production systems | W3 | Total water resources/precipitation | % | + | |
Total water supply | W4 | Statistics | 108 m³ | + | |
Water consumption per capita | W5 | Statistics | m³/person | − | |
Percentage of domestic water use | W6 | Domestic water consumption/total water consumption | % | + | |
Percentage of industrial water use | W7 | Industrial water consumption/total water consumption | % | − | |
Percentage of water used in agriculture | W8 | Agricultural water consumption/total water consumption | % | − | |
Percentage of ecological water use | W9 | Ecological water consumption/total water consumption | % | + | |
Water-saving irrigation area | W10 | Statistics | 103 hm2 | + | |
Urban sewage discharge | W11 | Statistics | 104 tons | − | |
Industrial wastewater discharge | W12 | Statistics | 104 tons | − | |
Industrial COD emissions | W13 | Statistics | tons | − | |
Urban sewage treatment rate | W14 | Statistics | % | + | |
Urban water conservation | W15 | Statistics | 104 m³ | + | |
Water consumption per CNY 10000 GDP | W16 | Total water consumption/GDP | m³/104 CNY | − | |
Energy | Total energy generation | E1 | Statistics | 104 tons of standard coal | + |
Electricity generation | E2 | Statistics | 108 Kw·h | + | |
Natural gas supply per capita | E3 | Statistics | m³/person | + | |
Energy consumption per capita | E4 | Total energy consumption/total population | Tons of standard coal/person | − | |
Total electricity consumption | E5 | Statistics | 108 Kw·h | − | |
Coal consumption | E6 | Statistics | 104 tons | − | |
Percentage of natural gas consumption | E7 | Natural gas consumption/total energy consumption | % | − | |
Energy consumption per unit of GDP | E8 | Total energy consumption/GDP | Ton of standard coal/104 CNY | − | |
Electricity consumption per unit of GDP | E9 | Total electricity consumption/GDP | Kw·h/104 yuan | − | |
Energy consumption elasticity coefficient | E10 | Statistics | — | − | |
Electricity consumption elasticity coefficient | E11 | Statistics | — | − | |
Total CO2 emissions | E12 | Statistics | tons | − | |
Industrial SO2 emissions | E13 | Statistics | tons | − | |
Land | Land area occupied per capita | L1 | Total land area/total population | hm2/104 person | + |
Relief amplitude | L2 | Difference between maximum and minimum altitude | — | − | |
Area of built-up | L3 | Statistics | km² | + | |
Urban road area per capita | L4 | Statistics | m2/person | + | |
Greening coverage of built-up areas | L5 | Statistics | % | + | |
Forestry land area | L6 | Statistics | 104 hm2 | + | |
Arable land area ratio | L7 | Arable land area/total land area | % | + | |
Wetland area ratio | L8 | Statistics | % | + | |
Rate of forest cover | L9 | Statistics | % | + | |
Agricultural land conversion | L10 | Statistics | hm2 | − | |
Sanded land area | L11 | Statistics | hm2 | − | |
Forestation area | L12 | Statistics | hm2 | + | |
GDP per land | L13 | GDP/land area | 104 yuan/hm2 | + | |
Food | Total crop area sown | F1 | Statistics | 103 hm2 | + |
Proportion of grain sown area | F2 | Food sown area/land area | % | + | |
Agricultural machinery power | F3 | Total machinery power/crop sown area | Kw/hm2 | + | |
Amount of mulch per unit of grain sown area | F4 | Amount of mulch used/area of grain sown | t/hm2 | − | |
Amount of chemical fertilizer per unit of grain sown area | F5 | Discounted fertilizer application/grain sown area | t/hm2 | − | |
Amount of pesticides per unit of grain sown area | F6 | Pesticide application/grain sown area | t/hm2 | − | |
Natural disaster incidence | F7 | Crop damage area/crop sown area | % | − | |
Food production per capita | F8 | Total food production/total population | Kg/person | + | |
Grain yield | F9 | Total grain production/grain sown area | t/hm2 | + | |
Total agricultural output | F10 | Statistics | 108 yuan | + | |
Natural population growth rate | F11 | Statistics | % | − | |
Consumer price index for food | F12 | Statistics | - | − |
D | Level | Characteristic |
---|---|---|
0.00–0.10 | Extreme disorder | Subsystems hinder each other’s development |
0.10–0.20 | Severe disorders | There are serious negative effects between subsystems |
0.20–0.30 | Moderate disorder | The dominance of mutual containment between subsystems |
0.30–0.40 | Mild disorders | The negative impact between subsystems is more obvious |
0.40–0.50 | Near-disorder | The phenomenon of negative influence between subsystems is highlighted |
0.50–0.60 | Barely coordinated | Positive effects among subsystems almost compensate for negative effects |
0.60–0.70 | Primary coordination | Positive impact between subsystems is more obvious |
0.70–0.80 | Intermediate coordination | Subsystem interactions dominate |
0.80–0.90 | Virtuous coordination | Good facilitating relationships exist between subsystems |
0.90–1.00 | Quality coordination | Effective coordination between subsystems can be developed |
Province | 2006 | 2010 | 2014 | 2019 | Average |
---|---|---|---|---|---|
Beijing | 0.5672 | 0.5792 | 0.5469 | 0.5930 | 0.5668 |
Tianjin | 0.5322 | 0.5519 | 0.5358 | 0.5524 | 0.5414 |
Hebei | 0.5817 | 0.5905 | 0.5626 | 0.5775 | 0.5678 |
Shanxi | 0.5783 | 0.5707 | 0.5363 | 0.5464 | 0.5427 |
Inner Mongolia | 0.6081 | 0.6634 | 0.6308 | 0.6429 | 0.6311 |
Liaoning | 0.5620 | 0.5711 | 0.5311 | 0.5442 | 0.5419 |
Jilin | 0.5598 | 0.5835 | 0.5482 | 0.5589 | 0.5522 |
Heilongjiang | 0.6099 | 0.6342 | 0.5931 | 0.6020 | 0.5980 |
Shanghai | 0.5874 | 0.5903 | 0.5678 | 0.5734 | 0.5687 |
Jiangsu | 0.6187 | 0.6176 | 0.6004 | 0.5985 | 0.5977 |
Zhejiang | 0.5830 | 0.5850 | 0.5537 | 0.5555 | 0.5598 |
Anhui | 0.5507 | 0.5751 | 0.5589 | 0.5573 | 0.5725 |
Fujian | 0.5340 | 0.5437 | 0.5120 | 0.5310 | 0.5307 |
Jiangxi | 0.5645 | 0.5844 | 0.5232 | 0.5218 | 0.5366 |
Shandong | 0.6382 | 0.6422 | 0.6169 | 0.6026 | 0.6139 |
Henan | 0.6134 | 0.6056 | 0.5559 | 0.5879 | 0.5763 |
Hubei | 0.5681 | 0.5677 | 0.5391 | 0.5572 | 0.5512 |
Hunan | 0.5861 | 0.5900 | 0.5574 | 0.5755 | 0.5589 |
Guangdong | 0.5892 | 0.5908 | 0.5554 | 0.5760 | 0.5693 |
Guangxi | 0.5700 | 0.5567 | 0.5338 | 0.5318 | 0.5361 |
Hainan | 0.4840 | 0.4946 | 0.4489 | 0.4619 | 0.4704 |
Chongqing | 0.4905 | 0.5364 | 0.5757 | 0.5198 | 0.5121 |
Sichuan | 0.5997 | 0.6106 | 0.5678 | 0.5980 | 0.5889 |
Guizhou | 0.5054 | 0.5168 | 0.5064 | 0.5097 | 0.5020 |
Yunnan | 0.5663 | 0.5785 | 0.5445 | 0.5345 | 0.5532 |
Shaanxi | 0.5485 | 0.5653 | 0.5364 | 0.5514 | 0.5461 |
Gansu | 0.5197 | 0.5149 | 0.4903 | 0.5211 | 0.5023 |
Qinghai | 0.5597 | 0.5772 | 0.5401 | 0.5685 | 0.5633 |
Ningxia | 0.4688 | 0.4815 | 0.4508 | 0.4520 | 0.4585 |
Xinjiang | 0.5962 | 0.6008 | 0.5503 | 0.5946 | 0.5702 |
Year | National | Intra-Regional Gini Coefficients | ||
---|---|---|---|---|
East | Middle | West | ||
2006 | 0.0394 | 0.0391 | 0.0206 | 0.0459 |
2007 | 0.0373 | 0.0388 | 0.0161 | 0.0428 |
2008 | 0.0499 | 0.0381 | 0.0541 | 0.0475 |
2009 | 0.0410 | 0.0347 | 0.0276 | 0.0522 |
2010 | 0.0377 | 0.0343 | 0.0182 | 0.0483 |
2011 | 0.0411 | 0.0389 | 0.0232 | 0.0514 |
2012 | 0.0344 | 0.0319 | 0.0193 | 0.0417 |
2013 | 0.0407 | 0.0402 | 0.0208 | 0.0468 |
2014 | 0.0380 | 0.0412 | 0.0189 | 0.0450 |
2015 | 0.0367 | 0.0349 | 0.0153 | 0.0485 |
2016 | 0.0396 | 0.0332 | 0.0205 | 0.0531 |
2017 | 0.0384 | 0.0320 | 0.0251 | 0.0497 |
2018 | 0.0393 | 0.0349 | 0.0236 | 0.0499 |
2019 | 0.0394 | 0.0351 | 0.0229 | 0.0497 |
Year | Inter-Regional Differences | Variance in Contribution Rate (%) | ||||
---|---|---|---|---|---|---|
East—Middle | East—West | Middle-West | Intra-Regional | Inter-Regional | Hypervariable Density | |
2006 | 0.0320 | 0.0462 | 0.0405 | 32.5112 | 30.3715 | 37.1173 |
2007 | 0.0328 | 0.0440 | 0.0356 | 32.4489 | 23.0123 | 44.5387 |
2008 | 0.0503 | 0.0481 | 0.0601 | 30.6870 | 32.6326 | 36.6804 |
2009 | 0.0335 | 0.0456 | 0.0438 | 33.1863 | 14.1369 | 52.6768 |
2010 | 0.0281 | 0.0444 | 0.0408 | 32.6500 | 24.9665 | 42.3835 |
2011 | 0.0323 | 0.0475 | 0.0421 | 33.4238 | 16.8178 | 49.7584 |
2012 | 0.0268 | 0.0401 | 0.0365 | 32.6211 | 22.1026 | 45.2762 |
2013 | 0.0337 | 0.0497 | 0.0388 | 32.3035 | 26.7726 | 40.9239 |
2014 | 0.0317 | 0.0446 | 0.0353 | 33.9297 | 13.6805 | 52.3898 |
2015 | 0.0266 | 0.0445 | 0.0373 | 33.3829 | 18.2844 | 48.3326 |
2016 | 0.0279 | 0.0476 | 0.0429 | 32.7557 | 22.0087 | 45.2356 |
2017 | 0.0294 | 0.0442 | 0.0411 | 33.1568 | 15.2031 | 51.6401 |
2018 | 0.0311 | 0.0457 | 0.0405 | 33.1482 | 13.6465 | 53.2054 |
2019 | 0.0302 | 0.0464 | 0.0414 | 32.8915 | 16.1355 | 50.9730 |
Year | Moran’s I | E(I) | Sd(I) | Z | p-Value |
---|---|---|---|---|---|
2006 | 0.000 | −0.034 | 0.033 | 1.051 | 0.147 |
2007 | 0.013 | −0.034 | 0.034 | 1.421 | 0.078 |
2008 | 0.035 | −0.034 | 0.030 | 2.336 | 0.010 |
2009 | −0.015 | −0.034 | 0.033 | 0.574 | 0.283 |
2010 | 0.013 | −0.034 | 0.033 | 1.454 | 0.073 |
2011 | 0.037 | −0.034 | 0.033 | 2.182 | 0.015 |
2012 | 0.026 | −0.034 | 0.033 | 1.849 | 0.032 |
2013 | 0.023 | −0.034 | 0.033 | 1.748 | 0.040 |
2014 | 0.012 | −0.034 | 0.032 | 1.445 | 0.074 |
2015 | 0.015 | −0.034 | 0.032 | 1.540 | 0.062 |
2016 | 0.019 | −0.034 | 0.033 | 1.649 | 0.050 |
2017 | 0.003 | −0.034 | 0.033 | 1.150 | 0.125 |
2018 | 0.026 | −0.034 | 0.033 | 1.830 | 0.034 |
2019 | 0.024 | −0.034 | 0.033 | 1.785 | 0.037 |
Region | National | East | Middle | West |
---|---|---|---|---|
Model Type | Time-space double fixed effects SAR model | Time-space double fixed effects SAR model | Time-space double fixed effects SAR model | Time-space double fixed effects SAR model |
−0.5334 ***(0.0422) | −0.5066 ***(0.0675) | −0.7130 ***(0.0905) | −0.4554 ***(0.0613) | |
0.6869 ***(0.0384) | 0.6284 ***(0.0533) | 0.3757 ***(0.0922) | 0.6997 ***(0.0456) | |
0.2773 | 0.2817 | 0.3124 | 0.3246 | |
N | 390 | 143 | 104 | 143 |
0.0586 | 0.0543 | 0.0960 | 0.0467 |
Region | National | East | Middle | West |
---|---|---|---|---|
Model Type | Time-space double fixed effects SDM model | Time-space double fixed effects SAR model | Time-space double fixed effects SEM model | Time-space double fixed effects SAR model |
−0.8423 *** (0.0451) | −0.9693 *** (0.0839) | −0.7116 *** (0.0474) | −0.5037 *** (0.0641) | |
0.7546 *** (0.0481) | 0.6489 *** (0.0637) | 0.6823 *** (0.0465) | ||
−0.7546 *** (0.2346) | ||||
Popu | −0.0139 *** (0.0018) | −0.0026 (0.0029) | −0.0469 *** (0.0027) | −0.0195 (0.0276) |
Pgdp | 0.0085 *** (0.0021) | 0.0036 (0.0027) | 0.0171 * (0.0091) | −0.0014 (0.0039) |
Urba | −0.0023 *** (0.0005) | −0.0003 (0.0010) | 0.0014 ** (0.0006) | 0.0002 (0.0007) |
Indu | 0.0305 (0.0299) | −0.0293 (0.0573) | −0.0543 (0.0480) | 0.0453 (0.0420) |
Envi | 0.0001 (0.0001) | 0.0013 (0.0017) | 0.0027 (0.0021) | 0.0001 (0.0001) |
Weat | −0.0085 *** (0.0029) | −0.0021 (0.0054) | −0.0061 ** (0.0028) | −0.0068 ** (0.0033) |
Huma | −0.0078 (0.0050) | −0.0009 (0.0084) | −0.0012 (0.0090) | 0.0054 (0.0071) |
0.0323 | 0.1213 | 0.0460 | 0.1239 | |
N | 390 | 143 | 104 | 143 |
0.1421 | 0.2680 | 0.0956 | 0.0539 |
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Li, Q.; Yang, L.; Jiang, F.; Liu, Y.; Guo, C.; Han, S. Distribution Characteristics, Regional Differences and Spatial Convergence of the Water-Energy-Land-Food Nexus: A Case Study of China. Land 2022, 11, 1543. https://doi.org/10.3390/land11091543
Li Q, Yang L, Jiang F, Liu Y, Guo C, Han S. Distribution Characteristics, Regional Differences and Spatial Convergence of the Water-Energy-Land-Food Nexus: A Case Study of China. Land. 2022; 11(9):1543. https://doi.org/10.3390/land11091543
Chicago/Turabian StyleLi, Qiangyi, Lan Yang, Fangxin Jiang, Yangqing Liu, Chenyang Guo, and Shuya Han. 2022. "Distribution Characteristics, Regional Differences and Spatial Convergence of the Water-Energy-Land-Food Nexus: A Case Study of China" Land 11, no. 9: 1543. https://doi.org/10.3390/land11091543
APA StyleLi, Q., Yang, L., Jiang, F., Liu, Y., Guo, C., & Han, S. (2022). Distribution Characteristics, Regional Differences and Spatial Convergence of the Water-Energy-Land-Food Nexus: A Case Study of China. Land, 11(9), 1543. https://doi.org/10.3390/land11091543