The Spatiotemporal Measurement of Coordinated Development of Resource-Environment-Economy Based on Empirical Analysis from China’s 30 Provinces
Abstract
:1. Introduction
2. Literature Review
3. Research Methods
3.1. Indicator System Construction
3.2. Research Data and Processing
3.2.1. Research Data
3.2.2. Data Processing
3.3. Empowerment of Indicators
3.4. Evaluation Model Designing
3.4.1. Integrated Evaluation Function
3.4.2. Coupling Coordination Model
3.4.3. Spatial Autocorrelation Analysis
4. Results
4.1. Analysis of Time-Series Evolutionary
4.2. Analysis of Spatial Distribution Characteristics
4.2.1. Analysis of the Spatial Distribution Characteristics of the Two Systems
4.2.2. Analysis of the Spatial Distribution Characteristics of the Three Systems
4.3. Spatial Correlation Analysis
4.3.1. Global Moran’s I Test
4.3.2. The LISA Diagram
5. Conclusions
6. Discussion and Policy Recommendations
6.1. Limitations
6.2. Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Systems | Tier 1 Indicators | Tier 2 Indicators | Code | Unit | Direction |
---|---|---|---|---|---|
Resource | Resource consumption | Total energy consumption | X1 | million tons of standard coal | - |
Coal consumption | X2 | million tons | - | ||
Natural gas consumption | X3 | billion cubic meters | - | ||
Electricity consumption | X4 | billion kWh | - | ||
Water consumption | X5 | billion cubic meters | - | ||
Resource efficiency | Energy consumption elasticity factor | X6 | - | - | |
Electricity consumption elasticity factor | X7 | - | - | ||
Energy consumption per CNY 10,000 of GDP | X8 | tons of standard coal/CNY 10,000 | - | ||
Electricity consumption per CNY 10,000 of GDP | X9 | kWh/million | - | ||
Water consumption per CNY 10,000 of GDP | X10 | cubic meters/CNY 10,000 | - | ||
Environment | Environmental pollution | Sulfur dioxide emissions | Y1 | ton | - |
Industrial fume emissions | Y2 | ton | - | ||
Industrial wastewater discharge | Y3 | million tons | - | ||
Respirable particulate matter PM10 | Y4 | mg/m3 | - | ||
Environmental quality | Greenery coverage | Y5 | % | + | |
Green space per capita | Y6 | cubic meters per person | + | ||
Integrated industrial solid waste volume | Y7 | million tons | + | ||
Annual completed investment in industrial pollution control | Y8 | million | + | ||
Harmless disposal rate of domestic waste | Y9 | % | + | ||
Economy | Economy scale | GDP per capita | Z1 | billion | + |
Total investment in fixed assets | Z2 | billion | + | ||
Total retail sales of social consumer goods | Z3 | billion | + | ||
Total imports and exports | Z4 | billion | + | ||
General budget revenue of local finance | Z5 | billion | + | ||
Economic quality | Ratio of urban to rural disposable income | Z6 | % | - | |
Fixed asset input–output ratio | Z7 | % | + | ||
Urbanization rate | Z8 | % | + | ||
Economic structure | GDP percentage of tertiary sector | Z9 | % | + | |
GDP percentage of industry | Z10 | % | - | ||
Construction as a share of GDP | Z11 | % | - | ||
Scientific research expenditure as a percentage of GDP | Z12 | % | + |
No. | Coupling Coordination | Grade Stage | Type | Stage Characteristics |
---|---|---|---|---|
1 | (0.0, 0.1] | Extreme disorders | Types of dysfunctional decline | Poorly developed inter-system coordination, complex systems in a dysfunctional decline stage |
2 | (0.1, 0.2] | Severe disorders | ||
3 | (0.2, 0.3] | Severe disorders | ||
4 | (0.3, 0.4] | Mild disorders | ||
5 | (0.4, 0.5] | On the verge of disorder | ||
6 | (0.5, 0.6] | Reluctantly coordinated | Type of coordinated development | The system begins to enter a phase of coordinated development and synergies between systems begin to develop |
7 | (0.6, 0.7] | Primary coordination | ||
8 | (0.7, 0.8] | Intermediate coordination | ||
9 | (0.8, 0.9] | Good coordination | ||
10 | (0.9, 1.0] | Quality coordination | Inter-system synergy development |
Provinces | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.436 | 0.487 | 0.576 | 0.640 | 0.636 | 0.667 | 0.724 | 0.730 | 0.774 | 0.806 | 0.840 | 0.819 | 0.848 | 0.818 | 0.862 |
Tianjin | 0.467 | 0.517 | 0.561 | 0.602 | 0.629 | 0.640 | 0.672 | 0.687 | 0.696 | 0.731 | 0.778 | 0.825 | 0.841 | 0.811 | 0.842 |
Hebei | 0.450 | 0.462 | 0.532 | 0.588 | 0.615 | 0.671 | 0.674 | 0.688 | 0.719 | 0.775 | 0.817 | 0.822 | 0.850 | 0.842 | 0.883 |
Shanxi | 0.453 | 0.474 | 0.548 | 0.644 | 0.660 | 0.642 | 0.655 | 0.688 | 0.713 | 0.736 | 0.781 | 0.808 | 0.778 | 0.781 | 0.824 |
Inner Mongolia | 0.530 | 0.507 | 0.587 | 0.646 | 0.676 | 0.681 | 0.682 | 0.705 | 0.743 | 0.794 | 0.805 | 0.836 | 0.823 | 0.796 | 0.803 |
Liaoning | 0.532 | 0.560 | 0.565 | 0.605 | 0.628 | 0.656 | 0.666 | 0.711 | 0.732 | 0.744 | 0.771 | 0.774 | 0.822 | 0.821 | 0.818 |
Jilin | 0.481 | 0.519 | 0.569 | 0.590 | 0.603 | 0.627 | 0.620 | 0.648 | 0.678 | 0.721 | 0.759 | 0.824 | 0.824 | 0.827 | 0.864 |
Heilongjiang | 0.406 | 0.435 | 0.509 | 0.578 | 0.613 | 0.659 | 0.687 | 0.683 | 0.742 | 0.762 | 0.795 | 0.814 | 0.809 | 0.845 | 0.860 |
Jiangsu | 0.465 | 0.475 | 0.568 | 0.616 | 0.596 | 0.627 | 0.663 | 0.695 | 0.726 | 0.762 | 0.800 | 0.816 | 0.844 | 0.861 | 0.864 |
Shanghai | 0.469 | 0.529 | 0.560 | 0.602 | 0.651 | 0.626 | 0.641 | 0.683 | 0.673 | 0.739 | 0.732 | 0.790 | 0.819 | 0.844 | 0.861 |
Zhejiang | 0.410 | 0.427 | 0.453 | 0.502 | 0.565 | 0.573 | 0.598 | 0.685 | 0.689 | 0.758 | 0.809 | 0.827 | 0.839 | 0.848 | 0.911 |
Anhui | 0.540 | 0.525 | 0.551 | 0.568 | 0.570 | 0.580 | 0.602 | 0.640 | 0.670 | 0.708 | 0.729 | 0.805 | 0.846 | 0.853 | 0.856 |
Fujian | 0.546 | 0.513 | 0.549 | 0.584 | 0.621 | 0.595 | 0.554 | 0.665 | 0.683 | 0.661 | 0.749 | 0.778 | 0.788 | 0.786 | 0.821 |
Jiangxi | 0.509 | 0.499 | 0.537 | 0.627 | 0.652 | 0.674 | 0.669 | 0.710 | 0.724 | 0.731 | 0.754 | 0.770 | 0.820 | 0.842 | 0.848 |
Shandong | 0.432 | 0.489 | 0.529 | 0.592 | 0.620 | 0.643 | 0.686 | 0.732 | 0.756 | 0.805 | 0.792 | 0.846 | 0.882 | 0.866 | 0.861 |
Hubei | 0.496 | 0.508 | 0.561 | 0.595 | 0.600 | 0.621 | 0.609 | 0.640 | 0.698 | 0.719 | 0.742 | 0.832 | 0.844 | 0.854 | 0.861 |
Henan | 0.434 | 0.441 | 0.499 | 0.530 | 0.566 | 0.575 | 0.613 | 0.660 | 0.677 | 0.732 | 0.747 | 0.825 | 0.849 | 0.849 | 0.883 |
Hunan | 0.431 | 0.510 | 0.560 | 0.593 | 0.615 | 0.639 | 0.635 | 0.670 | 0.703 | 0.733 | 0.784 | 0.803 | 0.834 | 0.827 | 0.861 |
Guangdong | 0.344 | 0.395 | 0.468 | 0.557 | 0.586 | 0.615 | 0.647 | 0.706 | 0.741 | 0.737 | 0.808 | 0.838 | 0.861 | 0.869 | 0.877 |
Guangxi | 0.462 | 0.498 | 0.544 | 0.585 | 0.637 | 0.627 | 0.689 | 0.730 | 0.752 | 0.775 | 0.818 | 0.847 | 0.858 | 0.830 | 0.868 |
Hainan | 0.568 | 0.540 | 0.556 | 0.603 | 0.638 | 0.659 | 0.672 | 0.724 | 0.754 | 0.775 | 0.757 | 0.816 | 0.834 | 0.798 | 0.801 |
Chongqing | 0.411 | 0.421 | 0.550 | 0.633 | 0.626 | 0.667 | 0.694 | 0.724 | 0.771 | 0.754 | 0.795 | 0.813 | 0.850 | 0.868 | 0.868 |
Sichuan | 0.481 | 0.507 | 0.559 | 0.603 | 0.577 | 0.615 | 0.637 | 0.674 | 0.711 | 0.742 | 0.762 | 0.794 | 0.824 | 0.841 | 0.858 |
Gansu | 0.473 | 0.495 | 0.507 | 0.564 | 0.606 | 0.601 | 0.607 | 0.650 | 0.675 | 0.701 | 0.728 | 0.830 | 0.791 | 0.793 | 0.858 |
Ningxia | 0.542 | 0.533 | 0.558 | 0.592 | 0.619 | 0.688 | 0.639 | 0.703 | 0.739 | 0.797 | 0.770 | 0.860 | 0.819 | 0.823 | 0.827 |
Shaanxi | 0.544 | 0.544 | 0.570 | 0.604 | 0.650 | 0.678 | 0.682 | 0.688 | 0.714 | 0.729 | 0.768 | 0.766 | 0.777 | 0.807 | 0.831 |
Qinghai | 0.564 | 0.558 | 0.585 | 0.557 | 0.607 | 0.626 | 0.679 | 0.695 | 0.681 | 0.739 | 0.764 | 0.816 | 0.791 | 0.800 | 0.837 |
Xinjiang | 0.518 | 0.484 | 0.573 | 0.588 | 0.661 | 0.676 | 0.687 | 0.671 | 0.673 | 0.706 | 0.740 | 0.796 | 0.830 | 0.839 | 0.831 |
Yunnan | 0.478 | 0.485 | 0.549 | 0.580 | 0.632 | 0.647 | 0.647 | 0.677 | 0.720 | 0.759 | 0.796 | 0.828 | 0.841 | 0.854 | 0.883 |
Guizhou | 0.488 | 0.468 | 0.483 | 0.584 | 0.604 | 0.630 | 0.633 | 0.665 | 0.717 | 0.751 | 0.791 | 0.805 | 0.818 | 0.838 | 0.872 |
Mean-value | 0.479 | 0.493 | 0.544 | 0.592 | 0.619 | 0.637 | 0.652 | 0.688 | 0.715 | 0.746 | 0.776 | 0.814 | 0.828 | 0.831 | 0.853 |
Coupling Coordination Level | 2005 | 2010 | 2015 | 2019 |
---|---|---|---|---|
Reluctantly coordinated | Xinjiang, Ningxia, Shanxi | Guizhou, Xinjiang, Qinghai, Gansu | Gansu, Xinjiang | Xinjiang |
Primary coordination | Jilin, Hubei, Shandong, Hunan, Jiangxi, Anhui, Heilongjiang, Hebei, Guangxi, Sichuan, Henan, Chongqing, Shaanxi, Yunnan, Qinghai, Inner Mongolia, Guizhou, Gansu | Shanxi, Henan, Yunnan, Guangxi, Ningxia, Sichuan, Inner Mongolia, Anhui, Hebei, Shaanxi, Heilongjiang, Jilin, Hunan, Jiangxi, Liaoning, Hubei | Hunan, Hubei, Heilongjiang, Chongqing, Liaoning, Anhui, Jilin, Shandong, Guangxi, Inner Mongolia, Sichuan, Hebei, Jiangxi, Shaanxi, Yunnan, Henan, Guizhou, Shanxi, Ningxia, Qinghai | Hunan, Anhui, Chongqing, Henan, Shandong, Jiangxi, Sichuan, Heilongjiang, Guangxi, Liaoning, Jilin, Shaanxi, Yunnan, Hebei, Shanxi, Guizhou, Inner Mongolia, Gansu, Ningxia, Qinghai |
Intermediate coordination | Guangdong, Tianjin, Jiangsu, Fujian, Zhejiang, Hainan, Liaoning | Tianjin, Guangdong, Zhejiang, Jiangsu, Fujian, Hainan, Shandong, Chongqing | Tianjin, Zhejiang, Guangdong, Hainan, Fujian, Jiangsu | Tianjin, Zhejiang, Guangdong, Hubei, Fujian, Hainan, Jiangsu |
Good coordination | Beijing, Shanghai | Beijing, Shanghai | Beijing, Shanghai | Beijing, Shanghai |
2005 | 2010 | 2015 | 2019 | |
---|---|---|---|---|
Moran’s I Index | 0.475 | 0.459 | 0.452 | 0.432 |
Z-value | 4.172 | 4.114 | 4.015 | 3.891 |
p-value | 0.001 | 0.001 | 0.001 | 0.001 |
2005 | 2010 | 2015 | 2019 | |
---|---|---|---|---|
Moran’s I Index | 0.488 | 0.489 | 0.469 | 0.447 |
Z-value | 4.253 | 4.313 | 4.166 | 3.973 |
p-value | 0.001 | 0.001 | 0.001 | 0.001 |
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Wang, H.; Lu, X.; Guo, Q.; Zhang, Y. The Spatiotemporal Measurement of Coordinated Development of Resource-Environment-Economy Based on Empirical Analysis from China’s 30 Provinces. Sustainability 2023, 15, 6995. https://doi.org/10.3390/su15086995
Wang H, Lu X, Guo Q, Zhang Y. The Spatiotemporal Measurement of Coordinated Development of Resource-Environment-Economy Based on Empirical Analysis from China’s 30 Provinces. Sustainability. 2023; 15(8):6995. https://doi.org/10.3390/su15086995
Chicago/Turabian StyleWang, Hongqiang, Xiaochang Lu, Qiujing Guo, and Yingjie Zhang. 2023. "The Spatiotemporal Measurement of Coordinated Development of Resource-Environment-Economy Based on Empirical Analysis from China’s 30 Provinces" Sustainability 15, no. 8: 6995. https://doi.org/10.3390/su15086995