The Coupling and Coordination of Urban Modernization and Low-Carbon Development
Abstract
:1. Introduction
2. Materials and Methods
2.1. Evaluation Indicator System
2.2. Study Models
2.2.1. Entropy Method
- (1)
- Standardized processing:
- (2)
- Calculate the proportion of the jth indicator in province i:
- (3)
- Calculate the entropy value of the indicator:
- (4)
- Calculate the coefficient of difference:
- (5)
- Calculate the proportion of a single indicator:
- (6)
- Evaluate the collaborative comprehensive score of different provinces [30]:
2.2.2. Coupling Coordination Degree Model
2.2.3. Spatial Autocorrelation Model
2.2.4. Gray Correlation Model
- (1)
- After converting all data to a range of , use the formula to obtain the correlation coefficients between each value:
- (2)
- Calculate the gray correlation degree through the correlation coefficient, with the formula:
2.3. Data Sources
3. Results
3.1. Comprehensive Level of Urban Modernization and Low-Carbon Development
3.2. Coupling Coordination Degree
3.3. Spatial Evolution Pattern of Collaborative Value
3.3.1. Spatial Evolution Pattern of Coupling Coordination Degree
3.3.2. Test for Spatial Correlation
4. Influencing Factors of Coupling Coordination
5. Conclusions and Suggestions
5.1. Conclusions
- (1)
- The coupling coordination degree of 31 provinces varied over the course of the 12-year study period, clearly displaying temporal evolution characteristics. The majority of the provinces have the highest coupling coordination degree in the first stage and the lowest coupling coordination degree in the second stage, according to the variation trend of the average coupling coordination degree of each province. It returns to a specific coupling level in the third stage (the average coupling coordination degree for the entire nation in the three stages is 0.379, 0.359, and 0.364, respectively). In three stages, several provinces in Northeast China show a decreasing trend. The coupling coordination degree has the traits of leading in the east, finishing in the west, and being centered in the middle and northeast from the standpoint of spatial heterogeneity. Overall, during the study period, the coupling level increased, but 2018 saw significant swings.
- (2)
- The coupling and coordination between the two during the inquiry had a sizable spatial relationship. First, using the natural discontinuity approach, the coupling coordination degree values of each province were separated into six groups in accordance with the clustering principle. Second, over the course of the three research periods, the coupling coordination levels of the 31 provinces were summarized in chronological order. The Moran index was then utilized to confirm the spatial association between urban modernization and low-carbon development using spatial correlation testing. Finally, the findings show that there is a distinct center of coupling, coordination, and agglomeration between the two. Agglomeration has the following unique features: some eastern provinces (such as Shanghai, Jiangsu, Zhejiang, etc.) exhibit HH agglomeration characteristics, whereas other western provinces (such as Gansu, Qinghai, etc.) exhibit LL agglomeration characteristics.
- (3)
- Economics, talent, life, and policy evaluation are the four variables that have the biggest effects on linked collaborative scheduling. Due to the numerous elements examined in this article, a significant number of provinces and regions are taken into consideration, and the location of the provinces affects the impact of numerous influencing factors on the coupling coordination of the provinces. As a result, from a broad perspective, the impact of various factors on the eastern region is well balanced; in the central region, policy evaluations have a stronger influence on the cooperation and coordination between the two. The western and northeastern regions are most affected by talent evaluation and life assessment.
5.2. Suggestions
- (1)
- The eastern provinces should make the transformation of the low-carbon industry a top priority [40]. This area has had rather rapid technological progress and innovation. High-quality demographic traits and degrees of scientific research not only support one another in economic development but also establish powerful partnerships in the development of low-carbon industrial innovation, raising everyone’s standard of living [24]. Large-scale low-carbon transformation can have structural consequences, encourage investment in environmentally friendly enterprises in the region, and support the coordination and coupling of low-carbon urbanization development. They should utilize the idea of “people-oriented” to coordinate the growth of diverse components, capitalize on the driving force of favorable regions, and advance comprehensive development [20].
- (2)
- The central provinces should keep stepping up their efforts to strengthen their economies [41]. A collaborative model of business, academia, and research can be established in this area by utilizing the benefits of asset investment and gathering educational resources. The government should also stimulate the development of low-carbon societies, take part in some economic pilot zones, and speed up the development of inland vertical and horizontal opening channels to the outside world. In addition, we will continue to raise the level of intense use of urban resources while also accelerating the elimination of outmoded production capacity, implementing industrial structural reform, and maintaining the ecological and green development pattern [27].
- (3)
- In the western and northeastern regions, infrastructure development and education should be given primary attention. First and foremost, the western region needs to use all of its energy resources. It can improve the supply capacity of strategic resources in numerous places, create competition in the resource market on a broad scale, and draw a lot of investment capital due to the abundance of clean energy and its strong industrial supporting capabilities. They should use educational resources in other disciplines, promote the use of renewable energy, and diversify capital investment in the education sector [38]. In order to achieve economic complementarity and industrial structural complementarity between the east and the west, the northeast region plays a critical strategic role. The northeast region has a lot of space for improvement in terms of low-carbon development patterns. At the same time, we will actively change the economic development model, increase the treatment of wastewater and waste gas, and accomplish comprehensive environmental improvement with the aid of government regulations and financial support. This should promote low-carbon development and urban upgrading in the end.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Primary Indicator | Secondary Indicator | Third Level Indicators | Attribute |
---|---|---|---|
Urban modernization subsystem (UM) | U1 Economic development | M1 Per capita GDP (yuan) | + |
M2 Per capita disposable income of urban residents (yuan) | + | ||
M3 Proportion of secondary and tertiary sector of the economy in GDP (%) | + | ||
M4 Local general public budgeting revenue (100 million yuan) | + | ||
U2 Infrastructure | M5 Per capita urban road area (square meters) | + | |
M6 Urban water usage penetration rate (%) | + | ||
U3 Population Humanities | M7 Proportion of urban population at the end of the year (%) | + | |
M8 Population at the end of the year (10,000 people) | + | ||
M9 Per capita possession of public library collections (volumes) | + | ||
U4 Innovation | M10 Education expenditure as a percentage of total fiscal expenditure (%) | + | |
M11 Science and technology expenditure (100 million yuan) | + | ||
M12 Internal expenditure of research and development (R&D) funds (10,000 yuan) | + | ||
Low-carbon subsystem (GL) | G1 Ecological endowment | L1 Green coverage rate in built-up areas (%) | + |
L2 Per capita park green area (square meters) | + | ||
L3 Area of urban green space (hectare) | + | ||
G2 Environmental governance | L4 Harmless treatment capacity of household waste (10,000 tons) | + | |
L5 Daily sewage treatment capacity (10,000 cubic meters) | + | ||
G3 Low-carbon development | L6 Energy conservation and environmental protection expenditure (100 million yuan) | + | |
L7 Electricity consumption (100 million kilowatt hours) | - | ||
G4 Pollution discharge | L8 Completion status of forestry investment (10,000 yuan) | + | |
L9 Exhaust gas sulfur dioxide emissions (10,000 tons) | - |
Coupling Value | Coordination Level | Coupling Value | Coordination Level |
---|---|---|---|
[0.000, 0.255) | Severe imbalance | [0.364, 0.406) | Mild synergy |
[0.255, 0.320) | Moderate imbalance | [0.406, 0.516) | Moderate synergy |
[0.320, 0.364) | Mild disorders | [0.516, 0.620) | Highly collaborative |
Province | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.490 | 0.497 | 0.502 | 0.430 | 0.461 | 0.505 | 0.510 | 0.516 | 0.451 | 0.504 | 0.504 | 0.492 |
Tianjin | 0.377 | 0.389 | 0.379 | 0.333 | 0.339 | 0.373 | 0.361 | 0.378 | 0.333 | 0.357 | 0.334 | 0.347 |
Hebei | 0.384 | 0.394 | 0.381 | 0.325 | 0.343 | 0.380 | 0.383 | 0.387 | 0.329 | 0.383 | 0.399 | 0.387 |
Shanxi | 0.335 | 0.346 | 0.332 | 0.287 | 0.300 | 0.320 | 0.320 | 0.320 | 0.280 | 0.323 | 0.329 | 0.335 |
Inner Mongolia | 0.343 | 0.357 | 0.364 | 0.314 | 0.324 | 0.360 | 0.363 | 0.335 | 0.289 | 0.324 | 0.324 | 0.331 |
Liaoning | 0.405 | 0.432 | 0.436 | 0.372 | 0.375 | 0.390 | 0.367 | 0.371 | 0.321 | 0.353 | 0.357 | 0.357 |
Jilin | 0.330 | 0.334 | 0.338 | 0.297 | 0.303 | 0.331 | 0.327 | 0.314 | 0.292 | 0.297 | 0.303 | 0.306 |
Heilongjiang | 0.346 | 0.352 | 0.353 | 0.311 | 0.312 | 0.341 | 0.322 | 0.314 | 0.269 | 0.291 | 0.311 | 0.300 |
Shanghai | 0.492 | 0.485 | 0.489 | 0.418 | 0.430 | 0.465 | 0.465 | 0.481 | 0.418 | 0.454 | 0.458 | 0.464 |
Jiangsu | 0.547 | 0.569 | 0.572 | 0.485 | 0.506 | 0.555 | 0.548 | 0.546 | 0.462 | 0.524 | 0.529 | 0.535 |
Zhejiang | 0.462 | 0.484 | 0.483 | 0.415 | 0.432 | 0.478 | 0.479 | 0.478 | 0.426 | 0.489 | 0.477 | 0.483 |
Anhui | 0.364 | 0.387 | 0.384 | 0.334 | 0.347 | 0.381 | 0.384 | 0.390 | 0.343 | 0.397 | 0.394 | 0.415 |
Fujian | 0.381 | 0.409 | 0.412 | 0.351 | 0.366 | 0.400 | 0.400 | 0.406 | 0.364 | 0.408 | 0.395 | 0.404 |
Jiangxi | 0.342 | 0.353 | 0.351 | 0.431 | 0.311 | 0.343 | 0.346 | 0.364 | 0.321 | 0.367 | 0.370 | 0.380 |
Shandong | 0.475 | 0.505 | 0.514 | 0.439 | 0.453 | 0.497 | 0.497 | 0.492 | 0.426 | 0.466 | 0.472 | 0.482 |
Henan | 0.364 | 0.377 | 0.384 | 0.327 | 0.344 | 0.374 | 0.380 | 0.393 | 0.347 | 0.405 | 0.403 | 0.409 |
Hubei | 0.374 | 0.390 | 0.387 | 0.333 | 0.356 | 0.394 | 0.397 | 0.400 | 0.353 | 0.412 | 0.400 | 0.409 |
Hunan | 0.364 | 0.371 | 0.380 | 0.326 | 0.341 | 0.374 | 0.376 | 0.380 | 0.338 | 0.367 | 0.393 | 0.400 |
Guangdong | 0.585 | 0.600 | 0.602 | 0.519 | 0.528 | 0.594 | 0.596 | 0.600 | 0.531 | 0.606 | 0.606 | 0.612 |
Guangxi | 0.411 | 0.398 | 0.396 | 0.346 | 0.374 | 0.396 | 0.389 | 0.389 | 0.348 | 0.341 | 0.371 | 0.381 |
Hainan | 0.287 | 0.311 | 0.307 | 0.245 | 0.258 | 0.283 | 0.274 | 0.282 | 0.255 | 0.333 | 0.269 | 0.277 |
Chongqing | 0.345 | 0.361 | 0.371 | 0.303 | 0.414 | 0.349 | 0.353 | 0.350 | 0.323 | 0.356 | 0.352 | 0.359 |
Sichuan | 0.390 | 0.389 | 0.396 | 0.341 | 0.352 | 0.390 | 0.392 | 0.388 | 0.349 | 0.392 | 0.404 | 0.417 |
Guizhou | 0.274 | 0.264 | 0.274 | 0.245 | 0.258 | 0.291 | 0.303 | 0.304 | 0.287 | 0.332 | 0.333 | 0.340 |
Yunnan | 0.331 | 0.331 | 0.322 | 0.272 | 0.282 | 0.311 | 0.311 | 0.314 | 0.273 | 0.307 | 0.307 | 0.315 |
Tibet | 0.228 | 0.214 | 0.201 | 0.173 | 0.188 | 0.241 | 0.197 | 0.218 | 0.209 | 0.222 | 0.239 | 0.251 |
Shaanxi | 0.357 | 0.357 | 0.357 | 0.308 | 0.319 | 0.357 | 0.350 | 0.350 | 0.314 | 0.339 | 0.338 | 0.353 |
Gansu | 0.280 | 0.277 | 0.273 | 0.248 | 0.255 | 0.278 | 0.277 | 0.287 | 0.255 | 0.286 | 0.280 | 0.291 |
Qinghai | 0.269 | 0.283 | 0.255 | 0.226 | 0.233 | 0.255 | 0.245 | 0.255 | 0.240 | 0.259 | 0.250 | 0.255 |
Ningxia | 0.320 | 0.316 | 0.299 | 0.266 | 0.271 | 0.304 | 0.300 | 0.295 | 0.278 | 0.306 | 0.300 | 0.304 |
Xinjiang | 0.316 | 0.324 | 0.307 | 0.266 | 0.278 | 0.304 | 0.299 | 0.304 | 0.387 | 0.304 | 0.295 | 0.307 |
Nationwide | 0.373 | 0.382 | 0.381 | 0.332 | 0.344 | 0.375 | 0.371 | 0.374 | 0.336 | 0.371 | 0.371 | 0.377 |
Period | Coordination Level | Eastern | Central | West | Northeast |
---|---|---|---|---|---|
2010–2012 | Highly coordinated | Jiangsu and Guangdong | |||
Moderate coordination | Beijing, Shanghai, Zhejiang, and Shandong | Liaoning | |||
Mild synergy | Tianjin, Hebei, and Fujian | Anhui, Henan, Hubei, and Hunan | Guangxi and Sichuan | ||
Mild disorders | Shanxi and Jiangxi | Inner Mongolia, Chongqing, Yunnan, and Shaanxi | Jilin and Heilongjiang | ||
Moderate imbalance | Hainan | Guizhou, Gansu, Qinghai, Ningxia, and Xinjiang | |||
Severe imbalance | Tibet | ||||
2014–2017 | Highly coordinated | Jiangsu and Guangdong | |||
Moderate coordination | Beijing, Shanghai, Zhejiang, and Shandong | ||||
Mild coordination | Hebei and Fujian | Anhui, Henan, Hubei, and Hunan | Guangxi, Chongqing, and Sichuan | Liaoning | |
Mild disorders | Tianjin | Jiangxi | Inner Mongolia and Shaanxi | Heilongjiang | |
Moderate imbalance | Hainan | Shanxi | Guizhou, Yunnan, Gansu, Ningxia, and Xinjiang | Jilin | |
Severe imbalance | Tibet and Qinghai | ||||
2019–2021 | Highly coordinated | Guangdong and Jiangsu | |||
Moderate coordination | Beijing, Shanghai, Zhejiang, and Shandong | Henan and Hubei | |||
Mild coordination | Hebei and Fujian | Anhui, Jiangxi, and Hunan | Guangxi and Sichuan | ||
Mild disorders | Tianjin | Shanxi | Inner Mongolia, Chongqing, Guizhou, and Shaanxi | Liaoning | |
Moderate imbalance | Hainan | Yunnan, Gansu, Qinghai, Ningxia, and Xinjiang | Jilin and Heilongjiang | ||
Severe imbalance | Tibet |
Year | Moran Index I | z-Value | p-Value | Year | Moran Index I | z-Value | p-Value |
---|---|---|---|---|---|---|---|
2010 | 0.256 | 2.500 | 0.006 | 2016 | 0.292 | 2.810 | 0.002 |
2011 | 0.328 | 3.118 | 0.001 | 2017 | 0.326 | 3.106 | 0.001 |
2012 | 0.351 | 3.320 | 0.000 | 2018 | 0.166 | 1.720 | 0.043 |
2013 | 0.380 | 3.572 | 0.000 | 2019 | 0.345 | 3.270 | 0.001 |
2014 | 0.289 | 2.784 | 0.003 | 2020 | 0.267 | 2.593 | 0.005 |
2015 | 0.289 | 2.788 | 0.003 | 2021 | 0.287 | 2.765 | 0.003 |
Spatial Association Mode | 2010–2021 |
---|---|
HH | Shanghai, Jiangsu, Zhejiang, Fujian, and Shandong |
LL | Gansu, Qinghai, Ningxia, and Xinjiang |
Area | Province | Influencing Factors | Top Four Impact Indicators | Ranking of Regional Indicators | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Economic Dominance | Policy Direction | Talent Literacy | Quality of Life | |||||||
Eastern | Beijing | 0.632 | 0.652 | 0.613 | 0.651 | M4 | L2 | M5 | M8 | L2 |
Tianjin | 0.565 | 0.529 | 0.619 | 0.640 | M5 | L9 | M3 | L2 | M6 | |
Hebei | 0.637 | 0.630 | 0.571 | 0.639 | M6 | L4 | M8 | L1 | L6 | |
Shanghai | 0.572 | 0.597 | 0.620 | 0.580 | L1 | L6 | M10 | M7 | M3 | |
Jiangsu | 0.565 | 0.624 | 0.608 | 0.631 | L9 | L6 | L2 | L4 | M5 | |
Zhejiang | 0.632 | 0.630 | 0.563 | 0.655 | L4 | M9 | L2 | M6 | M8 | |
Fujian | 0.624 | 0.631 | 0.645 | 0.612 | L6 | M10 | M6 | M3 | M10 | |
Shandong | 0.564 | 0.609 | 0.725 | 0.576 | M10 | M6 | M3 | L9 | L4 | |
Guangdong | 0.651 | 0.648 | 0.600 | 0.679 | L2 | M8 | M2 | M4 | L9 | |
Hainan | 0.683 | 0.584 | 0.652 | 0.614 | M9 | L6 | M5 | M2 | \ | |
Central | Shanxi | 0.654 | 0.642 | 0.647 | 0.680 | L3 | L2 | M8 | M7 | M4 |
Anhui | 0.735 | 0.730 | 0.685 | 0.652 | L1 | M2 | M4 | M7 | M2 | |
Jiangxi | 0.721 | 0.591 | 0.691 | 0.764 | L2 | M2 | M5 | L8 | M7 | |
Henan | 0.732 | 0.735 | 0.530 | 0.688 | M4 | M2 | L4 | M8 | \ | |
Hubei | 0.664 | 0.690 | 0.547 | 0.639 | M11 | M4 | L4 | M6 | \ | |
Hunan | 0.696 | 0.708 | 0.574 | 0.681 | L5 | L1 | M4 | M7 | \ | |
West | Inner Mongolia | 0.526 | 0.591 | 0.590 | 0.582 | M3 | L6 | L9 | M8 | M2 |
Guangxi | 0.570 | 0.579 | 0.710 | 0.578 | M10 | L9 | L5 | L3 | M10 | |
Chongqing | 0.665 | 0.568 | 0.687 | 0.583 | M10 | M2 | L8 | M9 | M6 | |
Sichuan | 0.739 | 0.713 | 0.655 | 0.658 | M2 | M10 | M4 | L3 | L3 | |
Guizhou | 0.791 | 0.771 | 0.692 | 0.733 | L1 | M6 | M8 | M1 | M3 | |
Yunnan | 0.652 | 0.626 | 0.638 | 0.607 | M2 | M3 | L9 | M10 | M9 | |
Tibet | 0.815 | 0.647 | 0.681 | 0.749 | M2 | L8 | L2 | M12 | L5 | |
Shaanxi | 0.615 | 0.593 | 0.551 | 0.620 | L3 | M6 | M11 | L5 | L9 | |
Gansu | 0.728 | 0.696 | 0.623 | 0.658 | M2 | M6 | M4 | L3 | \ | |
Qinghai | 0.706 | 0.579 | 0.738 | 0.617 | M10 | M2 | M6 | M9 | \ | |
Ningxia | 0.694 | 0.606 | 0.710 | 0.586 | M10 | M2 | L5 | L8 | \ | |
Xinjiang | 0.712 | 0.565 | 0.653 | 0.624 | M2 | L6 | M3 | M10 | \ | |
Northeast | Liaoning | 0.563 | 0.603 | 0.714 | 0.600 | M8 | L9 | M10 | L8 | M8 |
Jilin | 0.566 | 0.508 | 0.671 | 0.525 | L9 | M8 | M10 | M3 | M10 | |
Heilongjiang | 0.541 | 0.550 | 0.675 | 0.554 | L9 | M3 | M8 | M10 | L9 |
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Hu, W.; Liu, J. The Coupling and Coordination of Urban Modernization and Low-Carbon Development. Sustainability 2023, 15, 14335. https://doi.org/10.3390/su151914335
Hu W, Liu J. The Coupling and Coordination of Urban Modernization and Low-Carbon Development. Sustainability. 2023; 15(19):14335. https://doi.org/10.3390/su151914335
Chicago/Turabian StyleHu, Wei, and Jingsong Liu. 2023. "The Coupling and Coordination of Urban Modernization and Low-Carbon Development" Sustainability 15, no. 19: 14335. https://doi.org/10.3390/su151914335