Evaluation and Analysis of the County-Level Sustainable Development Process in Guangxi, China in 2014–2020
Abstract
:1. Introduction
2. Study Area and Data
2.1. Overview of the Study Area
2.2. Data
3. Method
3.1. SDGCI Calculation and Change Analysis
3.2. Classification and Cluster Analysis of SDG Indicator System
4. Results
4.1. Spatial and Temporal Changes of SDGCI
4.2. SDGCI Changes and Relationship Analysis for Different Systems
4.2.1. SDGCI Changes Based on United Nations SDG Classification
4.2.2. Spatial and Temporal Variations of SDGCI Coupling Degree (SDGCI_CD) among Different Systems
4.2.3. Spatial and Temporal Variations of SDGCI Coupling Coordination Degree (SDGCI_CCD) among Different Systems
4.3. Differences and Linkages of SDGCI in Townships: Results of Cluster Analysis
5. Discussions
- (1)
- Protection of cultivated land. The results show that both cultivated land and rice cultivation areas have a strongly positive relationship with SDGCI. While guaranteeing the quantity of cultivated land, we should ensure its spatial production suitability and improve the matching of crops and cultivated land [59].
- (2)
- Protection of the natural environment, especially the surface water environment. Ecosystem quality index and forest area contribute to SDGCI positively at a significant level, while surface water area and potential evapotranspiration have a strong relationship with SDGCI. Ecosystem conservation could integrate biodiversity conservation, ecological restoration, and water resource exploitation and protection, and promote the establishment of comprehensive conservation policies, laws and ecological compensation measures [60,61].
- (3)
- Improvement of infrastructure construction. Although the contribution of the distance to a railway, road, and hospital to SDGCI in Guangxi is low, the relationships between distance to a railway and road and SDGCI are significant (* p < 0.05). Guangxi is famous for its beautiful natural environment and rich tourism resources. The coordinated development of interregional, urban and rural transportation could meet economic and tourism development needs [62].
- (4)
- Control of environmental pollution: Atmospheric pollutants such as PM2.5, PM10, SO2, CO, O3 and fossil fuel emissions negatively correlate with SDGCI in Guangxi. The following suggestions are put forward: vigorously develo** green transportation and controlling traffic pollution, regulating high-polluting industries, improving clean production in various industries, and develo** clean energy [63].
6. Conclusions
- (1)
- The average value of SDGCI in Guangxi is around 0.12. The SDGCI level of townships in Guangxi is low, and the development of towns in the northern and southern regions needs to be strengthened.
- (2)
- The SDGCI value of the prosperity system contributes the most to the overall SDGCI. It is necessary to strengthen the development of people and planet system indicators, such as strengthening the utilization and protection of cultivated land, controlling pollution emissions, and strengthening infrastructure construction.
- (3)
- The towns in Guangxi are categorized into three clusters based on SDGCI values of the three systems. Thus, the sustainable development capacity of Guangxi could be improved by adapting to the local conditions of different towns.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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SDG Indicators | Datasets (Unit) | Period | Spatial Resolution | Data Sources | Incline |
---|---|---|---|---|---|
2.4.1 Proportion of agricultural area under productive and sustainable agriculture | Maize cultivation area (1 km2) | 2000–2019 | 1 km | Luo et al., 2020 [41] | + |
Rice cultivation area (1 km2) | 2000–2019 | 1 km | Luo et al., 2020 [41] | + | |
Percentage of cultivated land area (%) | 1990–2020 | - | Yang and Huang, 2021 [42] | + | |
3.8.1 Coverage of essential health services | Distance to hospital (1 km) | 2014–2021 | 1 km | Open Street Map (OSM) | − |
3.9.1 Mortality rate attributed to household and ambient air pollution | Near-surface CO concentration (mg/m3) | 2013–2020 | 10 km | Wei et al., 2023 [43] | − |
Near-surface SO2 concentration (µg/m3) | 2013–2020 | 10 km | Wei et al., 2023 [43] | − | |
4.a.1 Proportion of schools offering basic services, by type of service | Distance to school (1 km) | 2014–2021 | 1 km | Open Street Map (OSM) | − |
6.6.1 Change in the extent of water-related ecosystems over time | Precipitation (mm/year) | 2001–2020 | 1 km | Zhao et al., 2022 [44] | + |
Potential evapotranspiration (mm/year) | 1990–2021 | 1 km | Peng, 2022 [45] | − | |
Percentage of surface water area (%) | 1990–2020 | - | Yang and Huang, 2021 [42] | + | |
Distance to river (1 km) | 2014–2023 | 1 km | Open Street Map (OSM) | − | |
7.1.1 Proportion of population with access to electricity | Nighttime lighting index (-) | 2000–2020 | 1 km | Zhong et al., 2022 [46] | + |
7.3.1 Energy intensity measured in terms of primary energy and GDP | Actual electricity consumption (kWh) | 1992–2019 | 1 km | Chen et al., 2022 [47] | + |
Distance to energy-consuming industrial heat sources (1 km) | 2012–2022 | 1 km | Ma and **.org | + |
Year | SDGCI | Number (Percentage) of Towns for Different SDGCI Intervals | ||||||
---|---|---|---|---|---|---|---|---|
Mean | Median | Max | ≤0.1 | (0.1,0.2] | (0.2,0.3] | (0.3,0.4] | >0.4 | |
2014 | 0.129 | 0.117 | 0.415 | 403 (32.5%) | 729 (58.7%) | 93 (7.5%) | 14 (1.1%) | 2 (0.2%) |
2015 | 0.134 | 0.122 | 0.472 | 344 (27.7%) | 774 (62.4%) | 104 (8.4%) | 16 (1.3%) | 3 (0.2%) |
2016 | 0.127 | 0.115 | 0.474 | 441 (35.5%) | 701 (56.5%) | 85 (6.8%) | 12 (1.0%) | 2 (0.2%) |
2017 | 0.129 | 0.117 | 0.481 | 406 (32.7%) | 725 (58.4%) | 96 (7.7%) | 11 (0.9%) | 3 (0.2%) |
2018 | 0.123 | 0.111 | 0.502 | 480 (38.7%) | 669 (53.9%) | 81 (6.5%) | 9 (0.7%) | 2 (0.2%) |
2019 | 0.122 | 0.110 | 0.481 | 497 (40.0%) | 649 (52.3%) | 83 (6.7%) | 9 (0.7%) | 3 (0.2%) |
2020 | 0.125 | 0.114 | 0.479 | 442 (35.6%) | 697 (56.2%) | 91 (7.3%) | 8 (0.6%) | 3 (0.2%) |
Mean | 0.127 | 0.115 | 0.472 | 430 (34.7%) | 706 (56.9%) | 90 (7.3%) | 11 (0.9%) | 3 (0.2%) |
Year | Systems | SDGCI | Number (Percentage) of Towns for Different SDGCI Intervals | |||||
---|---|---|---|---|---|---|---|---|
Mean | Median | Max | ≤0.2 | (0.2,0.4] | (0.4,0.6] | >0.6 | ||
2015 | people | 0.107 | 0.091 | 0.468 | 1161 (93.6%) | 78 (6.3%) | 2 (0.2%) | 0 (0.0%) |
planet | 0.117 | 0.085 | 0.982 | 1111 (89.5%) | 99 (8.0%) | 27 (2.2%) | 4 (0.3%) | |
prosperity | 0.280 | 0.269 | 0.795 | 269 (21.7%) | 845 (68.1%) | 114 (9.2%) | 13 (1.0%) | |
2020 | people | 0.103 | 0.089 | 0.489 | 1172 (94.4%) | 67 (5.4%) | 2 (0.2%) | 0 (0.0%) |
planet | 0.112 | 0.078 | 0.984 | 1120 (90.2%) | 93 (7.5%) | 24 (1.9%) | 4 (0.3%) | |
prosperity | 0.238 | 0.222 | 0.785 | 497 (40.0%) | 658 (53.0%) | 72 (5.8%) | 14 (1.1%) |
Year | SDGCI_CD | Number (Percentage) of Towns for Different SDGCI_CD Intervals | |||||
---|---|---|---|---|---|---|---|
Mean | Median | Max | ≤0.6 | (0.6,0.7] | (0.7,0.8] | >0.8 | |
2015 | 0.852 | 0.867 | 0.9999 | 9 (0.7%) | 63 (5.1%) | 263 (21.2%) | 906 (73.0%) |
2020 | 0.869 | 0.886 | 0.9995 | 7 (0.6%) | 52 (4.2%) | 214 (17.2%) | 968 (78.0%) |
Year | SDGCI_CCD | Number (Percentage) of Towns for Different SDGCI_CCD Intervals | |||||
---|---|---|---|---|---|---|---|
Mean | Median | Max | ≤0.30 | (0.30,0.40] | (0.40,0.50] | >0.50 | |
2015 | 0.373 | 0.361 | 0.682 | 55 (4.5%) | 864 (69.6%) | 277 (22.3%) | 45 (3.6%) |
2020 | 0.356 | 0.344 | 0.668 | 124 (9.9%) | 903 (72.8%) | 181 (14.6%) | 33 (2.7%) |
Year | Systems | SDGCI Intervals for Clustering Results of Towns | ||
---|---|---|---|---|
Cluster 1 | Cluster 2 | Cluster 3 | ||
2015 | people | [0.056,0.414] | [0.093,0.434] | [0.113,0.350] |
planet | [0.061,0.322] | [0.134,0.570] | [0.313,0.640] | |
prosperity | [0.047,0.849] | [0.200,0.705] | [0.107,0.458] | |
Number of towns | 531 | 622 | 88 | |
2020 | people | [0.066,0.523] | [0.135,0.466] | [0.190,0.394] |
planet | [0.059,0.448] | [0.170,0.579] | [0.430,0.669] | |
prosperity | [0.253,0.847] | [0.168,0.615] | [0.090,0.342] | |
Number of towns | 733 | 414 | 94 |
Factors | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | R Value Mean | Correlation |
---|---|---|---|---|---|---|---|---|---|
Gross Domestic Product | 0.666 *** | 0.655 *** | 0.694 *** | 0.652 *** | 0.675 *** | 0.671 *** | 0.666 *** | 0.668 | Positive |
Actual electricity consumption | 0.665 *** | 0.654 *** | 0.691 *** | 0.648 *** | 0.671 *** | 0.665 *** | 0.660 *** | 0.665 | Positive |
Percentage of cultivated land area | 0.611 *** | 0.603 *** | 0.596 *** | 0.617 *** | 0.603 *** | 0.636 *** | 0.632 *** | 0.614 | Positive |
Nighttime lighting index | 0.581 *** | 0.574 *** | 0.612 *** | 0.569 *** | 0.598 *** | 0.601 *** | 0.605 *** | 0.591 | Positive |
Percentage of surface water area | 0.522 *** | 0.506 *** | 0.536 *** | 0.546 *** | 0.557 *** | 0.571 *** | 0.563 *** | 0.543 | Positive |
Rice cultivation area | 0.396 *** | 0.425 *** | 0.503 *** | 0.557 *** | 0.475 *** | 0.527 *** | 0.531 *** | 0.488 | Positive |
Maize cultivation area | 0.457 *** | 0.461 *** | 0.361 *** | 0.400 *** | 0.412 *** | 0.398 *** | 0.397 *** | 0.412 | Positive |
Ecosystem quality index | 0.313 *** | 0.346 *** | 0.336 *** | 0.373 *** | 0.3523 *** | 0.335 *** | 0.341 *** | 0.342 | Positive |
Rare plant and animal biodiversity | 0.245 *** | 0.263 *** | 0.267 *** | 0.286 *** | 0.317 *** | 0.308 *** | 0.312 *** | 0.285 | Positive |
Percentage of forest area | 0.202 *** | 0.239 *** | 0.229 *** | 0.267 *** | 0.252 *** | 0.226 *** | 0.223 *** | 0.234 | Positive |
Distance to natural attractions | 0.217 *** | 0.179 *** | 0.269 *** | 0.243 *** | 0.242 *** | 0.215 *** | 0.227 *** | 0.227 | Positive |
Distance to railway | 0.061 * | 0.127 *** | 0.123 *** | 0.113 *** | 0.139 *** | 0.133 *** | 0.131 *** | 0.118 | Positive |
Distance to river | 0.077 ** | 0.072 *** | 0.076 *** | 0.058 * | 0.053 | 0.062 * | 0.067 * | 0.066 | Positive |
Distance to road | 0.059 * | 0.033 | 0.032 | 0.012 | 0.063 * | 0.091 ** | 0.088 ** | 0.054 | Positive |
Distance to hospital | −0.013 | 0.021 | −0.041 | −0.021 | 0.049 | 0.048 | 0.158 *** | 0.029 | - |
Distance to school | −0.046 | 0.009 | −0.051 | −0.035 | 0.049 | 0.047 | 0.052 | 0.004 | - |
Vegetation index | −0.048 | 0.022 | −0.014 | 0.023 | 0.005 | 0.024 | 0.014 | 0.004 | - |
Distance to energy-consuming industrial heat sources | −0.012 | −0.042 | −0.035 | 0.010 | 0.010 | 0.025 | 0.014 | −0.004 | - |
Precipitation | 0.087 ** | −0.089 ** | −0.068 * | 0.047 | −0.060 * | −0.147 *** | −0.097 *** | −0.047 | - |
Near-surface SO2 concentration | −0.234 *** | −0.265 *** | −0.259 *** | −0.301 *** | −0.291 *** | −0.274 *** | −0.280 *** | −0.272 | Negative |
Near-surface CO concentration | −0.236 *** | −0.263 *** | −0.262 *** | −0.323 *** | −0.300 *** | −0.290 *** | −0.288 *** | −0.280 | Negative |
O3 concentration | −0.264 *** | −0.307 *** | −0.273 *** | −0.294 *** | −0.291 *** | −0.287 *** | −0.290 *** | −0.287 | Negative |
PM2.5 concentration | −0.412 *** | −0.471 *** | −0.455 *** | −0.499 *** | −0.486 *** | −0.479 *** | −0.467 *** | −0.467 | Negative |
Potential evapotranspiration | −0.454 *** | −0.470 *** | −0.456 *** | −0.496 *** | −0.478 *** | −0.468 *** | −0.468 *** | −0.470 | Negative |
PM10 concentration | −0.428 *** | −0.470 *** | −0.454 *** | −0.497 *** | −0.486 *** | −0.495 *** | −0.475 *** | −0.472 | Negative |
Fossil fuel emission | −0.582 *** | −0.567 *** | −0.597 *** | −0.550 *** | −0.573 *** | −0.570 *** | −0.574 *** | −0.573 | Negative |
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Shao, L.; Jia, G.; Qiu, Y.; Liu, J. Evaluation and Analysis of the County-Level Sustainable Development Process in Guangxi, China in 2014–2020. Sustainability 2024, 16, 1641. https://doi.org/10.3390/su16041641
Shao L, Jia G, Qiu Y, Liu J. Evaluation and Analysis of the County-Level Sustainable Development Process in Guangxi, China in 2014–2020. Sustainability. 2024; 16(4):1641. https://doi.org/10.3390/su16041641
Chicago/Turabian StyleShao, Lanqing, Guoqiang Jia, Yubao Qiu, and Jianming Liu. 2024. "Evaluation and Analysis of the County-Level Sustainable Development Process in Guangxi, China in 2014–2020" Sustainability 16, no. 4: 1641. https://doi.org/10.3390/su16041641