Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area
2.2. Data Sources
2.3. Research Methods
2.3.1. Localized SDG 1 Index System for Districts and Counties
2.3.2. Localized SDG 1 Evaluation Model for Districts and Counties Based on Multiple Linear Regression
2.3.3. Localized Evaluation Model for District- and County-Level SDG 1 Indicators Based on a Machine Learning Algorithm
3. Results
3.1. Results and Accuracy Verification of the Evaluation Model of Localized SDG 1 Indicators in Districts and Counties
3.2. Spatial Distribution Pattern of Localized SDG 1 Evaluation Values in Districts and Counties
3.3. Spatial Distribution Pattern of SDGs in Poverty-Stricken Counties in Hunan Province
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Global SDG 1 Indicator Framework | Localized SDG 1 Index for China | Localized SDG 1 Indicators for Districts and Counties |
---|---|---|
1. Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural) | 1. Incidence of poverty | 1. Incidence of poverty |
2. Proportion of population living below the national poverty line by sex and age | ||
3. Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions | 2. Percentage of urban residents with minimum living allowance in urban populations 3. Proportion of rural residents with minimum living security in rural populations 4. Proportion of urban and rural persons with disabilities included in the minimum living allowance in the total population | 2. Percentage of urban residents with minimum living allowance in urban populations 3. Proportion of rural residents with minimum living security in rural populations |
4. Proportion of population covered by social protection floors/systems by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-based injury victims and persons who are poor and vulnerable | ||
5. Number of deaths, missing persons and persons directly affected by disasters per 100,000 persons | 5. Proportion of the population affected by natural disasters to the total population | 4. Disposable income of urban residents 5. Disposable income of rural residents |
6. Direct economic loss attributed to disasters in relation to global gross domestic product (GDP) | ||
7. Proportion of total government spending on essential services (education, health and social protection) |
Localized SDG 1 Indicators for Districts and Counties | Weight | Indicator Attributes |
---|---|---|
1. Incidence of poverty | 0.1379 | — |
2. Percentage of urban residents with minimum living allowance in urban populations | 0.1138 | — |
3. Proportion of rural residents with minimum living security in rural populations | 0.1895 | — |
4. Disposable income of urban residents | 0.1624 | + |
5. Disposable income of rural residents | 0.3964 | + |
Influencing Factor | Data | Characteristic Factor |
---|---|---|
Social economy | NPP-VIIRS Nighttime light image | Total logarithm of nighttime lights () |
Percentage of nighttime light area at night () | ||
Land cover | Land use data | Proportion of urban land area () |
Topography | SRTM DEM data | Mean elevation () |
Average slope () | ||
Traffic network | OSM road data | Road network density () |
Machine Learning Model | Parameter Type | Parameter Value |
---|---|---|
Regression tree; Model VI | Minimum leaf size | 12 |
Support vector machine; Model VII | Kernel function | Linear function |
Gaussian process regression; Model VIII | Kernel function | Matern 5/2 function |
Integrated tree; Model IX | Minimum leaf size | 8 |
Evaluation Model | R2 | RMSE |
---|---|---|
Model I | 0.76 | 0.10 |
Model II | 0.75 | 0.10 |
Model III | 0.57 | 0.13 |
Model IV | 0.74 | 0.11 |
Model V | 0.64 | 0.12 |
Model VI | 0.50 | 0.14 |
Model VII | 0.72 | 0.11 |
Model VIII | 0.73 | 0.11 |
Model IX | 0.68 | 0.12 |
Poverty Indicator System | R2 | Literature Source | |
---|---|---|---|
1 | Localized SDG 1 indicators for districts and counties | 0.76 | Our study |
2 | Household wealth index | 0.70 | Zhao et al. [45] |
3 | Integrated poverty index | 0.86 | Yu et al. [47] |
4 | Household wealth index | 0.76 | Steele et al. [50] |
5 | Integrated poverty index | 0.68 | Xu et al. [51] |
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Wang, Y.; Wang, M.; Huang, B.; Li, S.; Lin, Y. Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China. Remote Sens. 2021, 13, 4778. https://doi.org/10.3390/rs13234778
Wang Y, Wang M, Huang B, Li S, Lin Y. Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China. Remote Sensing. 2021; 13(23):4778. https://doi.org/10.3390/rs13234778
Chicago/Turabian StyleWang, Yanjun, Mengjie Wang, Bo Huang, Shaochun Li, and Yunhao Lin. 2021. "Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China" Remote Sensing 13, no. 23: 4778. https://doi.org/10.3390/rs13234778