China’s poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

Wang, Yanjun; Wang, Yanjun; Teng, Fei; Li, Shaochun; Lin, Yunhao; Cai, Hengfan

doi:10.1080/10095020.2022.2108346

Cited by 6 publications

(3 citation statements)

References 75 publications

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“…They can support the quantitative assessment of the progress of SDGs. Wang et al assessed the progress of SDG1 at the district and county level in China based on multi-source remote sensing data such as night light imagery and land cover data [34]. Liang et al constructed a comprehensive assessment framework for the sustainable development of urbanisation in Hainan Island, China, and used remote sensing to analyse the progress of the relevant targets in time and space from 2011 to 2020 [35].…”

Section: Keyword Clusteringmentioning

confidence: 99%

Bibliometric Analysis of Research Hotspots and Frontiers in Progress towards the Sustainable Development Goals

Xin,

Dong,

Cui

et al. 2024

Sustainability

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The United Nations Sustainable Development Goals (SDGs), established in 2015, are binding targets for monitoring the sustainable status of all countries in the world. Developing localized SDGs indicator systems, assessing SDGs progress comprehensively, and creating policy tools have gradually become the hotspots of scientific research and practical application of the SDGs. To systematically sort out the research status of the SDGs progress and identify problematic gaps, this paper uses bibliometric methods to analyse the scientific knowledge mapping of SDGs research, sort out the general characteristics, scientific cooperation, cutting-edge hotspots and future research trends of SDGs research. Besides, this paper promotes the integration of academic research into concrete practice by linking it to mainstream SDGs progress reports. The results show that: ① The amount of SDGs research literature in 2015–2022 is on a growing trend, and the existing research is characterised by multidisciplinary crossover. Research institutions such as Utrecht University, the Chinese Academy of Sciences, and the University of the Chinese Academy of Sciences have contributed the most to SDGs research. ② The research hotspots include the SDGs practice of different responsible subjects, the comprehensive assessment of the progress of SDGs, the scientific research of SDGs indicators, and the research on the interaction between the goals of SDGs. ③ Environmental issues, indicators, energy transition, education and agriculture are the hot directions of SDGs research. Energy saving and carbon reduction, sustainable practices of supply chain management, and promoting the progress of environmental dimension goals are the focus of subsequent research. According to the results of the bibliometric analysis, future SDGs research should pay attention to strengthening multi-party cooperation, using innovative technologies to support the assessment of SDGs progress, and formulating sustainable development strategies.

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Section: Keyword Clusteringmentioning

confidence: 99%

Bibliometric Analysis of Research Hotspots and Frontiers in Progress towards the Sustainable Development Goals

Xin,

Dong,

Cui

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…To compensate for this shortcoming, some studies have attempted to incorporate additional data sources alongside NTL data. For example, Wang et al [39] combined NTL data with land cover and digital elevation model (DEM) data to identify poverty levels in county-level administrative regions in China. Moreover, Shi et al [40] developed a comprehensive poverty index by integrating NTL data, DEM, normalized difference vegetation index (NDVI), and point-of-interest (POI) data to generate a poverty map of Chongqing, China.…”

Section: Introductionmentioning

confidence: 99%

“…Studies utilizing multiple data sources have achieved more accurate poverty assessments than those using only NTL data [40]. In current poverty assessment, NTL data [41] is often combined with other data such as land cover data [39,42], NDVI [43,44], Google Earth imagery [17,45], and DEM [40,42] to enhance the accuracy of the assessment.…”

Section: Introductionmentioning

confidence: 99%

Poverty Estimation Using a ConvLSTM-Based Model With Multisource Remote Sensing Data: A Case Study in Nigeria

Tang,

Zhao,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Poverty is a global challenge, the effects of which are felt on the individual to national scale. To develop effective support policies to reduce poverty, local governments require precise poverty distribution data, which are lacking for many areas. In this study, we proposed a model to estimate poverty on a spatial scale of 10 × 10 km by combining features extracted from multiple data sources, including nighttime light remote sensing data, normalized difference vegetation index, surface reflectance, land cover type, and slope data, and applied the model to Nigeria. Considering that the trends of environmental factors contain valid information related to poverty, time-series features were extracted through convolutional long short-term memory and used for the assessment. The poverty level is represented by the wealth index derived from the Demographic and Health Survey Program. The model exhibited a good ability to estimate poverty, with an R 2 of 0.73 between the actual and estimated wealth index in Nigeria in 2018. Applying the proposed model to poverty estimation for Nigeria in 2021 yielded an R 2 value of 0.69, indicating good generalization ability. To further validate model reliability, we compared the assessment results with high-resolution satellite imagery and a state-level multidimensional poverty index. We also investigated the impact of incorporating time-series features on the accuracy of poverty assessment. Results showed that the addition of time-series features increased the accuracy of poverty estimation from 0.64 to 0.73. The proposed method has valuable applications for estimating poverty at the grid scale in countries without such data.

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Evaluation and obstacle analysis of sustainable development in small towns based on multi-source big data: A case study of 782 top small towns in China

Chen,

Jin

et al. 2024

Journal of Environmental Management

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China’s poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

Cited by 6 publications

References 75 publications

Bibliometric Analysis of Research Hotspots and Frontiers in Progress towards the Sustainable Development Goals

Bibliometric Analysis of Research Hotspots and Frontiers in Progress towards the Sustainable Development Goals

Poverty Estimation Using a ConvLSTM-Based Model With Multisource Remote Sensing Data: A Case Study in Nigeria

Evaluation and obstacle analysis of sustainable development in small towns based on multi-source big data: A case study of 782 top small towns in China

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