Abstract
Water is fundamental to human well-being, social development and the environment. Water development, particularly hydropower, provides an important source of renewable energy. Water development is strongly affected by poverty, but only few attempts have been made to understand the links between water development and poverty from a global water development point of view. In this work, this linkage was explored using reservoir construction, hydroenergy and water use data along with six derived indicators. We used association rule mining and classification and regression trees (CART) to identify the links. Random forests were employed to search for factors sensitive to poverty. This study shows that the reservoir density is significantly related to poverty, and reservoir densities are lower in countries with higher poverty rates. Countries with a higher use of small hydropower (SHP) systems are generally more prosperous as follows: an SHP utilization rate above 27% corresponds to a poverty rate below 4.9%. The ratio of water utilization, water availability per capita (WAPC) and reservoir density were essential for the prediction of the poverty class. All three ratios could be related to poverty alleviation as they enable the identification of the potential for water resource development and their constraints. This study concludes that water development in poor countries needs to receive more attention.
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Notes
Poverty rate derived from https://www.borgenmagazine.com/10-facts-poverty-in-somalia/
Poverty rate derived from http://timesofoman.com/article/78972
Poverty rate derived from https://en.wikipedia.org/wiki/Economy_of_Qatar
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Acknowledgements
We thank the World bank, FAO, World Energy Council and International Center on Small Hydro Power for providing the data used in this research. This study was financially supported by the National Key Research and Development Program (2016YFA0600304) and the National Natural Science Foundation of China (41561144013).
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Highlights
1. We investigated the water and poverty links from a global water development view of poverty alleviation.
2. Data mining methods was applied to explore the links between water development and poverty rates in a world-wide context.
3. We find that the ratio of water utilization, water availability per capita and reservoir density contributed most to predict poverty class and related to poverty alleviation.
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Tian, F., Wu, B., Zeng, H. et al. Identifying the Links Among Poverty, Hydroenergy and Water Use Using Data Mining Methods. Water Resour Manage 34, 1725–1741 (2020). https://doi.org/10.1007/s11269-020-02524-5
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DOI: https://doi.org/10.1007/s11269-020-02524-5