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# The contribution of data-driven poverty alleviation funds in achieving mid-21st-Century multidimensional poverty alleviation planning

## Abstract

The first Sustainable Development Goal (SDG) is intended to eradicate multi-dimensional poverty globally. The same multidimensional poverty indices for India and the Middle East/Africa in 2020 indicate that 10–14 years are still required to reach the level of China’s poverty eradication. Using machine learning, spatial statistics, and a scenario analysis, we demonstrate how a Monte Carlo simulation of poverty alleviation funds-guided shared socioeconomic pathways (PAFs-SSPs) in China reveals the necessity to adopt an integrated poverty alleviation strategy. This approach employs multi-dimensional development indicators to reduce wide regional differences. We developed the data-driven model framework of a PAFs-SSPs to analyze the multifaceted and long-term planning needs of poverty alleviation policies, which can be applied to the formulation of poverty alleviation policies in different developing countries. Our findings point to the importance of implementing multidimensional development policies in China to achieve the first SDG worldwide.

## Introduction

The first of the United Nations (UN) Sustainable Development Goals (SDGs) aims to “end poverty in all its forms everywhere in 2030” (Ravallion, 2013), requiring a coordinated and comprehensive sustainable development in the economy, environment, resources, welfare, and other sectors. The concept of poverty has expanded from a single economic index to a multidimensional poverty index (MPI) (Cuaresma et al., 2018). In recent decades, the UN Millennium Development Goals (MDGs) have lifted more than a billion people out of extreme poverty. However, in many countries, progress in the development and implementation of multifaceted social indicators is still insufficient (Liu and Xu, 2016). While the MDGs focused on developing countries, the SDGs seek to establish future global frameworks, with periodic progress reviews at the national, regional and global levels.

### Future tendency and spatial distribution of MPI in China

As an important developing country of Asia, China accounts for nearly one-fifth of the world’s population for eliminating absolute poverty, with its poverty rate (World Bank standard) decreasing from 66.3% in 1990 to 0.5% in 2016. Considering China’s successful implementation of targeted poverty alleviation (Zhou et al., 2018) and fulfillment of the first UN SDG, the definition and standard of poverty in China after 2020 would benefit from being expanded to a multidimensional perspective. Thus, we developed a poverty evaluation index in terms of resources, environment, welfare, and economy using multisource data (including that from remote sensing imaging and statistical yearbooks) to identify deep MPA and spatiotemporal evolution characteristics with the help of the wide application of remote sensing technology (Sambah and Miura, 2016). We took the list of China’s 592 key poverty alleviation counties published by the National Poverty Alleviation and Development group in 2012. Due to the incomplete nature of indices from yearbook data, we selected 574 of the key poverty alleviation counties for analysis. The MPI rankings of 2369 counties in China were divided into six categories using the G* fracture method: extreme-poverty, generally poverty, poverty, primary developed, medium developed, and advanced developed. The MPI generally poverty is mainly in Henan-Shandong in 2025, and the MPI poverty is mainly in Guangdong-Hainan, Yunan-Guangxi in 2025. The primary developed is mainly in Yunan in 2035. Spatial distribution of MPI in SSP2–SSP5 scenarios were also provided in Supplementary Fig. 7.

Using the Monte Carlo method with 107 simulation times, economic dimension weight (0.467 ± 0.033) > social welfare dimension weight (0.385 ± 0.014) > resource dimension weight (0.131 ± 0.02) > environment dimension weight (0.017 ± 0.003) when the identification accuracy of poverty alleviation counties is more than 80%. The identification accuracy of poverty-stricken counties in different provinces and regions of China is presented in Supplementary Fig. S1. Furthermore, a unique model is designed for every region by using the relationship between PAFs and MPI for explaining the region’s poverty alleviation policy. The poverty alleviation policy models from different regions can use different model parameters, reflecting their respective regional property. Taking Anhui province as an example, the complex relationship of each model is presented in (Supplementary Fig. S2). Using these PAFs and MPI models, we built development scenarios for five poor counties, which covers a wide range of uncertainties caused by varying the model parameters in Monte Carlo experiments. Considering the investment of poverty alleviation funds at various times, in different regions, and of different amounts, we conducted 1,000,000 simulations for the future scenario simulation. The spatial patterns of our MPI evolution property in China vary substantially across scenarios in 2025 and 2050 (see Fig. 2). Our proposed model is the first to reveal the future spatial changes of MPI at the county level. The differences can be affected by the amount, timescale, and direction of PAFs provided by government. The proposed model was able to capture the spatially explicit divergence between scenarios because it updates the change rate of MPI with PAFs at every time step (i.e., one year). These changes reflect how this region can be affected by PAFs, which allowed spatial patterns under different scenarios to evolve. For all SSP scenarios, a banded poverty zone involving Yunnan-Guangxi-Guizhou, Chongqing, and Henan deserves special attention in 2050. Scenarios SSP1, SSP4, and SSP5 yield a smaller number of less developed counties in 2050, while SSP2 and SSP3 have the slowest speed of poverty alleviation. The SSP2 and SSP3 scenarios rely too heavily on their own ability to become rich, which carries the risk that the area could return to poverty quickly.

### Future investment in PAFs and the distribution of MPI

Although China’s poverty population decreased by 770 million during 1978–2020, when we consider the balance of MPI and the future goal of realizing developed counties, our model can reveal the impact of different SSP paths on China’s future. Using diverse investment strategies by PAF to achieve developed county level by 2050 could lead to uncertainty. The evolution of investment PAFs is decreasing from 2020 to 2050 (see Fig. 4a). SSP1, SSP4, and SSP5 can reach the developed county goal in 2025, and SSP1 yields the minimum difference in regional balance. SSP2 has the best overall development level although regional differences will be intensified. The ksdensity estimate of the MPI distribution in different SSP scenarios is also presented in Fig. 4b. The ratio of poverty alleviation fund to MPI growth rate is used for evaluating the effectiveness of various SSP pathways. SSP1 is higher than SSP2, SSP3, and SSP4 by ratios of 0.0314, 0.2, and 0.029, respectively, and SSP1 is lower than SSP5 by a ratio of 0.024 because it damages the interests of resources and the environment. Therefore, the SSP1 path is more suitable for China to take to achieve the SDGs.

## Discussion

In this paper, we present the long-term scenario prediction for future MPI at both county and country scale. The MPI predictions on a country scale are based on the latest SSP scenarios, while the simulated predictions for Chinese counties indicate the future tendency under PAFs-SSPs. This enhances the usefulness of the scientific approach for formulating poverty alleviation policies. In addition, the proposed modeling framework can be extended to such other developing countries as India (Wim et al., 2011), Tajikistan (Jha et al., 2010), and Peru (Flachsbarth et al., 2018).

Great progress has been made in poverty reduction globally. The proportion of the world’s extremely poor population is decreasing, from 35.85% in 1990 to 9.2% in 2017. Affected by COVID-19, the proportion of the world’s extremely poor population had a reverse growth from 9.2% in 2017 to 9.1–9.4%, and the number of extremely poor people has reached 115 million. The pandemic has brought great challenges to global poverty reduction. At present, 95% of the world’s poor regions lie in East Asia and the Pacific, South Asia, and sub-Saharan Africa (based on the international poverty line at US \$1.90 per day). To mitigate these challenges, poverty alleviation policies should place more emphasis on multidimensional coordinated development rather than a single indicator.

With respect to MPI in China, the mean MPI scores are gradually increasing, and their distribution changes from a “peak” in 2012 to “wide flat” in 2017, which indicates that the depth of poverty is gradually being reduced and the economic development of counties is steadily improving. The MPI variance between China’s counties were displayed in Supplementary Table SV and Supplementary Fig. S3, which is slowly increasing, which indicates the poverty gap between counties is gradually reducing and the comprehensive development level of counties is steadily improving. Furthermore, the MPI poverty boundary is decreasing gradually with the number of national poverty alleviation counties (Supplementary Fig. S4a–c). With the increase of PAFs, the number of poverty-stricken counties has also decreased rapidly (see Supplementary Fig. S4d). The success of the targeted poverty alleviation strategy adopted in China demonstrates the importance of poverty reduction programs guided by PAFs in other developing counties. Great changes have taken place in the structure of poverty, and the MPI properties are shown in Supplementary Fig. S5. After the implementation of the targeted poverty alleviation strategy in 2012, the situation in China has improved, but it is difficult to completely reverse the current inter-provincial poverty situation in the short term. Yunnan, Guangxi, Sichuan, Gansu, Qinghai, Ningxia, Liaoning, Jilin, and other concentrated contiguous poverty-stricken areas are widely distributed. The connection of poverty zones is the key area to target to improve China’s collaborative approach to poverty reduction.

With China’s realization of its absolute poverty reduction goal in 2020, the formulation of a novel MPI standard has become necessary. The contiguous MPAs are an important obstacle. Using data-driven scenario simulations to predict future tendencies will enable accurate multidimensional poverty alleviation through a feedback and correction mechanism. At the same time, the effective evaluation of the PAFs along with monitoring regional poverty alleviation policies is also necessary.

There are limitations in our future tendency prediction for MPI due to lack of available data. First, we analyzed the MPI at a national level using just two factors (the economy and environment) due to the absence of other fields of data. Second, poverty generally occurs in remote villages, we provide the index of MPI at a county and country scale in this study due to the lack of related village information. With the continuous development of information technology and increasing data access via the Internet, we aim to solve this limitation in our future work by complementing point-of-interest data to obtain relevant community-level data for various scenarios.

## Data availability

The China Statistical Yearbook is available at https://data.cnki.net/YearData/Analysis. OECD GDP and population data is used for this study. The environmental dataset CMIP6 emissions is provided at https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=50. The MODIS dataset is available from https://modis.gsfc.nasa.gov/data/. The NLS data in terms of DMSP/OLS and NPP/VIIRS from 2000 to 2017 can be found at https://www.ngdc.noaa.gov/eog/download.html. Historical GDP and population statistics are available from https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=30. Codes and materials used to produce this work are available upon request.

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## Acknowledgements

This research has received funding from the key Project of National Natural Science Foundation (Grant No. 42030409).

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Correspondence to Weixin Luan, Jun Yang or Xiaoling Zhang.

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Yang, D., Luan, W., Yang, J. et al. The contribution of data-driven poverty alleviation funds in achieving mid-21st-Century multidimensional poverty alleviation planning. Humanit Soc Sci Commun 9, 179 (2022). https://doi.org/10.1057/s41599-022-01180-x