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Agricultural growth has long been recognized as an important instrument for poverty reduction. Yet, measurements of this relationship are still scarce and not always reliable. The authors present additional evidence at both the sectoral and household levels based on recent data. Results show that rural poverty reduction has been associated with growth in yields and in agricultural labor productivity, but that this relation varies sharply across regional contexts. GDP growth originating in agriculture induces income growth among the 40 percent poorest, which is on the order of three times larger than growth originating in the rest of the economy. The power of agriculture comes not only from its direct poverty reduction effect but also from its potentially strong growth linkage effects on the rest of the economy. Decomposing the aggregate decline in poverty into a rural contribution, an urban contribution, and a population shift component shows that rural areas contributed more than half the observed aggregate decline in poverty. Finally, using the example of Vietnam, the authors show that rapid growth in agriculture has opened pathways out of poverty for farming households. While the effectiveness of agricultural growth in reducing poverty is well established, the effectiveness of public investment in inducing agricultural growth is still incomplete and conditional on context.

Against the backdrop of consolidating the achievements of poverty alleviation and promoting rural revitalization, whether digital village construction can alleviate the risk of rural families returning to poverty and then help long-term poverty prevention needs systematic and scientific empirical investigation. Using data from the 2020 China Rural Revitalization Survey (CRRS), this paper analyzes the impact of digital village construction on rural household poverty vulnerability and explores its potential channels. The results show that (1) digital village construction can significantly alleviate the poverty vulnerability of rural households. (2) Improving entrepreneurial activity is an effective way for digital village construction to affect the risk of rural households returning to poverty. (3) Digital village construction has a greater effect on reducing the poverty vulnerability of households in the eastern and the plains regions, and it is necessary to be vigilant against the emergence of the “digital divide”. Based on these findings, this paper proposes policy recommendations to prevent the risk of rural areas returning to poverty through digital village construction, which achieves sustainable poverty reduction and rural revitalization goals.

Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive (PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals (SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index (MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis (ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following: (1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development (R&D) expenditure, and funding per capita for cultural undertakings. (2) From 2007 to 2017, provincial income poverty (IP), health poverty (HP), cultural poverty (CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces. (3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas. (4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.

Enhancing the synergies between agriculture and related sectors in rural areas is considered an important development strategy to eliminate rural poverty. This article provides evidence for this view by analyzing the effect of industry convergence between agriculture and related sectors on rural poverty. Based on China’s provincial panel data, we use two-way fixed effects model, system generalized method of moments and panel-corrected standard error estimator to quantitatively assess this effect. We find that: (1) the convergence of agriculture and tourism (ATOU), the convergence of agriculture and processing industry (APOS), and the convergence of planting and breeding industry (MIXA) have positive and significant effects on poverty reduction. The convergence of agriculture and the internet industry (AINT) has a positive but not significant effect. (2) Rural local employment plays an important role as a bridge in the impact of convergence on poverty reduction. ATOU and MIXA reduce poverty by increasing self-employment opportunities. APOS reduces poverty by providing more jobs. (3) Except for APOS, the effects of other types of convergence tend to stabilize or improve in the later period. (4) Convergence has the most significant impact on poverty reduction in western China. The findings provide inspiration for developing countries with agricultural foundations to choose appropriate rural development paths for reducing rural poverty.

This study investigated the energy poverty spatiotemporal interaction characteristics and socioeconomic determinants in rural China from 2000 to 2015 using exploratory time–space data analysis and a geographical detector model. We obtained the following results. (1) The overall trend of energy poverty in China’s rural areas was “rising first and then declining”, and the evolution trend of energy poverty in the three regions formed a “central–west–east” stepwise decreasing pattern. (2) There was a dynamic local spatial dependence and unstable spatial evolution process, and the spatial agglomeration of rural energy poverty in China had a relatively higher path dependence and locked spatial characteristics. (3) The provinces with negative connections were mainly concentrated in the central and western regions. Anhui and Henan, Inner Mongolia and Jilin, Jilin and Heilongjiang, Hebei and Shanxi, and Liaoning and Jilin constituted a strong synergistic growth period. (4) From a long-term perspective, the disposable income of rural residents had the greatest determinant power on rural energy poverty, followed by per capita GDP, rural labor education level, regulatory agencies, and energy investment. In addition, our findings showed that the selected driving factors all had enhanced effects on rural energy poverty in China through interaction effects.

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Environment and poverty are the focus of global concern, and green poverty reduction is China’s strategic choice to deal with these two major problems. However, due to the vast territory, there are regional differences in environment and poverty in China. On the basis of this, selecting renewable resource utilization, environmental protection, and incidence of poverty as measurement indicators from two dimensions of green and poverty reduction, this paper employed the analysis of spatial autocorrelation and spatial econometric regression based on dynamic spatial Durbin model to explore the internal mechanism of green and poverty in rural areas of 21 provinces in China. The results show that there was a significant spatial autocorrelation in the poverty in rural areas among provinces, which shows that poverty reduction has significant regional connections within spatial scopes. Meanwhile, it is green that has a spatial spillover effect on poverty reduction. Therefore, on the one hand, intergovernmental governments should establish effective communication and cooperation mechanisms between regions; on the other hand, intergovernmental governments should pay enough attention to the spatial spillover effects of green on poverty reduction in green poverty reduction, so as to promote green poverty reduction to achieve overall and sustainable development.

This article highlights why the Bill & Melinda Gates Foundation has focused on financial inclusion to advance women's economic empowerment and drive progress on gender equality. It highlights key lessons from financial inclusion-related projects the foundation has supported within the \"Putting Women and Girls at the Center of Development (WGCD) Grand Challenge\" in 2015. The article also shares the logic and research informing the foundation's strategy to close the gender gap in financial inclusion - a key pillar of its strategy on women's economic empowerment - and improve the lives and livelihoods of millions of women around the world.

Poverty threatens human development especially for developing countries, so ending poverty has become one of the most important United Nations Sustainable Development Goals (SDGs). This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model. The poverty reduction efficiency (PRE) is measured by the difference in the out-of-poverty rates (which measures the probability of being not poor) of 2016 and 2019. The study shows that the probability of poverty in all regions of China has shown an overall decreasing trend (PRE = 0.264), which indicates that the progress in poverty reduction during this period is significant. The Hu Huanyong Line (Hu Line) shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest China. From 2016 to 2019, the centroid of China's out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line, indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to 2019. The results imply that the government's future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor areas. This study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government's poverty reduction policy.

Scientific evaluation of the interaction between poverty reduction efficiency (PRE) and ecosystem services (ES) in state poverty counties is essential in promoting the rural revitalization strategy and the construction of an ecological civilization. Using the DEA model, the InVEST model, and fixed-effect panel data, this study was analyzed using the panel data of 832 poverty counties in China for 2010–2019 to evaluate the relationship between poverty reduction efficiency and ecosystem services. The main results are as follows: (1) The overall poverty reduction efficiency showed an upward trend, while ES exhibited a declining trend with spatial heterogeneity. The poverty reduction efficiency of state poverty counties in the western region increased rapidly. (2) The impact of different types of ecosystem services on poverty reduction efficiency varied considerably. Habitat quality was significantly negatively impacted, while food production and carbon storage showed significant positive effects. There was a significant positive relationship between ecosystem services and poverty reduction efficiency in all regions, with the eastern region having the strongest correlation. (3) The panel regression analysis showed a significant positive impact. The environmental parameters were the primary factors affecting poverty reduction efficiency, while economic and social factors were the driving and external factors. The rural revitalization strategy should strive towards the win-win effect of ecological protection and economic development.