Abstract Although rural livelihoods act as a critical mediator between poverty alleviation policies and sustainable outcomes, the spatial heterogeneity of this interaction remains underexplored within those agrarian systems that are crucial for food production. This study examines how China’s Targeted Poverty Alleviation policies shape livelihood strategies and the livelihood diversity of rural households across different spatial contexts in Jilin Province, a main grain production region of Northeast China. Using survey data from 2306 households, this study employs multiple logistic and linear regression models. The results indicate that (1) industrial and employment policies are associated with development-oriented strategies, whereas enterprise-driven and cash transfer policies tend to reinforce asset-based or welfare-dependent livelihoods; (2) these policy effects exhibit significant spatial heterogeneity, mediated by local agricultural productivity conditions, labor endowments, and off-farm livelihood availability; and (3) industrial policies show stronger associations with agricultural livelihoods in the east, while financial policies are more effective in sustaining agricultural engagement in the capital-constrained west. Integrating the Sustainable Livelihoods Framework with a spatial lens, this study shifts the focus of policy assessment from static outcome metrics to process-oriented analysis and reveals the mechanisms underlying the spatial divergence of livelihood strategies, providing a nuanced analytical framework for assessing the impacts of PAPs across diverse agricultural contexts. Based on these findings, this study highlights that spatially differentiated, livelihood context-sensitive policies are essential for securing sustainable and long-term poverty reduction in grain production regions, offering a replicable template for policy evaluation and practical implications for achieving SDGs 1 and 2 in agrarian regions. 1. Introduction Achieving No Poverty and Zero Hunger remain key priorities in the United Nations’ Sustainable Development Goals (SDGs), yet the persistence of poverty in agrarian regions continues to undermine progress towards sustainable rural development. While extreme poverty has been significantly reduced worldwide, the stability and sustainability of these achievements remain precarious, especially in areas where rural households face compound challenges, including climate vulnerability, market instability, and labor shortages. Therefore, the enhancement of smallholders’ developmental capacity and adaptive resilience is increasingly being emphasized in contemporary global poverty governance practices, and are considered as more sustainable objectives than short-term income gains or statistical poverty reduction [ 1]. China’s Targeted Poverty Alleviation (TPA) policy system, first implemented in 2013, integrates industrial development, employment facilitation, financial inclusion, and social security measures [ 12], and places particular emphasis on fostering sustainable livelihoods [ 13]. Despite its success in eliminating absolute poverty by 2020 [ 7], serious challenges remain regarding its long-term effectiveness in fostering households’ endogenous development capabilities [ 14]. This problem is pronounced in the northeast grain production region, where rural households grapple with an aging population, declining agricultural profit, deagrarianization, and limited employment opportunities, threatening both regional agrarian development and national food security. To address these gaps, this study introduces two innovations. First, it conducts a livelihood-centric evaluation of multiple PAPs, analyzing how policies reconfigure households’ engagement in labor-intensive activities (e.g., agriculture, non-farm employment) versus welfare-dependent strategies. Second, it integrates an explicit spatial analysis, examining the efficacy of the policies across eastern (mountainous), middle (fertile plains), and western (semi-arid) subregions. By explicitly embedding a spatial dimension into the livelihood–policy analysis, this study demonstrates how geographic context mediates policy outcomes and advances the SLF beyond static, context-blind assessments. These spatially nuanced insights provide an empirical foundation for balancing macro-level imperatives with micro-level interventions that are aligned with the constraints and opportunities of impoverished families [ 15]. Thus, the findings offer practical insights for aligning poverty alleviation policies with agricultural development needs in the considered grain production region, while providing a replicable framework for designing context-sensitive, sustainability-oriented poverty governance strategies elsewhere. 2. Literature Review and Theoretical Framework 2.1. PAPs Evaluation: From Income Metrics to Livelihood Processes 2.2. Livelihood Strategies and Poverty Alleviation Outcomes The SLF distinguishes multiple livelihood strategies based on how households combine their asset endowments (human, natural, financial, social, physical) to generate income and well-being [ 19, 20]. Empirical studies have consistently demonstrated that livelihood strategies characterized by autonomous labor input, income diversification, and non-farm participation generate more stable and durable poverty alleviation outcomes than those relying on governmental transfers or pure subsistence agriculture [ 21, 22, 23, 24, 25]. Livelihood diversification enhances stability and long-term poverty reduction through risk-hedging, and has therefore become a mainstream direction for livelihood development globally [ 26, 27, 28]. Strategies that incorporate mixed non-agricultural activities are particularly conducive to endogenous development and sustained poverty reduction [ 29]. In contrast, welfare-dependent strategies, which rely on external transfers or passive land leasing with limited adaptive capacity, may reduce long-term resilience and create dependency traps [ 5, 17]. This body of evidence underscores that distinguishing livelihood strategies by their divergent poverty outcomes provides a critical analytical lens for assessing the stability of poverty alleviation and understanding the endogenous development capacity of rural households [ 30, 31]. Building on this foundation, this study refines the livelihood strategy classification into two analytically distinct pathways (see Figure 1). Development-oriented strategies are characterized by active labor inputs, high income diversification across farm and non-farm activities, and adaptive capacity across agriculture, non-agriculture, and mixed forms, all of which generate endogenous growth potential [ 31]. By contrast, dependency-oriented strategies involve less labor inputs and generally have lower diversification. These comprise asset-based (passive land leasing) and welfare-based (reliance on subsidies) strategies, which provide immediate relief but may erode long-term adaptive capacity and create dependency traps [ 5, 17]. This refined classification moves beyond aggregate comparisons of income or asset levels, allowing us to examine how specific policy interventions shape the likelihood that households adopt one pathway over the other. The classification thus serves as the analytical lens through which the effects of poverty alleviation policies on household livelihoods are systematically evaluated in the subsequent sections. 2.3. The Spatiality of Rural Livelihoods and the Effects of PAPs Rural livelihoods and poverty outcomes are inherently embedded in regionally differentiated natural and socioeconomic contexts, giving rise to fundamental spatial attributes [ 32, 33, 34]. However, most PAPs are often spatially designed and implemented at the macro-scale, risking “one-size-fits-all” approaches [ 35, 36]. Consequently, the same policy may generate divergent outcomes, showing significant spatial heterogeneity. For instance, agricultural innovations that were successful in Europe and Asia have proven difficult to replicate in Africa [ 36]; cash transfer reduced child labor employment in Latin America but not in Sub-Saharan Africa [ 37]; and, in China, government-led industrial assistance boosted selected agriculture activities in the Longnan mountain area [ 38], yet hindered agricultural livelihoods in the Qinling–Daba mountain area [ 14]. The root cause of these divergent impacts lies in the path dependencies of rural livelihood systems, which are deeply shaped by long-standing local biophysical, institutional, and cultural contexts [ 35]. These cases underscore that the effectiveness of PAPs is mediated by spatial differences in livelihood contexts, necessitating spatially tailored policy designs [ 6]. Despite this recognition, few studies have systematically integrated household-level livelihood data with explicit spatial frameworks or examined how specific spatial contextual dimensions moderate heterogeneous policy effects. This study addresses this gap by conceptualizing spatial context as a multidimensional moderator (as illustrated in Figure 1) and empirically testing policy–livelihood heterogeneity across the eastern (mountainous, aging), middle (intensive, high off-farm), and western (capital-constrained, semi-arid) subregions of Jilin Province. This spatially explicit approach moves beyond acknowledging spatial heterogeneity to explicitly embedding contextual variation into the empirical specification, thereby providing a replicable framework for context-sensitive poverty governance in agrarian regions. 3. Materials and Methods 3.1. Study Area of Jilin Province in Northeast China Jilin Province is a major grain production region within the renowned black soil belt of Northeast China. It has nearly 7.5 million ha of cultivated land, and its per capita cultivated land area is about 0.58 ha, which is double the national average. Its natural and socioeconomic development shows distinct east–west zonal variations. The Eastern Area is located in the Changbai Mountain area, with a high proportion of aging population and the smallest area of farmland in the province. It contains 19 counties, characterized by serious problems of population outflow and socioeconomic decline. The Middle Area features semi-humid plains with fertile soil, abundant arable land, and high agricultural productivity. It has 15 counties, characterized by rapid urbanization. Its population size and proportion of non-agricultural economic activities are significantly larger than those in the other two areas. The Western Area, situated at the southern foot of the Great Khingan Mountains, is a semi-arid cropping–nomadic transition zone with flat terrain. Despite abundant arable land and grassland, it is restricted by poor soil quality and limited water resources. It contains 9 counties, characterized by a medium economic level ( Figure 2). Jilin is a less-developed province in China and is a key focus of TPA measures. It once faced serious rural poverty, with over 700,000 registered rural impoverished individuals in the national poverty alleviation information system for 2015. This population group is mainly characterized by an aging demographic, low educational attainment, and high disability and illness rates [ 39]. During the TPA implementation period (2013–2020), a series of policies were implemented based on the concept of long-term, sustainable poverty alleviation mechanisms, including assistance in industrial development (e.g., crop cultivation, livestock), the provision of employment opportunities, access to microcredit loans, and guiding enterprises/cooperatives to drive the alleviation of rural poverty. In addition, the government provided diverse subsidies for agricultural production, social security, and poverty alleviation, paid as direct cash transfers to those classified as rural impoverished. These payments have a wide coverage, and are the main source of income and livelihood foundation for impoverished populations. 3.2. Data The data used in this study were obtained from a survey issued for rural poverty assessment during the mid-stage of the TPA implementation period (i.e., in 2017–2018), organized by the Jilin Provincial Government and covering 43 counties. The survey households were randomly selected from the national register system of the rural impoverished population in each county. It has the characteristics of a large sample size, strong sample representativeness, and wide coverage. A total of 2306 sample households met the research requirements, with 1041, 754, and 511 sample households distributed in the Eastern, Middle, and Western areas, respectively. The survey data encompass the characteristics of livelihood capital, livelihood activities, income sources, income levels, and the poverty alleviation policies that the household received support through. The survey data indicate that 43.75% of rural households in Jilin rely on labor-input strategies, with Agriculture, Non-Agriculture, and Mixture strategies accounting for 21.64%, 14.70%, and 7.42%, respectively. The remaining 56.25% of households predominantly rely on Subsidies (52.73%) rather than Land Transfer (3.51%), reflecting the high welfare dependency of the rural impoverished population in Jilin. The Eastern Area exhibits significantly higher proportions of subsidy reliance compared to the Middle and Western areas, with Agriculture and Land Transfer showing lower rates. The Middle Area has a higher proportion of Agriculture, Land Transfer, and Mixture strategies, based on its characteristics as the provincial core area of grain production, population aggregation, and economic development. The Western Area has low proportions of Non-Agriculture and Mixture strategies and higher proportions of Agriculture and Land Transfer, which is related to its relatively low level of socioeconomic development and abundant farmland. Jilin Province exhibits an average livelihood diversity index of 0.54, with that in the Eastern Area being the lowest (0.43), while the Middle (0.62) and Western (0.65) areas show similar values (see Figure 3). 3.3. Methods 3.3.1. Multiple Logistic Regression and Linear Regression The choice of regression models is determined by the nature of the dependent variables. Multiple logistic regression was determined as a suitable method to examine the relationship between livelihood strategies and policies considered in this study, as the livelihood strategy is a nominal multi-categorical variable with no inherent ordinal ranking. The average marginal effect (AME) of multiple logistic regression is reported, which expresses the average effect of independent variables on dependent variables, and the differences across groups or models [ 40]. Linear regression was adopted to examine the effects of PAPs on livelihood diversity, as it is a continuous numerical variable measured using the Shannon–Wiener diversity index. The multicollinearity of variables within the model was inspected using the variance inflation factor (VIF). The VIF values of all variables were lower than 5 (1.02–2.04), thereby indicating the absence of multicollinearity. All statistical analyses were performed using StataMP 17, and the results were visualized as forest plots using GraphPad Prism 9.5. 3.3.2. Dependent Variable The second dependent variable is the level of livelihood diversity. The Shannon–Wiener index is widely applied in livelihood diversity research [ 44, 45], and is calculated as follows: l i v e l i h o o d d i v e r s i t y = − ∑ i = 1 n P i ln P i (1) In Equation (1), n represents the number of livelihood activity types; i denotes the specific income source type; and Pi is the proportion of income of type i to the households’ total income. 3.3.3. Independent Variable Based on the actual policy mix implemented during the TPA implementation period in Jilin Province, the explanatory variables are five main PAPs that directly relate to the livelihood activities of the rural impoverished population. The industrial policy (IP) represents whether the households receive industrial development assistance from the government, such as assistance to develop cultivation, breeding, or other non-agricultural industries. The employment policy (EP) represents whether the households receive employment assistance through the governmental provision of employment opportunities or skills training. The enterprise driving policy (EDP) reflects whether a household receives driving assistance from enterprises or cooperatives. The financial policy (FP) refers to access to microcredit for poverty alleviation provided by banks. The cash transfer policy (CAP) represents the proportion of various types of cash transfer income from governments (e.g., agricultural subsidies, social security subsidies as part of the minimum living standard subsidy, disability subsidies, pensions, family planning subsidies) to the total household income. 3.3.4. Control Variable 4. Results 4.1. Impact of PAPs on Rural Livelihoods 4.1.1. Impact of PAPs on Livelihood Strategy The regression results regarding the effects of PAPs on livelihood strategies are shown in Figure 4. Model 1 presents the regression results without any control variables, which reveal that all PAPs have significant effects on the livelihood strategies of rural households. Model 2 controls for livelihood capital variables, while Model 3 further controls for rural–urban interactions and geographical context. The results for Models 2 and 3 are largely consistent with those of Model 1, suggesting that the observed associations between the PAPs and livelihood strategies are robust to the inclusion of these controls. IP positively affects the Agriculture strategy and promotes rural livelihoods under this strategy, thus reducing the possibility of Non-Agriculture and Land Transfer strategies. This is consistent with the design of IP in Jilin Province, which mainly focuses on crop planting and livestock breeding. Households receiving IP tend to remain in or intensify their agricultural production, which corresponds to a higher probability of adopting an Agriculture strategy. By contrast, EP is positively associated with the Mixture strategy, with households receiving EP being more likely to combine agricultural and non-agricultural activities. A plausible explanation is that EP provides employment opportunities in nearby areas, enabling family members to work off-farm while others continue farming, thereby supporting a mixed livelihood pattern. EDP, which relies on enterprises or cooperatives to assist the rural impoverished population, has a significant positive relationship with the Subsidies strategy and negative relationships with the Agriculture and Mixture strategies. Thus, households receiving EDP tend to rely more on subsidy income and are less likely to adopt labor-intensive livelihoods. This likely reflects the implementation context: due to the high employment thresholds or limited job opportunities offered by enterprises/cooperatives, in addition to the weak labor capacity of impoverished households, EDP is often delivered as direct cash subsidies rather than labor-linked support. FP is correlated with a lower probability of Land Transfer and a higher probability of adopting the Agriculture strategy. This indicates that rural impoverished individuals who are able to access microcredit are more likely to engage in agricultural activities, rather than leasing out their land. CTP significantly increases the possibility of relying on Subsidies, while reducing the possibility of adopting other livelihood strategies. Households with a high proportion of subsidy income tend to have low labor capacity, making it difficult to develop livelihood strategies with labor inputs. Moreover, a high share of cash transfers corresponds to a lower probability of adopting diversified or development-oriented strategies, consistent with the possibility that such transfers may reinforce welfare dependence. 4.1.2. Impacts of PAPs on Livelihood Diversity The effects of PAPs on livelihood diversity are shown in Figure 5. Models 1, 2, and 3 were constructed similarly to those used to obtain the results shown in Figure 4. The results of Models 2 and 3 are basically consistent with those of Model 1, demonstrating that the PAPs bear significant associations with variations in household livelihood diversity. IP and EP are positively correlated with higher livelihood diversity; in particular, IP encourages agricultural diversification (e.g., crop–livestock integration), while EP expands off-farm work opportunities. Both mechanisms diversify household income sources, thereby enhancing livelihood diversity and strengthening risk-coping capacity. In contrast, EDP, FP, and CTP are all associated with decreased livelihood diversity. Households receiving EDP or CTP tend to rely on a single source of income—namely, subsidies or transfers—which restricts their engagement in varied productive activities and traps them in dependency-oriented, low-diversity livelihood modes. Notably, FP presents a negative influence on livelihood diversity while positively linking with Agriculture strategies, as revealed in Section 4.1.1. This is because households receiving FP are more prone to expanding their cultivation or breeding scale, or enhancing their agricultural production conditions, which further drives their specialization in agriculture rather than diversification. 4.2. Spatial Heterogeneity of PAP Impacts on Rural Livelihoods 4.2.1. The Spatial Heterogeneity of the Impact of PAPs on Livelihood Strategy Policy effects on livelihood strategies were found to exhibit pronounced spatial heterogeneity across eastern, middle, and western Jilin ( Figure 6). IP is only positively associated with Agriculture in the Eastern and Western areas. This pattern reflects the low socioeconomic development and underdeveloped non-agricultural markets in these areas, thus channeling IP support primarily toward crop cultivation and livestock breeding, reinforcing agricultural specialization. By contrast, IP shows a positive association with Mixture in the Middle Area, due to its higher socioeconomic development and robust non-agricultural sectors, enabling households to combine agricultural activities with non-farm employment. The synergy between agricultural and non-agricultural conditions further facilitates policies promoting mixed livelihoods locally. EP positively impacts the Mixture strategy across all three areas, with the strongest association in the Middle Area due to its developed non-farm labor markets, enabling households to easily combine agricultural activities with non-farm employment. EP negatively impacts Subsidies only in the Eastern Area, where high baseline welfare reliance is reduced by access to non-farm jobs. EDP’s positive impact on Subsidies decreases from east to west, and it is also positively linked to Land Transfer in the east. This disparity stems from differences in regional policy intensity and demographic conditions. The Eastern Area, as an ethnic border and poverty-stricken region, receives concentrated policy support; here, enterprises and cooperatives often deliver assistance through direct cash subsidies rather than job-linked support. Combined with severe aging and labor shortages, land transfer to large farms, cooperatives, or enterprises has become a primary channel for poverty reduction. Consequently, EDP is linked to both higher subsidy income and land transfer participation in the east. In the Middle and Western areas, lower policy intensity and more off-farm opportunities weaken these associations. FP is negatively associated with Land Transfer in all three areas, with the strongest link in the Western Area. The positive association is only significant in the west, where capital is extremely scarce and off-farm opportunities are very limited; here, land—despite its poor quality—is the primary productive asset that households can rely on. Microcredit therefore enables individuals in this area to farm their own land rather than leasing it out, which also explains why a positive link between FP and Agriculture strategy appears only in the west. CTP’s positive association with Subsidies strengthens from east to west. Its negative associations with Non-Agriculture, Mixture, and Land Transfer strategies also intensify in a westward gradient, while its negative association with Agriculture is significant only in the east. These spatial heterogeneities are closely related to the regional labor constraints, subsidy coverage, and economic structure. In the eastern region, baseline subsidy coverage shares are already high, leaving limited marginal gains from additional transfers. Moreover, severe labor shortages cause households to tend to abandon farming when their basic needs are met through transfers, explaining the significant negative association with Agriculture only in the east. The Middle Area’s more diversified economy partially offsets these negative impacts. In the west, limited off-farm options and a larger agricultural population amplify the reliance on subsidies. 4.2.2. The Spatial Heterogeneity of the Impact of PAPs on Livelihood Diversity The PAPs exhibit marked spatial heterogeneity in their associations with livelihood diversity across eastern, middle, and western Jilin ( Figure 7). The positive effect of IP on livelihood diversity is stronger in the central plain than in the west and east. Benefiting from diversified economies and sound non-farm markets, central households readily combine IP-supported agricultural diversification with off-farm work. By contrast, water and soil constraints in the west limit agricultural diversification, while labor loss and aging in the east restrict multi-activity engagement, weakening the IP–diversity association. EP presents the opposite spatial pattern, with significantly stronger positive associations in the Eastern and Western areas. Lower baseline livelihood diversity in these lagging regions allows for larger marginal gains from EP-provided non-farm opportunities, which effectively compensate for terrain constraints in the east and scarce non-agricultural options in the west. The already diversified livelihoods in the central plain substantially reduce EP’s marginal contribution. Although EDP and FP are negatively correlated with livelihood diversity in the Middle and Eastern areas, these effects are statistically insignificant. In the Middle and Eastern areas, the policies are found to drive agricultural specialization and subsidy dependence, inhibiting diversified livelihood development. In western Jilin, financial support mainly prevents land abandonment and maintains basic agricultural production. Given its inherently low initial diversity, no significant further decline is observed. CTP’s negative association with livelihood diversity weakens gradually from east to west. Severe aging and labor shortages make eastern households highly dependent on transfer income, trapping them in low-diversity livelihoods. Central households partially offset these negative effects through diverse livelihood opportunities. In the west, the already very low baseline diversity limits any further decline, resulting in a weaker observed negative association. 5. Discussion 5.1. Reframing PAPs from Outcome-Centric to Process-Oriented Evaluation Through SLF 5.2. The Policy–Livelihood Relationship Across Spatial and Agrarian Contexts This study further demonstrates a finer sub-scale spatial heterogeneity in Jilin Province, which reflects region-specific mechanisms affecting the PAP–livelihood relationships. The strongest subsidy reliance effect observed in the west aligns with evidence that unconditional cash transfers reduce labor supply incentives, particularly where off-farm alternatives are limited [ 49, 50]. The effects of IP in east Jilin mirror findings from the Longnan mountain area [ 38], but differ from those from the Qinling–Daba mountain area [ 14], showing that the effectiveness of PAPs is fundamentally shaped by diverse human–environment systems in rural areas [ 55]. Financial policies were found to reduce land transfer most strongly in the west, where capital scarcity makes microcredit a binding constraint on farming. This echoes broader evidence that tailored credit products are essential for smallholders to maintain productive engagement [ 56]. 5.3. Tracing Policy–Livelihood Pathways to Agricultural Sustainability in Grain Production Regions As a major black soil grain-producing region, Jilin features a coupled human–land system, where policy-driven livelihood transitions fundamentally determine long-term farmland sustainability. Long-term intensive cultivation has reduced topsoil organic matter and caused ecological fragility in the black soil ecosystem. Unlike most agricultural sustainability studies, which have prioritized technical conservation measures while overlooking the micro-behavioral mechanisms linking policy interventions, household livelihood choices, and land-use risks [ 8, 19], the findings of this study suggest that spatially heterogeneous livelihood pathways generate divergent ecological risks, rather than delivering uniform grain production and environmental benefits. 5.4. Optimizing PAPs Through a Contextually Grounded and Livelihood-Sensitive Framework in Grain Production Areas In the context of sustainable grain security and rural revitalization, the continuation of PAPs needs to be implemented with a comprehensive understanding of both the rural impoverished population’s specific livelihood needs and the spatial variations in the livelihood context. Considering the results of this study, several implications can be drawn. For the aging population in the eastern mountains, cash transfers should be complemented by ecological compensation for fallow land to prevent abandonment without creating welfare traps. Enterprise-driven subsidies need to be replaced with conditional support tied to job creation, skills training, or cooperative land use. More broadly, labor-saving technologies or social services are needed to compensate for demographic deficits. In the central plain, industrial and employment policies should be prioritized, as they effectively foster mixed livelihoods and reduce welfare dependency. Conservation tillage incentives should accompany these policies, thus ensuring that productivity gains do not come at the cost of soil health [ 57]. For the seriously capital-constrained western semi-arid region, microcredit reduces land transfer and sustains farming; however, green credit conditions (e.g., limits on fertilizer and groundwater use) are required to avoid secondary salinization. Market linkages for industrial output should be strengthened, especially where remittance inflows are significant, in order to ensure that cash transfers translate into productive investment rather than passive consumption. Across all regions, the evaluation of poverty must move beyond income metrics. Environmental indicators—for example, land use intensity, soil organic matter, and input use—should be integrated into policy assessments. Spatially adaptive governance that aligns policy design with the local labor endowment, economic structure, and agro-ecological conditions is essential for sustainable poverty reduction. 6. Conclusions This study evaluated the spatial heterogeneity of the effects of PAPs on rural livelihoods in Jilin Province, a critical grain production region of Northeast China. The findings demonstrated that the impacts of PAPs are systematically mediated by the local agro-economic context, revealing clear spatial differentiations across the eastern, middle, and western subregions of Jilin. The application of the SLF in this study shifts the impact assessment of PAPs from relying on static income metrics to a process-oriented analysis, which is more conducive to addressing the specific livelihood challenges faced by the rural impoverished population and achieving stable and sustainable poverty reduction. Embedding spatial heterogeneity into the PAP–livelihood nexus, this study further revealed the contextual mechanisms through which identical policies may produce divergent outcomes in black soil grain production areas, thereby deepening the understanding of PAP–livelihood interactions. Based on these findings, this study argues that context-sensitive implementations must be prioritized to support resilient and sustainable rural livelihoods in the context of poverty governance. Spatially targeted recommendations include ecological compensation for fallow land in the eastern part of Jilin, conservation tillage incentives in the central plain, and green credit conditions in the west. These findings can help local governments to precisely match their policy interventions to the livelihood needs of their communities, enhance the endogenous development capacity of households, and balance sustainable poverty reduction with long-term black soil protection. Together, these contributions offer practical implications for context-sensitive, livelihood-centric, and spatially adaptive governance, thereby serving as a useful reference for advancing SDGs 1 and 2 in agrarian economies. Several limitations should be addressed in future work. First, despite the efforts to control for observed confounders, the cross-sectional data preclude causal inference due to potential endogeneity and selection bias. Future research should use panel data or quasi-experimental designs to establish causality and track the long-term impacts of PAPs on rural livelihoods. Second, the policy variables in this study were measured as binary indicators, which do not capture differences in policy intensity, duration, implementation quality, or household participation. Third, the analysis covered only Jilin Province in China, and the generalizability of the obtained results to other agro-ecological zones (e.g., highlands, coasts) thus requires further verification. Nevertheless, the livelihood-centric, spatially adaptive framework used in this study provides a replicable template for the evaluation of PAPs in other agrarian regions. Author Contributions Conceptualization, L.M. and S.W.; methodology, L.M.; validation, L.M.; formal analysis, L.M.; investigation, L.M., S.W. and B.W.; data curation, L.M., S.W. and B.W.; writing—original draft preparation, L.M., B.W. and J.H.; writing—review and editing, B.W. and C.L.; visualization, L.M.; project administration, L.M.; funding acquisition, B.W. and L.M. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the Humanities and Social Science Fund of the Ministry of Education of China [grant number 24YJCZH207, 23YJC630076], Natural Science Foundation of Shaanxi Province of China [grant numbers: 2025JC-YBQN-413], National Natural Science Foundation of China [grant number 42171198], and Fundamental Research Funds for the Central Universities [grant number 2024CDJSKZK16]. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Conflicts of Interest The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: PAPs Poverty Alleviation Policies SLF Sustainable Livelihood Framework SDGs Sustainable Development Goals PTA Targeted Poverty Alleviation References Figure 1. The conceptual framework for the connections between policies, rural livelihoods, and poverty alleviation. Source: Drawn by the authors. Figure 1. The conceptual framework for the connections between policies, rural livelihoods, and poverty alleviation. Source: Drawn by the authors. Figure 2. An overview of Jilin Province. Source: Drawn by the authors. Figure 2. An overview of Jilin Province. Source: Drawn by the authors. Figure 3. The livelihood strategy structure and livelihood diversity of rural households in Jilin Province. Source: Authors’ calculation based on survey data. Figure 3. The livelihood strategy structure and livelihood diversity of rural households in Jilin Province. Source: Authors’ calculation based on survey data. Figure 4. The AME of poverty alleviation policies on the livelihood strategies of rural households. Source: Authors’ calculation based on survey data. Figure 4. The AME of poverty alleviation policies on the livelihood strategies of rural households. Source: Authors’ calculation based on survey data. Figure 5. The AME of poverty alleviation policies on the livelihood diversity of rural households. Source: Authors’ calculation based on survey data. Figure 5. The AME of poverty alleviation policies on the livelihood diversity of rural households. Source: Authors’ calculation based on survey data. Figure 6. The AME of poverty alleviation policies on the livelihood strategies in different geographical areas. Source: Authors’ calculation based on survey data. Figure 6. The AME of poverty alleviation policies on the livelihood strategies in different geographical areas. Source: Authors’ calculation based on survey data. Figure 7. The AME of poverty alleviation policies on the livelihood diversity in different geographical areas. Source: Authors’ calculation based on survey data. Figure 7. The AME of poverty alleviation policies on the livelihood diversity in different geographical areas. Source: Authors’ calculation based on survey data. Table 1. Descriptions and statistics of the variables used in this study. Table 1. Descriptions and statistics of the variables used in this study. Variables Description of Variables Mean Std. Dev Min. Max. Independent variable Industrial Policy Industry development assistance of governments (Yes = 1, No = 0) 0.12 0.32 0 1 Employment Policy Provision of employment opportunities or skills training (Yes = 1, No = 0) 0.11 0.31 0 1 Enterprise Driving Policy Driving assistance from enterprises or cooperatives (Yes = 1, No = 0) 0.74 0.44 0 1 Financial Policy Microcredit for poverty alleviation (Yes = 1, No = 0) 0.06 0.23 0 1 Cash Transfer Policy Proportion of governmental subsidies to household income (%) 49.26 26.87 0 100 Control variable Family size The size of the family (person) 2.16 1.07 1 7 Labor force The number of family members that have working capacity (person) 0.67 0.87 0 5 Health condition Health condition of family members. 1 = with serious illness or being disabled; 2 = only with chronic illness; 3 = all are healthy 1.66 0.60 0 1 Education level Education level of the household’s head. 1 = below the elementary school; 2 = junior high school; 3 = high school or above 1.30 0.50 0 1 Arable land area Area of arable land held by family (ha) 0.78 0.77 0 9 Housing condition 1 = have safe housing; 0 = no housing or have dangerous housing 0.92 0.27 0 1 Vehicle assets 1 = have cars or farming vehicles/machinery; 0 = no vehicles 0.04 0.19 0 1 Annual per capita net income 1 = CNY 13,066 2.14 0.79 1 4 Social connections 1 = Receive assistance from friends or relatives; 0 = No 0.28 0.45 0 1 Rural–urban linkage Distance to nearest urban areas. 1 = proximity; 2 = intermediate; 3 = outlying 2.45 0.75 0 1 Geographical context 1 = Eastern Area; 2 = Middle Area; 3 = Western Area 2.23 0.79 0 1 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. MDPI and ACS Style Ma, L.; Wang, S.; Wang, B.; Li, C.; Hu, J. Impacts of Poverty Alleviation Policies on Rural Livelihoods and Their Spatial Heterogeneity in a Main Grain Production Region of Northeast China. Sustainability 2026, 18, 5817. https://doi.org/10.3390/su18125817 AMA Style Ma L, Wang S, Wang B, Li C, Hu J. Impacts of Poverty Alleviation Policies on Rural Livelihoods and Their Spatial Heterogeneity in a Main Grain Production Region of Northeast China. Sustainability. 2026; 18(12):5817. https://doi.org/10.3390/su18125817 Chicago/Turabian Style Ma, Li, Shijun Wang, Binyan Wang, Chenxi Li, and Jialing Hu. 2026. "Impacts of Poverty Alleviation Policies on Rural Livelihoods and Their Spatial Heterogeneity in a Main Grain Production Region of Northeast China" Sustainability 18, no. 12: 5817. https://doi.org/10.3390/su18125817 APA Style Ma, L., Wang, S., Wang, B., Li, C., & Hu, J. (2026). Impacts of Poverty Alleviation Policies on Rural Livelihoods and Their Spatial Heterogeneity in a Main Grain Production Region of Northeast China. Sustainability, 18(12), 5817. https://doi.org/10.3390/su18125817
