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oai:figshare.com:article/33979333 2026-09-24T04:30:24Z category_575 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Social service accessibility and Green Agricultural Development Intensity as pathways to Rural Elderly Welfare: panel evidence from China’s underserved regions.pdf Linxin Wang (11761332) Fengyi Wang (9389802) Food Packaging, Preservation and Safety digital divide green agriculture livelihood resilience panel data analysis Rural Elderly Welfare social service accessibility Background<p>The elderly population in the rural areas of China, defined as 60 years or older, is over 121 million, representing 23.8% of the total rural population. The elderly population is growing rapidly and is characterized by the triple burden of poor access to healthcare, reduced agricultural income, and being digitally excluded. Although numerous national programs to expand social services and promote green agricultural transformation have been developed, the overall impact of these two policy streams on elderly wellbeing remains an untested area. This study aims to examine the independent and combined effects of social service accessibility (SSA) and Green Agricultural Development Intensity (GADI) on Rural Elderly Welfare (REW), while considering livelihood resilience (LR) as the mediating variable and the digital divide index (DDI) as the moderating condition. Both SSA and GADI are operationalized as province-level composite indices of service provision and agricultural development intensity, respectively, rather than as direct measures of individual elderly residents’ service use or farming behavior; this measurement level is made explicit throughout.</p>Methods<p>A panel data set was created, with the data collected at the provincial level and covering 30 provinces in China from 2013 to 2022 (N = 268). Three province-year cells are missing because Tibet and Hainan entered the relevant statistical series after 2013, and a further 29 cells were dropped through listwise deletion of province-years with incomplete sub-indicators. The data was collected from the China Statistical Yearbooks, CNNIC internet reports, and the China Health and Retirement Longitudinal Study. The estimation results were based on two-way fixed-effects regression with interaction terms, and a bootstrap mediation analysis with 5,000 replications. The results were also checked for robustness through IV-2SLS estimation, winsorization, alternative specification for the dependent variable, excluding municipalities, and dynamic panel (system-GMM) estimation.</p>Results<p>SSA exerted a significant positive effect on REW (β = 0.27, p < 0.05), as did GADI (β = 0.18, p < 0.10). The SSA × GADI interaction was significant and positive (β = 0.14, p < 0.05), implying that the combined effect exceeds the additive effect of individual components. Livelihood resilience partially mediated both the SSA–REW pathway (indirect effect = 0.09, 95% CI [0.03, 0.16]) and the GADI–REW pathway (indirect effect = 0.06, 95% CI [0.01, 0.12]). Contrary to expectations, DDI failed to significantly moderate the relationship between GADI and REW (β = −0.023, p = 0.412).</p>Conclusion<p>Coordinating social services with green agriculture was associated with wellbeing gains modestly but significantly greater than the sum of the two policy streams implemented in isolation. The synergy premium was estimated at 6% over the additive prediction (approximately 1–11% based on the 95% CI of the interaction coefficient), a pattern consistent with partial transmission through livelihood resilience. Interventions in the underserved rural areas should focus on integrated packages consisting of improved access to healthcare services and subsidies in green agriculture, rather than the two policy streams in isolation.</p> 2026-09-24T04:30:24Z Dataset Dataset 10.3389/fsufs.2026.1863204.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Social_service_accessibility_and_Green_Agricultural_Development_Intensity_as_pathways_to_Rural_Elderly_Welfare_panel_evidence_from_China_s_underserved_regions_pdf/33979333 CC BY 4.0