Evaluation of Fiscal Support Policies for Village-Level Collective Economies in Frontier Regions

This study evaluates the efficiency of fiscal support policies for village-level collective economies in S Province, a frontier region of China, over the analytical period of 2018–2023, which includes the policy implementation years (2019–2022) plus one pre-policy and one post-policy year. Integrating theories of collaborative governance, resource alertness, and inclusive rural development, we construct an efficiency measurement framework to assess policy performance across 13 regions. Static efficiency is measured using DEA-BCC and super-efficiency SE-DEA models, while dynamic total factor productivity (TFP) is analyzed via the DEA–Malmquist index. The entropy-weighted method is employed to ensure robust indicator weighting. The findings reveal the following: (1) The average super-efficiency is 0.855, indicating relatively high expenditure efficiency but significant regional disparities and room for improvement. (2) The TFP declined by an average of 9.7% over the analytical period (2018–2023), primarily due to technological regression, despite stable technical efficiency. Based on the TFP performance, regions are categorized into high-, middle-, and low-efficiency tiers. Accordingly, we propose policy recommendations including efficiency-driven funding allocation, long-term support mechanisms combining technological innovation and management empowerment, regionally differentiated strategies, and strengthened multi-stakeholder collaboration. This study provides empirical evidence for optimizing fiscal policies to promote the sustainable development of rural collective economies and advance inclusive rural development in frontier regions.

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Publication Details

Journal
Sustainability
Published
2026-05-18
DOI
https://doi.org/10.3390/su18105095
Primary Topic
Efficiency Analysis Using DEA
Type
article
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Evaluation of Fiscal Support Policies for Village-Level Collective Economies in Frontier Regions

Sustainability
Efficiency Analysis Using DEA
article

Evaluation of Fiscal Support Policies for Village-Level Collective Economies in Frontier Regions

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Abstract

This study evaluates the efficiency of fiscal support policies for village-level collective economies in S Province, a frontier region of China, over the analytical period of 2018–2023, which includes the policy implementation years (2019–2022) plus one pre-policy and one post-policy year. Integrating theories of collaborative governance, resource alertness, and inclusive rural development, we construct an efficiency measurement framework to assess policy performance across 13 regions. Static efficiency is measured using DEA-BCC and super-efficiency SE-DEA models, while dynamic total factor productivity (TFP) is analyzed via the DEA–Malmquist index. The entropy-weighted method is employed to ensure robust indicator weighting. The findings reveal the following: (1) The average super-efficiency is 0.855, indicating relatively high expenditure efficiency but significant regional disparities and room for improvement. (2) The TFP declined by an average of 9.7% over the analytical period (2018–2023), primarily due to technological regression, despite stable technical efficiency. Based on the TFP performance, regions are categorized into high-, middle-, and low-efficiency tiers. Accordingly, we propose policy recommendations including efficiency-driven funding allocation, long-term support mechanisms combining technological innovation and management empowerment, regionally differentiated strategies, and strengthened multi-stakeholder collaboration. This study provides empirical evidence for optimizing fiscal policies to promote the sustainable development of rural collective economies and advance inclusive rural development in frontier regions.

SustainabilityVol. 18(10)
University of Chinese Academy of Sciences (CN), Xinjiang University (CN)
Openalex Percentile: Top 3%
Efficiency Analysis Using DEA
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Evaluation of Fiscal Support Policies for Village-Level Collective Economies in Frontier Regions · Sustainability (2026) | TGRS Research Map | TGRS