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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/PBES.v9i9.15634</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on the Impact of the Digital Economy on the Efficiency of Agricultural Resource Allocation</title><url>https://artdesignp.com/journal/PBES/9/9/10.26689/PBES.v9i9.15634</url><author>DongQianyang</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>9</volume><issue>9</issue><history><date date-type="pub"><published-time>2026-09-30</published-time></date></history><abstract>Based on panel data from 31 provinces in China spanning 2011&amp;ndash;2023, this paper employs the DEA‑SBM model, the Malmquist index, and the Tobit model to measure agricultural resource allocation efficiency across provinces, and uses the entropy method to construct a comprehensive digital economy index to examine the impact of the digital economy on agricultural efficiency. On this basis, a mediating effect model is further constructed to identify the pathways through which the digital economy operates. The findings are threefold. First, China&amp;rsquo;s agricultural efficiency demonstrates an overall upward trend, with total factor productivity growing at an average annual rate of 3.5%, driven primarily by technological progress. Second, the comprehensive digital economy index exerts a significantly positive effect on agricultural efficiency at the 1% level, a conclusion that remains robust after multiple robustness checks. Heterogeneity analysis reveals that this effect is significantly stronger in non‑major grain‑producing areas (coefficient 1.171) than in major grain‑producing areas (coefficient 0.556), confirming the &amp;ldquo;late‑mover advantage&amp;rdquo; of the digital economy in regions with weaker foundational conditions. Third, the mediating effect test indicates that per capita telecommunications business volume plays a partial mediating role between the digital economy and agricultural efficiency, with the mediating effect accounting for approximately 10.9%, implying that the digital economy indirectly improves agricultural efficiency by enhancing telecommunications infrastructure. Accordingly, policy recommendations are proposed: strengthening rural telecommunications infrastructure, implementing differentiated strategies based on the distinction between major and non‑major grain‑producing areas, guiding consumption expenditure toward productive investment, and facilitating the transition of agricultural mechanization from quantitative expansion to qualitative upgrading.</abstract><keywords>Digital economy, Agricultural resource allocation efficiency, DEA model, Malmquist index, Tobit, Mediating effect</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Yao XB, Duan ZY, Che SF, et al., 2026, Spatiotemporal Evolution and Driving Factors of the Coupling Coordination Degree Between Digital Rural Construction and Agricultural Carbon Emissions.&amp;nbsp;Environmental Science, 1&amp;ndash;25.
[2] Liu ZY, Wang Z, 2026, The Impact of Digital Economy on Regional Carbon Emission Trade‑Out Rate.&amp;nbsp;China Business and Market, 1&amp;ndash;16.
[3] Liu M, 2025, Research on the Impact of Digital Inclusive Finance Development on Farmers&amp;rsquo; Production and Operation in Shandong Province, Master&amp;rsquo;s thesis, Shandong University of Technology.
[4] Li CD, Li NJ, 2025, Development of Applied Economics Disciplines at China Agricultural University: Review and Comparison.&amp;nbsp;Journal of China Agricultural University (Social Sciences Edition), 42(05): 32&amp;ndash;50.
[5] Li ML, Song DD, Zeng WT, et al., 2026, Digital New Quality Productive Forces and Agricultural and Rural Modernization Development&amp;mdash;Based on the Mediating Effects of Regional Industrial Division and Urban‑Rural Digital Divide.&amp;nbsp;Journal of Southwest University (Natural Science Edition), 48(03): 119&amp;ndash;131.
[6] Han G, Li C, 2023, Digital Economy, Spatial Effects, and Urban‑Rural Income Gap.&amp;nbsp;Journal of Southwest University of Science and Technology (Philosophy and Social Sciences Edition), 40(06): 25&amp;ndash;33.
[7] Gao SM, 2026, Digital Economy Empowers Rural Industrial Revitalization.&amp;nbsp;Chongqing Science and Technology News, March 27, 2026, 005.
[8] Zhen SS, 2025, Research on the Digital Economy Enhancing the Benefits of Leading Enterprises in Driving Farmers&amp;rsquo; Income from the Perspective of Transaction Costs, Master&amp;rsquo;s thesis, Sichuan Agricultural University.
[9] Yan M, Jingyi M, Yuhui L, 2022, Digital Villages Construction Accelerates High‑Quality Economic Development in Rural China Through Promoting Digital Entrepreneurship.&amp;nbsp;Sustainability, 14(21): 14224.
[10] Li Y, Cong HB, Zou DL, 2022, Research on the Impact of Digital Economy on the High‑Quality Development of Rural Industry&amp;mdash;Based on the Mediating Effect of Technological Innovation.&amp;nbsp;Science and Technology Management, 24(05): 23&amp;ndash;35 + 69.
[11] Wang YJ, Ji YY, 2023, Theoretical Analysis and Mechanism Testing of Rural Digital Economy Alleviating Multidimensional Relative Poverty.&amp;nbsp;Statistics Decision, 39(15): 57&amp;ndash;63.
[12] Sun YF, Du GF, Li JC, 2023, Analysis of Influencing Factors of Rural Digital Economy Development in Hunan Province.&amp;nbsp;Times Economy and Trade, 20(02): 132&amp;ndash;136.
[13] Chen LL, Cheng WX, 2023, Research on the Impact of Rural Digital Economy Development on Farmers&amp;rsquo; Income Increase.&amp;nbsp;Journal of Chengdu Normal University, 39(09): 105&amp;ndash;115.
[14] Zhang L, Xu ZM, Li CL, 2023, Impact of Rural Digital Economy Development on Farmers&amp;rsquo; Income Growth.&amp;nbsp;Journal of Jiangxi University of Finance and Economics, (03): 82&amp;ndash;94.
[15] Zhou H, Liu Y, 2026, Mechanisms, Spatial Spillover and Threshold Effects of Digital Economy Alleviating Urban‑Rural Factor Misallocation.&amp;nbsp;Journal of Chongqing University (Social Sciences Edition), 32(01): 95&amp;ndash;114.
[16] Li PZ, Jian LR, Liu Y, 2019, Research on Innovation Efficiency Evaluation of Regional High‑Tech Industries Based on DEA and Malmquist Index.&amp;nbsp;Industrial Technology Economy, 38(01): 27&amp;ndash;34.
[17] Dong K, Sun KK, 2026, Digital Economy, Technological Progress and Balanced Economic Development&amp;mdash;An Empirical Analysis Based on Nighttime Light Data.&amp;nbsp;Industrial Organization Review, 1&amp;ndash;23.
[18] Yang Q, Zhu HH, 2024, Spatial Differences and Driving Factors of Agricultural Resource Allocation Efficiency in China.&amp;nbsp;Statistics Decision, 2024.
[19] Shu XL, Min ZS, Guo XY, et al., 2024, Coupling Coordination and Driving Factors of Provincial Digital Economy and High‑Quality Development of Tourism.&amp;nbsp;Economic Geography, 44(01): 197&amp;ndash;208.
[20] Dong, Q, 2026, Research on the Supply Chain Spillover Effect of High‑Quality Innovation of &amp;ldquo;Chain‑Master&amp;rdquo; Enterprises.&amp;nbsp;Technical Economics and Management Research, (03): 108&amp;ndash;115.
[21] Wen ZL, Ye BJ, 2014, Mediation Effect Analysis: Methods and Model Development.&amp;nbsp;Advances in Psychological Science, 22(05): 731&amp;ndash;745.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
