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  [首页](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex.htm \"首页\")\n*   [机构职能](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fzfxxgk\u002Fjgzn.htm \"机构职能\")\n*   [政府信息公开](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fzfxxgk.htm \"政府信息公开\")\n*   [工作动态](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fgzdt.htm \"工作动态\")\n*   [机关党建](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fjgdj.htm \"机关党建\")\n*   [数据开放](http:\u002F\u002Flydata.sd.gov.cn\u002F \"数据开放\")\n*   [数字临沂](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fszly.htm \"数字临沂\")\n*   [服务大厅](http:\u002F\u002Flyzwfw.sd.gov.cn\u002Flys\u002Fdepartment\u002FdepartmentHall\u002FdepartmentHallInfo?id=371300DSJJ \"服务大厅\")\n\n当前位置： [首页](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex.htm)>>[数字临沂](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fszly.htm)>>正文\n\n罗庄区黄山镇：坚持低成本轻量化 探索基层数字治理新路径\n2026-09-07 14:45 临沂市大数据局网站\n\n近年来，罗庄区黄山镇依托低成本物联改造、轻量化数字履职、全维度巡防感知，因地制宜探索基层 数字 治理模式，走出一条“投入少、见效快、易推广”的数字乡村建设新路径。相关经验做法获评山东省大数据局2025年度公共视频智能应用示范案例，并在山东省委社会工作部简报刊发。\n\n**聚焦“普惠可及”，以“低成本改造”消除数字鸿沟**\n\n一是完成应急广播数字化改造。升级镇村老旧广播设施，布设55组智能接收终端，搭建全域覆盖、轻量运行的广播体系，提升政策宣讲等工作效率，有效破解农村信息传递“最后一米”难题。二是推广普及物联网感知终端。将重点风险纳入物联网监管体系，为46家重点企业加装智能烟感器，为344户重点农户安装 一氧化碳中毒 智能报警器，为31辆村级巡逻车加装GPS定位设备，实现精准巡逻防控、隐患实时预警、险情快速处置。\n\n**聚焦“务实管用”，以“轻量化应用”促进基层减负**\n\n一是打造“极简”工作模式。布设55套巡更设备，在防溺水巡河、森林防火巡山等工作场景构建低门槛、易操作、轻量化的智慧巡防工作体系，大幅简化工作流程，各重点区域监管到位率达99%。二是实现“一图”统管全局。创新应用“兰图绘”小程序，将391处限额以下工地、汛期转移安置点等关键信息“落图”管理，实现全周期可视化追溯。通过数据共享，国土卫片图斑数量常年保持全区最低，汛期转移用时户均提速15分钟，实现让数据跑路、让干部省心。\n\n**聚**焦“价值挖掘”，以“全维度感知”激活数据潜能****\n\n一是搭建“电力大数据”监管模型。归集全镇 816家加工经营单位用电数据，常态化分析生产动态，实现生产状况实时感知、违规作业智能预警，构建起“数据即线索、电力即探头”的无感监管新模式。二是构建“空天地一体”感知网络。推动智能化巡防力量全域下沉，搭建科技化巡查体系，将高空瞭望、智能监控、无人机航拍、雪亮工程 等数字化技术运用到秸秆禁烧、耕地保护、禁种铲毒、治安防控等重点领域，依靠科技手段精准识别隐患、快速排查问题、闭环高效处置。通过“电眼+天眼”互补，实现了从“人力蹲守”到“智能预警”的转变，大幅提升了风险防控的精准度。\n\n下一步，罗庄区黄山镇将继续深化基层 数字 治理，做强数据底座、拓展应用场景、输出治理经验，加快提升乡村治理精细化、智慧化、现代化水平。\n\n【[关闭窗口](javascript:window.opener=null;window.open('','_self');window.close();)】\n\n临沂市大数据局主办 临沂市网站集约化平台技术支持 网站标识码：3713000077\n\n联系电话：0539-8770501 0539-8770503\n\n[鲁ICP备2025159968号](http:\u002F\u002Fbeian.miit.gov.cn\u002F)![Image 1](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Fimages\u002Fbeian.png)[鲁公网安备 37130202372003号](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=37130202372003)[网站地图](https:\u002F\u002Fdsjj.linyi.gov.cn\u002Findex\u002Fwzdt.htm)","临沂市大数据局","2026-09-07T00:00:00Z",64,{"impact":16,"substance":17,"depth":167,"authority":168,"freshness":20,"relevant":21,"comment":169},15,9,"山东省级示范案例，低成本轻量化数字治理路径具体可复制，数据与场景细节扎实，对数字乡村建设有参考价值。",[171],{"name":163,"url":160},[26,173,28,174,175],"物联网","电力大数据","智能巡防",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":178},{"candidates":179,"agricultural_evidence":52,"reason":53},[180,184,189],{"code":38,"score":21,"supported":13,"title":181,"abstract":182,"tags":183},[],[],[28],{"code":185,"score":21,"supported":13,"title":186,"abstract":187,"tags":188},"I5",[],[],[173],{"code":43,"score":21,"supported":13,"title":190,"abstract":191,"tags":192},[],[],[26],[194,195],"电力大数据 基层治理 数字乡村 智能巡防","电力大数据 基层治理","电力大数据基层治理数字乡村智能巡防-2081","2026-09-11T00:04:18.642881Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":8,"content":203,"source_name":204,"source_url":8,"published_at":205,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":206,"score_detail":207,"sources":212,"tags":214,"primary_topic":8,"secondary_topics":218,"classification":219,"search_phrases":235,"slug":238,"view_count":107,"doi":8,"paper":8,"created_at":239},743,"中国农业新质生产力与绿色发展绩效的耦合协调度研究","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751","基于30省2013-2023面板,采用全球非径向VRS-SBM-GML模型测度农业绿色全要素生产率(AGTFP);耦合协调度从2013年0.3631升至2023年0.6072,新型劳动对象\u002F劳动资料维度率先进入初级协调,新型劳动力维度仍处勉强协调(0.5992)。","## 1. Introduction\n\nDeveloping agricultural new quality productive forces is an important pathway for shifting the drivers of agricultural development from conventional factor inputs toward scientific and technological innovation and optimized factor allocation, thereby improving the quality of agricultural development. Compared with agriculturally advanced countries, China still has considerable scope to improve agricultural labor productivity, the allocation of scientific and technological resources, and the commercialization of agricultural research outputs. Jiang [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B1-agriculture-16-01751)] estimated that, in 2020, agricultural labor productivity in the United States, Canada, and Israel was approximately 17.3, 19.8, and 25 times that of China, respectively. Meanwhile, increasingly stringent constraints on cultivated land, water resources, and the ecological environment have reduced the marginal returns of the conventional agricultural growth model, which relies primarily on expanding land, labor, machinery, and chemical inputs. A shift in development momentum through technological innovation, the reorganization of production factors, and changes in production modes is therefore urgently needed. The application of emerging technologies, including biological breeding, intelligent agricultural machinery, and artificial intelligence, has created new conditions for restructuring agricultural production functions and upgrading the form of agricultural productive forces, while drawing greater attention to the coordination between the upgrading of agricultural productive forces and resource–environmental performance.\n\nAgricultural new quality productive forces refer to an advanced form of agricultural productive forces led by agricultural scientific and technological innovation, grounded in improvements in worker quality, the modernization of the means of labor, and the optimization of the objects of labor, and formed through the reorganization of production factors and changes in production modes. They are characterized by high technology intensity, high efficiency, high quality, and greenness [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B2-agriculture-16-01751)]. Their “new quality” lies primarily in changes in factor quality, technological forms, and allocation patterns, rather than in a rejection of the basic components of conventional agricultural productive forces. Xi [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B3-agriculture-16-01751)] noted that green development is the defining feature of high-quality development and that new quality productive forces are inherently green productive forces, indicating an intrinsic consistency in the value orientation of the upgrading of productive forces and green development. Agricultural green development, in turn, emphasizes improving resource-use efficiency and reducing environmental burdens while maintaining agricultural output. The two concepts reflect the transformation of agricultural production modes from the perspectives of factor upgrading and resource–environmental performance improvement, respectively; however, their development levels and rates of evolution are not necessarily synchronized.\n\nIn recent years, China has made overall progress in agricultural green development. The composite agricultural green development index reported in the China Agricultural Green Development Report 2024 [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B4-agriculture-16-01751)] indicates that the national level of agricultural green development in 2023 was higher than both the previous year and 2015. Nevertheless, China’s agricultural green transition still faces practical constraints, including relatively low input-use efficiency. Data released by the Department of Science, Technology and Education of the Ministry of Agriculture and Rural Affairs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B5-agriculture-16-01751)] show that, in 2020, the fertilizer- and pesticide-use efficiencies of China’s three major grain crops were 40.2% and 40.6%, respectively. These indicators represent the shares of applied fertilizer nutrients absorbed by crops in the current season and of pesticides effectively used against target pests. Thus, although agricultural output has continued to grow, substantial room remains to improve resource-use efficiency and control environmental burdens. Whether the upgrading of agricultural productive forces can remain synchronized with improvements in resource–environmental performance has therefore become an important issue in transforming the agricultural development mode.\n\nExisting studies have examined the relationship between ANQPF and agricultural green development, but the coordination differences among the constituent dimensions of productive forces and the dynamic evolution of coordination states have not yet been systematically investigated. Because agricultural green development encompasses economic growth, resource use, ecological protection, social welfare, and governance institutions, this study does not construct a composite measure of agricultural green development in the broad sense. Instead, it focuses on the resource–environmental performance dimension and uses agricultural green total factor productivity (AGTFP) under resource and environmental constraints to represent AGDP. Against this background, the objective of this study is to characterize the temporal evolution, dimension-specific coordination, and state-transition patterns between ANQPF and AGDP across Chinese provinces. Specifically, using data for 30 Chinese provincial-level regions from 2013 to 2023, this study addresses three questions: (1) What annual evolutionary patterns characterize ANQPF and AGDP? (2) How does the coordination between AGDP and ANQPF as a whole, as well as its three constituent dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ? (3) How persistent are different coordination states, and in which directions do they transition?\n\n## 2. Literature Review\n\nResearch on agricultural green development has focused mainly on its conceptual connotation, influencing factors, and measurement methods. Existing studies generally define agricultural green development as the coordinated pursuit of agricultural economic growth, resource conservation, and ecological and environmental protection while ensuring an effective supply of agricultural products [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B6-agriculture-16-01751)]. Related research has further examined the determinants of agricultural green development from the perspectives of scientific and technological innovation, digital transformation, industrial restructuring, land-factor allocation, and agricultural socialized services, suggesting that regional resource endowments, technological foundations, and institutional conditions shape the effectiveness of the green transition [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B7-agriculture-16-01751)]. In terms of measurement, two principal approaches are used: composite indicator evaluation and green total factor productivity. The former captures the multidimensional objectives of agricultural green development, whereas the latter incorporates resource inputs, desirable outputs, and undesirable outputs into a unified production frontier and focuses on input–output performance under resource and environmental constraints [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B8-agriculture-16-01751),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B9-agriculture-16-01751),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B10-agriculture-16-01751)]. Because this study examines the coordination between the upgrading of productive forces and resource–environmental performance, AGTFP is used to represent AGDP, but it is not treated as equivalent to agricultural green development in the broad sense.\n\nAgricultural new quality productive forces are rooted in the Marxist theory of productive forces and represent a contextual development of this theory amid a new round of technological revolution and agricultural modernization. Workers, means of labor, and objects of labor remain the basic components of agricultural productive forces. ANQPF do not alter this fundamental logic; rather, they reflect the upgrading of factor quality, technological forms, and factor combinations under the leadership of scientific and technological innovation [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B11-agriculture-16-01751)]. Compared with conventional agricultural productive forces, which depend largely on quantitative expansion of land, labor, machinery, and chemical inputs, ANQPF place greater emphasis on technological innovation, the application of digital technologies, and optimized factor combinations [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B12-agriculture-16-01751)]. Existing studies have mainly addressed conceptual definition, functional effects, and level measurement. They generally characterize ANQPF as an advanced form of productive forces led by scientific and technological innovation and formed through qualitative upgrading and optimized combinations of production factors, and they construct evaluation systems either around the three elements of productive forces or around functional dimensions such as technology, digitalization, and greenness [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B15-agriculture-16-01751),[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751)]. Common measurement methods include the entropy-weight method, entropy-weight TOPSIS, and Markov chains. These studies generally find that ANQPFs in China have increased overall, although substantial regional disparities remain [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B17-agriculture-16-01751)].\n\nResearch on the relationship between ANQPF and green performance has followed two main analytical approaches: unidirectional effects and system coordination. Econometric studies primarily examine the effects of ANQPF on agricultural green production efficiency, carbon-emission efficiency, and sustainable-development performance, treating technological innovation, digital transformation, industrial upgrading, and factor optimization as potential channels [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B19-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)]. Other studies adopt a system-coordination perspective to investigate the relationships between new quality productive forces and agricultural green development, between digitalization and greening, and between ANQPF and green grain-production capacity. They generally find that coordination has improved, although regional disparities remain pronounced [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B21-agriculture-16-01751),[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B22-agriculture-16-01751),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B23-agriculture-16-01751)]. Consistent with these findings, research on sustainable intensification, precision agriculture, digital agriculture, and agricultural innovation systems indicates that technological upgrading can improve resource use and environmental performance, but the extent to which these benefits materialize depends on infrastructure, human capital, service systems, and the institutional environment [[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B24-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B27-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751),[29](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B29-agriculture-16-01751)]. The upgrading of agricultural productive forces and improvements in green performance are therefore not necessarily synchronized.\n\nOverall, the existing literature has three limitations. First, most studies focus on the unidirectional effect of ANQPF on green performance and pay insufficient attention to the synchronization and structural alignment of the two systems. Second, some studies include greening indicators in both ANQPF and agricultural green development assessments, leaving the measurement boundaries between the two systems insufficiently defined. Third, existing coordination studies primarily examine overall levels and spatial disparities, while giving limited attention to coordination differences among the three dimensions—new-quality means of labor, new-quality labor force, and new-quality objects of labor—or to the persistence, upgrading, and decline of coordination states. To address these gaps, this study uses AGTFP to represent AGDP, examines the coupling coordination between AGDP and both ANQPF as a whole and its three constituent dimensions, and applies a Markov chain to analyze the dynamic evolution of coordination states.\n\nThe marginal contributions of this study are threefold. First, AGTFP is used to represent AGDP, distinguishing this approach from composite assessments that incorporate economic, social, and ecological dimensions and thereby clarifying the measurement boundaries between ANQPF and AGDP. Second, the analysis extends from overall coordination to the three dimensions of new-quality means of labor, new-quality labor force, and new-quality objects of labor, allowing differences in the alignment between distinct elements of productive forces and AGDP to be compared. Third, in addition to examining coordination levels and spatial distributions, a Markov chain is introduced to identify the persistence, upgrading, and decline of coordination states. The coupling coordination degree reflects the synchronization, alignment, and coordination of the two systems; it is not used to identify the direction of influence or a causal relationship between them.\n\n## 3. Theoretical Analysis\n\nThis study draws on the theory of productive forces, system-coupling theory, and evolutionary and path-dependence theory to examine the relationship between ANQPF and AGDP at three levels: overall coordination, dimension-specific coordination, and state transitions. The purpose is to evaluate the synchronization and structural alignment of the two systems rather than to identify causal relationships among variables. Accordingly, the following theoretical propositions concern coordination states and their evolution and do not presuppose a causal direction.\n\n### 3.1. Coupling Coordination Between Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nSystem-coupling theory holds that the coordination state between systems depends on the relative alignment of their development levels, evolutionary rates, and internal structures. ANQPF and AGDP are jointly embedded in agricultural production and operation. New-quality means of labor, new-quality labor force, and new-quality objects of labor reflect qualitative upgrading in production conditions, production actors, and production objects, whereas AGDP captures changes in aggregate output under resource and environmental constraints. Changes in technological equipment, digital infrastructure, and human capital occur within the same production system as changes in agriculture-related environmental outputs. The development states of the two systems may therefore correspond, but they are not necessarily synchronized. Differences in technological suitability, resource endowments, and development stages may generate divergence in their levels and rates of change: some regions may accumulate new-quality factors rapidly while AGDP lags, whereas others may exhibit the opposite pattern [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B13-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[30](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B30-agriculture-16-01751)]. The coupling coordination degree measured in this study is intended to reflect the synchronization and alignment of the two systems rather than to identify causal effects. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 1****(H1).**\n\nANQPF and AGDP exhibit a general trend of joint improvement, but their growth trajectories are not fully synchronized; consequently, the overall level of coupling coordination increases over time.\n\n### 3.2. Differentiated Coordination Between the Dimensions of Agricultural New Quality Productive Forces and Agricultural Green Development Performance\n\nThe theory of productive forces regards workers, means of labor, and objects of labor as the basic components of productive forces. ANQPF thus represent the qualitative upgrading and recombination of these three components driven by scientific and technological innovation [[31](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B31-agriculture-16-01751)]. Because the foundations, evolutionary cycles, and regional dependence of the three dimensions differ, each may form a distinct coordination relationship with AGDP. New-quality means of labor reflect upgrades in technological equipment and digital infrastructure. Although technological renewal is relatively rapid, its diffusion is uneven, and its rate of change may be faster or slower than that of AGDP, producing different alignment states [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B14-agriculture-16-01751),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B18-agriculture-16-01751),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B25-agriculture-16-01751)]. The new-quality labor force reflects the accumulation of human capital and skills. Its formation and allocation require longer periods, while the efficiency with which it is transformed into productive outcomes depends on training and extension systems [[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B26-agriculture-16-01751),[28](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B28-agriculture-16-01751)]; its coordination with AGDP may therefore differ from that of the means-of-labor dimension. New-quality objects of labor reflect changes in how production objects are developed and utilized. They are strongly dependent on resources and location [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B16-agriculture-16-01751),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B20-agriculture-16-01751)], and their optimization and value realization exhibit marked path dependence, generating coordination characteristics distinct from those of the other two dimensions. Accordingly, the following hypothesis is proposed:\n\n**Hypothesis 2****(H2).**\n\nThe coordination levels and evolutionary pathways between AGDP and the three dimensions of ANQPF—new-quality means of labor, new-quality labor force, and new-quality objects of labor—differ.\n\n### 3.3. Dynamic Evolution of Coupling Coordination States\n\nEvolutionary and path-dependence theory indicates that technologies, knowledge, and institutional rules are cumulative. System transformation evolves incrementally along established trajectories but may stagnate or regress in response to external shocks [[32](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1751#B32-agriculture-16-01751)]. Coordination states are built on existing technological and institutional arrangements. Technology diffusion and human-capital development are generally long-term processes, making cross-stage transitions difficult to achieve in the short run. Interdependence between technologies and institutions creates path inertia, and transformation usually occurs through incremental reconfiguration [[27](https","MDPI Agriculture 16(16):1751 2026-08-15","2026-08-15T00:00:00Z",76,{"impact":208,"substance":118,"depth":17,"authority":209,"freshness":210,"relevant":21,"comment":211},20,12,4,"研究中国农业新质生产力与绿色发展绩效的耦合协调度，具有全国性政策意义，数据详实，但时效性稍差。",[213],{"name":204,"url":201},[26,27,215,216,217],"AGTFP","耦合协调度","30省",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":220},{"candidates":221,"agricultural_evidence":52,"reason":53},[222,227,231],{"code":223,"score":21,"supported":13,"title":224,"abstract":225,"tags":226},"I4",[],[],[216],{"code":43,"score":21,"supported":13,"title":228,"abstract":229,"tags":230},[],[],[26],{"code":48,"score":21,"supported":13,"title":232,"abstract":233,"tags":234},[],[],[27],[236,237],"新质生产力 耦合协调度 数字乡村 30省","新质生产力 耦合协调度","新质生产力耦合协调度数字乡村30省-743","2026-08-19T23:57:32.301681Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":8,"content":245,"source_name":246,"source_url":8,"published_at":247,"category":80,"cover_url":8,"hotness":12,"is_selected":13,"score":248,"score_detail":249,"sources":253,"tags":255,"primary_topic":8,"secondary_topics":258,"classification":259,"search_phrases":275,"slug":278,"view_count":251,"doi":8,"paper":8,"created_at":279},188,"解读丨人工智能与广袤乡村如何双向奔赴","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_1656a5f388296952","农民日报评论员文章指出:通过制度创新引导人才、资本、数据等要素向农业农村有序流动,构建多主体协同参与的良好生态,让人工智能落地生根;要做实应用场景,聚焦生产、经营、管理、服务全链条关键环节,推动技术应用从点状突破向面上铺开转变;要推进软硬并举,与农业基础设施改造、装备水平提升、流通体系优化等系统性工程统筹规划、一体推进,以软件赋能流程再造,以硬件升级支撑效能释放,形成软硬协同、迭代跃升的良性格局。","云图之上，“妈祖”预警横跨山海，风雨信息分秒必达；育种田间，“吉儿”精准授粉，数据回传种质库；手机屏里，“神农”随问随答，老经验秒变金处方……人工智能正以超乎想象的速度融入农业农村，让这片古老的土地焕发出前所未有的生机。\n\n近日，在2026世界人工智能大会暨人工智能全球治理高级别会议开幕式上，国家主席习近平提出：“要抓住难得的历史性机遇，鼓励开源开放、合作共享，全面促进人工智能科技创新、产业发展、场景应用，协同推进传统产业改造升级、新兴产业培育壮大、未来产业前瞻布局，让人工智能赋能千行百业。”\n\n当前，百年变局加速演进，人工智能技术创新进入前所未有的活跃期。放眼全球，科技实力已成为衡量国家综合国力的核心要素，人工智能作为引领新一轮科技革命与产业变革的战略性技术，具有极强的“头雁”效应，谁能在这一领域抢占先机，谁就能在国际舞台上掌握更多话语权。聚焦国内，人工智能更是推动高质量发展、实现中国式现代化的关键引擎。从“中国制造”向“中国智造”的跃升，到医疗、养老等民生领域的持续创新，人工智能正深度融入经济社会各领域。而“三农”作为国家发展的“压舱石”，既是智能化转型的重要战场，也是检验技术温度与治理水平的试金石。\n\n事实上，人工智能在为农业农村注入现代化“新动能”的同时，也在被“三农”的独特性反向塑造着技术发展路径。一方面，小农户与分散化经营决定了算法必须适应“碎片化”场景，这对数据采集精度与模型泛化能力提出更高要求；另一方面，乡村数字基建薄弱、留守群体老龄化等现实约束，也要求人工智能要学会听懂乡音、看懂乡土，倒逼智能终端向简易化与低成本方向迭代。","农民日报 2026-08-01","2026-08-01T00:00:00Z",77,{"impact":250,"substance":17,"depth":167,"authority":209,"freshness":251,"relevant":21,"comment":252},25,7,"央媒报道，结合国家领导人讲话，探讨AI与三农双向奔赴，具有全国性影响，内容有实质分析，但时效性稍差。",[254],{"name":246,"url":243},[26,27,256,257],"人工智能+农业","评论",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":260},{"candidates":261,"agricultural_evidence":52,"reason":53},[262,267,271],{"code":263,"score":21,"supported":13,"title":264,"abstract":265,"tags":266},"I2",[],[],[256],{"code":43,"score":21,"supported":13,"title":268,"abstract":269,"tags":270},[],[],[26],{"code":48,"score":21,"supported":13,"title":272,"abstract":273,"tags":274},[],[],[27],[276,277],"人工智能+农业 新质生产力 数字乡村","人工智能+农业 新质生产力","人工智能+农业新质生产力数字乡村-188","2026-08-04T01:15:42.970577Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":8,"source_name":286,"source_url":283,"published_at":287,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":116,"score_detail":288,"sources":290,"tags":292,"primary_topic":297,"secondary_topics":302,"classification":303,"search_phrases":331,"slug":334,"view_count":61,"doi":335,"paper":336,"created_at":353},5581,"How Does Digital Literacy Promote Inclusive Rural Growth? Micro-Level Evidence from Income Inequality Among Farm Households","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010689","Against the backdrop of the coordinated advancement of digital village construction and the common prosperity strategy, the widespread improvement in rural households’ digital literacy is emerging as a key force in reshaping rural income distribution and enabling sustainable rural development. Whether digital dividends can equitably benefit farm households with heterogeneous endowments has a direct bearing on the inclusive growth outcomes of digital village construction and the sustainable advancement of the common prosperity goal. Using three waves of panel data from the China Family Panel Studies (CFPS) covering 2018 to 2022, this paper empirically examines the impact of digital literacy on income inequality among rural households. The findings indicate that improvements in digital literacy significantly reduce rural household income inequality, and this conclusion remains robust across a series of robustness checks. Mechanism analysis reveals that digital literacy mainly curbs the widening of rural household income gaps through two core channels: expanding social capital and alleviating credit constraints. Heterogeneity analysis shows that the inequality-reducing effect of digital literacy is more pronounced among households that depend on non-agricultural income, have lower levels of human capital, or fall into relatively lower income brackets. Further analysis demonstrates that digital literacy significantly reduces inequalities in wage income, business income, and property income across rural households, whereas its mitigating effect on transfer income inequality is not statistically significant. Meanwhile, digital literacy significantly raises the probability of upward income mobility among rural households, thereby alleviating income inequality from a dynamic perspective. On the basis of these findings, this paper proposes policy implications in several areas: building a hierarchical and categorized rural digital literacy cultivation system, fostering a virtuous cycle of digital empowerment, equal opportunity, and income convergence, and optimizing income distribution structures and dynamic mobility mechanisms, with the aim of accelerating progress toward common prosperity.","在数字乡村建设与共同富裕战略协同推进的背景下，农户数字素养的普遍提升正成为重塑乡村收入分配格局、实现乡村可持续发展的重要力量。数字红利能否公平惠及禀赋异质性农户，直接关系到数字乡村建设的包容性增长成效与共同富裕目标的可持续推进。本文利用中国家庭追踪调查（CFPS）2018—2022年三期面板数据，实证检验数字素养对农户收入不平等的影响。研究发现，数字素养的提升显著降低了农户收入不平等，这一结论在一系列稳健性检验后依然成立。机制分析表明，数字素养主要通过拓展社会资本与缓解信贷约束两条核心渠道抑制农户收入差距的扩大。异质性分析显示，数字素养的减不平等效应在依赖非农收入、人力资本水平较低或处于相对较低收入区间的农户中更为明显。进一步分析发现，数字素养显著缩小了农户工资性收入、经营性收入和财产性收入的不平等，而对转移性收入不平等的缓解作用在统计上不显著。同时，数字素养显著提高了农户收入向上流动的概率，从而从动态视角缓解了收入不平等。基于上述发现，本文从构建分层分类的农户数字素养培育体系、形成数字赋能—机会均等—收入收敛的良性循环、优化收入分配结构与动态流动机制等方面提出政策启示，以期为加快共同富裕进程提供参考。","Sustainability","2026-10-21T00:00:00Z",{"impact":17,"substance":118,"depth":17,"authority":19,"freshness":168,"relevant":21,"comment":289},"基于CFPS三期面板数据的微观实证，方法规范、结论有新意，对数字乡村与共同富裕政策有参考价值，值得进入每日精选。",[291],{"name":286,"url":283},[26,293,294,295,296],"数字素养","共同富裕","农民收入","收入不平等",{"code":43,"slug":298,"name":299,"path":300,"parent_code":301},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":304,"locked":13,"version":33,"method":34,"evidence":305},"classified",{"candidates":306,"agricultural_evidence":52},[307,313,320,326],{"code":43,"score":308,"supported":52,"title":309,"abstract":310,"tags":312},8,[],[26,311],"digital village",[26,293],{"code":314,"score":20,"supported":52,"title":315,"abstract":316,"tags":319},"G6",[],[317,318],"农户收入","rural income",[],{"code":321,"score":58,"supported":52,"title":322,"abstract":323,"tags":325},"G",[],[324],"rural development",[],{"code":327,"score":21,"supported":13,"title":328,"abstract":329,"tags":330},"G4",[],[],[294],[332,333],"CFPS 数字素养 农户收入","数字素养 农村收入差距","CFPS数字素养农户收入-5581","10.3390\u002Fsu182010689",{"doi":335,"openalex_id":337,"authors":338,"venue":286,"cited_by_count":61,"oa_url":283,"card":347,"direction":66,"ingested_from":352},"W7223105459",[339,342,345],{"name":340,"orcid":341},"Yong Feng","https:\u002F\u002Forcid.org\u002F0000-0002-8151-8594",{"name":343,"orcid":344},"Shuokai  kai Wang","https:\u002F\u002Forcid.org\u002F0009-0002-7439-1764",{"name":346,"orcid":8},"Fangping Cao",{"tldr":348,"method":349,"finding":350,"direction":66,"opportunity":351},"基于CFPS三期面板数据，实证检验数字素养对农户收入不平等的影响。","使用CFPS 2018-2022三期面板数据，进行机制与异质性分析。","数字素养通过扩展社会资本和缓解信贷约束显著缩小农户收入差距。","可延伸研究数字素养对不同收入来源不平等的差异化机制及动态收入流动路径。","openalex","2026-10-22T11:20:47.675827Z"]