[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-942":3,"related-942":71},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":65,"slug":68,"view_count":20,"doi":69,"paper":8,"created_at":70},942,"数字乡村试点政策对土地利用效率与城乡差距的长期影响 基于中国县级面板的因果识别","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091531","原文标题：Smart Agriculture Construction and the Urban-Rural Gap in Land Use Efficiency: Evidence from Digital Agriculture in China。该研究系同篇Land 15(9)1531的数字乡村政策评估版本，扩展论证数字农业试点对城乡土地利用效率差距缩小的因果效应；识别策略包括双重机器学习、置换检验与子样本稳健性检验。研究为评估十五五数字乡村战略提供量化基准。",null,"MDPI Land 2026-08-22","2026-08-22T00:00:00Z","论文",10,false,80,{"impact":16,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},22,20,12,4,1,"基于县级面板的因果识别研究，为数字乡村政策评估提供量化基准，方法新颖，数据详实，但时效性稍差。",[23],{"name":9,"url":6},[25,26,27,28,29],"双重机器学习","数字乡村试点","城乡土地利用效率差距","MDPI Land","因果识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,49,58],{"code":31,"score":18,"supported":44,"title":45,"abstract":47,"tags":48},true,[46],"数字乡村",[46],[26],{"code":50,"score":51,"supported":44,"title":52,"abstract":53,"tags":57},"I1",9,[],[54,55,56],"数字农业","smart agriculture","digital agriculture",[],{"code":59,"score":60,"supported":44,"title":61,"abstract":62,"tags":64},"I2",3,[],[63],"机器学习",[],[66,67],"城乡土地利用效率差距 双重机器学习 数字乡村试点 因果识别","城乡土地利用效率差距 双重机器学习","城乡土地利用效率差距双重机器学习数字乡村试点因果识别-942","10.3390\u002Fland15091531","2026-08-25T00:03:29.567369Z",{"total":72,"page":20,"page_size":72,"items":73},6,[74,148,181,231,297,337],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":79,"content":8,"source_name":80,"source_url":77,"published_at":81,"category":11,"cover_url":8,"hotness":12,"is_selected":44,"score":82,"score_detail":83,"sources":89,"tags":91,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":124,"slug":127,"view_count":60,"doi":128,"paper":129,"created_at":147},3336,"Prosper or beggar thy neighbor? non-farm employment effects and spatial spillovers of China's National Digital Village Pilot Program","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1950504","Introduction Counties are a key spatial unit linking cities and villages in China's rural transformation, yet many still lack the industrial base, market scale, and non-farm job opportunities needed to absorb rural labor. This study examines whether China's National Digital Village Pilot Program expands rural non-farm employment and whether its effects extend beyond pilot counties. Methods Using a panel of 1,450 Chinese counties and 24,111 county-year observations from 2007 to 2024, this study exploits the staggered implementation of the 2020 and 2024 pilot cohorts as a quasi-natural experiment. The policy effect is estimated using a multi-period difference-in-differences model and cross-validated with stacked DID and the Callaway-Sant'Anna estimator. Robustness is further assessed through propensity score matching, inverse probability weighting, entropy balancing, placebo tests, and alternative samples and specifications. Transmission channels, heterogeneity, and spatial effects are also examined. Results The National Digital Village Pilot Program increased rural non-farm employment in pilot counties by approximately 25% relative to non-pilot counties, and the result remains robust across alternative identification and estimation strategies. The two-step transmission-channel analysis provides evidence consistent with two potential channels: enhanced county-level innovation and improved mobile network access capability. The policy effect is significantly stronger in central-region counties than in western counties and stronger in smaller counties than in larger ones, with weaker evidence of a larger effect in counties with lower human capital. Spatial analysis shows a positive direct effect on pilot counties but a negative spillover effect on neighboring counties. Discussion The findings indicate that digital village development can strengthen local non-farm employment absorption, but part of the local gain may reflect a spatial reallocation of economic activity rather than net regional job creation. Effective implementation therefore requires not only county-level digital development but also cross-county resource coordination and regional policy integration.","引言 县域是中国城乡转型中连接城市与乡村的关键空间单元，但许多县域仍缺乏吸纳农村劳动力所需的产业基础、市场规模和非农就业机会。本研究考察中国国家数字乡村试点项目是否扩大了农村非农就业，以及其效应是否超出试点县范围。方法 本研究使用2007年至2024年间1，450个中国县域、24，111个县—年观测值的面板数据，将2020年和2024年试点批次的分批实施作为准自然实验。政策效应采用多期双重差分模型估计，并通过堆叠双重差分和Callaway-Sant'Anna估计量进行交叉验证。稳健性进一步通过倾向得分匹配、逆概率加权、熵平衡、安慰剂检验以及替代样本和设定加以评估。研究还考察了传导渠道、异质性和空间效应。结果 国家数字乡村试点项目使试点县的农村非农就业相对于非试点县增加了约25%，且该结果在替代性识别和估计策略下保持稳健。两步传导渠道分析提供的证据与两个潜在渠道相一致：县域创新能力增强和移动网络接入能力改善。政策效应在中部地区县域显著强于西部地区县域，在较小县域强于较大县域，而在人力资本较低县域效应更大的证据较弱。空间分析显示，政策对试点县具有正向直接效应，但对邻近县域产生负向溢出效应。讨论 研究结果表明，数字乡村发展能够增强本地非农就业吸纳能力，但部分本地收益可能反映的是经济活动的空间再配置，而非区域净就业创造。因此，有效实施不仅需要县域层面的数字发展，还需要跨县域资源协调和区域政策整合。","Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z",89,{"impact":84,"substance":85,"depth":86,"authority":87,"freshness":51,"relevant":20,"comment":88},24,23,19,14,"基于1450个县2007-2024年面板数据的准自然实验研究，证实数字乡村试点使县域非农就业提升约25%，但存在对邻县的负向溢出，对政策协调具有实质参考价值。",[90],{"name":80,"url":77},[46,92,93,26,94],"空间溢出","县域经济","非农就业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":98},{"candidates":99,"agricultural_evidence":44},[100,106,114,119],{"code":31,"score":101,"supported":44,"title":102,"abstract":104,"tags":105},16,[103],"digital village",[46,103],[46,26],{"code":107,"score":108,"supported":44,"title":109,"abstract":110,"tags":113},"I4",7,[],[111,112],"空间分析","spatial analysis",[92],{"code":115,"score":20,"supported":13,"title":116,"abstract":117,"tags":118},"G4",[],[],[93],{"code":120,"score":20,"supported":13,"title":121,"abstract":122,"tags":123},"G6",[],[],[94],[125,126],"国家数字乡村试点 非农就业","县域 数字乡村 空间溢出","国家数字乡村试点非农就业-3336","10.3389\u002Ffsufs.2026.1950504",{"doi":128,"openalex_id":130,"authors":131,"venue":80,"cited_by_count":139,"oa_url":77,"card":140,"direction":144,"ingested_from":146},"W7214093544",[132,135,137],{"name":133,"orcid":134},"Wei Yang","https:\u002F\u002Forcid.org\u002F0000-0002-0540-5934",{"name":136,"orcid":8},"Lijun Wang",{"name":138,"orcid":8},"Yizhuo Ma",0,{"tldr":141,"method":142,"finding":143,"direction":144,"opportunity":145},"评估国家数字乡村试点对县域非农就业的影响及空间溢出效应。","2007-2024年1450个县面板数据，多期DID与Callaway-Sant","试点县非农就业增约25%，但邻县出现负向溢出，部分为就业空间再分配。","数字乡村与农业信息化","可探究数字乡村试点的跨县协同机制与负溢出治理，及不同区域差异化政策设计。","openalex","2026-09-24T23:30:07.434384Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":8,"content":153,"source_name":154,"source_url":8,"published_at":155,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":156,"score_detail":157,"sources":161,"tags":163,"primary_topic":167,"secondary_topics":168,"classification":169,"search_phrases":176,"slug":179,"view_count":60,"doi":8,"paper":8,"created_at":180},940,"数字乡村建设对土地约束下农业技术选择的影响 基于中国28省面板数据的证据","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400","原文标题：Digital Rural Construction and Agricultural Technological Choice Under Land Constraints: Evidence from China。基于2011-2024年中国28省面板数据，首先基于归一化要素增广CES生产函数测度资本-土地与劳动-土地偏向型技术进步，进而构建多维度数字乡村建设指数。实证结果显示数字乡村建设显著促进资本-土地与劳动-土地偏向型技术进步，对资本-土地维度效应更强，表明数字乡村建设主要通过资本体现与数字赋能技术推动农业技术选择。机制分析显示资本-土地效应主要由价格效应驱动，劳动-土地效应主要由规模效应驱动；异质性分析显示正向效应在东部地区更为显著。本研究为促进农业转型、改善土地利用可持续性与加强农业数字治理提供证据。","## 1. Introduction\n\nCultivated land is the basic carrier of agricultural production, but it is also one of the most rigidly constrained production factors. With rapid urbanization, ecological protection requirements, and the need to ensure food security, agricultural development increasingly faces the challenge of maintaining production capacity under limited land resources [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B1-land-15-01400),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B2-land-15-01400)]. For countries with large populations and scarce per capita farmland, this issue is particularly important. In this context, agricultural transformation cannot rely mainly on the continuous expansion of land input. A more realistic path is to improve how capital, labor, technology, and services support production on existing farmland [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B3-land-15-01400)]. With the rapid development of rural digitalization, digital technologies are becoming an important means of reorganizing agricultural production factors and improving the use of existing farmland. Therefore, understanding agricultural technological choice under land constraints requires attention not only to land scarcity itself, but also to how digital transformation reshapes the relationship among capital, labor, and land.\n\nDigital rural construction provides a new perspective for understanding this issue. It can be understood as the integration of digital infrastructure, smart agricultural technologies, digital finance, digital platforms, and data-based services into rural production and rural development. These technologies include broadband networks, smartphones, digital payment systems, e-commerce platforms, agricultural machinery service platforms, drones, sensors, GPS, remote sensing, smart irrigation systems, and data-driven decision-support tools [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B4-land-15-01400)]. They can improve information access, reduce transaction costs, support machinery and service matching, and strengthen production management in rural areas. However, the adoption of these technologies is not automatic. Farmers and agricultural producers may face barriers such as high initial investment costs, limited digital skills, insufficient rural infrastructure, weak agricultural service systems, fragmented land management, and uncertainty about the economic returns of digital technologies. Compared with traditional agricultural technologies, digital technologies are not only production tools; they also reshape the matching relationship among capital, labor, and land. Under land constraints, this may change the direction of agricultural technological progress and encourage agricultural production to rely more on the effective use of non-land factors rather than additional land inputs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B5-land-15-01400)].\n\nExisting studies have provided important insights into the relationship between digitalization and agriculture. One strand of literature focuses on the effects of digital rural construction, digital finance, and digital infrastructure on agricultural productivity, farmers’ income, agricultural modernization, and rural development. These studies generally find that digitalization can reduce information costs, improve resource allocation, and promote agricultural transformation [[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B6-land-15-01400),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B7-land-15-01400)]. Another strand of literature examines agricultural land use, land productivity, cultivated land protection, and food security, emphasizing that land scarcity and land-use constraints have become central issues in sustainable agricultural development [[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B8-land-15-01400),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B9-land-15-01400)]. A third strand of literature studies focus on biased technological progress, showing that technological progress may favor certain production factors and thereby change the relative efficiency of capital, labor, and land [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B10-land-15-01400),[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B11-land-15-01400),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B12-land-15-01400),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B13-land-15-01400)].\n\nHowever, the existing literature still leaves several issues insufficiently addressed. First, many studies examine whether digitalization improves agricultural output, total factor productivity, or green efficiency, but pay less attention to how digitalization changes the direction of agricultural technological choice under land constraints [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B14-land-15-01400),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B15-land-15-01400)]. Second, studies on land and agriculture often focus on land productivity, land transfer, or land-use efficiency, but rarely analyze how digital technologies enable capital and labor to support production on existing farmland [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B14-land-15-01400),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B16-land-15-01400)]. Third, although biased technological progress provides a useful framework for understanding factor-oriented technological change, few studies apply this framework to examine the land-related effects of digital rural construction. As a result, we still know relatively little about whether digital rural construction promotes capital–land and labor–land biased technological progress, and through which mechanisms this influence occurs.\n\nThis paper addresses these gaps by examining the impact of digital rural construction on agricultural technological choice under land constraints. The core research question is: Does digital rural construction reshape agricultural technological choice by changing the relative role of capital and labor in relation to land? To answer this question, this study focuses on two land-related dimensions of biased technological progress: the capital–land biased technological progress index and the labor–land biased technological progress index. These two indicators allow us to examine whether digital rural construction promotes a technological path in which capital and labor better support agricultural production on existing farmland.\n\nThe theoretical logic of this paper is as follows. Under land constraints, agricultural production cannot rely mainly on the expansion of cultivated land. Digital rural construction may promote agricultural technological choice by improving the way non-land factors are combined with land. On the one hand, digital infrastructure, smart equipment, machinery services, digital finance, and agricultural service platforms may strengthen the role of capital-embodied technologies in agricultural production. This may promote capital–land biased technological progress. On the other hand, digital platforms, technical guidance, production information, and service systems may improve farmers’ decision-making and production organization, enabling labor to participate more effectively in land management and agricultural production. This may promote labor–land biased technological progress.\n\nTo provide systematic empirical evidence on this relationship, this study uses provincial panel data from China and constructs a multidimensional index of digital rural construction. Based on the theoretical framework, this paper further builds empirical models to examine both the baseline effect and the mechanism effect of digital rural construction on agricultural technological choice under land constraints. Specifically, the baseline model is used to estimate the impact of digital rural construction on capital–land and labor–land biased technological progress. The mechanism model further examines whether digital rural construction affects agricultural technological choice through the scale effect and the price effect. This research design links the theoretical mechanism with empirical testing and helps clarify how digital rural construction reshapes the relationship among capital, labor, and land in agricultural production.\n\nThis paper makes several contributions. First, it introduces a land-constraint perspective into the study of digital rural construction. Instead of only asking whether digitalization improves agricultural productivity, this paper examines how digitalization reshapes agricultural technological choice when land resources are limited. Second, it links digital rural construction with land-related biased technological progress by distinguishing between capital–land and labor–land dimensions. This helps clarify how digital technologies change the relationship between non-land factors and existing farmland. Third, it examines the scale effect and price effect as mechanisms, providing a more detailed explanation of how digital rural construction influences agricultural technological choice. This study provides evidence from China, a major agricultural country facing strong land constraints, thereby offering useful implications for agricultural transformation, cultivated land protection, and land-related digital governance.\n\n## 2. Theoretical Analysis and Research Hypotheses\n\n### 2.1. Digital Rural Construction and Agricultural Technological Choice Under Land Constraints\n\nThe theory of biased technological progress suggests that technological progress does not improve the productivity of all production factors equally. Instead, it may improve the efficiency of one factor more rapidly than that of another factor [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B17-land-15-01400),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B18-land-15-01400)]. In agriculture, biased technological progress reflects changes in the relative productivity of capital, labor, and land. The direction of technological progress is shaped not only by factor endowments, but also by market conditions, institutional arrangements, and the diffusion of new technologies. Under the constraints of cultivated land protection and agricultural security, land has become a relatively rigid factor in agricultural production. Compared with capital and labor, land is more difficult to expand in the short term. Therefore, agricultural development increasingly depends on whether capital, labor, and digital technologies can better support production on existing farmland.\n\nDigital rural construction provides new technological conditions for agricultural production under land constraints. Through the development of rural digital infrastructure, digital production systems, digital finance, digital transactions, and digital public services, digital rural construction changes the way agricultural producers obtain information, allocate resources, adopt technologies, and organize production [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B19-land-15-01400)]. Rural networks, smart terminals, agricultural meteorological information, and digital platforms reduce the cost of information acquisition and transmission [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B20-land-15-01400)]. Digital finance and online transactions improve the accessibility of agricultural capital and production services [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B21-land-15-01400)]. Smart agricultural equipment, machinery services, sensors, irrigation systems, drones, and digital management platforms further promote the integration of digital technologies with agricultural production [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B22-land-15-01400)].\n\nFrom the perspective of land constraints, the key role of digital rural construction is not to expand land resources, but to reshape how non-land factors interact with land. Digital technologies can improve the ability of capital to support production on existing farmland. For example, smart machinery, digital equipment, agricultural machinery services, and platform-based production services allow capital to be more effectively embedded in land-related production processes [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B22-land-15-01400),[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B23-land-15-01400)]. This may lead agricultural technological progress to become more oriented toward capital relative to land. In this sense, capital-related technological progress reflects a pathway in which agricultural production responds to land constraints by strengthening the role of capital-embodied and digitally enabled technologies.\n\nAt the same time, digital rural construction may also improve the role of labor in agricultural production under land constraints. Digital platforms, technical guidance, market information, weather information, and production management tools can improve farmers’ information access and decision-making capacity [[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B24-land-15-01400)]. Labor is not only a traditional production input, but also an important carrier of digital information, production coordination, and land management. When farmers and agricultural operators are better connected with digital services and agricultural service systems, labor can more effectively support agricultural production on existing farmland. Therefore, digital rural construction may also promote labor–land biased technological progress.\n\nBased on the above analysis, this study proposes the following hypotheses:\n\n**Hypothesis****1a.**\n\nUnder land constraints, digital rural construction promotes capital–land biased technological progress in agriculture.\n\n**Hypothesis****1b.**\n\nUnder land constraints, digital rural construction promotes labor–land biased technological progress in agriculture.\n\n### 2.2. Scale Effect, Price Effect, and Biased Agricultural Technological Progress\n\nDigital rural construction may affect agricultural technological choice under land constraints through changes in factor allocation and relative factor returns. In agricultural production, capital, labor, and land are not independent of each other. The effect of digital technologies depends on how they change the relationship among these factors. Since cultivated land is difficult to expand, the adjustment of capital and labor around existing farmland becomes an important way for agriculture to respond to land constraints. In this process, the scale effect and the price effect are two important channels through which digital rural construction may influence biased technological progress.\n\nFirst, digital rural construction may affect agricultural technological progress through the scale effect. The improvement of digital infrastructure, agricultural information services, and digital production systems can reduce information asymmetry and transaction costs in agricultural production. It can also improve the efficiency of land transfer, machinery service matching, and production service coordination. These changes may promote more organized forms of agricultural production and improve the matching between land, capital, labor, and agricultural services [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B25-land-15-01400),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B26-land-15-01400)]. Under more organized production conditions, machinery services, digital management, standardized production, and platform-based agricultural services are more likely to be adopted. Therefore, the scale effect does not only refer to the expansion of operation size; it also reflects the reorganization of production factors around land. By improving factor coordination, digital rural construction may change the relative efficiency of capital, labor, and land, thereby affecting the direction of agricultural technological progress.\n\nSecond, digital rural construction may affect agricultural technological progress through the price effect. According to the theory of induced technological change, changes in relative factor prices can influence the direction of technological progress [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B17-land-15-01400),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B18-land-15-01400),[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#B27-land-15-01400)]. When the use cost of a factor declines or its relative return increases, technological progress is more likely to improve the efficiency of that factor. Digital rural construction can affect relative factor prices by reducing information costs, transaction costs, financing constraints, and service access costs. For example, digital finance can improve access to agricultural capital; digital platforms can reduce the cost of matching machinery services and production services; and online transactions can improve the efficiency of agricultural input and output markets. These changes may alter the cost–benefit conditions of capital, labor, and land use, thus influencing technological choice.\n\nFrom the perspective of land constraints, both the scale effect and the price effect are closely related to how agriculture uses existing farmland. The scale effect changes the organizational conditions under which capital and labor are combined with land, while the price effect changes the economic incentives for adopting different technologies. Together, these two channels may guide agricultural technological progress toward different factor directions. Therefore, digital rural construction may influence capital–land and labor–land biased technological progress by changing both factor organization and relative factor returns.\n\nBased on the above analysis, this study proposes the following hypothesis:\n\n**Hypothesis****2.**\n\nDigital rural construction affects biased agricultural technological progress through both the scale effect and the price effect.\n\n## 3. Research Design\n\n### 3.1. Measurement of Biased Agricultural Technological Progress\n\nAccording to classical economic growth theory, production depends on both factor inputs and technology. Since this study focuses on agricultural production, land is introduced as a key production factor in addition to capital and labor. The general production function can be written as follows:\n\n$$\nY = F \\left(\\right. \\Gamma_{t}^{K} K , \\Gamma_{t}^{N} N , \\Gamma_{t}^{C} C_{t} \\left.\\right)\n$$\n\n(1)\n\nwhere $Y$ denotes agricultural output; $K$, $L$, and $C$ represent capital, labor, and land inputs, respectively; $\\Gamma_{t}^{K}$, $\\Gamma_{t}^{N}$ and $\\Gamma_{t}^{C}$ denote capital-augmenting, labor-augmenting, and land-augmenting technological progress, respectively. Equation (1) is a general form that can describe different types of technological progress. When $\\Gamma_{t}^{K} \\neq \\Gamma_{t}^{N} \\neq \\Gamma_{t}^{C}$, technological progress is neutral. When the growth rates of $\\Gamma_{t}^{K}$, $\\Gamma_{t}^{N}$ and $\\Gamma_{t}^{C}$ differ, technological progress is factor-biased.\n\nTo construct an operational measurement of biased technological progress and avoid measurement bias caused by differences in index benchmarks, this study adopts a normalized factor-augmenting CES production function. The specific form is as follows[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F8\u002F1400#fn001-land-15-01400):\n\n$$\nY_{t} = Y_{0} \\left[\\alpha \\left(\\frac{K_{t}}{K_{0}} \\frac{\\Gamma_{t}^{K}}{\\Gamma_{0}^{K}}\\right)^{\\frac{\\sigma - 1}{\\sigma}} + \\beta \\left(\\frac{N_{t}}{N_{0}} \\frac{\\Gamma_{t}^{N}}{\\Gamma_{0}^{N}}\\right)^{\\frac{\\sigma - 1}{\\sigma}} + \\gamma \\left(\\frac{C_{t}}{C_{0}} \\frac{\\Gamma_{t}^{C}}{\\Gamma_{0}^{C}}\\right)^{\\frac{\\sigma - 1}{\\sigma}}\\right]^{\\frac{\\sigma}{\\sigma - 1}}\n$$\n\n(2)\n\nwhere the subscript 0 denotes the normalization benchmark; $Y_{0}$, $K_{0}$, $L_{0}$, and $C_{0}$ denote output, capital input, labor input, and land input at the benchmark point, respectively. $\\alpha , \\beta and \\gamma$ are distribution parameters, and $\\sigma$ denotes the elasticity of substitution among factors.\n\nBiased technological progress reflects changes in the relative marginal products of","MDPI Land 2026-08-04","2026-08-04T00:00:00Z",76,{"impact":17,"substance":16,"depth":158,"authority":87,"freshness":159,"relevant":20,"comment":160},18,2,"基于28省面板数据的实证研究，探讨数字乡村建设对农业技术选择的影响，数据扎实，结论具有政策参考价值，但发表于三周前，时效性较低。",[162],{"name":154,"url":151},[46,28,164,165,166],"农业技术选择","土地约束","28省面板",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":170},{"candidates":171,"agricultural_evidence":44},[172],{"code":31,"score":18,"supported":44,"title":173,"abstract":174,"tags":175},[46],[46],[46],[177,178],"农业技术选择 土地约束 数字乡村 28省面板","农业技术选择 土地约束","农业技术选择土地约束数字乡村28省面板-940","2026-08-25T00:03:29.495045Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":8,"content":186,"source_name":187,"source_url":8,"published_at":188,"category":189,"cover_url":8,"hotness":12,"is_selected":44,"score":82,"score_detail":190,"sources":194,"tags":196,"primary_topic":200,"secondary_topics":201,"classification":202,"search_phrases":226,"slug":229,"view_count":101,"doi":8,"paper":8,"created_at":230},319,"数字浪潮润乡村——我国数字乡村建设按下\"快进键\"","https:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202608\u002Ft20260810_3137018.shtml","经济日报8月10日报道,四川绵阳安州区作为第二批国家数字乡村试点地区,在金佛手种植基地部署智能降温系统与47个田间物联网监测点,让5万多株佛手树夏天的掉果率从80%降到30%。\"十四五\"末我国已实现\"县县通千兆、乡乡通5G、村村通宽带\",农村互联网普及率达69.5%,2025年农村网络零售额、农产品网络零售额分别达3万亿元、7833.1亿元。但部分数字乡村建设仍面临\"重建设、轻运营\"困境,福建农林大学雷馨圆副教授建议建立长效运营机制,坚持存量盘活与增量约束并重,同时把小农户覆盖率与增收纳入项目考核,防止建设投入转化为少数规模经营主体专属收益。","## 数字浪潮润乡村\n\n2026年08月10日 06:15 来源：经济日报 本报记者 李和风\n\n时值中伏，酷暑难耐，但在四川省绵阳市安州区花荄镇的金佛手种植基地，570多亩佛手柑吹上了“空调”，长势丝毫没有被高温影响。\n\n种植基地负责人雷顺德拿出遥控器轻轻一按，细密的水雾便从遍布田间的管网中喷出。雷顺德说，基地有5万多株佛手树，每株树都安了一个喷头。开启系统后3到5分钟，树周围便可降温10℃左右，这套智能降温设备让佛手柑夏天的掉果率从80%降到30%。除了喷雾降温，它还可以用于病虫害防治。\n\n什么时候该降温、打药？贴心的作物管护离不开精密的智能化农情监测设施。据了解，在当地现代农业园区，47个田间物联网监测点像田野的神经末梢，实现了园区全覆盖，可实时感知土壤墒情、温度、病虫害风险等，为每一项种植决策提供科学依据。\n\n安州区是第二批国家数字乡村试点地区之一。近年来，随着数字技术与乡村生产、生活日渐融合，我国数字乡村建设按下“快进键”。除了农业生产，乡村治理、农村电商、公共服务等，都享受到了数字乡村建设的红利。\n\n数据显示，截至“十四五”末，我国已实现“县县通千兆、乡乡通5G、村村通宽带”，农村互联网普及率达69.5%，城乡数字鸿沟持续缩小；2025年农村网络零售额、农产品网络零售额分别达3万亿元、7833.1亿元；乡村数字教育、数字医疗、数字文化、数字金融等公共服务均等化、普惠化、便捷化水平不断提升。同时，北斗卫星导航系统、高分辨率对地观测系统等一批数据“大动脉”的建设，为数字乡村建设提供了坚实技术支撑。\n\n数字乡村是乡村全面振兴的战略方向，也是建设数字中国的重要内容。自2018年起，“数字乡村”连续9年写入中央一号文件，国家先后出台数字乡村建设指南1.0、2.0，搭建起我国数字乡村建设的四梁八柱。\n\n在取得成绩的同时也要看到，部分地区的数字乡村建设还面临“重建设、轻运营”的窘境。\n\n福建农林大学经济与管理学院副教授雷馨圆分析，部分地区出现这一现象的原因在于供给导向的短期项目制逻辑挤压了需求导向的长期公共服务逻辑：“资金投入、设备采购和平台上线易于量化验收，而真实需求和运营绩效难以考核，容易导致‘为建而建’。同时，建设、所有、使用和维护主体相互分离，权责利不匹配，运维资金和激励约束不足；基层又缺少兼具数字技术、农业生产和乡村治理能力的复合型人才，最终形成建设有动力、运营无主体的困境。”\n\n建立数字乡村长效运营机制，应坚持存量盘活与增量约束并重。雷馨圆建议，对已建成的平台和设备开展全面检查，建立资产台账，摸清使用状况和实际效益，对低效闲置项目分类采取整合共享、功能转用或有序退出等措施。对拟建项目，应坚持需求导向和县域统筹，综合评估产业基础、人口结构和治理需求，严格控制重复建设。同时，在立项阶段同步明确产权归属、运营主体、维护责任、资金来源和绩效标准，并引进培养既懂数字技术又熟悉农业农村的复合型人才，推动数字乡村项目由重建设验收转向重运营服务。\n\n数字赋能乡村产业，还要警惕出现规模经营主体加速获益、小农户难以承接红利的情况。在兼顾规模化与普惠性方面，雷馨圆认为，可通过财政支持降低小农户参与数字产业的门槛，充分发挥数字平台的连接作用，将小农户覆盖率和增收纳入项目考核，防止相关建设投入转化为少数规模经营主体的专属收益。\n\n按照《数字乡村发展战略纲要》要求，到2035年，数字乡村建设将取得长足进展；到本世纪中叶，将全面建成数字乡村。数字红利正加快流向千家万户，持续赋能农业强、农村美、农民富。（经济日报记者 李和风）\n\n(责任编辑：王炬鹏)","经济日报 2026-08-10","2026-08-10T00:00:00Z","报道",{"impact":191,"substance":16,"depth":158,"authority":192,"freshness":51,"relevant":20,"comment":193},25,15,"央媒深度报道数字乡村建设成效与问题，含最新数据与专家建议，具有全国性影响。",[195],{"name":187,"url":184},[197,46,198,26,199],"农村电商","数字经济","经济日报",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":203},{"candidates":204,"agricultural_evidence":44},[205,209,215,221],{"code":31,"score":87,"supported":44,"title":206,"abstract":207,"tags":208},[46],[46],[46,26,198],{"code":210,"score":60,"supported":44,"title":211,"abstract":212,"tags":214},"G2",[],[213],"小农户",[],{"code":216,"score":60,"supported":44,"title":217,"abstract":218,"tags":220},"I5",[],[219],"物联网",[],{"code":222,"score":20,"supported":13,"title":223,"abstract":224,"tags":225},"E5",[],[],[197],[227,228],"数字乡村试点 农村电商 数字乡村 数字经济","数字乡村试点 农村电商","数字乡村试点农村电商数字乡村数字经济-319","2026-08-09T23:54:47.033859Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":8,"source_name":237,"source_url":234,"published_at":238,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":239,"sources":242,"tags":244,"primary_topic":249,"secondary_topics":250,"classification":251,"search_phrases":275,"slug":278,"view_count":139,"doi":279,"paper":280,"created_at":296},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":158,"substance":16,"depth":158,"authority":240,"freshness":51,"relevant":20,"comment":241},13,"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。",[243],{"name":237,"url":234},[46,245,246,247,248],"数字素养","共同富裕","农民收入","收入不平等",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":252},{"candidates":253,"agricultural_evidence":44},[254,259,265,271],{"code":31,"score":255,"supported":44,"title":256,"abstract":257,"tags":258},8,[],[46,103],[46,245],{"code":120,"score":72,"supported":44,"title":260,"abstract":261,"tags":264},[],[262,263],"农户收入","rural income",[],{"code":266,"score":60,"supported":44,"title":267,"abstract":268,"tags":270},"G",[],[269],"rural development",[],{"code":115,"score":20,"supported":13,"title":272,"abstract":273,"tags":274},[],[],[246],[276,277],"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":279,"openalex_id":281,"authors":282,"venue":237,"cited_by_count":139,"oa_url":234,"card":291,"direction":144,"ingested_from":146},"W7223105459",[283,286,289],{"name":284,"orcid":285},"Yong Feng","https:\u002F\u002Forcid.org\u002F0000-0002-8151-8594",{"name":287,"orcid":288},"Shuokai  kai Wang","https:\u002F\u002Forcid.org\u002F0009-0002-7439-1764",{"name":290,"orcid":8},"Fangping Cao",{"tldr":292,"method":293,"finding":294,"direction":144,"opportunity":295},"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。","2026-10-22T11:20:47.675827Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":8,"content":302,"source_name":303,"source_url":8,"published_at":304,"category":189,"cover_url":8,"hotness":12,"is_selected":13,"score":305,"score_detail":306,"sources":308,"tags":310,"primary_topic":315,"secondary_topics":316,"classification":317,"search_phrases":332,"slug":335,"view_count":139,"doi":8,"paper":8,"created_at":336},5505,"广西以赛聚智赋能「AI+农业」 桂字号品牌与东盟水果IP赛道决出","https:\u002F\u002Fwww.agri.cn\u002Fzt\u002Fzhzz\u002Ftp\u002F202610\u002Ft20261019_8877223.htm","10月16日2026年广西智慧农业（数字乡村）创新大赛「桂字号」农业品牌推广赛道、中国（广西）—东盟水果贸易第一IP打造赛道决赛举行，55个项目同台竞技。自治区农业农村厅提出广西正按「北上广研发+广西集成+东盟应用」路径打造中国（广西）—东盟智慧农业先行区，去年一批项目已入选广西人工智能赋能千行百业拟补贴名单。","自治区农业农村厅党组成员、副厅长欧毅霞表示，本次大赛是落实中央一号文件关于因地制宜发展农业新质生产力、大力发展智慧农业部署的具体行动，也是推动人工智能与农业深度融合的重要举措。今年大赛聚焦“桂字号”农业品牌推广和中国（广西）—东盟水果贸易第一IP打造这两个赛道，主要基于三点考虑：一是广西农业资源丰富，“桂字号”品牌基础扎实，但数字化运营和品牌传播仍是短板，需要以赛聚智、以赛促创，借助数字化手段让好产品卖出好价钱；二是广西面向东盟的区位优势得天独厚，水果贸易体量大、潜力足，亟需打造具有国际影响力的第一IP，以数字技术赋能贸易全链条，提升通关、溯源、营销等环节效率；三是这两个赛道切口小、场景实、应用性强，能够有效打通“技术—产业—市场”闭环，推动创新链与产业链精准对接，让更多“金种子”项目在八桂大地上落地生根、开花结果。\n\n欧毅霞提出三点希望：专业组勇当产业升级“领跑者”，形成可复制、可推广的解决方案；学生组争做乡村振兴“生力军”，把创新火花带回课堂、实验室和田间地头；各方共筑智慧农业“生态圈”，推动技术、资本、人才、政策等要素集聚。\n\n广西农业职业技术大学党委书记韦波在致辞中表示，作为承办方，学校将联合广西数字乡村研究院，在赛道组委会统一领导下做好组织、服务与保障，推动赛事成果与教育教学、产业需求有效对接，发挥面向东盟农业职业教育合作优势，为“桂字号”出海、中国—东盟水果贸易双向流通搭建桥梁。\n\n据介绍，广西正按照“北上广研发+广西集成+东盟应用”路径，打造中国（广西）—东盟智慧农业先行区。智慧农业创新大赛不仅是竞技舞台，更是创新成果转化、产业赋能的重要平台。去年，广西首次举办智慧农业创新大赛并取得丰硕成果，一批新技术、新理念、新模式脱颖而出，逐步被大家看见、被市场认可；如捷佳润科技跨境农业智能决策系统、北部湾产权交易所集团AI赋能广西桑蚕茧丝绸全产业链项目入选广西人工智能赋能千行百业拟补贴项目名单；荣获“最佳潜力奖”的“桂鸟先飞—滴滴打药平台”获得地方政府投资扩容实现了升级蝶变。\n\n以赛为媒，向新而行。随着多个项目在决赛中展开比拼，一批聚焦品牌数字化运营、跨境贸易、品控溯源和IP打造的创新项目将在八桂大地加速落地。未来，广西将持续以大赛为纽带，推动技术、资本、人才、政策等要素向智慧农业集聚，促进创新链、产业链、资金链、人才链深度融合，让更多“金种子”项目生根发芽、茁壮成长，为“桂字号”香飘东盟、中国—东盟水果贸易双向流通注入新动能，助力中国（广西）—东盟智慧农业先行区建设迈上新台阶。","中国农业农村信息网 2026-10-19","2026-10-19T00:00:00Z",66,{"impact":158,"substance":101,"depth":87,"authority":18,"freshness":72,"relevant":20,"comment":307},"省级智慧农业赛事报道，聚焦AI赋能品牌与东盟水果贸易，有具体路径与落地案例，但属活动通稿，时效稍滞后。",[309],{"name":303,"url":300},[46,311,312,313,314],"智慧农业","农业人工智能","桂字号","东盟水果贸易",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":318},{"candidates":319,"agricultural_evidence":44},[320,324,328],{"code":50,"score":19,"supported":44,"title":321,"abstract":322,"tags":323},[],[311],[311],{"code":31,"score":19,"supported":44,"title":325,"abstract":326,"tags":327},[],[46],[46],{"code":59,"score":20,"supported":13,"title":329,"abstract":330,"tags":331},[],[],[312],[333,334],"广西 智慧农业创新大赛","桂字号 东盟水果 IP","广西智慧农业创新大赛-5505","2026-10-22T00:06:58.572242Z",{"id":338,"title":339,"url":340,"summary":341,"summary_zh":342,"content":8,"source_name":343,"source_url":340,"published_at":344,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":355,"secondary_topics":356,"classification":357,"search_phrases":372,"slug":375,"view_count":139,"doi":376,"paper":377,"created_at":393},5493,"Mapping Research on the Implementation of Smart Village Policies in Indonesia: A Bibliometric Study for the Years 2017-2025","https:\u002F\u002Fdoi.org\u002F10.66254\u002Fjgssvol5issue2page333-347","This research aims to analyze the role of public sector policies in the development of Smart Villages in Indonesia, focusing on the utilization of information and communication technology (ICT) to improve the quality of life in rural communities. Although the Indonesian government has launched various programs such as the Digital Village Program and Smart City, the implementation of these policies still faces various challenges, including limited infrastructure, low digital literacy, and lack of inter-agency coordination. This research uses a bibliometric approach to map relevant research and policy trends, as well as to identify challenges and opportunities in the development of Smart Villages. The research findings indicate that despite progress, existing public sector policies have not yet been fully effective in addressing the digital divide between villages and cities. This research recommends the need for more inclusive and integrated policy improvements, as well as strengthening human resource capacities in villages to support the creation of sustainable and competitive Smart Villages.","本研究旨在分析公共部门政策在印度尼西亚智慧村（Smart Villages）发展中的作用，重点关注利用信息通信技术（ICT）提升农村社区生活质量。尽管印度尼西亚政府已推出数字村计划（Digital Village Program）和智慧城市（Smart City）等多项举措，但这些政策的实施仍面临诸多挑战，包括基础设施有限、数字素养偏低以及跨机构协调不足。本研究采用文献计量学方法，梳理相关研究与政策趋势，并识别智慧村发展中的挑战与机遇。研究结果表明，尽管取得了一定进展，现有公共部门政策在弥合城乡数字鸿沟方面尚未充分发挥效用。本研究建议有必要进行更具包容性和整合性的政策改进，同时加强农村人力资源能力建设，以支持创建可持续且具有竞争力的智慧村。","Journal of Government Science Studies\u002FJournal Of Government Science Studies","2026-10-20T00:00:00Z",71,{"impact":101,"substance":158,"depth":192,"authority":240,"freshness":51,"relevant":20,"comment":347},"该文献计量研究系统梳理了印尼智慧乡村政策实施进展与数字鸿沟挑战，对数字乡村国际比较有参考价值，但属区域性研究，影响层级有限。",[349],{"name":343,"url":340},[46,351,352,353,354],"印尼","数字鸿沟","文献计量","智慧乡村",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":358},{"candidates":359,"agricultural_evidence":44},[360,366],{"code":361,"score":192,"supported":44,"title":362,"abstract":364,"tags":365},"I7",[363],"bibliometric",[353,363],[353],{"code":31,"score":367,"supported":44,"title":368,"abstract":369,"tags":371},11,[],[352,103,370],"digital divide",[46,352],[373,374],"印尼 智慧乡村","印尼 数字村 数字鸿沟","印尼智慧乡村-5493","10.66254\u002Fjgssvol5issue2page333-347",{"doi":376,"openalex_id":378,"authors":379,"venue":343,"cited_by_count":139,"oa_url":340,"card":388,"direction":144,"ingested_from":146},"W7223067869",[380,382,385],{"name":381,"orcid":8},"Syaifulrijal Mahulauw",{"name":383,"orcid":384},"Wahab Tuanaya","https:\u002F\u002Forcid.org\u002F0009-0001-9236-8149",{"name":386,"orcid":387},"Marno Wance","https:\u002F\u002Forcid.org\u002F0000-0001-6663-7598",{"tldr":389,"method":390,"finding":391,"direction":144,"opportunity":392},"用文献计量法梳理2017-2025年印尼智慧村政策实施研究，识别挑战与机会。","文献计量分析，覆盖2017-2025年相关文献与政策趋势。","政策尚未有效弥合城乡数字鸿沟，需更包容整合的政策与人力建设。","可延伸至跨国比较或政策效果量化评估，聚焦数字鸿沟与跨部门协同机制。","2026-10-21T23:30:33.962760Z"]