[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-Sustainability":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":8,"page_size":9,"items":10},"Sustainability","tag",[4],4,1,100,[11,64,139,182],{"id":12,"title":13,"url":14,"summary":15,"summary_zh":16,"content":17,"source_name":18,"source_url":16,"published_at":19,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":23,"score_detail":24,"sources":29,"tags":31,"primary_topic":38,"secondary_topics":44,"classification":45,"search_phrases":58,"slug":61,"view_count":62,"doi":16,"paper":16,"created_at":63},970,"数字化与可持续农业：基于PRISMA的系统文献综述 涵盖62项2015-2026年实证研究","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807","原文标题: Digitalisation and Sustainable Agriculture: A PRISMA-Based Systematic Literature Review。作者: Zhanna Smagulova、Urpash Shalbolova、Aigul Mukhanova、Gulshat Duzelbayeva(哈萨克斯坦Korkyt Ata Kyzylorda大学、L.N. Gumilyov欧亚国立大学、纳尔霍兹大学)。本研究基于PRISMA 2020指南对2015-2026年间发表的实证证据进行批判性综合分析，考察数字化对可持续农业发展的影响。在Scopus和Web of Science数据库中识别并分析62项实证研究，采用演绎与归纳相结合的主题分析方法，围绕「三重底线」(经济、环境、社会)框架展开。研究发现2022年后学术关注度显著上升，研究高度集中在亚洲(特别是中国，占42%)、欧洲(22.58%)和非洲(17.7%)；现有研究主要凸显数字化在生产力提升、效率改善、收入增长与资源优化配置、排放减少方面的积极影响，但也记录了劳动力替代、不平等加剧、数字工资不透明等不均衡、情境依赖的社会维度负面结果；整合性分析极其匮乏——仅不到5%的研究同时考察经济、环境和社会三个维度。本综述识别出数字化对可持续农业复杂、不均衡且情境依赖影响的四个未来研究优先方向。",null,"## 1. Introduction\n\nAgriculture occupies a structurally irreplaceable position in the global economy. It contributes approximately 24% of global gross domestic product, employs around 1.3 billion people (roughly 16% of the world’s workforce), and underpins food security across approximately 1.2 to 1.5 billion hectares of cultivated land [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B1-sustainability-18-07807)]. Yet the sector faces mounting pressure on multiple fronts: a growing global population, rising per capita food demand, accelerating climate change, and persistent rural poverty [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B2-sustainability-18-07807)]. These intersecting challenges have renewed the urgency of a question posed by the International Assessment of Agriculture, Science and Technology for Development (IAASTD): how can hunger and poverty be alleviated, rural livelihoods enhanced, and equitable, environmentally, socially, and economically sustainable development advanced through the generation and application of agricultural knowledge and technology [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B3-sustainability-18-07807)]?\n\nOver the past decade, one of the most prominent responses to this challenge has been the rapid digitalisation of the agricultural sector. Digital technologies, including information and communication technologies (ICTs), precision agriculture tools, artificial intelligence, remote sensing, and digital platforms, are increasingly regarded as instruments of agricultural transformation. Existing research identifies a convergence of drivers: economic pressures that reward efficiency gains, environmental imperatives that demand more precise use of resources, and social objectives that require the inclusion of smallholder farmers and rural communities in the benefits of technological change [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B4-sustainability-18-07807)]. Studies from diverse national contexts reflect these dynamics: they document productivity gains in the Chinese agricultural sector [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B5-sustainability-18-07807),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B6-sustainability-18-07807)], report increased adoption of modern practices among Saudi Arabian farmers following access to digital information [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B7-sustainability-18-07807)], and link digital agriculture to reductions in agricultural carbon emissions [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B8-sustainability-18-07807)]. Finally, the literature highlights that the transformative potential of digital tools depends critically on inclusive, co-created knowledge transfer among farmers, consultants, and other stakeholders [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B9-sustainability-18-07807)]. Despite this growing body of work, the literature remains fragmented. Most studies focus on a single technology, country, or dimension of sustainability, typically the economic dimension. Integrative analyses that assess the impact of digitalisation across all three dimensions of sustainable development remain scarce, and potential trade-offs between dimensions are insufficiently explored [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B10-sustainability-18-07807)]. The present study addresses this gap by analysing the impact of digitalisation on the sustainable development of agriculture through a systematic synthesis of empirical evidence and by identifying directions for future research.\n\nTo achieve this aim, three objectives were set: to conduct a PRISMA-compliant systematic review of empirical literature on digitalisation and sustainable agriculture; to systematically identify and compare the positive and negative effects of digitalisation on the economic, environmental, and social dimensions of agricultural sustainability; and to identify research gaps and priority directions for further enquiry. The study is guided by two research questions: (1) Which geographical regions have been the focus of research on digitalisation and agricultural sustainability, and how has the volume of related publications changed from 2015 to 2026? (2) Do all studies report positive sustainability outcomes associated with digitalisation, or have adverse effects also been documented?\n\n### Theoretical Framework\n\nThe analytical structure of this review is grounded in the concept of sustainable development, as defined by the World Commission on Environment and Development (the Brundtland Commission), which characterised sustainability as development that “meets the needs of the present without compromising the ability of future generations to meet their own needs” [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B11-sustainability-18-07807)]. This foundational definition has since been operationalised through the triple-bottom-line framework, which recognises three interdependent dimensions of sustainability: economic, environmental, and social [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B12-sustainability-18-07807)]. The framework is now embedded in the United Nations’ 2030 Agenda and its 17 Sustainable Development Goals (SDGs) [[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B13-sustainability-18-07807)]. Several of these, including SDG 2 (Zero Hunger), SDG 8 (Decent Work and Economic Growth), SDG 13 (Climate Action), and SDG 15 (Life on Land), are directly relevant to agriculture.\n\nIn the agricultural context, these three dimensions can be defined as follows. Economic sustainability is the capacity of agricultural systems to generate productive output, maintain profitability, and support rural livelihoods over the long term, as reflected in indicators such as total factor productivity, crop yields, income, and market participation. Environmental sustainability is the capacity to preserve natural resources and ecological functions, particularly through reduced carbon emissions, efficient use of land and water, and improvements in biodiversity and soil health. Social sustainability concerns the conditions under which agricultural labour is performed, including the distribution of income gains, equitable access to technology, the quality of working conditions, and the protection of rural communities from displacement or marginalisation.\n\nThis three-dimensional framework directly informs the review’s organisational structure. The synthesis of empirical findings in [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec3-sustainability-18-07807) is organised around these three dimensions, enabling a systematic examination of whether digitalisation contributes to, detracts from, or produces uneven outcomes across economic, environmental, and social sustainability. Critically, the framework also highlights potential trade-offs between dimensions, for example, when gains in economic productivity come at the cost of labour displacement, or when environmental improvements require capital investments that disadvantage smallholders. Recognising these trade-offs is a central objective of this review and a distinctive contribution to the existing literature.\n\n## 2. Materials and Methods\n\n### 2.1. Overview and Design\n\nThis study employs a systematic literature review in line with the PRISMA 2020 guidelines [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)], and the checklist could be found in the [Supplementary Material](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#app1-sustainability-18-07807). The PRISMA framework was selected for its transparent, reproducible protocol for identifying, screening, and synthesising empirical evidence, thereby minimising selection bias and enhancing the reliability of conclusions. The review adopts a combined deductive–inductive analytical approach: deductive methods were used to map the geographical and temporal distribution of research (RQ1), while inductive thematic analysis was applied to synthesise substantive findings across sustainability dimensions (RQ2). Data analysis drew on both quantitative methods, aggregating numerical indicators such as yield changes and productivity growth, and qualitative thematic coding, in which the 62 included studies were systematically categorised according to the economic, environmental, and social dimensions of the theoretical framework described in the “Theoretical Framework” section. A formal meta-analysis was not conducted because preliminary analysis revealed substantial heterogeneity across the included studies in outcome measures, research designs, analytical methods, and reported effect estimates. Consequently, calculating comparable pooled effect sizes and applying statistical methods for quantitative synthesis, including a funnel plot and Egger’s test to assess publication bias, were not methodologically justified.\n\n### 2.2. Data Sources and Search Strategy\n\nA systematic search was conducted in two stages: the first stage was conducted on 29 January 2025 using a single electronic database; Scopus and Web of Science (WoS) was incorporated only in the second stage, the 2026 update (see [Section 2.4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec2dot4-sustainability-18-07807)).\n\nScopus was selected as the primary database for its extensive multidisciplinary coverage and strong performance in systematic search retrieval. WoS was included as a complementary source to broaden disciplinary coverage and reduce the risk of database-specific indexing gaps, consistent with best practice in systematic reviewing [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)].\n\nThe search string actually executed in Scopus (Article title, Abstract, Keywords field) was the free-text phrase “digitalisation of agriculture”, which Scopus parsed as digitalisation AND of AND agriculture. The term “precision agriculture” was not used as a separate search term, as the review aimed to examine the broader phenomenon of agricultural digitalisation rather than a single technological approach. Precision agriculture was therefore treated as one form of agricultural digitalisation within the review’s broader conceptual scope. It is important to note that studies focused on precision agriculture and related technologies were identified through the search strategy and are represented among the 62 studies included in the final sample.\n\nThe following filters were then applied sequentially, with the resulting record counts: publication years 2015–2025 (n = 1353); subject areas Computer Science, Environmental Science, Agricultural and Biological Sciences, Social Sciences, Business, Management and Accounting, Economics, Econometrics and Finance, Decision Sciences, and Multidisciplinary (n = 1233); document language limited to English (n = 1186); source\u002Fdocument type limited to Journal and Conference Proceeding (n = 927); the Environmental Science subject category was then excluded to avoid duplication with other categories (n = 717).\n\n### 2.3. Eligibility Criteria\n\nInclusion and exclusion criteria were developed collaboratively by all four authors (Zh.S., U.S., A.M., G.D.) before screening and are presented in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t001). The criteria were designed to include only empirical studies that directly examined the impact of digitalisation on agricultural sustainability outcomes, while excluding review articles, theoretical or conceptual papers, and studies focused on non-agricultural sectors.\n\n**Table 1.** Inclusion and exclusion criteria applied in the systematic literature review.\n\n### 2.4. Study Selection\n\nAfter database searches and deduplication, 739 records were retained for title and abstract screening. Screening was conducted independently by three authors (Zh.S., A.M., G.D.) using the eligibility criteria outlined in [Section 2.3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec2dot3-sustainability-18-07807). Discrepancies between screeners were recorded: Zh.S. identified 42 eligible articles, A.M. 45, and G.D. 42. All discrepancies were resolved through discussion and consensus arbitration by the fourth author (U.S.). A total of 40 studies were included in the final synthesis. The full selection process is documented in the PRISMA 2020 flow diagram ([Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#fig_body_display_sustainability-18-07807-f001)).\n\n**Figure 1.** PRISMA flowchart illustrating the study selection process for the first search round (Round 1, Scopus, January 2025). See [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t002) for the second search round (Round 2, 2026 update). Source: (Page et al., 2021) [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)].\n\n**Table 2.** PRISMA-style flow for the second search round (Round 2, 2026 update), combining Scopus and Web of Science.\n\n**Figure 1.** PRISMA flowchart illustrating the study selection process for the first search round (Round 1, Scopus, January 2025). See [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t002) for the second search round (Round 2, 2026 update). Source: (Page et al., 2021) [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B14-sustainability-18-07807)].\n\n[![Image 1: Sustainability 18 07807 g001](https:\u002F\u002Fwww.mdpi.com\u002Fsustainability\u002Fsustainability-18-07807\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fsustainability-18-07807-g001.png)](https:\u002F\u002Fwww.mdpi.com\u002Fsustainability\u002Fsustainability-18-07807\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fsustainability-18-07807-g001.png)\n\n#### Search Update (Round 2)\n\nOn 5 February 2026, the Scopus search was re-run, retrieving 278 additional records published in 2026. On 17 February 2026, Web of Science was searched for the first time, covering 2015–2026 and retrieving 259 records. The authors judged that a single-database search was insufficient for a comprehensive review and therefore incorporated WoS at this stage. The combined total of 537 records (278 Scopus + 259 WoS) was screened for duplicates; 25 duplicate records were identified and removed, leaving 512 distinct records (278 Scopus + 234 WoS) for screening against the eligibility criteria in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#table_body_display_sustainability-18-07807-t001). Applying these criteria to the 512 records identified 22 additional eligible studies, which were added to the 40 studies retained from the first search round, yielding a final total of 62 studies included in the review.\n\n### 2.5. Data Extraction and Quality Assessment\n\nData from the 62 included studies were extracted by three authors (Zh.S., A.M., G.D.) using a standardised extraction template. For each study, the following information was recorded: author(s) and year; country\u002Fregion of study; research design and data collection method; digital technology or intervention examined; sustainability dimension(s) addressed; key quantitative or qualitative findings; and direction of effect (positive, negative, or mixed). The full extraction is presented in the [Appendix A](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#app2-sustainability-18-07807).\n\nNo formal quality appraisal tool (such as the Mixed Methods Appraisal Tool, MMAT [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B15-sustainability-18-07807)], or the Joanna Briggs Institute checklists) [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B16-sustainability-18-07807)] was used in this review, given the heterogeneity of study designs and methodological approaches across the 62 included studies. This is acknowledged as a limitation of the review (see [Section 4.5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#sec4dot5-sustainability-18-07807)).\n\n### 2.6. Synthesis Approach\n\nGiven the heterogeneity of study designs, geographic contexts, and outcome measures across the included studies, a narrative synthesis was adopted rather than a meta-analysis. Synthesis proceeded in two stages. First, a descriptive analysis mapped the distribution of studies by geographical region, publication year, and sustainability dimension addressed (addressing RQ1). Second, a deductive–inductive thematic analysis synthesised the substantive findings within each sustainability dimension (addressing RQ2). Initial codes were derived deductively from the three-dimensional theoretical framework (economic, environmental, and social sustainability. Sub-themes were then developed inductively from patterns emerging within each dimension. Coding was conducted by three authors (Zh.S., A.M., G.D.) and reviewed for consistency by the fourth (U.S.).\n\n## 3. Results\n\nThis section is organised in accordance with the study’s four guiding research questions. The first question provides contextual information on the digitalisation of agriculture, whereas the subsequent three questions focus on a detailed analysis of how digitalisation affects the sustainable development of the agricultural sector.\n\n### 3.1. RQ1. Which Geographical Regions Have Been the Focus of Research on the Impact of Digital Technologies on Agricultural Sustainability, and How Has the Volume of Related Publications Evolved in Recent Years?\n\nAll studies included in this review were conducted in countries across three continents, Europe, Asia and Africa. However, the geographic distribution of studies was uneven across these regions (see [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#fig_body_display_sustainability-18-07807-f002)). Among the 37 studies conducted in Asia (59.7% of the studies), the overwhelming majority, 26 studies or 42% of the total sample, were conducted in China, which is not surprising. China is one of the world’s largest producers of agricultural products and has been striving not only to expand production volumes but also to improve product quality. The Chinese government has strengthened agricultural land protection and implemented various incentives to promote agricultural production [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B17-sustainability-18-07807)]. In addition, the country continues to liberalise trade in agricultural products [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B18-sustainability-18-07807)]. As a result of these combined factors, China has effectively integrated digital technologies into both the production and trade sectors of its agriculture. Following China in the ranking are India (4.84%) and Kazakhstan (3.2%), both of which are major producers of staple crops such as rice and wheat. Other contributing countries include Indonesia (1.6%), Uzbekistan (1.6%), Vietnam (3.2%), Azerbaijan (1.6%) and members of the South Asian Association for Regional Cooperation (SAARC), including Bangladesh, Nepal, Pakistan, Bhutan, the Maldives, and Afghanistan (1.6%).\n\n**Figure 2.** Geographical distribution of studies. Source: Author’s elaboration using Bing Maps.\n\nEurope accounts for 22.58% of the studies (14 articles), making it the second most represented continent. Germany stands out as the most researched country in Europe (6.45%). As one of the leading agricultural producers in the European Union [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B19-sustainability-18-07807)], German researchers, citing previous studies, argue that nearly 50% of agricultural professional activities in the country could be replaced by digital technologies [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F15\u002F7807#B20-sustainability-18-07807)]. These trends have contributed to Germany’s heightened interest in the digitalisation of its agricultural sector.\n\nAfrica ranks third in terms of the number of studies, accounting for 17.7% of the total. In Africa, the greatest interest in the impact of digitalisation on agriculture was shown by researchers in Ghana (6.45%) and Sub-Saharan African countries (4.84%). Additional studies were carried out in Tanzania (1.6%), Nigeria (1.6%), Somali (1.6%) and other African nations (1.6%). The focus on digital agriculture in Africa is lar","Sustainability 2026, 18(15), 7807","2026-08-02T00:00:00Z","论文",10,false,78,{"impact":25,"substance":26,"depth":25,"authority":27,"freshness":7,"relevant":8,"comment":28},20,22,12,"系统综述涵盖62项研究，全面分析数字化对农业可持续性的多维影响，具有较高学术价值和信息增量，但时效性稍差。",[30],{"name":18,"url":14},[32,4,33,34,35,36,37],"数字化","可持续农业","哈萨克斯坦","PRISMA","三重底线","文献综述",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51],{"code":39,"score":52,"supported":53,"title":54,"abstract":55,"tags":57},15,true,[33],[33,56],"sustainable agriculture",[33],[59,60],"可持续农业 哈萨克斯坦 三重底线 文献综述","可持续农业 哈萨克斯坦","可持续农业哈萨克斯坦三重底线文献综述-970",3,"2026-08-26T00:09:32.321765Z",{"id":65,"title":66,"url":67,"summary":68,"summary_zh":16,"content":69,"source_name":70,"source_url":16,"published_at":71,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":72,"score_detail":73,"sources":78,"tags":80,"primary_topic":85,"secondary_topics":91,"classification":92,"search_phrases":133,"slug":136,"view_count":137,"doi":16,"paper":16,"created_at":138},791,"数字乡村发展与牧户非农就业:来自甘青川藏26县2790户牧户调查的证据(原标题:From Grasslands to Markets: Digital Rural Development and Herders' Non-Farm Employment)","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503","兰州大学草地农业生态系统国家重点实验室吴忠安等团队基于2023-2025年甘青川藏26县2790户牧户调查数据,考察县级数字乡村发展与牧户非农就业关系。研究发现数字乡村发展与户主主要从事非农就业的可能性正相关,在系列稳健性和内生性检验后结果稳定;在第一产业基础较大的地区以及从事饲草种植的牧户中,正向关联较弱,表明当地生产结构和家庭劳动配置制约了数字机会向就业多元化转化。进一步分析表明数字金融和数字消费是数字乡村发展支持非农就业的两种潜在渠道。研究强调牧区就业收益既取决于数字接入,又取决于家庭劳动配置灵活性及非农机会可得性。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: sustainability-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fsustainability-logo.png?3798e4e58c765aed)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fsustainability)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nZhongan Wu\n\n *[](mailto:wuzha2023@lzu.edu.cn), \nToba Stephen Olasehinde\n\n and \nYubing Fan\n\nState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, Chinese Grass Industry Development Strategy Research Center, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 3 July 2026 \u002F Revised: 11 August 2026 \u002F Accepted: 12 August 2026 \u002F Published: 19 August 2026\n\n## Abstract\n\nAlthough natural resources and geographical factors limit herders’ employment options, the development of digital rural helps herding households compete on an equal footing by expanding employment opportunities. Using survey data collected from 2790 herder households in 26 counties across Gansu, Qinghai, Sichuan and Tibet from 2023 to 2025, this study examines the relationship between county-level digital rural development and non-farm employment. We find that digital rural development is positively associated with the likelihood that household heads engage primarily in non-farm employment. This finding remains stable across a series of robustness and endogeneity checks. Moreover, the positive association is weaker in areas with a larger primary-industry base and among households engaged in forage cultivation, suggesting that local production structures and household labor commitments constrain the conversion of digital opportunities into employment diversification. Further analysis indicates that digital finance and digital consumption are two potential channels through which digital rural development supports non-farm employment. These findings highlight that the employment benefits of digital development depend not only on digital access but also on the flexibility of household labor allocation and the availability of non-farm opportunities in pastoral areas.\n\n## 1. Introduction\n\nArtificial intelligence, Big Data, and mobile internet are examples of digital technology that are spreading to all parts of the economy and society, changing how production is organized and people work [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B1-sustainability-18-08503)]. Given the above, China released the Digital Rural Development Plan in 2018 to promote the construction of a digital rural society and rural revitalization. As 5G networks expand into rural and pastoral areas, the all-weather demonstration project for e-commerce in rural areas continues to advance, and the coverage of digital inclusive finance is gradually expanding. At the same time, digital infrastructure and services are being rolled out in the grassland region at an ever-increasing rate [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B2-sustainability-18-08503)]. However, because of their remote areas, fragile ecosystems and unique cultures, these large pastoral areas have been lacking in development resources for a long time. Traditional livestock farming is limited by the productivity of natural grasslands, ecological red lines and fluctuations in market prices, and a single grazing-based livelihood model cannot provide stable income growth for herder households [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B3-sustainability-18-08503)]. Therefore, expanding non-farm employment opportunities and promoting the transfer of the pastoral workforce to the secondary and tertiary sectors are urgently needed to consolidate the results of poverty alleviation and prevent a relapse into poverty, as well as to achieve all-round rejuvenation of pastoral areas.\n\nNon-farm employment provides herder households with an alternative to relying entirely on traditional livestock production. This resulting income mix can reduce exposure to livestock losses and strengthen resilience to market, climatic and health shocks [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B4-sustainability-18-08503)]. Especially against the backdrop of increasing resource and environmental constraints in pastoral areas and shrinking growth prospects for traditional livestock farming, promoting the rational flow of labor into non-farm sectors is not only a practical necessity for herding households to enhance the resilience of their livelihoods, but also a key direction for industrial restructuring and socioeconomic transformation in pastoral areas. However, the formation of non-farm employment is not simply a matter of individual choice, as herders’ decision-making is influenced by a range of factors, including their ability to access information, job search costs, financial constraints, and the extent of market access [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B5-sustainability-18-08503),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B6-sustainability-18-08503)]. The reason why the development of digital rural may have an impact on herders’ non-farm employment is that it can, to some extent, alleviate these constraints.\n\nExisting research has provided a wealth of discussion on the relationship between the digital economy, information and communication technologies, and rural labor migration, as well as the growth of farm household income [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B7-sustainability-18-08503),[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B8-sustainability-18-08503),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B9-sustainability-18-08503),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B10-sustainability-18-08503)]. A large body of the literature indicates that digital development can improve the information environment, expand market access, alleviate financial exclusion, and, to some extent, facilitate the shift in rural labor toward the non-farm sector [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B11-sustainability-18-08503),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B12-sustainability-18-08503),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B13-sustainability-18-08503)]. Some studies have also indicated that the development of internet usage, digital finance, and e-commerce has a positive impact on farmers’ non-farm employment, entrepreneurial activities, and income growth [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B14-sustainability-18-08503),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B15-sustainability-18-08503),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B16-sustainability-18-08503),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B17-sustainability-18-08503)]. More broadly, infrastructure development and technological progress can promote economic diversification and strengthen livelihood resilience in peripheral and resource-dependent regions [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B18-sustainability-18-08503)]. In terms of research subjects, most of the existing studies have focused on farmers and have paid relatively little attention to herders, another group in pastoral areas. Herders’ employment decisions are influenced by the general labor market, but they are also restricted by other factors, such as grassland management systems, livestock production cycles, household livestock assets and grazing practices; therefore, their non-farm employment behavior is relatively context-specific and constrained [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B19-sustainability-18-08503),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B20-sustainability-18-08503)]. Most studies have focused on the general inclusive benefits of digital technology for research, but there is a lack of specific research on how digital rural initiatives operate in the particular spatial conditions of pastoral areas and whether their effects differ among different groups and regions.\n\nThis paper takes as its focus the non-farm employment of herders and studies the effect of digital rural development on the transformation of herders’ employment, as well as the mechanisms through which this change occurs. Based on this, this paper seeks to answer the following questions: can digital rural development promote non-farm employment among herders?; through what mechanisms does it primarily achieve this?; and does this impact vary depending on individual characteristics, spatial conditions, and the level of development in each county? This study makes three contributions. First, it conceptualizes digital rural development in pastoral areas as a mechanism for translating digital access into non-farm employment opportunities, emphasizing the role of household labor constraints in livestock-based livelihoods. In doing so, it extends the literature on digital rural development and labor transition by showing that improved digital access does not automatically lead to labor reallocation, because pastoral production commitments may constrain herders’ ability to respond to new employment opportunities. Second, it integrates survey data from 2790 herding households across Gansu, Qinghai, Sichuan, and Tibet, with a county-level Digital Rural Development Index, providing evidence from underrepresented pastoral production systems. Third, it examines how structural and household-level conditions shape the relationship between digital rural development and non-farm employment, highlighting the importance of contextual heterogeneity in pastoral regions. By linking digital development with livelihood diversification, the study also contributes to the rural sustainability literature by showing that the employment benefits of digitalization depend on household labor flexibility and local economic conditions.\n\n## 2. Theoretical Framework\n\n### 2.1. Direct Effects of Digital Rural Development on Herders’ Non-Farm Employment\n\nCompared to typical rural areas, pastoral regions face long-standing constraints such as geographical dispersion, poor transportation, limited access to information and insufficient market access. As a result, employment options for herding households are often confined to traditional livestock farming. Access to non-farm employment depends largely on personal network or occasional face-to-face contact [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B21-sustainability-18-08503)]. In light of the above, the construction of an information infrastructure, the extension of platform coverage and the broad application of digital tools in the development of digital rural have created new employment opportunities for herders outside of traditional sectors.\n\nAccording to the theory of information asymmetry, if market participants cannot obtain the required information in time and to their full satisfaction, they will bear a higher search cost, be less efficient in matching, and thus lose opportunities in their decision-making [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B22-sustainability-18-08503),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B23-sustainability-18-08503)]. The development of digital rural is changing how herders get jobs and market information. New technologies are now available to help herders stay informed about new employment prospects outside farming, wage levels, working locations and required skills [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B24-sustainability-18-08503)]. In this way, information barriers between herders and the outside labor market have been reduced. Thus, employment information is no longer scarce and fragmented, but rather open and accessible. As a result, herders’ awareness of non-farm employment and their opportunities for employment have significantly increased [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B25-sustainability-18-08503)].\n\nAs is well-known, non-farm employment not only involves the movement of labor across sectors, but also entails costs associated with information search, transportation, payment settlements and job matching. Transaction cost theory suggests that improvements in market efficiency depend largely on reductions in transaction costs [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B26-sustainability-18-08503),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B27-sustainability-18-08503)]. The development of digital rural has reduced the time costs, communication costs, and institutional friction faced by herders when seeking non-farm employment through information dissemination, digital payments and platform integration. Looking further, improvements in the information environment and reductions in transaction costs do not stop at the level of access to employment opportunities. Rather, they continue to influence labor allocation within herding households. In this context, non-farm employment reflects the reallocation of household labor from livestock production to non-farm sector [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B28-sustainability-18-08503),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B29-sustainability-18-08503)]. Consequently, household labor may gradually shift away from traditional livestock production toward a wider range of market-based non-farm activities. In light of the above, this paper proposes the following hypothesis:\n\n**H1.**\n\nDigital rural development is positively associated with household non-farm employment.\n\n### 2.2. Boundary Conditions of the Employment Effects of Digital Rural Development\n\nDigital rural development can improve information access, reduce job-search costs, and strengthen links to external markets. Nevertheless, these improvements do not automatically generate non-farm employment. Whether herders convert digital opportunities into employment also depends on local industrial structure and household production systems. We therefore examine the moderating roles of primary-industry development and forage cultivation.\n\nStructural transformation involves the reallocation of labor from primary production toward secondary and tertiary activities [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B30-sustainability-18-08503)]. Primary-industry value added at the county level measures the absolute scale of agricultural and livestock output, rather than the sector’s share of the local economy or its relative return. A higher value may nevertheless indicate a larger agricultural and livestock production base. In such counties, digital tools may be used more extensively for production management, input procurement and product marketing within the primary sector [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B31-sustainability-18-08503)]. Digitalization may lower search, matching, and market-access costs while also improving production within the primary sector. Where digital services are used mainly for production management and product marketing, these benefits may be absorbed within agricultural and pastoral activities rather than translated into labor reallocation [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B32-sustainability-18-08503)]. The positive association between digital rural development and herders’ participation in non-farm employment may therefore be weaker in counties with a larger primary industry base. We propose:\n\n**H2a.**\n\nHigher primary industry value added in a county weakens the positive association between digital rural development and household non-farm employment.\n\nForage cultivation is closely integrated with livestock production and requires labor for planting, management, harvesting, transport and storage [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B33-sustainability-18-08503)]. These tasks increase household labor demands, particularly during busy seasons. With limited household labor, forage cultivation may reduce the time available for non-farm employment [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B34-sustainability-18-08503)]. It also binds cropland, forage, livestock and family labor into a more integrated production system. Households may therefore have less flexibility to act on digital employment information and market opportunities.\n\n**H2b.**\n\nForage cultivation weakens the positive association between digital rural development and household non-farm employment.\n\n### 2.3. Transmission Mechanisms of Digital Rural Development\n\nIn most cases, funding is a key factor limiting herders’ ability to engage in any activities. Continuing livestock production, starting a business, and seeking employment elsewhere may all require financial resources, while reliable payment services can facilitate related transactions [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B35-sustainability-18-08503),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B36-sustainability-18-08503)]. However, due to factors such as remote locations, a lack of financial service outlets and exclusion from the traditional financial system, herders have long faced challenges in accessing financial services [[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B37-sustainability-18-08503)]. The development of digital rural has driven the shift in financial services toward online, convenient and grassroots-level delivery, providing herders with more accessible channels for payments, credit and financial services. The expansion of financial services to grassroots levels represents not only an increase in the scale of financial operations but also an extension of service coverage and an improvement in the efficiency of resource allocation. Digital rural development can facilitate the expansion of digital finance. Greater access to digital financial services may ease the credit and liquidity constraints faced by herding households. This, in turn, can lower the financial barriers to participation in non-farm employment and entrepreneurial activities [[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B38-sustainability-18-08503)]. At the same time, development of digital finance has reduced the transaction costs for labor transfer and provided a relatively easy way for herders to access external labor markets [[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B39-sustainability-18-08503)]. Therefore, the development of digital rural areas should not only increase the supply of financial products but also improve the financial situation for herders’ non-market activities at a deeper level and enhance their ability to engage in non-farm employment. Based on the above analysis, this study puts forward the following hypothesis:\n\n**H3.**\n\nDigital rural development promotes herders’ non-farm employment by expanding digital finance.\n\nWith the spread of online shopping, digital payments, and platform services, herding households have become more closely connected to digital markets. These developments have changed how households purchase goods, make payments, and access consumer services [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B40-sustainability-18-08503)].\n\nThe shift in consumption patterns is not merely a change in lifestyle. It also influences household employment choices through adjustments in income needs and labor allocation. Traditional herding households often rely on self-produced goods and local supplies. Digital rural development has expanded their access to external markets. Their consumption may therefore become more dependent on purchased goods and services. While households now have a wider range of choices regarding goods and services, they also have a greater need for a stable cash flow to support these choices [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B41-sustainability-18-08503)]. The increase in digital consumption suggests that more money will be needed by households. As a result, there has been a change in demand for labor, and many people have begun to leave traditional pastoralism for non-agricultural work in search of a more stable in","MDPI Sustainability 18(16):8503 2026-08-19","2026-08-19T00:00:00Z",81,{"impact":74,"substance":26,"depth":25,"authority":75,"freshness":76,"relevant":8,"comment":77},18,13,8,"基于2790户牧户调查的实证研究，揭示数字乡村对非农就业的促进作用及条件，数据详实，结论可靠，对牧区数字乡村政策有参考价值。",[79],{"name":70,"url":67},[81,4,82,83,84],"数字乡村","数字金融","非农就业","牧户",{"code":86,"slug":87,"name":88,"path":89,"parent_code":90},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":93},{"candidates":94,"agricultural_evidence":53},[95,99,105,111,117,123,128],{"code":86,"score":27,"supported":53,"title":96,"abstract":97,"tags":98},[81],[81],[81],{"code":100,"score":76,"supported":53,"title":101,"abstract":103,"tags":104},"G",[102],"rural development",[],[],{"code":106,"score":62,"supported":53,"title":107,"abstract":108,"tags":110},"C4",[],[109],"饲草",[],{"code":112,"score":62,"supported":53,"title":113,"abstract":114,"tags":116},"F5",[],[115],"农机",[],{"code":118,"score":62,"supported":53,"title":119,"abstract":120,"tags":122},"H6",[],[121],"农业生态",[],{"code":124,"score":8,"supported":22,"title":125,"abstract":126,"tags":127},"G3",[],[],[82],{"code":129,"score":8,"supported":22,"title":130,"abstract":131,"tags":132},"G6",[],[],[83],[134,135],"数字乡村 数字金融 非农就业 牧户","数字乡村 数字金融","数字乡村数字金融非农就业牧户-791",5,"2026-08-21T00:05:57.571383Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":16,"content":144,"source_name":145,"source_url":16,"published_at":146,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":147,"score_detail":148,"sources":151,"tags":153,"primary_topic":158,"secondary_topics":164,"classification":165,"search_phrases":177,"slug":180,"view_count":62,"doi":16,"paper":16,"created_at":181},512,"Can Certification of Geographical Indication Agricultural Products Promote Agricultural Green Total Factor Productivity? Moderation and Threshold Effects from China","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265","Chunlan Zou与Zaijin Zou(西南林业大学经济管理学院)基于2012—2023年中国31个省份面板数据,采用双向固定效应模型估计基准效应,调节效应模型检验农业产业集聚作用,面板门槛回归捕捉潜在非线性关系。研究表明:地理标志农产品认证显著提高AGTFP,每个认证在1%显著性水平上提高AGTFP约0.001单位;农业产业集聚正向调节这一关系,认证效应仅在高集聚地区显著;认证效应呈门槛依赖型,低于农业发展水平0.048时呈显著负效应-0.0023,高于时呈显著正效应0.0015。","## 1. Introduction\n\nThe green transformation of agriculture is a central pillar of China’s agricultural modernization agenda. At the end of 2024, the Ministry of Agriculture and Rural Affairs officially issued the Guiding Opinions on Accelerating the Comprehensive Green Transformation of Agricultural Development and Promoting Ecological Revitalization in Rural Areas, marking a new, more systematic phase of China’s agricultural green transformation [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B1-sustainability-18-08265)]. The Plan for Accelerating the Construction of an Agricultural Powerhouse (2024–2035) further emphasizes the need to enhance total factor productivity in agriculture and to build a modern production system that is both resource-efficient and environmentally friendly [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B2-sustainability-18-08265)]. This policy momentum, however, contrasts sharply with the persistent challenges that remain in practice.\n\nDespite this policy momentum, the gap between policy ambition and practical implementation remains substantial. According to the China Agricultural Green Development Report 2024, total agricultural fertilizer application nationwide reached 49.88 million metric tons in 2024. This figure indicates continued pressure on agricultural pollution control and, correspondingly, considerable room for improvement in Agricultural Green Total Factor Productivity (AGTFP). It also suggests that technological progress and increased use of inputs alone are insufficient to achieve a fundamental breakthrough in agriculture’s green transformation. Institutional mechanisms that combine regulatory constraints with market-based incentives are equally indispensable.\n\nAgainst this backdrop, a notable tension has emerged in China’s agricultural sector. The number of registered Geographical Indication (GI) products reached approximately 82,000 in 2024, a year-on-year increase of 7% [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B3-sustainability-18-08265)]. Yet agricultural non-point source pollution continues to impose a heavy environmental burden. The Outline of the 15th Five-Year Plan for National Economic and Social Development explicitly identifies green and branded agriculture as strategic priorities for agricultural modernization, calling for the vigorous development of GI products and distinctive agricultural brands [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B4-sustainability-18-08265)]. This juxtaposition raises a question of both theoretical and practical urgency: can GI certification—an institution originally designed to protect origin-specific product quality—simultaneously boost agricultural output and reduce pollution, thereby contributing to AGTFP improvement? Addressing this question is the central motivation of the present study.\n\nA substantial body of literature has examined the determinants of AGTFP, which can be broadly grouped into four strands. The first strand focuses on digital technology and financial innovation. Studies show that digital village construction [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B5-sustainability-18-08265)], digital inclusive finance [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B6-sustainability-18-08265)], and agricultural technological innovation and modernization [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B7-sustainability-18-08265)] enhance AGTFP by improving information accessibility, resource allocation efficiency, and innovation capacity. The second strand examines policy and institutional factors, including environmental regulation [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B8-sustainability-18-08265)], fiscal support for agriculture [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B9-sustainability-18-08265)], agricultural insurance [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B10-sustainability-18-08265)], and farmland transfer rights [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B11-sustainability-18-08265)]. These studies consistently find that the effectiveness of such policy instruments varies considerably across regional and institutional contexts [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B12-sustainability-18-08265),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B13-sustainability-18-08265)]. The third strand investigates industrial development and factor endowments, including agricultural industrial agglomeration [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B14-sustainability-18-08265)], agricultural product trade [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B15-sustainability-18-08265)], rural population aging [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B16-sustainability-18-08265)], and agricultural socialized services [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B17-sustainability-18-08265),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B18-sustainability-18-08265)]. This research shows that structural transformations in the agricultural economy can affect AGTFP through multiple pathways, with some studies reporting positive effects and others reporting negative or insignificant effects [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B19-sustainability-18-08265),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B20-sustainability-18-08265)]. The fourth strand considers macroeconomic factors, such as market conditions and urbanization, which also shape AGTFP at the aggregate level [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B21-sustainability-18-08265)]. Collectively, this literature has identified diverse technological, institutional, structural, and macroeconomic drivers of AGTFP, though most studies examine these determinants in isolation, leaving the role of market-based quality certification schemes such as GI largely unexplored.\n\nResearch on GI agricultural products has predominantly focused on the economic effects of certification. A substantial body of research confirms that GI certification raises farm household income, primarily through price premiums and improved market access [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B22-sustainability-18-08265)]. Other studies link GI certification to rural industrial development [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B23-sustainability-18-08265)] and county-level economic growth [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B24-sustainability-18-08265)], emphasizing the roles of brand value and agritourism in stimulating local economies [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B25-sustainability-18-08265)]. A smaller but growing body of literature examines the link between GI certification and environmentally sustainable production at the farm level. Several studies find that certified producers adopt environmentally friendly inputs and practices significantly more often than non-certified producers [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B26-sustainability-18-08265)]. These findings suggest that GI certification has meaningful implications for sustainable production. However, few studies have systematically examined the effects of GI certification on AGTFP specifically, and the conditions under which its potential environmental benefits are realized remain underexplored.\n\nIntegrating these two research streams reveals a critical blind spot. Although existing research has established that GI certification shapes producer behavior and that structural and institutional factors drive AGTFP, no systematic framework has yet been developed to examine whether GI certification can function as an institutional driver of green productivity improvement. Three questions remain unanswered. Does GI certification, as a formal institutional arrangement, exert a meaningful effect on AGTFP? If so, under what regional conditions is this effect realized—and why does it appear to vary so markedly across contexts? And is the relationship between certification and green productivity truly linear and homogeneous, or might it exhibit threshold-dependent non-linearities that prior studies have been unable to detect? Accordingly, this study draws on provincial panel data from China spanning 2012 to 2023 and employs a two-way fixed-effects model, a moderation model, and a panel threshold regression model to conduct a systematic empirical analysis.\n\nThis study offers three principal contributions to the existing literature. First, we integrate Geographical Indication (GI) certification into the analytical framework for AGTFP determinants. Unlike prior research that primarily focuses on command-and-control policy instruments such as environmental regulations [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B8-sustainability-18-08265),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B9-sustainability-18-08265)], this study conceptualizes GI certification as a market-based institutional mechanism. From an institutional economics perspective, we show that GI certification functions as a governance tool that embeds environmental constraints into production standards, thereby extending the institutional dimension of AGTFP research beyond conventional policy instruments [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B20-sustainability-18-08265)].\n\nSecond, we move beyond aggregate-level analysis to explain regional heterogeneity in certification outcomes by introducing agricultural industrial agglomeration as a moderating variable. We theorize that industrial agglomeration amplifies the green productivity effects of GI certification through two interrelated channels: improved factor supply conditions and the diffusion of green technology. This helps explain regional variations in policy effectiveness and offers empirical grounding for coordinating GI promotion with industrial policy.\n\nThird, this study identifies non-linearities in the certification effect that prior empirical studies have largely overlooked. Using a panel threshold regression model, we identify a critical agricultural development threshold: below this threshold, GI certification has a statistically significant negative effect on AGTFP, whereas above it, the effect reverses and becomes statistically significant and positive. This finding challenges the implicit assumption that certification effects are linear and homogeneous across regions and provides evidence supporting more targeted, region-specific design of agricultural green transformation policies.\n\n## 2. Theoretical Framework and Research Hypotheses\n\n### 2.1. The Direct Effect of GI Agricultural Product Certification on AGTFP\n\nGeographical Indication (GI) certification is more than a market label; it is an institutional system with a built-in green development orientation, typically requiring products to originate from a specific region and adhere to traditional ecological production standards that limit chemical inputs and protect the production environment. Because this green transformation occurs at the source, Agricultural Green Total Factor Productivity (AGTFP)—which integrates traditional inputs such as land, labor, and machinery while accounting for reductions in fertilizers, pesticides, and plastic mulch—provides an suitable indicator for capturing the resulting environmental and economic performance.\n\nAs illustrated in the “direct effect” section of [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#fig_body_display_sustainability-18-08265-f001), this study identifies three interrelated channels through which GI certification affects AGTFP: standard constraints, market incentives, and collective organization.\n\n**Figure 1.** Mechanism framework of GI agricultural products and AGTFP.\n\nFirst, the certification standards system imposes binding constraints on production inputs at the source. GI certification is governed by national standards and local regulations covering the production environment, cultivation techniques, and input use, with core requirements including pollutant limits and maximum pesticide residue levels [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B27-sustainability-18-08265)]. Through the credible-commitment mechanism of granting and revoking certification status, producers who violate input standards face certification disqualification and the loss of premium revenues, creating endogenous incentives for compliance. This constrains the use of pesticides and fertilizers, directly regulates pollution-generating behavior, and improves the coordination between origin-environment protection and factor-use efficiency—collectively driving AGTFP improvement.\n\nSecond, market price premiums provide sustained economic incentives for green production. Because information asymmetry prevents consumers from independently verifying product quality and ecological attributes—a problem long recognized in the signaling literature on quality uncertainty [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B28-sustainability-18-08265)]—GI certification serves as an officially endorsed quality signal that reduces search costs and raises consumers’ willingness to pay, generating stable premiums. When the long-term returns from retaining these premiums exceed the short-term gains from non-compliance, producers rationally reduce chemical inputs and adopt greener production methods, directly improving AGTFP. This mechanism’s effectiveness, however, depends on consumers’ ability to identify green attributes and on the maturity of market-based reputation systems.\n\nThird, drawing on the theory of collective action and knowledge spillovers [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B29-sustainability-18-08265)], the collective certification application process fosters coordinated constraints on green production at the regional level. Because GI applications are submitted on behalf of regional producers by cooperatives or industry associations, dispersed farm households, cooperatives, and agribusinesses must adopt unified production standards. This common regulatory framework accelerates the diffusion of green technologies, enables centralized processing of agricultural waste, and internalizes externalities that arise from fragmented, decentralized management—reducing redundant resource use and promoting AGTFP growth through improved technical efficiency and regional factor allocation. Based on the above analysis, we propose the following hypothesis:\n\n**H1.**\n\nGI agricultural product certification has a positive and significant effect on agricultural green total factor productivity.\n\n### 2.2. The Moderating Role of Agricultural Industrial Agglomeration in the Relationship Between GI Certification and AGTFP\n\nThe direct channels discussed in [Section 2.1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#sec2dot1-sustainability-18-08265) do not operate in isolation from the broader industrial environment. As illustrated in the moderating-effect component of [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#fig_body_display_sustainability-18-08265-f001), the extent to which certification’s green incentives translate into actual productivity gains depends critically on the level of agricultural industrial agglomeration surrounding certified producers. Two interrelated channels underlie this moderating role: a factor supply channel rooted in agglomeration economies, and a technology diffusion channel rooted in knowledge spillover theory.\n\nFrom the factor-supply perspective, agglomeration economy theory suggests that the spatial concentration of production activities improves factor market efficiency by lowering transaction costs and enhancing resource matching [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B30-sustainability-18-08265),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B31-sustainability-18-08265)]. Realizing the productivity effects of GI certification requires not only willingness but also the capacity to comply: producers need adequate financial resources, technological inputs, and human capital to implement certification standards effectively. In regions with higher industrial agglomeration, factor markets are more developed and resource acquisition is more accessible, providing the resources needed to convert certification compliance into genuine efficiency gains. Conversely, where agglomeration is low, factor market segmentation and restricted mobility create binding financial and technological constraints, leaving certified producers unable to implement green production standards in practice and diminishing certification’s net effect on AGTFP.\n\nFrom the technology-diffusion perspective, knowledge spillover theory holds that geographic proximity facilitates the transmission of tacit knowledge, which attenuates rapidly with distance [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B32-sustainability-18-08265),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B33-sustainability-18-08265)]. Green agricultural production relies heavily on tacit, hard-to-codify knowledge—such as site-specific cultivation techniques and input management experience—that spreads primarily through direct interaction. Industrial agglomeration reduces information transmission costs and provides an effective channel for such knowledge to diffuse between certified and non-certified producers. In regions with higher agglomeration density, more frequent interaction among producers lowers the marginal cost of adopting new green technologies, amplifying the overall effect of GI certification on AGTFP. In regions with lower agglomeration density, by contrast, dispersed producers weaken learning and demonstration effects among producers, leaving green production knowledge localized and adoption costs high, such that the spillover benefits fail to materialize.\n\nTaken together, these two channels indicate that industrial agglomeration does not merely provide a favorable context for GI certification—it actively amplifies certification’s green productivity effects through both factor supply and knowledge diffusion. The moderating role of agglomeration is therefore structural rather than incidental: it determines the extent to which the institutional incentives embedded in GI certification are converted into realized productivity gains. Based on the above analysis, we propose the following hypothesis:\n\n**H2.**\n\nThe positive effect of GI agricultural product certification on AGTFP is strengthened as agricultural industrial agglomeration increases.\n\n### 2.3. The Non-Linear Effect of GI Agricultural Product Certification on AGTFP\n\nThe direct and moderating effects discussed in [Section 2.1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#sec2dot1-sustainability-18-08265) and [Section 2.2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#sec2dot2-sustainability-18-08265) suggest that the green productivity effects of GI certification are neither universal nor uniform. As illustrated in the threshold-effect component of [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#fig_body_display_sustainability-18-08265-f001), this relationship may also exhibit structural non-linearity that varies with the level of regional agricultural development, giving rise to two distinct regimes: a sub-threshold regime in which certification suppresses AGTFP, and a supra-threshold regime in which its positive effects are activated. This pattern reflects institutional complementarity: GI certification cannot generate green productivity effects in isolation but requires complementary conditions—adequate infrastructure, functioning market access, and accessible factor supply—to activate its embedded incentives [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8265#B34-sustainability-18-08265)]. These conditions do not accumulate gradually but form in a threshold-dependent manner; below a critical level of agricultural development, key complementary conditions remain absent and the certification system’s incentive structure breaks down, whereas above it, these conditions become sufficiently developed to unlock the institution’s full potential.\n\nBelow the threshold, the inhibitory effect operates ","Sustainability 2026-08-12","2026-08-11T16:00:00Z",72,{"impact":74,"substance":26,"depth":74,"authority":27,"freshness":149,"relevant":8,"comment":150},2,"论文探讨地理标志认证对农业绿色全要素生产率的影响，主题相关，有实证数据，但发表于MDPI期刊，权威性一般，且非最新发布。",[152],{"name":145,"url":142},[154,155,156,4,157],"中国","门槛效应","地理标志","绿色全要素生产率",{"code":159,"slug":160,"name":161,"path":162,"parent_code":163},"E5","branding-commerce","品牌流通与电商","\u002Ftopic\u002Ffood-supply\u002Fbranding-commerce","E",[],{"status":46,"locked":22,"version":47,"method":48,"evidence":166},{"candidates":167,"agricultural_evidence":53},[168,172],{"code":159,"score":7,"supported":53,"title":169,"abstract":170,"tags":171},[],[156],[156],{"code":173,"score":8,"supported":22,"title":174,"abstract":175,"tags":176},"H4",[],[],[157],[178,179],"绿色全要素生产率 地理标志 门槛效应 中国","绿色全要素生产率 地理标志","绿色全要素生产率地理标志门槛效应中国-512","2026-08-13T23:53:47.475503Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":16,"content":187,"source_name":188,"source_url":16,"published_at":189,"category":20,"cover_url":16,"hotness":21,"is_selected":22,"score":190,"score_detail":191,"sources":194,"tags":196,"primary_topic":201,"secondary_topics":207,"classification":213,"search_phrases":235,"slug":238,"view_count":62,"doi":16,"paper":16,"created_at":239},380,"农户数字能力对低碳农业采纳的影响——基于陕黔川10057户猕猴桃种植户的机器学习分析","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088","基于陕西、贵州、四川三省10057名猕猴桃种植户的微观调查数据,西安建筑科技大学向雯与西北农林大学高建中等于Sustainability发表论文。以农户对低碳农业技术的初始与持续采纳为因变量,构建涵盖数字接入、数字获取、数字处理和数字共享4个维度的数字能力综合评价体系。比较Lasso回归、极端梯度提升、随机森林和反向传播神经网络等多种实证模型,结果显示随机森林模型表现最优。采用SHAP方法定量评估各变量边际贡献,研究发现农户数字能力显著正向促进低碳农业技术的初始与持续采纳,且具有明显的维度异质性:数字接入和获取能力是初始采纳的核心驱动因素,数字处理和共享能力对持续采纳起决定性作用。","## 1. Introduction\n\nIn light of the ongoing decline in atmospheric quality and the momentous deterioration of the ecological environment, the pursuit of low-carbon development has emerged as an imperative response to these challenges. The sustainable development model of low-carbon development, which is characterised by energy conservation, reduced consumption, and pollution reduction, has become a crucial avenue for addressing environmental issues [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B1-sustainability-18-08088),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B2-sustainability-18-08088),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B3-sustainability-18-08088),[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B4-sustainability-18-08088)]. In response to the specific conditions of its own development, China has put forth proposals for carbon peaking and carbon neutrality programmes. In 2020, China formally proposed a double carbon goal, committing to the international community to peak its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060 [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B5-sustainability-18-08088)]. It is widely acknowledged that agriculture represents a significant global source of greenhouse gas emissions. Consequently, it has become evident that farmland has an integral part to play in achieving the double carbon goal [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B6-sustainability-18-08088),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B7-sustainability-18-08088)].\n\nNevertheless, for an extended period, the direct cost of agricultural production has been the sole factor considered, with the cost of resource and environmental losses incurred during the production process remaining unaccounted for. The excessive and inefficient use of agricultural resources, such as pesticides and chemical fertilisers, in agricultural production has a direct impact on the quality and safety of agricultural products, the ecological environment, and human health. The negative externalities of this practice are becoming increasingly apparent [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B8-sustainability-18-08088),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B9-sustainability-18-08088),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B10-sustainability-18-08088)].\n\nOver the past few decades, China’s traditional agricultural production and cultivation patterns have undergone significant expansion, resulting in a considerable accumulation of high carbon emissions and a multitude of ecological damage issues [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B11-sustainability-18-08088),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B12-sustainability-18-08088),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B13-sustainability-18-08088)]. In recent years, the central No. 1 document has consistently emphasised the necessity of promoting environmentally sustainable agricultural practices. This entails accelerating the promotion and application of agricultural input reduction and efficiency technology, integrating water and fertiliser management, and establishing and enhancing the collection and utilisation of straw, agricultural film, pesticide packaging waste, livestock and poultry manure, and other agricultural waste collection and treatment systems. Furthermore, General Secretary Xi Jinping emphasised the necessity of developing ecological and low-carbon agricultural practices, asserting that environmentally sustainable practices are the foundation of modern agriculture.\n\nGreen and low-carbon agriculture has the potential to save energy and reduce emissions, facilitate the sequestration of carbon in an ecological manner, enhance the utilisation of agricultural resources, produce safe and high-quality agricultural products, and provide robust support for the realisation of China’s double carbon target [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B14-sustainability-18-08088),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B15-sustainability-18-08088)]. The foundation of low-carbon agricultural production is the utilisation of cutting-edge low-carbon agricultural technology [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B16-sustainability-18-08088),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B17-sustainability-18-08088)]. It is for this reason that research and development, promotion, and application of low-carbon agricultural technology have become pivotal measures to achieve carbon sequestration and emission reduction in agricultural production. The contemporary understanding of low-carbon agricultural technology is primarily concerned with the minimisation of inputs, including energy, fertilisers, pesticides and water, in agricultural production. This approach ultimately aims to curtail greenhouse gas emissions from agricultural sources. In particular, low-carbon agricultural technology encompasses a range of techniques and practices, including formula fertilisation technology, commercial organic fertiliser application, farm fertiliser application, no-till technology, and water-saving irrigation technology.\n\nThe implementation of the household contract responsibility system in China has resulted in the farming household becoming the primary entity engaged in production and management activities within rural areas, and is thus the most fundamental decision-making unit. The uptake of low-carbon agricultural technology by farmers is contingent upon the comprehensive promotion and implementation of ecological low-carbon agriculture in China [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B18-sustainability-18-08088)]. The decision to adopt technology is a behavioural choice made by an individual, influenced by the opinions of others and based on an evaluation of the advantages offered by the technology and their own suitability for it [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B19-sustainability-18-08088)]. The dissemination and uptake of low-carbon agricultural technology can be conceptualised as a process of technology diffusion [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B20-sustainability-18-08088)]. The process can be divided into two distinct stages: the initial stage concerns farmers making the decision to adopt low-carbon agricultural technology; the subsequent stage pertains to farmers deciding to continue the behaviour after adopting low-carbon agricultural technology [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B21-sustainability-18-08088)]. In accordance with the conventional perspective on economics, a considerable number of scholars have examined the internal and external elements influencing the initial adoption of low-carbon agricultural technology by farmers at both the theoretical and empirical levels. Internal factors include gender, age, health, education, peer effects, ecological awareness, household income, non-farm labour migration, time preference, and risk perception [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B22-sustainability-18-08088),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B23-sustainability-18-08088),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B24-sustainability-18-08088),[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B25-sustainability-18-08088),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B26-sustainability-18-08088),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B27-sustainability-18-08088),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B28-sustainability-18-08088)]. The adoption of low-carbon agricultural technology by farmers is influenced by several external factors, including training, social capital, market incentives, government regulation, subsidies, agricultural insurance, participation in cooperatives, and machinery outsourcing services [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B29-sustainability-18-08088),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B30-sustainability-18-08088),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B31-sustainability-18-08088),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B32-sustainability-18-08088),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B33-sustainability-18-08088),[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B34-sustainability-18-08088)]. A review of the literature reveals a paucity of studies investigating the sustained adoption of low-carbon agricultural technology by farmers. Many scholars have employed sustained use modelling to examine the influence of various factors, including technology cost and efficacy, technology complexity, government technology promotion, and policy subsidies, on the sustained adoption of low-carbon agricultural technology by farmers [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B35-sustainability-18-08088),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B36-sustainability-18-08088),[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B37-sustainability-18-08088)]. A consensus was reached on the fundamental premise that the continued adoption of low-carbon agricultural technology by farmers represents an extension of the technology adoption process. This extension is based on the evaluation of the actual results of technology adoption in relation to individual expectations of the effects of low-carbon agricultural technology. It is frequently observed that the performance of a technology is at its lowest during the initial adoption phase. It is only through continued utilisation of the technology that its intrinsic value can be fully appreciated. Despite the ascendance of green agriculture in China in the context of production and development, a discrepancy persists between the initial and sustained adoption of green and low-carbon agricultural technologies by farmers. Nevertheless, the majority of existing studies focus on the initial decision-making process surrounding the adoption of low-carbon technology, neglecting to consider the dynamic characteristics of technology adoption behaviour across different stages. Furthermore, there is a tendency to conflate initial adoption with sustained adoption.\n\nThe digital economy is particularly adept at optimising the low-carbon agricultural industry landscape [[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B38-sustainability-18-08088)]. The advent of the digital economy has given rise to a plethora of novel technologies, industries, and business models. Moreover, the digitisation of knowledge and information has become a primary factor in the production process [[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B39-sustainability-18-08088)]. The integration of modern information technology with agriculture, including the internet, the Internet of Things, cloud computing, big data, and artificial intelligence, enables real-time collection of agricultural data, real-time monitoring of the agricultural ecological environment, precise input of agricultural production factors, quantitative decision-making in the agricultural production process, and intelligent regulation and control of agricultural production. These functions collectively constitute a new low-carbon agricultural production model. It has been demonstrated that the digital economy plays a notable role in the implementation of low-carbon agricultural technology by farmers [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B40-sustainability-18-08088)]. Concurrently, the digital economy has considerable expectations regarding the digital capability of farmers. Those with superior digital abilities are more adept at navigating digital technology and are more likely to receive a premium for their skills in the context of the advancement of the digital economy.\n\nThe disparate digital capability of farmers gives rise to variations in the digital divide and the digital dividend. This raises the question of whether digital capability has a significant positive effect on the initial adoption of low-carbon agricultural technology by farmers. Does digital capability also have a significant positive effect on the sustained adoption of low-carbon agricultural technology? Given the established link between initial and sustained adoption of technology, it is pertinent to inquire whether the factors influencing the initial behaviour of farmers in the adoption of low-carbon agricultural technology also exert an influence on their sustained adoption behaviour. This study proposes an innovative perspective to study the gap between the initial and sustained adoption of existing low-carbon agricultural technology based on a comprehensive analysis of the preceding findings. The objective of this study is to achieve an accurate match between low-carbon agricultural technology and farmers.\n\nFurthermore, it is worthwhile to mention that the conventional methodology utilised in agricultural policy research typically entails the gathering of data, the application of analytical models to elucidate the interrelationships between variables, and the formulation of policy recommendations. A review of the existing literature reveals that the majority of studies have formulated hypotheses and conducted regression analyses, primarily utilising econometric methods such as binary probit, propensity score matching, and double difference methods. Nevertheless, the inherent bias in establishing linear relationships between indicators in explanatory modelling results in insufficient discriminatory precision, which may lead to a wide range of policy recommendations that are disparate. The conventional techniques employed in statistical analysis are gradually being shown to have certain inherent limitations.\n\nIn recent years, machine learning methods have been employed extensively in agri-environmental and economic research due to their ability to effectively address complex non-linear problems. The field of machine learning is predicated on the analysis and learning of the mapping of existing data to expert classification models. Techniques for processing data and information are developed through algorithms that facilitate the stable and reliable classification of unknown categorical data. In the context of artificial intelligence and big data technology, the computational analysis method of machine learning can be applied to discover data laws or implicit models from multi-dimensional data that are difficult to perceive and discover. The computational analysis method offers several advantages, including high accuracy, rapid training speed, and straightforward implementation and interpretation. The method enhances the existing research paradigms and is extensively employed in diverse classification and regression tasks [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B41-sustainability-18-08088)].\n\nIn light of the aforementioned evidence, this study proceeded to devise and administer a questionnaire survey comprising 10,057 kiwifruit farmers situated in the provinces of Shaanxi, Guizhou, and Sichuan, which collectively represent the three leading kiwifruit-producing regions in China. The current status and challenges associated with the initial and sustained adoption of low-carbon agricultural technology by kiwifruit growers were further elucidated. The ordered logit model, which has been demonstrated to offer a robust explanatory framework for behavioural decisions, was selected as a reference for the analysis of the results obtained from other machine learning methods. The dataset was divided into training and testing sets based on the results of the experimental analysis, which employed a range of machine learning techniques. The optimal model is selected on the basis of the expected goodness of fit. Subsequently, the SHAP value interpretive method is implemented to ascertain the contribution rate of each digital capability dimension to the initial and sustained adoption of low-carbon agricultural technology by farmers, thereby facilitating the effective identification of the most significant influencing factors. The influence of digital capability and its constituent elements on the initial and sustained adoption of low-carbon agricultural technology by farmers is subjected to rigorous analysis, and recommendations for pertinent policy measures are put forth.\n\nThe contributions of this study are mainly reflected in the following three aspects: (1) Research Theme: This study investigates the characteristics of a sample of farmers and their initial and sustained adoption of low-carbon agricultural technology through micro-field research. In accordance with the findings of static technology adoption studies, further consideration is given to the continued adoption of technology. Accordingly, the objective of this study is to address the existing gap in the literature on the sustained adoption of low-carbon agricultural technology by farmers. (2) Research Content: This study provides a comprehensive examination of the composition of the digital capability of farmers, with the utilisation of factor analysis to construct and measure the digital capability index from four dimensions: digital access capability, digital acquisition capability, digital processing capability, and digital sharing capability. This offers a reference paradigm for the construction of indicators for subsequent related studies. Concurrently, it furnishes novel evidence to elucidate the influence of the digital capability of farmers and their multifaceted dimensions on the initial and sustained adoption of low-carbon agricultural technology by farmers. (3) Research Method: This study offers a comprehensive integration of the strengths inherent to both traditional econometric research methods and machine learning methods. In order to more accurately capture the impact of digital capability on the initial and sustained adoption of low-carbon agricultural technology by farmers, and to provide more scientific policy recommendations for the decision-making community, an ordered logit model, a Lasso regression method, a random forest method, an XGBoost model, and a BP neural network model were used.\n\n## 2. Theoretical Analysis and Research Hypotheses\n\n### 2.1. Theoretical Analysis Framework\n\nThe behaviours of farmers in the adoption of green and low-carbon agricultural technologies can be divided into two progressive stages: initial trial and long-term sustained application. However, there exist significant discrepancies in the behavioural constraint mechanisms between the two stages [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B42-sustainability-18-08088)]. The former stage is primarily restricted by decision-making uncertainty arising from insufficient information, whereas the latter stage hinges on perceived technical benefits and long-term production preferences. As a core endowment enabling farmers to adapt to the digital transformation of modern agriculture, digital capability can systematically resolve practical dilemmas hindering the adoption of low-carbon technology by farmers through two pathways: alleviating endogenous decision-making constraints and optimising exogenous production mechanisms. Digital capability effectively drives the behavioural transition of farmers from passive wait-and-see to proactive trials, and from one-off usage to regular, in-depth implementation. On the one hand, grounded in the bounded rationality decision-making theory and farmer behaviour theory, comprehensive digital capability integrated by multi-dimensional capability including digital access, information acquisition, data processing, and information sharing can break the ingrained cognitive habits shaped by the traditional farming experiences of farmers, expand supply channels of agricultural technical information, correct farmers’ biased risk perceptions of low-carbon technology, and reshape their long-term income expectations. From the endogenous cognitive dimension, it thereby facilitates farmers’ acceptance of innovative low-carbon production modes. On the other hand, drawing upon the sustained technology application theory, a sound d","Sustainability 2026-08-08","2026-08-08T00:00:00Z",75,{"impact":74,"substance":25,"depth":74,"authority":27,"freshness":192,"relevant":8,"comment":193},7,"基于大规模农户数据的机器学习研究，探讨数字能力对低碳农业采纳的影响，方法新颖，数据量大，结论可靠，对农业信息化与低碳发展有参考价值。",[195],{"name":188,"url":185},[197,198,199,200,4],"数字能力","低碳农业","SHAP","随机森林",{"code":202,"slug":203,"name":204,"path":205,"parent_code":206},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[208],{"code":209,"slug":210,"name":211,"path":212,"parent_code":90},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":46,"locked":22,"version":47,"method":48,"evidence":214},{"candidates":215,"agricultural_evidence":53},[216,222,227,231],{"code":202,"score":217,"supported":53,"title":218,"abstract":220,"tags":221},11,[219],"猕猴桃",[219],[],{"code":209,"score":21,"supported":53,"title":223,"abstract":225,"tags":226},[224],"机器学习",[],[199,200],{"code":173,"score":8,"supported":22,"title":228,"abstract":229,"tags":230},[],[],[198],{"code":86,"score":8,"supported":22,"title":232,"abstract":233,"tags":234},[],[],[197],[236,237],"低碳农业 数字能力 随机森林 Sustainability","低碳农业 数字能力","低碳农业数字能力随机森林Sustainability-380","2026-08-10T23:54:46.360428Z"]