[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-CLDS":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"CLDS","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":29,"tags":31,"primary_topic":16,"secondary_topics":36,"classification":37,"search_phrases":55,"slug":58,"view_count":59,"doi":60,"paper":61,"created_at":81},3761,"Multidimensional climate physical risk and reported farmland abandonment among rural farm households in China: evidence from household panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948860","Introduction For family-operated farms, keeping contracted land in cultivation is one practical dimension of resilience to climate stress. This study asks whether multidimensional climate physical risk is associated with reported farmland abandonment among rural households in China. Methods We matched the 2014, 2016, and 2018 China Labor-force Dynamics Survey (CLDS) waves to province-level Climate Physical Risk Index (CPRI) values from the preceding agricultural years. Abandonment is coded from a household report that some contracted cropland was uncultivated during the reference period; the survey does not show whether that status was temporary or persistent or the reason for it. The resulting unbalanced panel contains 19,311 household-year observations for 11,982 households in 27 provincial-level units. Household fixed-effects linear probability models include survey-wave fixed effects, province-clustered standard errors, and Webb-weight wild cluster bootstrap inference. Results A one-standard-deviation within-province increase in CPRI is associated with a 3.02-percentage-point increase in reported abandonment, approximately one-fifth of the sample mean. The association remains positive under alternative outcomes, exposure dates, sample restrictions, and model choices. The four hazards are jointly significant; only extreme low temperature is individually significant in the exploratory decomposition. Discussion Although the estimates are observational rather than causal, they suggest that multidimensional climate exposure may weaken land-use continuity in household farming systems.","引言 对于家庭经营的农场而言，保持承包地处于耕作状态是应对气候压力的一种实际韧性维度。本研究旨在探讨多维气候物理风险是否与中国农村家庭报告的耕地撂荒存在关联。方法 我们将2014年、2016年和2018年中国劳动力动态调查（CLDS）数据与前一农业年份的省级气候物理风险指数（CPRI）值进行匹配。撂荒的编码依据是家庭报告在参考期内部分承包耕地未进行耕作；该调查并未显示该状态是暂时性的还是持续性的，也未显示其原因。由此得到的非平衡面板包含27个省级单位中11,982个家庭的19,311个家庭-年份观测值。家庭固定效应线性概率模型纳入了调查波次固定效应、省级聚类标准误以及Webb权重野生聚类自举推断。结果 省内CPRI每增加一个标准差，与报告撂荒增加3.02个百分点相关，约为样本均值的五分之一。在替代结果变量、暴露日期、样本限制和模型选择下，该关联仍为正向。四种灾害联合显著；在探索性分解中，仅极端低温单独显著。讨论 尽管这些估计是观察性的而非因果性的，但它们表明多维气候暴露可能削弱家庭农业系统中土地利用的连续性。",null,"Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z","论文",10,false,80,{"impact":24,"substance":25,"depth":24,"authority":26,"freshness":27,"relevant":7,"comment":28},18,22,13,9,"基于CLDS三期面板与省级气候风险指数，量化气候物理风险与农户耕地撂荒的关联，方法规范、数据规模大，对农业风险管理有参考价值。",[30],{"name":17,"url":13},[32,33,34,35,4],"气候变化","农业风险","农户生计","耕地撂荒",[],{"status":38,"locked":21,"version":39,"method":40,"evidence":41},"unclassified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":53,"reason":54},[43,48],{"code":44,"score":7,"supported":21,"title":45,"abstract":46,"tags":47},"G6",[],[],[34],{"code":49,"score":7,"supported":21,"title":50,"abstract":51,"tags":52},"H7",[],[],[32],true,"缺少足够的主题证据",[56,57],"中国劳动力动态调查 耕地撂荒","气候物理风险 农户 撂荒","中国劳动力动态调查耕地撂荒-3761",6,"10.3389\u002Ffsufs.2026.1948860",{"doi":60,"openalex_id":62,"authors":63,"venue":17,"cited_by_count":73,"oa_url":13,"card":74,"direction":78,"ingested_from":80},"W7214624950",[64,67,69,71],{"name":65,"orcid":66},"Ruofan Liao","https:\u002F\u002Forcid.org\u002F0009-0009-5058-7368",{"name":68,"orcid":16},"Kexin Zhang",{"name":70,"orcid":16},"Mi Tan",{"name":72,"orcid":16},"Jianxu Liu",0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"研究中国农户多维气候物理风险与报告耕地撂荒的关联。","匹配CLDS 2014-2018面板与省级气候物理风险指数，用固定效应模型。","省内CPRI每增一个标准差，报告撂荒率上升3.02个百分点。","农业绿色发展与碳","可结合遥感验证撂荒、区分暂时\u002F持续撂荒，并探究极端低温等具体致灾机制。","openalex","2026-09-29T23:30:07.148843Z"]