象山县农村数字金融服务覆盖度评估数据

象山县农村数字金融服务覆盖度评估模型能够系统评估县域数字金融服务的普及水平和质量，帮助农业农村局精准识别农村金融服务的薄弱环节与区域差异。通过量化分析手机银行、数字支付、线上信贷等关键指标，可为政策制定者提供数据支撑，优化金融资源配置，推动普惠金融向偏远地区和低收入群体延伸。同时，模型能强化金融机构与农村基层的联动，促进数字金融工具的创新与应用，提升农民金融素养，降低金融服务门槛。最终，该模型将助力构建更公平、高效、可持续的农村数字金融生态，为乡村振兴注入金融活水。根据村庄邮政编码经过特殊处理获得村庄唯一编码；根据农户抽样调查获得手机银行开通率Ta；根据银联交易数据与村级服务站台账获得数字支付覆盖率Na；根据人民银行征信系统与涉农贷款统计获得线上信贷渗透率J3；根据保险行业协会数据与承保机构报表获得农业保险数字化投保率Ma；根据金融机构网点GIS数据与现场核查获得村级金融触点密度J1；根据12396热线咨询记录与入户测试获得数字金融知识普及度S；根据核心企业ERP系统与合作社台账获得涉农供应链金融覆盖率J2；根据法院调解平台数据与司法所记录获得金融纠纷线上调解率Ht；所有采集汇总数据通过多因子归一化处理，最终通过线性加权法进行计算：Y=(Tax0.2+Nax0.2+J3x0.15)+(Max0.1+J1x8x0.1+Sx20x0.1)+(J2x0.1+Htx0.05)，最终获得象山县农村数字金融服务服务覆盖值，>=85分：优秀（引领示范），核心措施：优先纳入国家级数字普惠金融试点，推广成熟模式；70-85分（不包括85，包括70）：良好（优化提升），核心措施：针对性提升线上信贷渗透率与手机银行开通率（如通过村级服务站定向推广），优化触点布局（每村新增1-2个智能设备），开展金融知识普及活动（入户测试+12396热线联动）；60-70分（不包括70，包括60）：合格（重点改进），核心措施：县级专班督导整改，6个月内聚焦线上信贷率与数字支付率短板（如引入自动审批贷款工具），冻结非紧急支出，资金倾斜村级触点建设与保险数字化推广，组织村民代表赴示范点学习，修订村规民约强化金融意识；<60分：不合格（紧急干预），核心措施：省级挂牌督办，农业农村局直接问责，强制增加村级触点密度，引入第三方机构驻点培训（重点提升数字金融知识普及度），每日上报进度。

The Rural Digital Financial Service Coverage Evaluation Model for Xiangshan County systematically evaluates the popularity and quality of county-level digital financial services, enabling the Agricultural and Rural Affairs Bureau to accurately identify weak links and regional disparities in rural financial services. Through quantitative analysis of key indicators including mobile banking, digital payment, and online credit, the model provides data support for policymakers to optimize financial resource allocation and advance inclusive finance to remote areas and low-income groups. Additionally, the model strengthens the linkage between financial institutions and rural grassroots entities, promotes the innovation and application of digital financial tools, enhances farmers' financial literacy, and reduces the threshold for accessing financial services. Ultimately, this model facilitates the construction of a fairer, more efficient, and sustainable rural digital financial ecosystem, injecting financial vitality into rural revitalization efforts.

Unique village codes are generated via special processing of village postal codes. The mobile banking adoption rate Ta is derived from farmer sampling surveys; the digital payment coverage rate Na is obtained from UnionPay transaction data and village-level service station ledgers; the online credit penetration rate J3 is acquired from the Credit Reporting System of the People's Bank of China and agricultural-related loan statistics; the agricultural insurance digital enrollment rate Ma is collected from data of the insurance industry association and underwriting institution statements; the village-level financial contact point density J1 is obtained from GIS data of financial institution outlets and on-site inspections; the digital financial knowledge popularization rate S is based on 12396 hotline consultation records and household interviews; the agricultural supply chain finance coverage rate J2 is derived from core enterprise ERP systems and cooperative ledgers; the online mediation rate of financial disputes Ht is collected from court mediation platform data and judicial office records.

All collected and aggregated data undergo multi-factor normalization processing, and the final rural digital financial service coverage value of Xiangshan County is calculated using the linear weighting method:
Y=(Ta×0.2 + Na×0.2 + J3×0.15) + (Ma×0.1 + J1×8×0.1 + S×20×0.1) + (J2×0.1 + Ht×0.05)
The grading criteria are as follows:
1. ≥85 points: Excellent (Pilot Demonstration). Core measures: Prioritize inclusion in national digital inclusive finance pilot programs to promote mature models;
2. 70–85 points (excluding 85, including 70): Good (Optimization and Improvement). Core measures: Targetedly improve the online credit penetration rate and mobile banking adoption rate (e.g., targeted promotion via village-level service stations), optimize the layout of financial contact points (add 1–2 smart devices per village), carry out financial literacy popularization activities (household interviews + 12396 hotline linkage);
3. 60–70 points (excluding 70, including 60): Qualified (Key Improvement). Core measures: Implement county-level special team supervision and rectification, focus on addressing the shortcomings of online credit rate and digital payment rate within 6 months (e.g., introduce automatic loan approval tools), freeze non-urgent expenditures, allocate funds to village-level contact point construction and insurance digital promotion, organize villager representatives to visit demonstration sites, and revise village regulations to strengthen financial awareness;
4. <60 points: Unqualified (Emergency Intervention). Core measures: Designate for provincial-level key supervision, hold the Agricultural and Rural Affairs Bureau directly accountable, mandatorily increase village-level financial contact point density, introduce third-party institutions for on-site training (focus on improving digital financial knowledge popularization rate), and report progress daily.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于象山县农村数字金融服务覆盖度评估，包含1001条记录，每季度更新，通过手机银行开通率、数字支付覆盖率等11个关键指标量化金融服务水平。它采用线性加权算法计算覆盖指数，支持政策制定者识别区域差异、优化金融资源配置，并推动普惠金融发展和乡村振兴。