Evaluation and Improvement Strategy of Agricultural Product Logistics Capacity in Western Region
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摘要:
采用因子分析法,以2020年我国西部地区省域的农产品物流数据为研究样本,构建西部地区农产品物流能力评价指标体系,对西部地区农产品物流能力进行综合评价。剖析了西部地区农产品物流能力提升过程中现存的地区发展不均衡、发展动力不足、损失率较高等问题。此外,针对评价结果及提升过程中现存的问题,进一步分析问题产生的成因,提出了提高西部地区农产品物流能力的针对性建议,如夯实基础设施建设、完善信息平台搭建、优化农产品物流体系等对策,旨在整合农产品物流资源,提升农产品物流效率。
Abstract:Adopting factor analysis method and taking agricultural product logistics data in western region of China in 2020 as the sample, evaluation index system of agricultural product logistics capability of western region was constructed, and a comprehensive evaluation of agricultural product logistics capability in western region was constructed.In process of improving logistics capacity of agricultural products in western region, existing problems such as unbalanced regional development, insufficient development momentum, and high loss rate were analyzed.In addition, evaluation results and existing problems in improvement process were given, causes of problems and targeted suggestions to improve logistics capacity of agricultural products in western region were further analyzed, such as consolidating construction of infrastructure, perfecting construction of information platforms, optimizing logistics system of agricultural products.It aimed to integrate agricultural product logistics resources, improve efficiency of agricultural product logistics.
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表 1 西部地区农产品物流能力评价指标体系
Table 1. Evaluation index system of agricultural product logistics capacity in western region
一级指标 二级指标 对应变量 单位 需求能力 人均GDP X1 万元 社会消费品零售总额 X2 亿元 全体居民人均消费支出 X3 元 供给能力 农林牧渔业总产值 X4 亿元 农作物总播种面积 X5 ×103 hm2 农村居民人均可支配收入 X6 元 物流规模 公路里程 X7 ×104 km 公路货运量 X8 万t 民用汽车拥有量 X9 万辆 信息化水平 互联网宽带接入用户 X10 万户 表 2 KMO检验和巴特利特球形检验
Table 2. KMO test and Bartelett’s sphericity test
项目 数值 KMO 取样适切性量数 0.663 巴特利特球形度检验 近似卡方 127.427 自由度 45 显著性 <0.001 表 3 因子特征值和方差贡献率
Table 3. Factor eigenvalues and variance contribution rate
成分 初始特征值 提取载荷平方和 旋转载荷平方和 总计 方差/% 累积/% 总计 方差/% 累积/% 总计 方差/% 累积/% 1 6.523 65.226 65.226 6.523 65.226 65.226 5.990 59.905 59.905 2 2.002 20.021 85.247 2.002 20.021 85.247 2.534 25.342 85.247 3 0.485 4.850 90.097 4 0.370 3.696 93.793 5 0.326 3.259 97.052 6 0.211 2.112 99.164 7 0.046 0.465 99.629 8 0.021 0.212 99.841 9 0.012 0.116 99.957 10 0.004 0.043 100.000 注:提取方法为主成分分析法。 表 4 旋转成分矩阵
Table 4. Rotation component matrix
指标 成分1 成分2 X1 0.016 0.948 X2 0.882 0.300 X3 0.247 0.824 X4 0.985 0.029 X5 0.873 0.149 X6 0.200 0.840 X7 0.890 0.107 X8 0.873 0.149 X9 0.980 0.107 X10 0.929 0.178 注:提取方法为主成分分析法;采用凯撒正态化最大方差法进行旋转,下同。 表 5 主因子得分系数矩阵
Table 5. Score of main factor coefficient matrix
指标 成分1 成分2 X1 −0.103 0.433 X2 0.138 0.039 X3 −0.044 0.350 X4 0.188 −0.096 X5 0.153 −0.029 X6 −0.055 0.363 X7 0.161 −0.050 X8 0.140 0.024 X9 0.173 −0.037 X10 −0.055 0.363 表 6 西部地区各省份农产品物流能力总分
Table 6. Total score of provincial agricultural product logistics capacity in western region
地区 F1 排名 F2 排名 F 排名 内蒙古自治区 −0.024 6 1.438 2 0.411 3 广西壮族自治区 0.664 3 −0.454 9 0.332 5 重庆市 −0.305 9 2.046 1 0.394 4 四川省 2.220 1 0.483 3 1.703 1 贵州省 0.327 4 −1.179 11 −0.121 8 云南省 0.844 2 −0.568 10 0.424 2 西藏自治区 −1.268 12 −0.408 8 −1.012 12 陕西省 0.229 5 0.295 4 0.249 6 甘肃省 −0.176 8 −1.489 12 −0.566 9 青海省 −1.234 11 −0.121 6 −0.903 11 宁夏回族自治区 −1.226 10 0.210 5 −0.7989 10 新疆维吾尔自治区 −0.052 7 −0.253 7 −0.112 7 -
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