中国智慧冷链发展水平评价及对策建议

doi:10.12133/j.smartag.SA202302003

Smart Agriculture ›› 2023, Vol. 5 ›› Issue (1): 22-33.doi: 10.12133/j.smartag.SA202302003

• 专题--农产品智慧供应链 • 上一篇下一篇

中国智慧冷链发展水平评价及对策建议

杨霖¹^,²^,³^,⁴(), 杨斌¹^,²^,³, 任青山¹^,²^,³, 杨信廷¹^,²^,³, 韩佳伟¹^,²^,³()

^1.北京市农林科学院信息技术研究中心，北京 100097
^2.农产品质量安全追溯技术及应用国家工程研究中心，北京 100097
^3.农业农村部农产品冷链物流技术重点实验室，北京 100097
^4.仲恺农业工程学院信息科学与技术学院，广东广州 510225

收稿日期:2023-02-06 出版日期:2023-03-30
基金项目:
北京市农林科学院科技创新能力建设专项(KJCX20210408);国家重点研发计划课题(2022YFD2001804);北京市农林科学院科研创新平台建设(PT2023-24)
作者简介:杨霖，硕士研究生，研究方向为智慧供应链关键技术。E-mail：yanglinear@163.com
通信作者: 韩佳伟，博士，高级农艺师，研究方向为智慧供应链关键技术。E-mail：hjwlove8@163.com

Evaluation and Countermeasures on the Development Level of Intelligent Cold Chain in China

YANG Lin¹^,²^,³^,⁴(), YANG Bin¹^,²^,³, REN Qingshan¹^,²^,³, YANG Xinting¹^,²^,³, HAN Jiawei¹^,²^,³()

^1.Research Center of information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
^2.National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China
^3.Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
^4.College of Information Science and Technology, Zhongkai Agricultural Engineering College, GuangZhou 510225, China

Received:2023-02-06 Online:2023-03-30
Foundation items:Beijing Academy of Agricultural and Forestry Sciences Science and Technology Innovation Capacity Building Special Project (KJCX20210408); National Key R&D Program Project (2022YFD2001804); Construction of Research and Innovation Platform of Beijing Academy of Agriculture and Forestry Sciences (PT2023-24)
About author:YANG Lin, E-mail：yanglinear@163.com
Corresponding author:HAN Jiawei, E-mail：hjwlove8@163.com

摘要/Abstract

摘要：

新一代信息技术促使冷链智能化水平得以快速发展，精准把控智慧冷链发展水平是实现技术瓶颈重点突破与发展方向战略布局的前提基础与保障。基于此，本研究从供给能力、仓储能力、运输能力、经济效益、信息化水平等维度构建了中国智慧冷链发展评价指标体系，运用熵权法并结合优劣解距离法（Technique for Order Preference by Similarity to Ideal Solution，TOPSIS）对2017—2021年中国30个省市（不包含西藏、香港、澳门、台湾）的智慧冷链发展水平进行定量评价，通过探索性空间数据分析法（Exploratory Spatial Data Analys，ESDA）和地理加权回归（Geographically Weighted Regression，GWR）分析评价指标对不同省市的影响变化。研究结果表明，经济发展状况、配套设施建设与信息化水平对智慧冷链建设影响作用较大；东部沿海地区智慧冷链发展普遍较好，西南和西北地区发展缓慢，整体发展较为落后；评价指标具有显著时空异质性，影响程度随空间位置和时间发生变化。为提升中国智慧冷链整体发展水平，就加强配套设施建设、促进信息化转型升级提出相应发展对策。本研究可为实现中国智慧冷链全局性谋划、战略性布局、整体性推进等提供科学依据。

关键词: 智慧冷链, 熵权法, TOPSIS, 探索性空间分析, 地理加权回归, 评价指标体系, 信息化水平

Abstract:

The new generation of information technology has led to the rapid development of the intelligent level of the cold chain, and the precise control of the development level of the smart cold chain is the prerequisite foundation and guarantee to achieve the key breakthrough of the technical bottleneck and the strategic layout of the development direction. Based on this, an evaluation index system for China's intelligent cold chain development from the dimensions of supply capacity, storage capacity, transportation capacity, economic efficiency and informationization level was conducted. The entropy weight method combined with the technique for order preference by similarity to ideal solution (TOPSIS) was used to quantitatively evaluate the development of intelligent cold chain in 30 Chinese provinces and cities (excluding Tibet, Hong Kong, Macao and Taiwan) from 2017 to 2021. The quantitative evaluation of the level of intelligent cold chain development was conducted. The impact of the evaluation indicators on different provinces and cities was analysed by exploratory spatial data analyses (ESDA) and geographically weighted regression (GWR). The results showed that indicators such as economic development status, construction of supporting facilities and informationization level had greater weight and played a more important role in influencing the construction of intelligent cold chain. The overall level of intelligent cold chain development in China is divided into four levels, with most cities at the third and fourth levels. Beijing and the eastern coastal provinces and cities generally have a better level of intelligent cold chain development, while the southwest and northwest regions are developing slowly. In terms of overall development, the overall development of China's intelligent cold chain is relatively backward, with insufficient inter-regional synergy. The global spatial autocorrelation analysis shows that the variability in the development of China's intelligent cold chain logistics is gradually becoming greater. Through the local spatial autocorrelation analysis, it can be seen that there is a positive spatial correlation between the provinces and cities in East China, and negative spatiality in North China and South China. After geographically weighted regression analysis, it can be seen that the evaluation indicators have significant spatial and temporal heterogeneity in 2017, with the degree of influence changing with spatial location and time, and the spatial and temporal heterogeneity of the evaluation indicators is not significant in 2021. In order to improve the overall development level of China's intelligent cold chain, corresponding development countermeasures are proposed to strengthen the construction of supporting facilities and promote the transformation and upgrading of information technology. This study can provide a scientific basis for the global planning, strategic layout and overall promotion of China's intelligent cold chain.

Key words: intelligent cold chain, entropy power method, TOPSIS, exploratory spatial data analysis, geographically weighted regression, evaluation indicator system, informationization level

中图分类号:

F253.4

杨霖, 杨斌, 任青山, 杨信廷, 韩佳伟. 中国智慧冷链发展水平评价及对策建议[J]. 智慧农业(中英文), 2023, 5(1): 22-33.

YANG Lin, YANG Bin, REN Qingshan, YANG Xinting, HAN Jiawei. Evaluation and Countermeasures on the Development Level of Intelligent Cold Chain in China[J]. Smart Agriculture, 2023, 5(1): 22-33.

图/表 9

图1

表 1

图 2

图3

图4

图 5

表 2

中国智慧冷链发展水平GWR计算结果

指标	2017	2021
局部 $R 2$	0.738~0.769	0.7806~0.7809
$R 2$	0.7664	0.7808
调整 $R 2$	0.6884	0.7350
残差平方和	0.1138	0.1087

表 2

表 3

2017年智慧冷链GWR自变量回归系数与地区分布

指标名称	回归系数范围	地区
人均生鲜农产品产量	-0.0316~-0.0383	北京，天津，河北，内蒙古，辽宁，吉林，黑龙江
	-0.0383~-0.0493	山西，上海，江苏，浙江，安徽，江西，山东，河南，湖北，重庆，陕西，甘肃，青海，宁夏，新疆
	-0.0493~-0.0573	福建，湖南，广东，广西，四川，贵州，云南
	-0.0573~-0.0619	海南
货物运输量	0.1399~0.1559	辽宁，吉林，黑龙江
	0.1599~0.1699	北京，天津，河北，内蒙古，上海，江苏，山东
	0.1699~0.1797	山西，浙江，安徽，福建，河南
	0.1797~0.1897	江西，湖北，湖南，广东，陕西，宁夏
	0.1897~0.1998	广西，海南，重庆，四川，贵州，甘肃
	0.1998~0.2127	云南，青海，新疆
公路里程	0.0067~0.0166	云南，青海，新疆
	0.0166~0.0289	湖南，广东，广西，海南，重庆，四川，贵州，陕西，甘肃，宁夏
	0.0289~0.0399	河北，山西，内蒙古，江苏，浙江，安徽，福建，江西，河南，湖北
	0.0399~0.0482	北京，天津，辽宁，上海，山东
	0.0482~0.0587	吉林，黑龙江
人均需求量	0.1983~0.1995	黑龙江，新疆
	0.1995~0.2090	北京，天津，河北，山西，内蒙古，辽宁，吉林，上海，江苏，安徽，山东，河南，湖北，重庆，四川，陕西，甘肃，青海，宁夏
	0.2090~0.2166	浙江，福建，江西，湖南，广东，广西，海南，贵州，云南
每百家企业拥有网站数	0.1059~0.1090	广西，海南，云南
	0.1090~0.1189	山西，上海，江苏，浙江，安徽，福建，江西，河南，湖北，湖南，广东，重庆，四川，贵州，陕西，甘肃，青海，宁夏，新疆
	0.1189~0.1264	北京，天津，河北内蒙古，辽宁，山东
	0.1264~0.1345	吉林，黑龙江
每百人使用计算机数	0.3969~0.3997	青海，新疆
	0.3997~0.4013	黑龙江，四川，云南，甘肃
	0.4013~0.4029	内蒙古，辽宁，吉林，广西，海南，重庆，贵州，陕西，宁夏
	0.4029~0.4039	北京，天津，河北，山西，江苏，安徽，江西，山东，河南，湖北，湖南，广东
	0.4039~0.4043	上海，浙江，福建

表 3

表 4

参考文献 25

1	GOKARN S, CHOUDHARY A. Modeling the key factors influencing the reduction of food loss and waste in fresh produce supply chains[J]. Journal of environmental management, 2021, 294: ID 113063.
2	ANAND S, BARUA M K. Modeling the key factors leading to post-harvest loss and waste of fruits and vegetables in the agri-fresh produce supply chain[J]. Computers and electronics in agriculture, 2022, 198: ID 106936.
3	WIŚNIEWSKI T, TUNDYS B. Comparative analysis of sustainability factors in supply chain links. Evidence of empirical research[J]. Procedia computer science, 2022, 207(C): 3358-3366.
4	APEJI U D, SUNMOLA F T. Principles and factors influencing visibility in sustainable supply chains[J]. Procedia computer science, 2022, 200(C): 1516-1527.
5	郭明德, 李红. 农产品冷链物流发展水平评价——基于12典型省市数据分析[J]. 商业经济研究, 2019(1): 125-127.
	GUO M D, LI H. Evaluation on the development level of agricultural cold chain logistics—Based on the data analysis of 12 typical provinces and cities[J]. Journal of commercial economics, 2019(1): 125-127.
6	王姗姗, 魏遥. 中国农产品物流发展水平评价研究——基于因子分析法和熵值法的分析[J]. 信阳师范学院学报(哲学社会科学版), 2020, 40(4): 40-45.
	WANG S S, WEI Y. Research on evaluation of China's aqricultural product logistics development leve—Based on factor analysis and analysis of entropy method[J]. Journal of Xinyang normal university (philosophy and social sciences edition), 2020, 40(4): 40-45.
7	张妍. 基于AHP的生鲜农产品冷链物流影响因素评价分析[J]. 农村经济与科技, 2019, 30(1): 162-163.
	ZHANG Y. Evaluation and analysis on influencing factors of cold chain logistics of fresh agricultural products based on AHP[J]. Rural economy and science-technology, 2019, 30(1): 162-163.
8	剌美香, 李玉萍. 基于FAHP的山西省农产品冷链物流综合评价[J]. 山西农业科学, 2017, 45(8): 1349-1352, 1360.
	LA M X, LI Y P. Comprehensive evaluation of the cold-chain logistics for the agricultural products in Shanxi Province based on FAHP[J]. Journal of Shanxi agricultural sciences, 2017, 45(8): 1349-1352, 1360.
9	谢清玲. 中国东部地区水产品冷链物流发展水平评价——基于熵-模糊物元分析法[D]. 广州: 仲恺农业工程学院, 2020.
	XIE Q L. Evaluation of aquatic products cold chain logistics development level in eastern China: Based on Entropy-fuzzy Matter-element[D]. Guangzhou: Zhongkai University of Agriculture and Engineering, 2020.
10	田玉洁, 谢庆红, 王子豪. 基于AHP-熵权法的生鲜农产品冷链物流安全评价[J]. 保鲜与加工, 2019, 19(5): 185-190.
	TIAN Y J, XIE Q H, WANG Z H. Safety assessment of fresh agricultural products cold chain logistics based on AHP-entropy weight method[J]. Storage and process, 2019, 19(5): 185-190.
11	张于贤, 黄鑫, 刘瑞环. 基于熵权灰色关联法的中部地区农产品物流发展评价研究[J]. 商业经济研究, 2017(21): 88-91.
	ZHANG Y X, HUANG X, LIU R H. Research on evaluation of agricultural products logistics development in central China based on entropy weight grey correlation method[J]. Journal of commercial economics, 2017(21): 88-91.
12	李云, 桂海霞, 王向前, 等. 基于熵权-Topsis的中部地区低碳物流发展水平评价[J]. 衡阳师范学院学报, 2022, 43(3): 115-120.
	LI Y, GUI H X, WANG X Q, et al. Evaluation of low carbon logistics development level in central China based on entropy weight TOPSIS[J]. Journal of Hengyang normal university, 2022, 43(3): 115-120.
13	伦家兴. 中国31个省市物流产业发展水平综合评价研究[J]. 投资与创业, 2021, 32(2): 50-52, 55.
	LUN J X. Study on comprehensive evaluation of logistics industry development level in 31 provinces and cities of China[J]. Investment and entrepreneurship, 2021, 32(2): 50-52, 55.
14	石雪斌. 基于模糊物元的江苏省农产品物流发展水平评价与提升研究[D]. 南京: 南京农业大学, 2019.
	SHI X B. Development level of agricultural products logistics in Jiangsu province based on fuzzy matter element evaluation and promotion research[D]. Nanjing: Nanjing Agricultural University, 2019.
15	王晓宇, 黄铭. 基于熵权灰色关联法的物流发展水平评价——以安徽省为例[J]. 黑河学院学报, 2020, 11(9): 50-53.
	WANG X Y, HUANG M. Evaluation of logistics development level based on entropy grey relational method—Taking Anhui Province as an example[J]. Journal of Heihe university, 2020, 11(9): 50-53.
16	徐耀群, 程林. 基于TOPSIS的生鲜农产品冷链物流企业评价研究[J]. 物流技术, 2017, 36(10): 87-91.
	XU Y Q, CHENG L. Study on evaluation of fresh farm produce cold chain logistics enterprise based on TOPSIS[J]. Logistics technology, 2017, 36(10): 87-91.
17	饶淑雯. 福建省区域物流能力评价及提升建议——基于熵权-TOPSIS法[J]. 物流科技, 2022, 45(18): 45-49.
	RAO S W. On evaluation and improvement suggestions of regional logistics capacity in Fujian province—Based on entropy-weighting TOPSIS method[J]. Logistics sci-tech, 2022, 45(18): 45-49.
18	李丹丹, 刘锐, 陈动. 中国省域碳排放及其驱动因子的时空异质性研究[J]. 中国人口·资源与环境, 2013, 23(7): 84-92.
	LI D D, LIU R, CHEN D. Research on space-time heterogeneity of carbon emission and influencing factors in provinces of China[J]. China population, resources and environment, 2013, 23(7): 84-92.
19	孙忠秋, 李爽, 程承旗. 基于ESDA的长三角区域经济差异分析[J]. 地理信息世界, 2016, 23(1): 71-79.
	SUN Z Q, LI S, CHENG C Q. Analyzing regional economic disparities based on ESDA in Yangtze River Delta[J]. Geomatics world, 2016, 23(1): 71-79.
20	ANSELIN L. Local indicators of spatial association-LISA [J]. Geographical analysis, 2010, 27(2): 93-115.
21	周广亮, 吴明. 中原城市群物流业发展水平时空分异及影响因素分析[J]. 河南理工大学学报(自然科学版), 2021, 40(5): 90-98.
	ZHOU G L, WU M. Analysis of spatial-temporal differentiation and influencing factors of logistics industry development level in the central plains urban agglomeration[J]. Journal of Henan polytechnic university (natural science), 2021, 40(5): 90-98.
22	杨青, 彭若慧, 刘星星, 等. 基于地理加权回归的省域碳排放影响因素研究[J]. 环境工程技术学报, 2023, 13(1): 54-62.
	YANG Q, PENG R H, LIU X X, et al. Study on influencing factors of provincial carbon emission based on geographically weighted regression[J]. Journal of environmental engineering technology, 2023, 13(1): 54-62.
23	尹硕. 基于我国现状的冷链物流发展之路探究[J]. 中国储运, 2022(11): 205-207.
	YIN S. Research on the development of cold chain logistics based on China's current situation[J]. China storage & transport, 2022(11): 205-207.
24	吴佳佳. 冷链物流发展存在的问题及对策研究[J]. 中国集体经济, 2022(31): 106-108.
	WU J J. Research on the problems and countermeasures in the development of cold chain logistics[J]. China collective economy, 2022(31): 106-108.
25	韩佳伟, 李佳铖, 任青山, 等. 农产品智慧物流发展研究[J]. 中国工程科学, 2021, 23(4): 30-36.
	HAN J W, LI J C, REN Q S, et al. Development strategy of intelligent logistics for agricultural products[J]. Strategic study of CAE, 2021, 23(4): 30-36.

一级指标	权重	排序	二级指标	权重	排序
冷链供给能力	0.1087	5	人均生鲜农产品产量	0.0608	8
冷链供给能力	0.1087	5	货物运输量	0.0479	11
冷链仓储能力	0.2230	2	人均冷库容量	0.0966	4
			冷链企业数	0.0754	6
			从业人员	0.0510	10
冷链运输能力	0.1750	4	人均冷藏车	0.1343	1
冷链运输能力	0.1750	4	公路里程	0.0407	12
冷链经济效益	0.2873	1	人均需求量	0.0173	14
			人均可支配收入	0.0807	5
			人均GDP	0.0641	7
			餐饮企业营业额	0.1252	3
冷链信息化水平	0.2060	3	电子商务销售额	0.1274	2
			每百家企业拥有网站数	0.0202	13
			每百人使用计算机数量	0.0584	9

中国智慧冷链发展水平评价及对策建议

Evaluation and Countermeasures on the Development Level of Intelligent Cold Chain in China

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参考文献 25

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指标	回归系数范围
人均生鲜农产品产量	-0.1223—-0.1225
公路里程	0.0760—0.0761
人均需求量	0.2714—0.2715
每百家企业拥有网站数	0.1303—0.1304
每百人使用计算机数	0.2753—0.2754