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

doi:10.12133/j.smartag.SA202302003

Abstract

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

CLC Number:

F253.4

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.

Figures/Tables 9

Fig. 1

Table 1

Fig.2

Fig. 3

Fig. 4

Fig. 5

Table 2

GWR calculation results of China's intelligent cold chain development level

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

Table 2

Table 3

Regression coefficients and regional distribution of GWR independent variables for intelligent cold chain in 2017

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

Table 3

Table 4

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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