RETRACTED ARTICLE: Spatiotemporal evolution characteristics of China’s cold chain logistics resources and agricultural product using remote sensing perspective

Statement of Retraction We, the Editor and Publisher of the journal European Journal of Remote Sensing, have retracted the following articles that were published in the Special Issue titled “Remote Sensing in Water Management and Hydrology”: Marimuthu Karuppiah, Xiong Li & Shehzad Ashraf Chaudhry (2021) Guest editorial of the special issue “remote sensing in water management and hydrology”, European Journal of Remote Sensing, 54:sup2, 1-5, DOI: 10.1080/22797254.2021.1892335 Jian Sheng, Shiyi Jiang, Cunzhu Li, Quanfeng Liu & Hongyan Zhang (2021) Fluid-induced high seismicity in Songliao Basin of China, European Journal of Remote Sensing, 54:sup2, 6-10, DOI: 10.1080/22797254.2020.1720525 Guohua Wang, Jun Tan & Lingui Wang (2021) Numerical simulation of temperature field and temperature stress of thermal jet for water measurement, European Journal of Remote Sensing, 54:sup2, 11-20, DOI: 10.1080/22797254.2020.1743956 Le Wang, Guancheng Jiang & Xianmin Zhang (2021) Modeling and molecular simulation of natural gas hydrate stabilizers, European Journal of Remote Sensing, 54:sup2, 21-32, DOI: 10.1080/22797254.2020.1738901 Tianyi Chen, Lu Bao, Liu Bao Zhu, Yu Tian, Qing Xu & Yuandong Hu (2021) The diversity of birds in typical urban lake-wetlands and its response to the landscape heterogeneity in the buffer zone based on GIS and field investigation in Daqing, China, European Journal of Remote Sensing, 54:sup2, 33-41, DOI: 10.1080/22797254.2020.1738902 Zhiyong Wang (2021) Research on desert water management and desert control, European Journal of Remote Sensing, 54:sup2, 42-54, DOI: 10.1080/22797254.2020.1736953 Ji-Tao Li & Yong-Quan Liang (2021) Research on mesoscale eddy-tracking algorithm of Kalman filtering under density clustering on time scale, European Journal of Remote Sensing, 54:sup2, 55-64, DOI: 10.1080/22797254.2020.1740894 Wei Wang, R. Dinesh Jackson Samuel & Ching-Hsien Hsu (2021) Prediction architecture of deep learning assisted short long term neural network for advanced traffic critical prediction system using remote sensing data, European Journal of Remote Sensing, 54:sup2, 65-76, DOI: 10.1080/22797254.2020.1755998 Yan Chen, Ming Tan, Jiahua Wan, Thomas Weise & Zhize Wu (2021) Effectiveness evaluation of the coupled LIDs from the watershed scale based on remote sensing image processing and SWMM simulation, European Journal of Remote Sensing, 54:sup2, 77-91, DOI: 10.1080/22797254.2020.1758962 Ke Deng & Ming Chen (2021) Blasting excavation and stability control technology for ultra-high steep rock slope of hydropower engineering in China: a review, European Journal of Remote Sensing, 54:sup2, 92-106, DOI: 10.1080/22797254.2020.1752811 Yufa He, Xiaoqiang Guo, Jun Liu, Hongliang Zhao, Guorong Wang & Zhao Shu (2021) Dynamic boundary of floating platform and its influence on the deepwater testing tube, European Journal of Remote Sensing, 54:sup2, 107-116, DOI: 10.1080/22797254.2020.1762246 Kai Peng, Yunfeng Zhang, Wenfeng Gao & Zhen Lu (2021) Evaluation of human activity intensity in geological environment problems of Ji’nan City, European Journal of Remote Sensing, 54:sup2, 117-121, DOI: 10.1080/22797254.2020.1771214 Wei Zhu, XiaoSi Su & Qiang Liu (2021) Analysis of the relationships between the thermophysical properties of rocks in the Dandong Area of China, European Journal of Remote Sensing, 54:sup2, 122-131, DOI: 10.1080/22797254.2020.1763205 Yu Liu, Wen Hu, Shanwei Wang & Lingyun Sun (2021) Eco-environmental effects of urban expansion in Xinjiang and the corresponding mechanisms, European Journal of Remote Sensing, 54:sup2, 132-144, DOI: 10.1080/22797254.2020.1803768 Peng Qin & Zhihui Zhang (2021) Evolution of wetland landscape disturbance in Jiaozhou Gulf between 1973 and 2018 based on remote sensing, European Journal of Remote Sensing, 54:sup2, 145-154, DOI: 10.1080/22797254.2020.1758963 Mingyi Jin & Hongyan Zhang (2021) Investigating urban land dynamic change and its spatial determinants in Harbin city, China, European Journal of Remote Sensing, 54:sup2, 155-166, DOI: 10.1080/22797254.2020.1758964 Balaji L. & Muthukannan M. (2021) Investigation into valuation of land using remote sensing and GIS in Madurai, Tamilnadu, India, European Journal of Remote Sensing, 54:sup2, 167-175, DOI: 10.1080/22797254.2020.1772118 Xiaoyan Shi, Jianghui Song, Haijiang Wang & Xin Lv (2021) Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group, European Journal of Remote Sensing, 54:sup2, 176-188, DOI: 10.1080/22797254.2020.1762247 GN Vivekananda, R Swathi & AVLN Sujith (2021) Multi-temporal image analysis for LULC classification and change detection, European Journal of Remote Sensing, 54:sup2, 189-199, DOI: 10.1080/22797254.2020.1771215 Yiting Wang, Xianghui Liu & Weijie Hu (2021) The research on landscape restoration design of watercourse in mountainous city based on comprehensive management of water environment, European Journal of Remote Sensing, 54:sup2, 200-210, DOI: 10.1080/22797254.2020.1763206 Bao Qian, Cong Tang, Yu Yang & Xiao Xiao (2021) Pollution characteristics and risk assessment of heavy metals in the surface sediments of Dongting Lake water system during normal water period, European Journal of Remote Sensing, 54:sup2, 211-221, DOI: 10.1080/22797254.2020.1763207 Jin Zuo, Lei Meng, Chen Li, Heng Zhang, Yun Zeng & Jing Dong (2021) Construction of community life circle database based on high-resolution remote sensing technology and multi-source data fusion, European Journal of Remote Sensing, 54:sup2, 222-237, DOI: 10.1080/22797254.2020.1763208 Zilong Wang, Lu Yang, Ping Cheng, Youyi Yu, Zhigang Zhang & Hong Li (2021) Adsorption, degradation and leaching migration characteristics of chlorothalonil in different soils, European Journal of Remote Sensing, 54:sup2, 238-247, DOI: 10.1080/22797254.2020.1771216 R. Vijaya Geetha & S. Kalaivani (2021) A feature based change detection approach using multi-scale orientation for multi-temporal SAR images, European Journal of Remote Sensing, 54:sup2, 248-264, DOI: 10.1080/22797254.2020.1759457 LianJun Chen, BalaAnand Muthu & Sivaparthipan cb (2021) Estimating snow depth Inversion Model Assisted Vector Analysis based on temperature brightness for North Xinjiang region of China, European Journal of Remote Sensing, 54:sup2, 265-274, DOI: 10.1080/22797254.2020.1771217 Yajuan Zhang, Cuixia Li & Shuai Yao (2021) Spatiotemporal evolution characteristics of China’s cold chain logistics resources and agricultural product using remote sensing perspective, European Journal of Remote Sensing, 54:sup2, 275-283, DOI: 10.1080/22797254.2020.1765202 Guangping Liu, Jingmei Wei, BalaAnand Muthu & R. Dinesh Jackson Samuel (2021) Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling, European Journal of Remote Sensing, 54:sup2, 284-295, DOI: 10.1080/22797254.2020.1771774 Yishu Qiu, Zhenmin Zhu, Heping Huang & Zhenhua Bing (2021) Study on the evolution of B&Bs spatial distribution based on exploratory spatial data analysis (ESDA) and its influencing factors—with Yangtze River Delta as an example, European Journal of Remote Sensing, 54:sup2, 296-308, DOI: 10.1080/22797254.2020.1785950 Liang Li & Kangning Xiong (2021) Study on peak cluster-depression rocky desertification landscape evolution and human activity-influence in South of China, European Journal of Remote Sensing, 54:sup2, 309-317, DOI: 10.1080/22797254.2020.1777588 Juan Xu, Mengsheng Yang, Chaoping Hou, Ziliang Lu & Dan Liu (2021) Distribution of rural tourism development in geographical space: a case study of 323 traditional villages in Shaanxi, China, European Journal of Remote Sensing, 54:sup2, 318-333, DOI: 10.1080/22797254.2020.1788993 Lin Guo, Xiaojing Guo, Binghua Wu, Po Yang, Yafei Kou, Na Li & Hui Tang (2021) Geo-environmental suitability assessment for tunnel in sub-deep layer in Zhengzhou, European Journal of Remote Sensing, 54:sup2, 334-340, DOI: 10.1080/22797254.2020.1788994 Hui Zhou, Cheng Zhu, Li Wu, Chaogui Zheng, Xiaoling Sun, Qingchun Guo & Shuguang Lu (2021) Organic carbon isotope record since the Late Glacial period from peat in the North Bank of the Yangtze River, China, European Journal of Remote Sensing, 54:sup2, 341-347, DOI: 10.1080/22797254.2020.1795728 Chengyuan Hao, Linlin Song & Wei Zhao (2021) HYSPLIT-based demarcation of regions affected by water vapors from the South China Sea and the Bay of Bengal, European Journal of Remote Sensing, 54:sup2, 348-355, DOI: 10.1080/22797254.2020.1795730 Wei Chong, Zhang Lin-Jing, Wu Qing, Cao Lian-Hai, Zhang Lu, Yao Lun-Guang, Zhu Yun-Xian & Yang Feng (2021) Estimation of landscape pattern change on stream flow using SWAT-VRR, European Journal of Remote Sensing, 54:sup2, 356-362, DOI: 10.1080/22797254.2020.1790994 Kepeng Feng & Juncang Tian (2021) Forecasting reference evapotranspiration using data mining and limited climatic data, European Journal of Remote Sensing, 54:sup2, 363-371, DOI: 10.1080/22797254.2020.1801355 Kepeng Feng, Yang Hong, Juncang Tian, Xiangyu Luo, Guoqiang Tang & Guangyuan Kan (2021) Evaluating applicability of multi-source precipitation datasets for runoff simulation of small watersheds: a case study in the United States, European Journal of Remote Sensing, 54:sup2, 372-382, DOI: 10.1080/22797254.2020.1819169 Xiaowei Xu, Yinrong Chen, Junfeng Zhang, Yu Chen, Prathik Anandhan & Adhiyaman Manickam (2021) A novel approach for scene classification from remote sensing images using deep learning methods, European Journal of Remote Sensing, 54:sup2, 383-395, DOI: 10.1080/22797254.2020.1790995 Shanshan Hu, Zhaogang Fu, R. Dinesh Jackson Samuel & Prathik Anandhan (2021) Application of active remote sensing in confirmation rights and identification of mortgage supply-demand subjects of rural land in Guangdong Province, European Journal of Remote Sensing, 54:sup2, 396-404, DOI: 10.1080/22797254.2020.1790996 Chen Qiwei, Xiong Kangning & Zhao Rong (2021) Assessment on erosion risk based on GIS in typical Karst region of Southwest China, European Journal of Remote Sensing, 54:sup2, 405-416, DOI: 10.1080/22797254.2020.1793688 Zhengping Zhu, Bole Gao, Renfang Pan, Rong Li, Yang Li & Tianjun Huang (2021) A research on seismic forward modeling of hydrothermal dolomite:An example from Maokou formation in Wolonghe structure, eastern Sichuan Basin, SW China, European Journal of Remote Sensing, 54:sup2, 417-428, DOI: 10.1080/22797254.2020.1811160 Shaofeng Guo, Jianmin Zheng, Guohua Qiao & Xudong Wang (2021) A preliminary study on the Earth’s evolution and condensation, European Journal of Remote Sensing, 54:sup2, 429-437, DOI: 10.1080/22797254.2020.1830309 Yu Gao, Ying Zhang & Hedjar Alsulaiman (2021) Spatial structure system of land use along urban rail transit based on GIS spatial clustering, European Journal of Remote Sensing, 54:sup2, 438-445, DOI: 10.1080/22797254.2020.1801356 Xia Mu, Sihai Li, Haiyang Zhan & Zhuoran Yao (2021) On-orbit calibration of sun sensor’s central point error for triad, European Journal of Remote Sensing, 54:sup2, 446-457, DOI: 10.1080/22797254.2020.1814164 Following publication, the publisher identified concerns regarding the editorial handling of the special issue and the peer review process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. 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Introduction and basic survey on cold chain market and agricultural products
In recent years, consumer demand for the food products industry has increased rapidly and issues with food safety have been given a great deal of attention as it improves the individual's overall living standards (Kamilaris et al., 2017).Gradually, organic items became the primary source of food for the customer (Mercier et al., 2017;Zhang et al., 2018).Fresh agricultural products have certain features, including shorter shelf life, high market demand, high storage and transport requirements (Song et al., 2019).This provides a clear working model for an effective information management framework for guaranteeing product quality (Hiloidhari et al., 2017).This ensures intermediate relations between the consumers and suppliers in the distribution of agricultural products.However, in every logistics link, a great risk prevails, since fresh agricultural products are not completely maintained by specific environments during cold chain logistics (Jones et al., 2017).Therefore, it is important to consider how to accurately measure and prevent the challenge of cold chain logistics (Yue et al., 2017) on food product demand.Fresh agricultural products should be processed, packaged and delivered at appropriate temperatures as soon as possible to mitigate commodity required recreations, to ensure customer health and also to promote the rapid growth of cold chain loads (Genovese et al., 2017).The cold-chain logistics network has rapid economic growth but faces a number of environmental challenges.Unlike in the conventional logistics, the normal operation of cooling equipment emits CO 2 , and other exercise-generated gases from transport vehicles, leading to an increase in greenhouse gas emission, air pollution and environmental consequences (Singh et al., 2018).Optimized network architecture should be taken into account when moving supplies from supply points to distribution centers and ultimately transferring products from DC to the terminal demand point (TDP).A simple map of the cold chain logistics system in which the place delivery issue (PDI) and path issue (PI) are two major problems.The different frameworks for the vehicle path issue (VPI) leading outcome have PI planning and the PDI programs have the joint PDI-PI question (Baró et al., 2017) which is not addressed and the position problem in the cold chain logistics network, must be resolved (Cojoianu & Ascui, 2018;Ronco et al., 2017).While different methods of risk assessment are taken into account leads to poor validation which has been addressed in this research.Hence, this paper discusses the cold chain logistic network threats and production process with a maximum difference framework to develop a better technique for determining the cold chain logistics model for agricultural goods.This model must positively identify the functional and promotional importance of the specific risk structure using mathematical modelling.

A technological overview of the conventional database
The technological revolution (Erb et al., 2017) and the emergence of worldwide demands on agricultural products actively supported and introduced cold chain logistics for decades and created an interconnected cold chain network for agricultural products (Weersink et al., 2018).The cold chain has been slowly selling processed foods and agricultural products.Here, most cold chain studies actually concentrate primarily on electronics (Accorsi et al., 2018;Antle et al., 2017).Mingfei and Ting (2011) identified the processes of industrialization and growth of the agricultural distribution industry which becomes a major barrier to inefficient agricultural logistics.Here authors reported the implementation of a performance evaluation system and establishes the 3D value appraisal index approach for lateral goods, like cold chain logistics.This research offered a guide for planning and management of cold chain logistics services for the government departments using data creation review, sampling, monitoring and performance evaluations of Beijing, Shanghai, Tianjin, Hubei, Hunan, six middle provinces and towns.Huang et al. (2018) reported on the prices of fresh foods is steadily increasing along with that food safety problem in China in recent years due to the lower circulation capacity, inflation and moral reasons.This paper explores the current trends in the logistics of the Chinese cold chain.For instance, cold chain logistics facilities are falling behind; where the third parties' logistics is slowly growing based on the supply chain management which is poor; further the cold chain logistics requirements are lacking and hence the authors reported the model of cold chain logistics growth focused on resource integration.Chen et al. (2013) proposed the effective monitoring of cold chain logistics and transportation in agriculturally produced products which ensure quality, safety and reducing the cost of logistics.Here the authors explored three practical systems, namely mobile data collection, logistics alerting and mobile payment.The cold-chain logistic process and modular structure of the mobile service application system are based on the mobile appliance and wireless network hardware environment.To increase the logistic performance, the network tracks and controls cold-chain agricultural storage transportation processes through the mobile terminal was developed by the authors as reported.Sharma et al. (2018) discussed cold chain logistics for agricultural products which ignores the carbon model for production, which were addressed by the process analysis of agricultural logistics in the cold chain.They had used the low carbon concept, which established a third-party logistics business, further, the logistics supplier for agricultural products for cold chain, restore China's logistics system for the cold chain and increase the exchange of information.The results reveal that applying the low-carbon economy to reengineering cold chain logistics of agricultural products has advantages in increasing value-added products in cold chain agriculture.Hence based on the conventional analysis, the research has been conducted at a lab-scale that established an alternate interpretation between different regional variations in logistic capital of the ecological features of the cold chain logistics.The aim of this analysis is to validate the condition of the cold chain market based on the quantitative system of risk assessment on agricultural products using RS-STLR model which are discussed as follows.

Material and methods for the mathematical validation
The fundamental principle and steps for the method of catastrophe progression The aim of catastrophe theory is to activate the empirical laws of qualitative change in a secured environment, by examining the consistency of object structures.This principle is to build based on the complex mathematical principles, which have been commonly used for decades in physical, biological and social sciences.The key feature of this model is to analyze the subjectivity reduction, empirical information, rational data and logical estimates of the agricultural products.This paper, therefore, chooses the technique for catastrophe growth based on disrupted transition.The catastrophe is a sort of disrupted transition of power that contributes to frequent changes in agricultural products that affect individually or collectively based on the program risk factors for cold chain logistics in agriculture.The system is constantly evolving because of the dynamic nonlinear relationship between program risk factors.Further, the chain of agricultural products is shown in Figure 1.
Before using the metric of catastrophe progress, each index should be ordered according to its meaning.The maximal variance method is used for calculating the weight of each variable and determining the decreased subjective partiality in the final results, Hence, the subjectivity of different index series can be excluded.The key principle of the maximum exit method is to achieve results by calculating the combination of the average discrepancy between the variable and the cumulative variance of all indexes as inferred from Figure .1.

The correlation steps for the improved maximum deviation method as states as follows
Step 1: Here the processing of the dimensions for the original index data based on the uniform min-max method of printing is used to validate the higher value based on positive index results which are defined as follows in Equation ( 1), The lower value is better when the reverse index is present, and the data can be displayed as represented in Equation ( 2), As defined in Equation ( 2), where the number of raw data is represented as p ij .From which i is the evaluate object and j is the evaluate index.Further the p max j ð Þ is the maximum index number of j and p min j ð Þ is the minimum index number of j.After standardization, Z ij represents the index number based on the indexed formulation as defined as follows in Equation (3), Step 2: Design of the optimal weight vector solving model of the enhanced maximum deviation process has been validated in this step based on the weight vector of the variable which is assumed to be W Here, the weight vector of a specific hierarchical attribute is designed using the matrix for the determination of labelled structure with Z ¼ Z ij À Á nÂm after a standardized procedure.It can then be described as a structured weighted control matrix as represented as follows in Equation ( 4) As inferred from Equation (4), for a particular index j, d ij is the sum of the variance of the object i for a specific evaluation, and all the other analysis objects in the same sequence based on the d ij can be explained in the following Equation ( 5) The sum of the index difference j is shown as in Equation ( 6) based on the computational effect as represented as follows: Therefore, the cumulative deviation from the index method is as follows in Equation ( 7): Z ij À Z kj :w j :w Ã j i ¼ 1; 2; . . .; n; ð j ¼ 1; 2; . . .; mÞ (7) EUROPEAN JOURNAL OF REMOTE SENSING 277

R E T R A C T E D
Based on the above observations, it may create a model of deviation optimization which is combined with the related conditions as defined as follows in Equation ( 8), Step 3: Solving the model for optimizing deviations in Equation ( 8) the obtained results are processed as follows by processing Formula (9): The partial derivative analysis has been used to classify formula (9) to overcome a partial derivative result to obtain optimal model parameters as derived in Equation ( 10), Step 4: For a multilevel index system of measures, the value of the upper lobe is calculated with the combined utility of the corresponding lower lobes.The total sum of the component and relevant evaluation object data (both static and variable weights) is equal to the cumulative utility value based on the relation among upper, middle and lower lobes as represented in Figure 2 (a and b) When there is a one-dimensional component, it is termed as the flexible catastrophe which is represented as follows in Equation (10): If the variable control is two-dimensional, it is a cusp catastrophe represented as follows in Equation ( 10), The three-dimensional control variable is a swallowtail tragedy as shown in Figure 3, is capable of representing its potential function as shown in Equation ( 13): If the control variable is four-dimensional, then the following representation can be seen in the folding catastrophe based on the butterfly structure as shown in Figure 4.
Where f x ð Þ represents the possible role of the catastrophe system based on the pole position; x is a state variable for the catastrophe function; l; m; n; oare the different control state variables based on polar wander.
In brief, there is a lot of research on the risk assessments in place distribution issue (PDI) and VPI on cold chain logistics, based on the above analysis which has been represented in Figure 5. Hence, the operating costs are based on the management goal and rarely take carbon emissions into account.However, sustainable development is becoming popular a range of energy-saving and emission reduction strategies that are being implemented, and CO 2 emissions are becoming a key issue for logistics companies to address throughout the delivery of the cold chain on agricultural products.Hence, this paper proposes a green and environmental model that includes carbon emissions in logistics network optimization.Finally, the validity and viability of the model checks based on the

R E T R A C T E D
statistical experimental analysis which are modeled as follows.

Model formation on the distribution process
Several distribution centers for cold chain logistics are open for delivering supplies to different customers using cooled vehicles.It leaves a distribution center and returns after delivery to the nearest distribution center.The lowcarbon routing problem (LCLRP) model, which includes the lowest overall costs, establishes a road distribution system for economic and environmental security while taking due to the account of fixed cost, transport costs, refrigeration costs, fines, damage costs and carbon costs.

Design of models and objective system function analysis
Cost fixed It applies to operating costs for distribution centers, which primarily include routine maintenance and depreciation expenses of warehouses, equipment, labor costs for drivers and others.In this model, the fixed costs C 1 is expressed as in Equation ( 15) In Equation ( 15), where L g is the selection of the distribution center g for candidates.K g is the number of cooled trucks in middle g ð Þ.C k is the cost fixed to the truck with refrigeration k ð Þ. Z g refers to the distribution center.Y k refers to the refrigerated truck which is used in the distribution center.Y k refers to the cooled truck used in the distribution center.C g represents the cost fixed of the distribution center.All the above-mentioned variables are used to compute the cost for the distribution center.

Cost of transport
Transportation costs for cooled trucks are considered in this section and the cost of cooling is measured separately during transport.Fuel consumption, maintenance and other factors influence the transportation   costs for vehicles which are proportional to the distance.Hence, transport costs C 2 maybe expressed in this model as follows in Equation ( 16): Where C k ij refers to the cost of travel from customer to the customer for unit lengths.xk ij represents the cooled truck passing between the customer i ð Þ and the customer j ð Þ.Further, the cost for refrigeration has been derived as follows.

Cost for refrigeration
The energy must be continually stored to maintain the same temperature in storage.This cooling cost is required to maintain the correct temperature.Hence, the cooling prices of shipping C 31 of cooled trucks as follows in Equation ( 17): Where V g is the consumer delegated to the middle for the formulation,g.- refers to the waiting time for the vehicle to service the customer j ð Þ without unloading.t k ij represents the time of travel from the customer i ð Þ to customer j ð Þ of the cooled truck k ð Þ.Further, the penalty cost is estimated as follows.

Penalty costs
The pollution of the metropolitan traffic in the cold chain logistics study brought great challenges to the delivery of logistics.If the customer is unable to deliver the shipment, then a certain interest fee C 4 presented as follows in Equation ( 18),

Damage costs
This paper presents the variable function of the quality of refrigerated goods.The cost of the cargoes is split into two parts during the distribution process, including the expense of the loads that have accrued with the duration of the moving of refrigerated trucks and the cost of load losses near the door when the customer is served by opening the door.The cost of freight damage caused by cooled trucks during C 51 can be described as follows: When cooled trucks arrive at the customer's site for service, the rate of spoilage is now assumed to be @ 2 ð@ 2 > @ 1 Þ.The cost of loss C 52 is defined as follows in Equation (20): Based on the above expression, thus the total damage costs are represented as follows in Equation ( 21):

Costs for carbon emission
In the cycle of distribution and transport fuel, the carbon emissions are produced from two sources.Linear fuel consumption formula in unit distance Ś (X) as follows and export emission, The carbon emissions from the transport process presented as follows: The model's carbon emission impacts include the logistic center's fixed carbon emissions and production and export carbon emissions is shown in Equation ( 24): Hence, the aim of this analysis is to validate the condition of the cold chain market based on the quantitative system of risk assessment on agricultural produces.The risk assessment model is designed based on the empirical results for cold chain logistics and hence this paper provides low carbon, environmentally friendly and perishable goods solution which blends with the overall optimization of the distribution system.Furthermore, the PDI model is built with reduced total costs as a priority function including the green and low carbon problem(s) in the logistics chain of China based on the experimental analysis is discussed as follows.

Results and discussion
The cold chain logistic fresh agricultural products from the database for the objective of validating the efficiency and applicability of this model to improve the risk assessment for disaster development has been considered and the results are summarized in Table 1.
Here, the original nonstatistical data collection has been obtained based on the exact value of the catastrophe membership function under Equations ( 1) and ( 2).The noncomplementary butterfly disaster model is defined by four technical equipment indexes X1,X2, X3, and X4, and the value of which resides in the X1 > X3 > X4 > X2.On the basis of the original subordinate function of the catastrophe, the progression of catastrophe in the upper indexes can be calculated by Equation ( 11) and the subordinate feature has been shown in Table 2.
A subordinate feature value of the top index of area 1 and area 2 may be produced by the result from the initial catastrophe as shown in Table 2. Figure 6(a,b) shows the result of subordinate feature values of both area 1 and 2. The above indices, W, X, Y, and Z, are obviously noncomplementary, and the order of priority of the butterfly catastrophe model is W > Z > X > Y.
For a multilevel index system of measures, the value of the upper lobe is calculated with the combined utility of the corresponding lower lobes.The total sum of the component and relevant evaluation object data (both static and variable weights) is equal to the cumulative utility value based on the relation among upper, middle and lower lobes has been used to analyze the progression values of top indices which are shown in Table 3.
A new risk management model is generated by analyzing the minimal catastrophe development for the performance of upper indexes as shown in Tables 3 and 4 for the overall subordinate progression value of the businesses over different years.Figure 7(a,b) represents the progression values of area 1 and area 2 using the catastrophe method as represented in Figure 7(a,b), the upper indices in area 1 and area 2 with a catastrophe progression value of a total safe is of range of 1 ~1.2.However, in terms of technical equipment (W), drug supply (X), management structure (z) relative to region 1, has still large gaps that are numerically listed in Table 4.

R E T R A C T E D
agricultural cold chain logistics in the enhanced catastrophe progression evaluation model developed in this article.While studying in this phase of estimation, the approach will minimize subjectivity by considering vector weights and reference weights between different factors in the decision-making process.On the other side, it is also relatively simple and comfortable to quantify.

Conclusion and future scope
A new maximum deviation technique is suggested for the improved method of catastrophic progression, which measures the weight of each predictor to calculate the ratio between the global difference and the combined variance of all the indexes of the system results are more rational and reasonable since enormous businesses are using the data.This paper can, therefore, guarantee its generality by the technique and variety of topics that merit to improve the overall comprehensiveness of the index system, where a higher dimension systemic model of catastrophe development may be further discussed in the future.Furthermore, potential studies should focus on the development of a general risk index framework for the cold chain logistics of agricultural products to answer demands from different businesses for risk assessment.This research focuses on the risk assessment framework for cold chain logistics agricultural products.In the future, this paper offers ways to prevent and control risks.

Figure 1 .
Figure 1.The supply chain of Agricultural products.

Figure 5 .
Figure 5. Risk validation of cold chain logistics.

Figure 7 .
Figure 7. (a) Progression Values of Top Indices of Area 1 (b) Progression Values of Top Indices of Area 2.

Table 1 .
Index values of cold chain processing..

Table 2 .
Subordinate feature value of top indices..
Figure 6.(a) Subordinate Feature Value of Top Indices of Area 1 (b) Subordinate Feature Value of Top Indices of Area 2. EUROPEAN JOURNAL OF REMOTE SENSING 281

Table 3 .
Progression values of top indices..