Examine the Impacts of the Coronavirus Crisis on Vegetable Pricing
Yanze Sun
a
Institute of Problem Solving, Northeast Forestry University, Mount Hua Street, Jinan, China
Keywords: Vegetable Commodities, Supply Chain, Evolutionary Game Modeling.
Abstract: Vegetables, as a necessity of people's lives, are of great concern to the community in terms of their price
changes. Vegetable prices have experienced significant fluctuations under the tremendous impact of the New
Crown epidemic. Using the theoretical approach rooted in the principles of evolutionary game theory to
evaluate and map out the strategic interactions between vegetable growers and distributors. It provides an in-
depth examination of how the pandemic has influenced the availability and distribution of these vital goods,
and based on the relevant literature, puts forward countermeasure suggestions to maintain the stability of
vegetable prices. Studying the fluctuation pattern of vegetable prices under the influence of the epidemic has
far-reaching and important significance and value for guaranteeing market supply, safeguarding consumers'
rights and interests, improving farmers' income as well as precisely regulating the market. Through rigorous
research and analysis, this article can respond more effectively to to the swiftly shifting market conditions
during health emergencies and promote the sustainable prosperity of the market.
1 INTRODUCTION
The cost of perishable goods is significantly linked to
public well-being and is essential for maintaining
balance and fostering long-term development within
the broader socio-economic system. Maintaining
fresh commodities is particularly important for the
healthy development of society, and in recent years,
the e-commerce sector for fresh food in China has
experienced a significant surge in growth. In 2022,
the revenue from online transactions of perishable
goods in China reached 363.75 billion yuan,
representing a 16.7% rise from the prior year. The
COVID-19 outbreak has notably boosted the public's
online purchasing behavior for fresh produce,
enhancing their reliance on e-commerce platforms
specializing in fresh foods. Forecasts suggest that by
2026, the market for fresh foods in China could
expand to 630.20 billion yuan. (iResearch, 2024).
Meanwhile, according to Shenggen Fan (2021),
the Food and Land Use Coalition estimates that the
environmental, health, and social costs amount to at
least $12 trillion per year, a figure that exceeds the
value of the food system's global output. This
suggests that the food system, including fresh
commodities, is a significant part of the global
a
https://orcid.org/0009-0007-9980-4189
economy and that its impacts extend far beyond direct
economic transactions The price volatility of fresh
commodities, as an important part of the socio-
economic fabric and a necessity of people's lives, is
widely publicized. However, the outbreak of the New
Crown epidemic has created unprecedented
challenges for society as a whole, including the
agricultural sector.
The New Crown epidemic significantly
influenced every phase of the vegetable distribution
network, and vegetables have received much
attention from the community due to the significant
changes in pricing dynamics following the outbreak,
this study aims to delve into the complex interplay of
factors affecting the pricing of vegetables following
COVID-19. The early stages of the epidemic led to
widespread disruptions in labor supply, particularly in
the labor-intensive vegetable farming industry.
Blockades and mobility restrictions prevented the
movement of seasonal workers, leading to labor
shortages that directly affected the harvesting and
processing of vegetables. These disruptions led to
higher production costs, which inevitably led to
higher prices for consumers. According to Alam, G.
M. M., & Khatun, M. N. (2021) found that due to the
perishable nature of vegetables, small-scale vegetable
Sun, Y.
Examine the Impacts of the Coronavirus Crisis on Vegetable Pricing.
DOI: 10.5220/0012972200004508
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 747-753
ISBN: 978-989-758-713-9
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
747

growers are particularly affected. The blockade
restricted the ability of vegetable growers to reach
marketplaces, consequently constraining their ability
to produce and sell. In addition, the outbreak
triggered a surge in demand for vegetables, with
consumers engaging in panic buying and hoarding to
ensure food security. This sudden surge in demand,
coupled with a reduced supply chain due to
operational challenges, created an imbalance between
supply and demand that exerted upward pressure on
vegetable prices. Not only that the impact of COVID-
19 was not limited to production and demand but also
extended to the logistics and transportation sectors.
Disruptions in these sectors have resulted in higher
transportation costs to move vegetables from farm to
market. Border closures and restrictions have also
affected the import and export of vegetables, leading
to localized shortages and price volatility in different
regions. At the same time, government policies and
interventions have played a key role in shaping
vegetable pricing patterns. Export bans, import
restrictions and incentives aimed at supporting
farmers and the agricultural sector directly and
indirectly affected vegetable pricing. Epidemics
highlight the importance of resilient and adaptive
food supply chains, and of effective policy responses
to mitigate pricing impacts and ensure food safety
(OECD, 2020).
By applying the principles of evolutionary game
theory, this study develops a model of strategic
interactions that encompasses producers and retailers
to simulate the strategic choices of both parties under
the influence of the New Crown epidemic and its
impact on vegetable pricing, to provide insights into
the impact of the New Crown epidemic on the pricing
of vegetable-based commodities and examine the
factors that lead to price volatility. By understanding
these dynamics, policymakers, and stakeholders can
develop strategies to stabilize vegetable prices and
increase the resilience of the food system to future
crises.
2 LITERATURE REVIEW
The outbreak of the Xinguan epidemic had a major
impact on the pricing of vegetable commodities due
to the following aspects. In terms of market behavior,
it was found that the blockade policy led to significant
increases in vegetable prices and increased price
dispersion, reflecting the severe disruption of the
distribution network. With respect to specific
categories, Ruan, Cai, and Jin (2021) the price of
Chinese cabbage increased by about 46% after the
outbreak blockade and peaked in the fourth week of
the blockade, subsequently experienced a steady
decrease. Research revealed that amidst the
restrictions imposed by the COVID-19 pandemic,
there was a notable upsurge in the costs of wheat
flour, referred to as atta, and rice within the Indian
market. In contrast, the cost of onions saw a
significant drop. The price increases may be related
to increased demand or supply shortages, while the
price decreases in onions may be related to their semi-
perishable nature. GU & WANG (2020) examined
the effects of the health crisis on the cultivation of
vegetables by conducting questionnaires and oral
inquiries. with 46 agricultural cooperatives in
Shanghai and showed that several segments of the
vegetable supply chain were affected due to the
blockade and self-isolation policies stemming from
the COVID-19 outbreak. Particularly during the
cultivation and distribution stages, labor shortages
and reduced transport efficiency led to increased
costs. At the same time, the vulnerability to market
fluctuations in the cultivation of vegetables has risen.
significantly during the New Crown epidemic.
Consequently, the disparity in pricing from the farm
gate to the marketplace has expanded. This indicates
that the uncertainty and risk of the supply chain
increased, making the selling price higher (Gu &
Wang, 2020).
In this paper, the authors use evolutionary game
theory to study the vegetable pricing problem, which
was pioneered by von Neumann and Morgenstern
within the realm of economic theory (von Neumann
and Morgenstern, 1994) John Nash (Nash, 1950) and
Lloyd Shapley (Shapley, 1953) further developed this
theory, laying down the foundation of the modern
noncooperative The cornerstone of modern non-
cooperative game theory was laid. Next, evolutionary
game theory incorporates population ecology. In this
perspective, participants are understood to be finitely
rational and pursue evolutionarily stable strategies
through repeated experimentation, imitation, and
learning.
Game theory is often used in the study of
vegetable supply chains, Mousapour Mamoudan et al.
(2022) used game theory to study game theory
models showing how to develop pricing strategies for
perishable food products taking into account brand
value and competitors' prices and how to optimize
supply chain coordination through quantity discount
contracts. It is constructed as a game model of the
fresh food supply chain incorporating techniques for
initial cooling and the mitigation of greenhouse gas
emissions. Exploring the Impact of Energy-Efficient
and Emission-Reducing Technologies on the
EMITI 2024 - International Conference on Engineering Management, Information Technology and Intelligence
748

Sustainability of Fresh Food Distribution Networks
(Liu, Huang, Shang, Zhao, Yang, & Zhao, 2022).
Omkar D. Palsule-Desai in his study also used a game
theory model to study the rationalization of fruit and
vegetable cooperatives in India and the impact and
stability of decentralization (Palsule-Desai, 2015).
3 MODEL BUILDING
As a static idea evolutionary stable strategy is the
basic concept of evolutionary game, which can
describe the basic stability of the existence of a
dynamic system. Evolutionary game theory
highlights the concept of bounded rationality in the
decision-making process, that the main body involved
in the game theory is finite rationality of both sides,
their behavior and choice will be in the process of
continuous change, and ultimately tends to be
constantly stable, the main body of the game for the
vegetable is the producer of A, and vegetable retailers
B, as finite rationality of the main body, participants
in the process of the mature strategy respectively, the
producer of the vegetable commodity supply chain
strategies to give Producer's strategy in the vegetable
commodity supply chain is to give "high price" and
"low price"; retailer's strategy in the vegetable
commodity supply chain also has to give "high price"
and "low price". The retailer's strategic choices in the
vegetable commodity supply chain are also "high
price" and "low price".
x denotes the probability that the producer gives a
high price, 1-x denotes the probability that the
producer gives a low price; The variable 'y' represents
the likelihood of a retailer setting a high price for their
goods, whereas '1-y' corresponds to the chance that
they opt for a lower pricing strategy.; R1 denotes the
producer's revenue, C1 denotes the producer's cost,
R2 denotes the retailer's revenue, C2 denotes the
retailer's cost. The cooperation between the two
parties results in a synergistic revenue value P. The
additional revenue value is allocated according to the
participation ratio. The producer's allocation ratio is
a, and the value of the additional gain is denoted as
aP, while the retailer's allocation ratio is (1-a), and the
additional gain is (1-a)P, where 0≦a≦1.
Among them, the producer's revenue R1 includes
sales and government subsidies, and producer's costs
include vegetable planting costs, such as seeds,
pesticides, land, transportation and logistics costs; the
retailer's revenue R2 mainly includes sales, and the
retailer's costs C2 include purchasing costs,
transportation costs, storage costs, and operating
costs, for the producer's additional revenue aP is the
stabilization of the sales channel, i.e., the
establishment of stable cooperative relationships with
retailers, which also includes the reduction of market
risks, and the reduction of market risks. cooperative
relationship, also includes the reduction of market
risk, producers through cooperation with retailers,
producers can diversify the production risk, through
the retailer's diversified sales to help producers in
different markets and consumer groups; for the
retailer's additional benefits (1-a) P embodied in the
stability of the supply chain, cooperation with the
producer can ensure the stability of the supply of
vegetables to mitigate the impact of seasonal or other
factors.
4 INTEREST MATRIX AND
EVOLUTIONARY MODELING
The strategic outcomes matrix for the grower and the
seller in the bilateral interaction game is presented in
Table 1 below.
Table 1: Translation of the Payoff Matrix in the Game
between Manufacturer A and Retailer B.
Event
Producer
coo
p
eration
Non-cooperation
of
p
roducers
Vendor
Collaboration
𝑅

+

1 −𝑎

𝑃−𝐶

𝑅

+ 𝑎𝑃 − 𝐶

𝑅

−𝐶

𝑅

Uncooperati
ve vendors
𝑅

𝑅

−𝐶

𝑅

𝑅

Based on assumptions in Table 1, the expected
and average returns when producer B adopts the
"cooperative" and "uncooperative" strategies are
𝑈

、𝑈

and 𝑈

respectively, which are determined
using the subsequent methodology：
𝑈

= 𝑦(𝑅

+ 𝑎𝑃 − 𝐶

)+(1-y)(𝑅

−
𝐶

)=𝑦𝑎𝑃 + 𝑅

−𝐶

(1)
𝑈

= 𝑦𝑅

+

1 −𝑦

𝑅

= 𝑅

(2)
𝑈

= 𝑥𝑈

+

1 −𝑥

𝑈

= 𝑥

𝑦𝑎𝑃 − 𝐶


+ 𝑅

(3)
Replication dynamic equation are equations that
model the rate at which a collective embraces
particular strategies amidst evolving conditions. The
replication dynamic equation for the evolutionary
game with probability x that a producer chooses to
"cooperate" is:
𝐹


𝑥

=
𝑑𝑥
𝑑𝑡
= 𝑥

𝑈

−𝑈


= 𝑥

1 −𝑥

(𝑎𝑃𝑦 −𝐶

)
(4)
Examine the Impacts of the Coronavirus Crisis on Vegetable Pricing
749

The anticipated and mean profits when the vendor
implements "cooperative" and "uncooperative"
strategies are 𝑈

, 𝑈

and 𝑈

respectively, which are
determined using the subsequent methodology：
𝑈

= 𝑥
(
𝑅

+
(
1 −𝑎
)
𝑃−𝐶

)
+
(
1 −𝑥
)(
𝑅

−𝐶

)
= 𝑥
(
1 −𝑎
)
𝑃 + 𝑅

−𝐶

(5)
𝑈

= 𝑥𝑅

+(1−𝑥)𝑅

=𝑅

(6)
𝑈

= 𝑦𝑈

+
(
1 −𝑦
)
𝑈

= 𝑦
(
𝑥
(
1 −𝑎
)
𝑃−
𝐶

)
+ 𝑅

(7)
The formula that describes the replication
dynamics for a vendor opting to engage in a
"collaborative" approach with a given likelihood y is:
𝐹

(
𝑦
)
=
𝑑𝑦
𝑑𝑡
= 𝑦
(
𝑈

−𝑈

)
= 𝑦(1 −𝑦)(
(
1 −𝑎
)
𝑃𝑥
−𝐶

)
(8)
When 𝐹

(x)= 𝐹

(y)=0the equilibrium points can
be derived from A(0,1),B(0,0),C(1,0),D(1,1)and
E(


(

)

, 𝐶

/𝑎𝑃).
According to Friedman's theory, by replicating the
system of dynamic equations in trial (4), the Jacobi
matrix can be obtained as Table 2.
Based on the stipulated premise, the significance
of both the starting and the resulting points is
confined to a plane with two axes𝑉 =

(
𝑥, 𝑦
)
|0 ≪
𝑥≪1,0 ≪𝑦≪1,

Noting that The determinant of
the matrix is represented as det(J), while the sum of
the diagonal elements is denoted by tr(J); the
evaluation of stability for the five points of
equilibrium is detailed in Table 2.and evolutionary
stabilization points for both sides of the game can be
seen in table 3.
𝑑𝑒𝑡𝐽 =
()

∗
()

−
()

×
()

(9)
𝑡𝑟𝐽 =
()

+
()

(10)
Table 2: Jacobi matrix determinant values and traces for
each equilibrium point.
equilibriu
m points
detJ trJ
(0,0)
𝐶

𝐶

𝐶

−𝐶

(0,1)
𝐶

(𝑎𝑃 − 𝐶

)
𝐶

+ 𝐶

−𝑎𝑃
(1,0)
𝐶

(𝐶

− (1 −𝑎)𝑃)
(1
− a)P
−𝐶

−𝐶

(1,1)
(𝐶

−𝑎𝑃)((1-a)P-𝐶

)
(
2𝑎
− 1
)
𝑃
+ 𝐶

−𝐶

(𝑥

, 𝑦

)
[


(

)




(

)




] [


(



)()
]
0
Table 3: Evolutionary stabilization points for both sides of
the game.
equilibrium
p
oints
detJ
notation
trJ
notation
Equilibrium
results
(
0,0
)
+ - ESS
(0,1) + + point of
instability
(1,0) + + point of
instabilit
y
(1,1) + - ESS
(𝑥

, 𝑦

)
+ x saddle point
From the analysis of the graph, (0,0) and (1,1) are
the ESS equilibrium points, indicating that both
producers and retailers choose "no cooperation" or
"cooperation" and the evolutionary phase diagram of
manufacturer A and retailer B can be seen in figure 1.
Figure 1: Evolutionary Phase Diagram of Manufacturer A
and Retailer B. (Picture credit: Original).
The coordinates B(0,0) and D(1,1) represent two
points of equilibrium that emerge as stable solutions,
signifying that both producer and marketer
replication dynamic curves converge to these two
point locations. When both imitators' dynamic curves
converge to B(0,0), producers and marketers do not
cooperate to become the norm, and when both
imitators' dynamic curves converge to point D(1,1),
producers and marketers cooperate to become the
norm, in which point E is the key point for judging
that the two simulated dynamics trajectories coalesce
EMITI 2024 - International Conference on Engineering Management, Information Technology and Intelligence
750

at points B and D. And the final direction of the game
is related to the area 𝑆

of ABCE and the area𝑆

of
AECD, when 𝑆

< 𝑆

both participants in the
strategic interaction are inclined to collaborate as the
outcomes develop, when𝑆

> 𝑆

, the two participants
in the strategic interaction are inclined to select the
path of non-cooperation in the evolution of the
analysis of the factors affecting the size of the area of
𝑆

is shown below:
𝑆

=1− 1/2[


(

)

+ 𝐶

/𝑎𝑃] (11)
From the formula𝑆

, it can be seen that the image
𝑆

area size of the parameters are𝐶

、𝐶

、a、P, with
𝑆

on these parameters for the partial derivation, "+"
indicates a positive correlation, "-" indicates a
negative correlation, "*" indicates that can not discern
correlation, the results are shown in Table 4. "*"
indicates that the correlation cannot be discerned, and
the results are obtained as shown in Table 4:
Table 4: Analysis of the influence of parameters on the
choice of cooperative strategies of the game-participating
subjects.
parameters partial derivative
Effect on 𝑆

𝐶

、𝐶

<0 -
a * *
P >0 +
As can be seen from Table 4, the cost of producers
and retailers choosing to cooperate with 𝐶

、𝐶

and
𝑆

is negatively correlated, that is, when choosing a
cooperative strategy, when the mutual collaboration
expenses surpass a specific threshold, it will make
both sides to obtain the benefits of less than the cost,
which leads to the two sides to choose the strategy of
non-collaboration.
a and (1-a) are the proportion of collaboration
benefit distribution of producers and retailers,
respectively, where the range of a is [0, 1] but the
effect on the size of the area of 𝑆

cannot be judged.
P is the collaboration benefit of producers and
retailers choosing cooperation strategy, aP and (1-a)P
are the amount of benefit distribution between the two
parties respectively, when P increases it will have a
positive correlation on the area of 𝑆

, which can be
obtained that with the increase of the collaboration
benefit, the stability and attractiveness of the
cooperation between the producers and retailers are
enhanced. This implies that increasing the benefits of
cooperation not only promotes the growth of both
parties' revenues but also strengthens their
cooperative relationship, making cooperation a
preferred strategic choice for both parties. This
positive correlation also implies that through an
appropriate benefit distribution mechanism, both
parties can be incentivized to seek more efficient
ways of cooperation, thus jointly expanding the
revenue space.
5 SUGGESTIONS
5.1 Increased Transparency in the
Vegetable Supply Chain
Supply chain transparency helps to stabilize
vegetable supply and thus stabilize vegetable prices
According to the results of this paper, the cooperation
between producers and sellers has an important
impact on the stable supply of vegetables, and on the
cooperation between the two sides, through the
transparent supply chain management, vegetable
producers can have a clearer understanding of the
costs of planting, picking, transportation and other
links, which helps to carry out cost control and
management. This can help producers avoid
unnecessary waste and extra costs, thus stabilizing
production costs and influencing the final selling
price fluctuations. At the same time, a transparent
supply chain in producer-retailer cooperation enables
producers, wholesalers, and retailers to better
understand market demand and supply, so that they
can adjust production and inventory more effectively
and keep supply in balance with demand. When
supply and demand are stabilized, vegetable price
volatility may be reduced.
Enhancing the openness within the vegetable
distribution network can be realized through the
utilization of advanced 5G and analytics
technologies. By integrating 5G with data analytics,
supply chain managers can access and analyze data
from all parts of the supply chain in real-time, thus
achieving higher transparency and efficiency,
Combining 5G and Big Data, supply chain managers
can access and analyze data from all parts of the
supply chain in real time, thus achieving higher
transparency and efficiency. For example, real-time
data transmitted over 5G networks can be used to
dynamically adjust vegetable production plans,
optimize vegetable delivery routes, predict potential
supply chain disruptions such as policy impacts under
epidemics, vegetable demand impacts, etc., and take
timely countermeasures (Bai & Sarkis, 2020). It is
also possible to improve supply chain transparency by
publicizing market information to consumers and
establishing price indices to maintain stable supply
Examine the Impacts of the Coronavirus Crisis on Vegetable Pricing
751

relationships in response to the impact of public
events such as epidemics on vegetable prices.
5.2 Controlling Costs for Producers
and Distributors
Based on the findings from the strategic interaction
analysis, between producers and retailers in this
paper, the cost of both sides plays an important role
in deciding whether to cooperate, so controlling the
cost of both sides is better for maintaining supply and
demand and thus stabilizing the price of vegetables,
taking China as an example In China, the distribution
cost of vegetable agricultural products accounts for
50%-60% of the selling price, or even higher. The
reasons for this high cost include the inconsistency of
transportation management around the world, which
adds unnecessary costs to logistics enterprises, such
as according to “The Paper ”the different definitions
of overloading in different provinces and the
prevalence of indiscriminate fees and fines (2021).
Therefore, optimizing the logistics and transportation
of vegetable commodities can better control
production costs.
Optimizing the logistics and transportation of
vegetable commodities can enhance supply chain
decision-making by employing digital twin
technology how to predict the quality and
marketability metrics of food in transit through real-
time sensing and virtual models. According to
“Innovation in Fruit and vegetable supply chains”
Chandrima Shrivastava and colleagues at the
University of Bern used digital twins to characterize
the hot and humid conditions that preserve freshness,
prevent infestation by fruit fly eggs, and steer clear of
cooling system harm (2022). It is also possible to
design different scenarios for fresh fruit and vegetable
transportation and distribution networks employing
Geographic Information Systems (GIS) for analysis.
The research merged issues of site allocation with
transportation routing via computational modeling to
assess the efficacy of various scenarios. (Suraraksa &
Shin, 2019).
5.3 Increase the Benefits of Collaboration
Between Producers and Retailers
Based on the findings from the strategic interaction
analysis, the collaborative revenue of producers and
retailers significantly influences the collaborative
decision-making process for both parties involved.,
so improving the collaborative revenue of the two
parties can increase the probability of cooperation
between the two parties and maintain the stability of
the supply chain, thus making the price stable. The
establishment of a long-term cooperative relationship
can promote the trust between the two parties, reduce
the conflict caused by the pursuit of short-term
interests, and jointly respond to market changes to
achieve long-term stable collaborative revenue, at the
same time, improve the collaborative revenue,
producers and retailers need to recognize and respect
the retailers' anticipations regarding the equitableness
of earnings allocation, through the profit distribution
mechanism in the theory of the cooperative game to
solve the problem (Zhang, Ma, Si, Liu, & Liao, 2011),
which is the most important role in the decision-
making of both parties. (Si, Liu, & Liao, 2021). It is
also possible to help farmers improve their production
skills, reduce costs, and improve product quality by
collectively purchasing agricultural materials,
sharing best practices and technologies, and
providing training to increase the benefits of the
collaboration or to help farmers obtain the necessary
financial support through the establishment of a credit
cooperation mechanism to reduce the production risk
and improve the ability to adapt to market changes
(Wang, Luo, & Liu, 2021).
6 CONCLUSION
This study offers a perspective on how the COVID-
19 crisis has influenced the valuation of fresh produce,
utilizing the principles of evolutionary strategic
analysis. Taking vegetable producers and retailers as
the two sides of the game and considering the
influence of the outbreak on the distribution network
of fresh produce resume evolutionary game model,
the study finds that the epidemic leads to significant
disruptions in all segments of the vegetable supply
chain, especially labour shortage and logistics
disruption, which together lead to significant
fluctuations in vegetable prices. The evolutionary
game model constructed in the article reveals the
strategic choices of producers and retailers under the
influence of the epidemic and their specific impacts
on vegetable pricing, pointing out that the balance
between cooperative and non-cooperative strategies
plays an important role in price stability. This study
examines how supply chain disruptions due to
epidemics affect vegetable price volatility and
proposes strategies to enhance supply chain resilience,
which can help future research on how to maintain
price stability in the face of similar crises fills a
research gap on the relationship between price
volatility as well as the adaptive capacity of the
EMITI 2024 - International Conference on Engineering Management, Information Technology and Intelligence
752

sequence of processes involved in the procurement,
production, and distribution of goods.
The results of this research highlight the critical
need for enhancing the clarity of supply chain
operations, managing expenses, and boosting the
mutual advantages derived from collaborative efforts
among growers and sellers. These measures can be
effective in stabilising vegetable prices and
increasing the resilience of the supply chain, thus
better coping with rapid changes in the market
environment and possible future crises.
For the outlook of future research, the article
suggests that it could further explore how
technological innovations, technologies like 5G and
advanced data analytics could potentially enhance
both the openness and operational effectiveness of the
supply chain oversight. Meanwhile, research could be
extended to other types of agricultural products, as
well as considering the impact of policies and market
conditions on vegetable pricing in different regions
and countries. In addition, future research could
provide insights into the fairness of benefit
distribution in cooperative mechanisms and how
policy interventions can optimise the partnership
between producers and retailers for the sustainability
of the whole food system.
REFERENCES
Bai, C., & Sarkis, J. (2020). A supply chain transparency
and sustainability technology appraisal model for
blockchain technology. International Journal of
Production Research, 58(7), 2142–2162. https://doi.
org/10.1080/00207543.2019.1708989
Bairagi, S., Mishra, A. K., & Mottaleb, K. A. (n.d.). Impacts
of the COVID-19 pandemic on food prices: Evidence
from storable and perishable commodities in India.
PLOS ONE. https://doi.org/10.1371/journal.pone.
0264355
Europe PMC. (2021). Impact of COVID-19 on vegetable
supply chain and food security: Empirical evidence
from Bangladesh. PLoS ONE, 16(3), e0248120.
https://doi.org/10.1371/journal.pone.0248120
Food supply chains and COVID-19: Impacts and policy
lessons. (2020). OECD Policy Responses to
Coronavirus (COVID-19). https://doi.org/10.1787/71b
57aea-en
GU, H., & WANG, C. (2020). Impacts of the COVID-19
pandemic on vegetable production and
countermeasures from an agricultural insurance
perspective. Journal of Integrative Agriculture, 19(12),
2866–2876. https://doi.org/10.1016/s2095-3119(20)
63429-3
Guo, Y., Wang, B., Li, X., Xu, J., & Chen, H. (2024).
Identifying key nodes in the fresh food cold chain: A
novel hybrid information diffusion algorithm.
Computers and Electronics in Agriculture, 196,
105862.https://doi.org/10.1016/j.compag.2023.105862
Innovation in Fruit and vegetable supply chains. (2022).
Nature Food, 3(6), 387–388. https://doi.org/10.
1038/s43016-022-00548-1
iResearch. (2024). 2023-2024 China's Fresh E-commerce
Operation Big Data and Development Prospect
Research Report.
Liu, Z., Huang, Y.-Q., Shang, W.-L., Zhao, Y.-J., Yang, Z.-
L., & Zhao, Z. (2022). Precooling energy and carbon
emission reduction technology investment model in a
fresh food cold chain based on a differential game.
Applied Energy, 326, 119945. https://doi.org/10.10
16/j.apenergy.2022.119945
Mamoudan, M. M., Mohammadnazari, Z., Ostadi, A., &
Esfahbodi, A. (2022). Food products pricing theory
with application of machine learning and game theory
approach. International Journal of Production Research,
1–21. https://doi.org/10.1080/00207543.2022.2128921
Ruan, J., Cai, Q., & Jin, S. (2021). Impact of COVID-19
and nationwide lockdowns on vegetable prices:
Evidence from wholesale markets in China. American
Journal of Agricultural Economics, 103(5), 1574–1594.
doi:10.1111/ajae.12211
Suraraksa, J., & Shin, K. S. (2019). Urban transportation
network design for fresh fruit and vegetables using
GIS–the case of Bangkok. Applied Sciences, 9(23),
5048. https://doi.org/10.3390/app9235048
von Neumann, J., & Morgenstern, O. (1994). Theory of
Games and Economic Behavior. Princeton, NJ:
Princeton University Press.
The Paper. (2021, June 12). How high are China's
circulation costs? People's Daily investigation: Over
5% price increase at each link.
Wallace, C., & Young, H. P. (2015). Chapter 6 - Stochastic
Evolutionary Game Dynamics. In H. P. Young & S.
Zamir (Eds.), Handbook of Game Theory with
Economic Applications (Vol. 4). Elsevier.
Wang, L., Luo, J., & Liu, Y. (2021). Agricultural
Cooperatives Participating in Vegetable Supply Chain
Integration: A Case Study of San Yuan Cooperative in
China. PLOS ONE, 16(6).https://doi.org/10.1371
/journal.pone.0253668
Zhang, R., Ma, W., Si, H., Liu, J., & Liao, L. (2021).
Cooperative game analysis of coordination mechanisms
under fairness concerns of a green retailer. Journal of
Retailing and Consumer Services, 59, 102361.
https://doi.org/10.1016/j.jretconser.2020.102361
Examine the Impacts of the Coronavirus Crisis on Vegetable Pricing
753