Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability

2025-05-03 0 0 1002.44KB 37 页 10玖币
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Shock propagation from the Russia-Ukraine conflict
on international multilayer food production network
determines global food availability
Moritz Laber1,2, Peter Klimek1,3,4, Martin Bruckner5,6, Liuhuaying Yang1, and Stefan
Thurner1,3,4,7,*
1Complexity Science Hub Vienna, A-1080 Vienna, Austria
2Network Science Institute, Northeastern University, Boston, MA 02115, USA
3Center for Medical Data Science CeDAS, Medical University of Vienna, A-1090 Vienna, Austria
4Supply Chain Intelligence Institute Austria, A-1080 Vienna, Austria
5Institute for Ecological Economics, Vienna University of Economics and Buisness, A-1020 Vienna, Austria
6ETH Zurich, Institute of Environmental Engineering, 8093 Zurich, Switzerland
7Santa Fe Institute, Santa Fe, NM 85701, USA
*stefan.thurner@meduniwien.ac.at
Abstract
Dependencies in the global food production network can lead to shortages in numerous regions, as demonstrated by
the impacts of the Russia-Ukraine conflict on global food supplies. Here, we reveal the losses of
125
food products
after a localized shock to agricultural production in
192
countries and territories using a multilayer network model of
trade (direct) and conversion of food products (indirect), thereby quantifying
108
shock transmissions. We find that a
complete agricultural production loss in Ukraine has heterogeneous impacts on other countries, causing relative
losses of up to
89%
in sunflower oil and
85%
in maize via direct effects, and up to
25%
in poultry meat via indirect
impacts. Whilst previous studies often treated products in isolation and did not account for product conversion
during production, our model studies the global propagation of local supply shocks along both production and trade
relations, allowing comparison of different response strategies.
1 Introduction
Trade relations among countries create a global network
1–7
. This trade network facilitates the propagation of locally confined
shocks in the food system around the globe
7–11
. Such shocks can result from a variety of often overlapping causes, most notably
extreme weather events or economic and geopolitical crises
12–14
, and they have been found to become more frequent over
time
13
. Building dynamical models of shock propagation
15–20
makes it possible to assess the effect of a locally confined event
on the economy in distant places and to compare different response strategies
16,19
. These modelling efforts have highlighted
that the set of affected countries extends beyond direct trading partners
18
and that countries differ in their ability to deal with
shocks depending on their position in the trade network
15,16
next to their access to food reserves
17,19
. Previous work
21
has
shown that food crises are not always correlated with spiking food prices, thereby calling for methods that complement the
modeling of food prices22,23.
Even though it has been recognized that shocks co-occur in different parts of the food system
13
, shock propagation models have
so far often treated each commodity in isolation and neglected that products may be converted into other products along the
food production chain. Input–output models provide a well-established formalism to account for the conversion of products
into each other
24,25
. In the context of food systems, this framework was successfully employed to shed light on the use of
resources in foreign countries
26,27
. As a demand-driven model the input–output formalism is, however, less suitable to assess
the propagation of shocks caused by changes in supply rather than demand24,28.
Here, we present a multilayer network model that takes into account both trade between countries and the production
dependencies among products. We base all model parameters on data on supply and use of individual food products in different
countries
26
. Our model allows us to simulate shocks to the production of individual products and to assess the resulting losses
of the same and other products in countries around the globe. We employ our model in three different case studies, that are
based on the ongoing war in Ukraine, one of the worlds largest producers of maize, wheat, and sunflower seeds
29
: First, we
arXiv:2210.01846v3 [econ.GN] 16 Jun 2023
simulate a shock assuming a complete loss of agricultural production in Ukraine and show that the availability of various
products in different world regions is severely reduced. Second, we study production shocks across the entire spectrum of food
products in Ukraine and the resulting loss of different products. Here, we focus on maize and sunflower oil, which make up the
largest shares of Ukraine’s food exports. Finally, to demonstrate the versatility of our model to explore other kinds of food
shocks, we consider the availability of pork in Germany and identify critical suppliers and production inputs, i.e. country and
product pairs that would reduce the availability of pork in Germany if they suffer a shock. With this work we therefore establish
a tool to assess multiple impacts emerging from shocks on the interconnected trade and production networks.
2 Results
Understanding trade and production as a multilayer network Our model describes trade and production of different food
products in different countries as an iterated three-step process. These steps are (i) the allocation of products to different
purposes, (ii) trade with other countries and (iii) food conversion and processing activities, i.e. production of products using
other products as input. Figure 1represents a simplified version of our model illustrated by means of a multilayer network. For
simplicity, it includes only two products, maize and pigs, and three countries, Ukraine, France and Germany. First, a country,
c
,
possesses an amount,
xi
c(t)
, of a product,
i
, in iteration
t
, and constitutes a node in the multilayer network. In a first step this
amount is allocated in fixed but country- and product-specific proportions to different purposes, namely consumption as food,
export, further processing and other uses. This split is represented as a pie chart within each country in fig. 1.
Second, countries trade with other countries. A country,
c
, directs a fraction,
Ti
dc
, of its exports of product
i
towards country
d
.
The trade of a product,
i
, corresponds to a single layer of the multilayer network and the fractions,
Ti
dc
, form a matrix describing
this weighted, directed In a third step, countries produce new products by converting input products to output products using
different types of production processes. The production process of type
k
in a country,
c
, is modelled by a production function,
fk
c
, which maps the available amount of input products to the amount of output products. These functions constitute a second
type of node, that acts as an intermediary between replicas of the same country on different product-layers. In the example
depicted in fig. 1the process pig husbandry connects countries in the maize-layer to their replica in the pig-layer, as maize can
be used to feed pigs. The simplified depiction does not show other possible fodder crops included in the model nor does it
depict other processes that use maize as an input.
Performing each of the three steps once in every country constitutes a single iteration (model time step) of the algorithm. We
define a baseline scenario that consists of
10
iterations of the dynamics described above and denote
xi
c(t)
the amount of a
product,
i
, in a country,
c
in iteration
t
. We obtain parameters and initial conditions,
xi
c(t=0)
, from trade and production data
26
of the year
2013
(see methods). The trade matrices
Ti
and production functions
fk
c
are not re-calibrated to data during the
simulation but stay fixed. The variable
xi
c(t)
is updated according to the production, trade and allocation steps and, therefore,
changes each iteration. As we calibrate our model parameters to yearly data, we can think of an iteration as a model year.
Shocks propagate through different channels We compare the baseline scenario to a shocked scenario. In the shocked
scenario, the produced amount of one or more products in a specific country is removed from the model in each time step. We
denote the amount of a product,
i
, in a country,
c
, after a shock to product
j
in country
d
as
Xji
dc(t)
, where
t
denotes the time
step. The relative loss of a product, i, in a country, c, after a shock to a product, j, in a country, d,
RLji
dc(t) = xi
c(t)−Xji
dc(t)
xi
c(t),(1)
describes the relative reduction of the amount of product
i
in country
c
, in the shocked scenario with respect to the baseline
scenario at iteration
t
. We omit the dependence on the iteration
t
when referring to the relative loss in the last iteration
tend
,
RLji
dc =RLji
dc(tend).
The quantity
RL
captures the combined effect of several shock propagation channels. We illustrated the different shock
propagation channels with a toy example in fig. 2. The direct trade relations of a country comprise only one such channel
(fig 2a)). The propagation of shocks on trade networks allows us to capture the effect of trade via third party countries (fig. 2b))
or a even higher number of intermediaries. Our model accounts for two additional shock propagation channels that result from
the possibility to convert products into other products. Losses of a product,
i
, in a country,
d
, can occur if country
d
, can no
longer import a product
j
, that it relies on to locally produce product
i
, (fig. 2c)). In addition, such losses can occur if a trading
partner, c, lacks an input to produce a product, i, and reduces its exports of product ito country d(fig. 2d)).
Dynamic unfolding of different types of shock transmission In general, a shock
(d,j)
to product
j
in country
d
can
lead to three different types of impact, namely on (i) different products in the same country (local production), (ii) the same
product in different countries (direct or indirect trade), or (iii) different products in different countries. Figure 3shows for
2/37
the example of shocking product
j=maize
in country
d=UKR
(Ukraine), the time evolution of the available amount in a
baseline case without shock,
xi
c(t)
, and in the shocked scenario,
xi
c(t)
, next to the resulting relative loss,
RLji
dc(t)
. First (different
product, same country), this shock can reduce the amount of another product, shown here is
i=poultry
, in the same country
d
(fig. 3a)). Second (same product, different country), the shock propagates through trade relations and reduces the availability
of the same product,
j=maize
, in another country, Portugal
c=PRT
(fig. 3b)). Third and finally (different product, different
country), losses of different products, shown again for
i=poultry
, can occur in different countries, e.g.,
c=PRT
, either
through reduced production in
c
or in other countries (fig. 3c)). Note, that the onset of losses is delayed, if the shock propagates
through multiple production processes and trade (fig. 3d)). In this example it may take several years until the full production
losses after a shock have been realized via all direct and indirect shock transmission channels. Further details of both scenarios
are described in the methods section.
We characterize the network topology of those layers of the trade network that are most central to our analysis in the
supplementary information (SI) and show that Ukraine occupies a prominent position among the exporters. Here, we focus
on shocks to the agricultural production of Ukraine but the effects of shocks to a product of choice in other countries can be
explored in our interactive online data visualization30.
The role of Ukraine in the global food system We examine the losses that occur after a simultaneous shock to the production
of all food products,
F
, in Ukraine,
UKR
. The resulting losses,
RLF,i
UKR,C
, of different products,
i
, in different world regions,
C
,
are shown in fig.4. The availability of sunflower oil is substantially reduced in several world regions. The two most strongly
affected regions are located in Asia, with relative losses of
67.8%
arising in Southern and
48.8%
in Eastern Asia. Western Asia
ranks fourth with relative losses of
27.1%
. The third most affected region, Northern Africa, suffers losses of
48.3%
. The effect
on Europe is felt most intensely in the north (
38.23%
) and less so in the south (
12.5%
), west (
10.3%
) and east (
2.3%
). In the
latter case we exclude the losses occurring in Ukraine itself. However, in contrast to Asian regions, Europe and Africa are also
affected in their availability of other edible oils, such as rape seed and mustard seed oil (up to
21.1%
) or maize germ oil (up to
23.0%).
The shock in Ukraine also leads to considerable losses of maize in many world regions. Northern and Southern Europe are hit
strongest with losses of
39.1%
and
30.1%
, respectively, followed by Western Asia with
22.2%
and Northern Africa
17.1%
.
The latter also faces a relative loss of 24.7% of wheat.
Substantial losses also occur for animal products such as poultry meat. Southern Europe suffers losses of
17.2%
of poultry and
12.9%
of pork. Northern Africa loses
12.4%
and
6.6%
of the respective products. Losses reach
8.0%
(
1.3%
) of poultry (pig)
meat in central and 6.8% (7.0%) in Western Asia.
Regions differ considerably by the number of products for which they exhibit a direct or indirect dependence on Ukraine.
Southern Europe is strongly affected, with
19
out of
125
products having losses of more than
10%
, followed by Western Asia
and Northern Africa, where this is the case for 15 and 11 products, respectively. In contrast, North America and Australia are
least affected with only 5 and 7 out of 125 products with a relative loss that exceeds 1%.
In the following, we assess the role of production versus trade in the shock propagation. We compare the relative loss of
different products,
i
, after two types of shock. On the one hand, we shock a fixed product
j
in Ukraine and compute the relative
loss,
RLji
URK,C
, of other products,
i
, in different world regions,
C
. On the other hand, we shock the same products,
i
, in Ukraine
and monitor the losses for
i
in other regions, i.e. we compute
RLii
UKR,C
. The former quantifies the losses that arise on a different
layer and therefore involve the conversion of products into other products, while the latter quantifies the effect within one layer,
i.e. international trade. While a shocked country can still reexport products that it imported, we found that the share of reexports
is low in case of Ukraine and the within layer effect constitutes a good measure for trade-related losses, see also SI.
Downstream impacts of a shock to Ukrainian maize production
In fig. 5a) these production- and trade-related contributions to the relative losses are shown for a shock to the Ukrainian maize
production. Colors reflect the size of losses and each cell describes the losses of an affected product,
i
, in an affected region,
C
.
Cells are split into two parts. The left half captures the production-related losses after a shock to Ukrainian maize,
RLmaize,i
UKR,C
,
and therefore quantifies an effect across layers. The right half, on the other hand, captures the losses after a trade-related shock
in Ukraine to product iitself, RLii
UKR,C, and therefore quantifies the importance of Ukraine within one layer.
For the product maize itself both sides are equal by definition. The strongest effects of maize in Ukraine on maize in other
countries occur in Northern Europe with losses of
39.1%
, but Southern Europe (
30.1%
), Western Asia (
22.2%
) and Northern
Africa (
17.1%
) are affected as well. Note that these losses incorporate not only the lack of maize that is directly imported from
Ukraine, but also reduced domestic production due to lack of seeds and the trade with third party countries, that might also rely
on imports from Ukraine.
In addition, the upper part of fig 5shows that the shock to Ukrainian maize influences the availability of pig and poultry meat in
Europe, Northern Africa and Western Asia. For poultry meat the relative loss after a shock to maize in Ukraine amounts to
15.4%
in Southern Europe,
4.9%
in Northern Africa and
3.9%
in Western Asia. This contrasts the losses after a shock to the
3/37
Ukrainian poultry meat production, which stay below
1%
in these world regions. This indicates that the losses in these world
regions arise from a lack of fodder maize in the domestic poultry meat production and not from a trade of poultry meat with
Ukraine. A similar pattern can be observed in the supply of other products that rely on maize as an input to production.
The availability of sweeteners in central Asia is an exception. Here the effect within one layer, with losses of
28.3%
, is larger
than the effect across layers, with a loss of
11.1%
, after a shock to the Ukrainian maize production. Similarly, the relative loss
of alcoholic beverages in central Asia after a shock to this product in Ukraine amounts to
1.5%
and exceed the losses of
0.7%
,
that occur after a shock of the Ukrainian maize production. This situation arises as both products can be made from a variety of
input products other than maize.
Ukraine is a critical supplier of sunflower oil
Fig. 5b) shows results for a shock to the sunflower seed production of Ukraine. In contrast to the maize shock in fig. 5a) the
losses induced across layers (left half) and within the same layer (right half) are of similar size. In Southern and Eastern Asia
relative losses of
67.7%
and
48.7%
of sunflower oil are observed for a shock to the Ukrainian sunflower seed production. A
shock to Ukrainian sunflower oil leads to equal losses in these world regions. Losses of similar size occur in Northern Africa,
which loses
48.2%
due to a shock to sunflower seeds and
48.1%
after a shock to sunflower oil, and Northern Europe with losses
of
38.2%
and
38.1%
respectively. The difference between the two types of shock is largest in Western Asia where losses of
27.0%
of sunflower oil arise after a shock to Ukrainian sunflower seeds and
24.8%
after a shock to Ukrainian sunflower oil. As
a shock to Ukrainian sunflower seeds also causes losses of
5.2%
of sunflower seeds in this region, the additional losses are
likely due to a reduced local production of oil as a result of the reduced availability of sunflower seeds. Most world regions also
suffer losses of sunflower cake, a residue from oil seed crushing that can be used as fodder. The largest losses occur in Western
Africa
42.9%
, Northern Europe
39.1%
and Western Europe
28.1%
for a shock to Ukrainian sunflower seeds. For a shock to
Ukrainian sunflower cake itself losses are slightly lower or equal, amounting to
42.4%
,
39.1%
and
28.1%
in the respective
world regions. The relative losses of sunflower seeds in all world regions are much weaker than the losses of sunflower oil. Our
multilayer modelling framework can readily be applied to other types of shocks, which we demonstrate in the SI within a case
study to identify dependencies of the German pork production.
4/37
Allocation:
consumption
exports
processing
other
fk
c
fk
d
fk
e
Ti
dc
Tj
ce
d
e
c
d
e
c
Figure 1. Schematic representation of trade and production as a multilayer network for three countries, Ukraine,
c
, France,
d
,
and Germany, e, and two products maize (lower layer i) and pigs (upper layer j). The allocation of products to different
purposes is represented as a pie chart within each country. Trade is described by the weighted directed links (solid arrows)
within each layer. The entry Ti
cd describes the share of country d’s exports (green) of a product, i, directed towards country c.
Production processes, modelled as a second type of node, turn products into other products, thereby connecting different layers
(dashed arrows). Here, the production function, fk
c, for the process type, k, pig husbandry, in country c, turns maize into pigs.
Country cis therefore an in-neighbor of the process on the maize-layer and an out-neighbor on the pig-layer. Note that
production processes can take more than one input and supply more than one output.
5/37
摘要：

ShockpropagationfromtheRussia-UkraineconflictoninternationalmultilayerfoodproductionnetworkdeterminesglobalfoodavailabilityMoritzLaber1,2,PeterKlimek1,3,4,MartinBruckner5,6,LiuhuayingYang1,andStefanThurner1,3,4,7,*1ComplexityScienceHubVienna,A-1080Vienna,Austria2NetworkScienceInstitute,NortheasternU...

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