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评估中国食品行业在全球供应链中基于消费的碳足迹(SPC)) 2023-5-23 135818 1.pdf
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Assessing consumption-based carbon footprint of China's food industry
in global supply chain
Boqiang Lin
⁎
, Chunxu Guan
School of Management, China Institute for Studies in Energy Policy, Xiamen University, Fujian, 361005, China
abstractarticle info
Article history:
Received 6 August 2022
Received in revised form 30 October 2022
Accepted 15 November 2022
Available online 19 November 2022
Editor: Dr. Peter Bradley
With the acceleration of industrialization and rapid demand growth, carbon emissions increased dramatically in
China's food industry during the past two decades. However, the uncertainty of the emission level of China's food
industry alongside the global supply chain restricts China from implementing more sustainable food policies.
Thus, this paper first estimates the carbon footprint in China's food manufacturing industry domestically and in-
ternationally by identifying major trade partners and the corresponding industries using a multi-regional input-
output model. Then, this paper further decomposed the determinants of carbon footprint change. The results
show that agriculture, food industry, transport, and energy supply are primarily responsible for carbon emission
growth. The structural decomposition results further demonstrate that increasing demand and consumption pat-
tern changes are important factors leading to a growth in carbon emission, and energy efficiency improvement
helps China's food industry mitigate the emission level. Finally, policy recommendations are provided based on
the decomposition results.
© 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
Keywords:
Food industry
Structural decomposition analysis
Food policy
Input-output analysis
Carbon emissions
1. Introduction
The food industry has been characterized as an essential industry to
a society based on its function of providing basic needs for people's daily
consumption. China has one of the world's largest food industry and
food trade volume. The total domestic sales value of China's food indus-
try accounts for 10.4 % of total industry sales in 2016, and the sales value
of the food industry increased from 108 billion USD to 988 billion USD in
the last two decades, with an average annual growth rate at about 53.5 %
(National Bureau of Statistics of China, 2022). The value of food
import in China raised from 32.36 billion US$ in 2000 to 222.34 billion
US$ in 2019 (World Bank, 2015). At the same time, globalization and
economic development have also caused increasing envi ronmental
challenges for the food industry (Lin and Su, 2022). Agri-food system
is estimated to have contributed 31 % of the total 54 Gt CO2eq anthropo-
genic greenhouse gas emissions in 2021 (FAO, 2021); and the food
manufacturing process accounts for around 8–10 % of total GHG emis-
sions (Rosenzweig et al., 2020; Xu et al., 2021a). In China, carbon emis-
sion from the food industry is a growing threat to the environment.
Though the food manufacturing industry is not considered a traditional
energy-intensive industry like the production of metals or construction
industries, the carbon dioxide emission of the food manufacturing
industry in China is still high, whether by volume or intensity. According
to the WIOD input-output database, the total carbon emission of the
food industry in China ranks top 10 out of 55 industries throughout
the years based on International Standard Industrial Clas sification
(ISIC); moreover, the food industry generates more carbon emissions
than some typical energy-intensive industries like coking industry and
manufacture of paper products in the 2000s and 2010s (Corsatea
et al., 2019). In addition, the China's food industry has higher carbon in-
tensity than over two-thirds of manufacturing industries like construc-
tion, manufacture of fabricated metals, and production of machinery
and equipment. The energy consumption of the food manufacturing in-
dustry raised from 28.8 million tons of standard coal equivalent in 2010
to 74.3 million tce in 2019 (National Bureau of Statistics of China, 2022).
The food manufacturing industry in China generated around 108 Mt.
CO2 in 2016, which is larger than the carbon emission of the food indus-
try in Germany, the United States, the UK, Russia, Canada, and France
together (Corsat ea et al., 2016). Thus, the serious impact of China's
food-related activities on climate change has received greater attention.
The sustainable goals for food sec tor are hard to achieve without
acknowledging the emission volume of food-related industries and het-
erogeneity in production pattern alongside the global supply chain. As
an intermediate industry, food sector primarily used agricultural prod-
ucts as raw materials to prepare the food into saleable goods by packag-
ing and processing (Manzini and Accorsi, 2013). On the one hand, with
the thrive of international trade, food industry relies more on trade of
Sustainable Production and Consumption 35 (2023) 365–375
⁎ Corresponding author.
E-mail addresses: bqlin@xmu.edu.cn, bqlin2004@vip.sina.com (B. Lin).
https://doi.org/10.1016/j.spc.2022.11.013
2352-5509/© 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
Contents lists available at ScienceDirect
Sustainable Production and Consumption
journal homepage: www.elsevier.com/locate/spc
final goods and intermediate inputs throughout the years. China's food
industry increased the demand for imported materials dram atically
since China joined World Trade Organization (WTO) in 2001 (Zhu,
2016). According to FAO, China imports over $110 billion value of agri-
cultural products in 2020 (FAO, 2022). On the other hand, growing
global trade induces greenhouse gas emission in long-distance interna-
tional transportation (Wiedmann and Lenzen, 2018). Foo d transport
accounts for about 12 % of total carbon emissions alongside the food
supply chain, roughly the same size of emissions generated by food
catering and fo od retail industries combined (Garnett, 2011). Thus,
the con cept of “food miles”, representing environmental footprint
created by food transport alongside the food supply chain, has received
more attention recently (Paxton, 1994; Feenstra, 1997; Bellows and
Hamm, 2001). Food miles is a simple concept which suggests that the
larger the distance food travel, the higher the environmental impact it
creates. Later, some studies point out that food miles are oversimplified
and suggest that the assessment of environmental impact should also
consider heterogeneity in greenhouse gas emission intensity based on
the country of production (Van Passel, 2010). For example, suppose a
country has lower energy efficiency in agriculture or food manufactur-
ing production, replacing the domestic food products with import coun-
terparts with a higher efficiency level may reduce the overall territorial
carbon emission (Gustavsson et al., 2011).
China faces more c omplicated situation in promoting low-carbon
food development as how big the environmental impact of China's
food sector has along the global food supply chain is still ambiguous.
On the one hand, China has one of the most efficient coal-based produc-
tion structures. China's coal-based energy system has consistently
decre ased in emitting air pollutants in the last two decades (Wang
et al., 2021a). The clean coal technology in China now generates fewer
particle matters than in Japan, the United States, and Germany (Wang
et al., 2020). Further, a s one of the largest food manufacturers and a
booming economic entity, China also has the advantage of industrial
agglom eration and factor substitution to lower the emission in food
production (Li et al., 2012; Lin and Xie, 2015). Thus, China's domestic
food manufacturing production should have an advantage compared
to other countries' food industries with a coal-based production system.
On the other hand, a coal-based energy system is still known as the
primary source of carbon dioxide emission, no matter how efficient it
is. According to the World Integrated Trade Solution database (WIOD),
China's primary food import sources are diversified, including develop-
ing countries like Brazil, Peru, Argentina, and developed countries such
as the United States, France, and New Zealand. Consequently, since
some of China's food trade partners have already finished the energy
transition and others are still using relatively lower efficiency energy
to produce, it is hard to determine whether China should import or con-
sume more domestically produced food products from an environmen-
tal perspective. While the uncertainty of China's carbon emissions level
of food industry presents a dilemma for China to whether promote local
or imported food supply, the environmental pressure requires a low-
carbon development path for food industry since the food demand is
booming in China for last two decades. From the policy perspective,
while an open food market is vital for global food security (Anderson,
2022
), the self-sufficient strategies targeted at local food security are
still the priority of China's food policy. On the one hand, feeding over
1.4 billion people with domestically produced food is im portant for
the stable development of China. On the other hand, the massive
income growth at the household level drives Chinese consumers to
acquire more imported food (Huang and Yang, 2017; Anderson an d
Strutt, 2014).
Previous studies have tried to estimate the environmental impact of
China's food industry. Feng et al. (2020a) used input-output model to
evaluate carbon emissions change of China's food industry from 1992
to 2007 induced by household consumption. Jianyi et al. (2015) adopted
a life-cycle assessment to quantify production-based carbon footprint of
China's food sector by examining 15 food types from 1979 to 2009. Lin
and Lei (2015a) use LMDI to decompose carbon emission of China's
food industry induced by domestic production activities. Song et al.
(2015) estimated the water, carbon, and ecological footprints of
Chinese households' food consumption by combining surveys and emis-
sion factor database . Though earlier research has seek to assess the
environmental impact of China's food industry, most stud ies focused
on the greenhouse gas emissions induced by domestic production or
consumption; and emissions embodied within the international trade
for food industry are often neglected.
Thus, in order to understand which food production and consump-
tion patterns are more environment-friendly in China, the main objec-
tives and contributions of this paper are: (1) estimate the domestic
emissions for China's food industry by input industry (2) estimate emis-
sions embedded in international trade by country and intermediate
inpu t industry (3) decompose the factors influencing the change of
emission level throughout years and evaluate how demand and supply
factors (population, per capita demand, industry emissions intensity,
trade changes, trade pattern of intermediate input, and production
technology) have each contributed to changes over time in China's
food industry emissions profile.
This paper is organized as follows: Section 2 provides the context by
offering the description of methodology adopted in exploring the
consumption-based carbon emission and its determinants. Section 3
presents the results of empirical analysis. Section 4 further discusses
the results in the previous section. Based on the results and initial anal-
ysis, Section 5 gives policy recommendation for future food policy in
China from environmental perspective.
2. Methodology and Data
Input-output analysis (IOA) is an appropriate methodology for
exploring the inter-regional and inter-industry carbon emissions of
the food industry. Since professor Wassily Leontief first proposed the
concept of input-output analysis to understand the interdependencies
of the in dustries within the society (Leontief, 1936), scientists h ave
soon included environmental and energy satellite accounts to construct
the extended environmental input-output (EIO). EIO is adopted to study
the environmental impact of prod uction or consumption activities
(Huang et al., 1994; Hawdon and Pearson, 1995; Gemechu et al.,
2014). Environmental input-output analysis can reflect the ecological
footprint induced by the final demand of an industry. Accompanied by
the rising of national-based environmental input-output analysis, the
concept of the multi-regional environmentally extended input-output
(MREEIO) model is also proposed to study the effect of carbon emission
flow in the global system. While the global input-output databases like
WIOD, EXIOBASE, and Eora have been constructed in recent years, it is
now possible to investigate the interregional carbon emission induced
by international trade (Moran and Wood, 2014; Stadler et al., 2018
;
Aguiar et al., 2019). Multi-regional input-output models (MRIO) have
several advantages in evaluating the environmental impact. First,
compared to direct accounting of emissions, input-output analysis can
capture both direct, indirect, and induced trade effects in a more com-
prehensive way by tracking do wn the source of carbon emission s
throughout the global supply chain (Wiedmann et al., 2007). Second,
MRIO models can consider the heterogeneity of emission intensity in
different countries (Tukker and Dietzenbacher, 2013). Third, double
accounting issues is frequently seen in overestimating carbon emissions
when the emissions are calculated from production and consumption
sides simultaneously (Caro et al., 2013). MRIO models can avoid such
bias by clearly defining the boundaries at both industry and region
level (Daniels et al., 2011).
Production-based accou nting (PBA) and consumption-based ac-
counting (CBA) are two mainstream emission estimation mechanisms
that prevail for assessing carbon emissions . The difference between
PBA and CBA is the treatment of indirect carbon emissions. For a given
indu stry, direct emissions refer to operational emissions within the
B. Lin and C. Guan Sustainable Production and Consumption 35 (2023) 365–375
366
industry's production activity, and indirect emissions are the emissions
embodied in the industry that are generated alongside the energy sup-
ply chain. Unlike traditional production-based environmental account-
ing (PBA) which evaluates an in dustry's direct carbon emissions, the
consumption-based accounting (CBA) can capture both direct and indi-
rect carbon emissions. Take the food industry in China as an example:
while direct emission will be the carbon dioxide released by the food
manufacturing process, indirect emission will be the agricultural raw
materials and transportation services used by food production. There
has been a heated debate over the accountability of carbon emission
between consumption-based accounting (CBA) and production-based
accounting (PBA). Currently, production-based accounting is prevailing
in national carbon evaluation; the Intergovernmental Panel on Climate
Change (IPCC) recommends th at countries follow produc tion-bas ed
accounting as guidelines to estimate the territorial carbon emission
(Franzen and Mader, 2018). However, the concept was challenged
by environmental scientist s who believe that the nature of
production-based accounting is flawed due to an i naccurate defini-
tion of emission responsibility. The r esul ts of production-based car-
bon accounting infer that the producers of the carbon dioxide
should be responsible for the environmental consequences. On the
contrary, consumption-based accountin g follows the principle that
consumers should answer f or the emissions regardless of the origin
of products (Fan et a l., 2016). As the problem of carbon leakage has
become more serious r ecently, consumption-based carbon account-
ing has received more a ttention. Carbon leakage is the phenomenon
of more developed countries transferring carbon-intensive indus-
tries to places where the environmental constraints are looser
(Babiker, 2005). While production-based carbon accounting failed
to consider the issue of carbon leakage, consumption-based carbon
accounting captures the carbon embodied in global trade.
Input-output analysis has been widely adopted to investigate green-
house gas emissions in China for the last two decades. By using China's
national input-output tables, Feng et al. (2020b) studied China's carbon
dioxide emission in the food industry from a household perspective; the
results show that though expenditures and physical volumes of food
consumption have increased by 35.9 % and 20.7 %, respectively, the em-
bodied carbon dioxide emission displays a decreasing trend over 1992
to 2007. Moreover, Lin and Xie (2015) explore China's domestic carbon
footprint of food production by combining environmental input-output
analysis and life cycle assessment; the conclusion suggests that China's
total carbon footprint has doubled from 1979 to 2009. By incorporating
structural decomposition analysis and input-output model, Liu and
Liang (2017) examines 41 industrial sub-sectors in China and finds
out that the food industry leads the technological progress in emission
mitigation. Xu et al. (2021b) use China's input-output tables from
2000 to 2 017 to inspect the growth of emissions in China's food
consu mption. The research concludes that greenhouse ga s emission
has tripled during the fifteen-year window, and the primary driv ing
forces of the emission are high emission intensities in the agriculture
and food manufacturing industry (Hu et al., 2021). While many scholars
have already deployed input-output models to evaluate the environ-
mental impact of food-related industries in China, the studies are
mainly limited to the domestic supply chain. Thus, by adopting a
multi-regional input-outp ut model, the current study extends the
scope of research to the global supply chain.
With the results fr om the extended environmental input-output
analysis, scholars have proposed various ways to study the driving
forces that underlie the changes in carbon emissions. Index decomposi-
tion analysis (IDA) and structural decomposition analysis (SDA) are two
mainstream approaches to determining the influencing factors of car-
bon emission shift across years (Su and Ang, 2012). While IDA favors
sectoral aggregate data, SDA requires input-output tables to function.
Hoekstra and Van den Bergh (2003) compa red the SDA a nd IDA and
concluded that SDA had several advantages in exploring determinants
of changes in carbon emission. First, while IDA can only catch the direct
impact of the determin ants, SDA can capture both direct and indirect
demand effec ts. Sec ond, though SDA requires a higher qu ality of
information like input-output tables t han IDA, it c an decompose a
broader range of driving forces. Furthe rmore, SDA can take different
forms according to research purposes. Based on the purpose of
decomposition, a multiplicative m ethod is adopted when analyzing
intensity indicators, and an additive t echnique is desi gned for
decomposing absolute values like carbon emission a nd ene rgy
consumption change. One wel l-known pro blem with S DA is that
the decomposition is non-unique; the deco mposition process gener-
ates n! decomposition f orms where n represent s the number of
determinants selected. Dietzenbacher and Los (1998a) and Zheng
et al. (2019) addressed the problem of non-uniqueness and recom-
mended to use the av erage of two polar decompositions to estimate
full sets of de compositions.
Considering both the research purpose and the characteristics of
the methodologies, this paper adopts multi-regional environmen-
tally extended input-output (MREEIO) model to explore embodied
carbon emission of China's food ma nufacturing industry alongside
the global supply chain. To f urther investigate the driving forces of
carbon emission changes , polar form structural d ecomposition anal-
ysis is performed to determine the primary factors which increase or
mitigate overall carbon emission generated by food industry in
China.
2.1. Consumption-Based Multi-Regional Input-Output Analysis (MRIO)
Since this paper tries to analyze the global influence of China's food
industry induced by final demand change on climate change, the multi-
regional input-output analysis (MRIO) model and consumption-based
carbon accounting methodology are adopted. The emission embedded
in consumption is formulated as:
C ¼ eI−AðÞ
−1
Y ¼ eLY
where C denotes the embodied carbon emission. e is a r ow vec tor
representing emission intensity per unit of output. e is a row vector
representing the carbon emission per unit of output. A is input re-
quirement matrix where diagonal sub-mat rix represents domest ic
input requirements and off-diagonal matrix indicates import inter-
mediate goods re quirements. Y is the final demand. L is equal to
(I-A)
−1
and is also called Leontief i nverse matrix. To study the im-
pact of a specific industry, setti ng the non-food se ctors to zero
within total final demand matrix Y will suffice.
For simplicity, a step-by-step example of three regions is adopted to
explain the estimation process of consumption-based carbon emission.
Suppose carbon emissions induced by the finaldemandofregion1are
sourced from total three regions and k industries, then the following ex-
pression shows the carbon emissions embodied in the production:
C
11
F
C
21
F
C
31
F
0
B
@
1
C
A
¼ e
1
k
e
2
k
e
3
k
I−A
11
−A
12
−A
13
−A
21
I−A
22
−A
23
−A
31
−A
32
I−A
33
0
@
1
A
−1
y
11
F
y
21
F
y
31
F
0
@
1
A
where C
F
11
represents the region 1's carbon emission for food industry
induced by domestic production, and vector C
F
i1
(where i≠1) denotes
the food industry's carbon emission of region 1 caused by the
production activi ties of other regions. e
k
1
are the domestic emission
intensity vectors of k
th
industry, and e
k
i
means the emission intensity
of k
th
industry in other countries. The sub-matrix A
ii
at diagonal of the
block matrix represents domestic input requirement for region i, and
the off-diagonal matrix A
ij
(where j≠i) is the international trade of inter-
mediate products between the three regions. At last, while y
F
11
indicates
the fi nal demand of food ind ustry in region 1 that are produced
domestically, y
F
i1
(where i≠1) means the imported volume for final
demand of food industry in region 1.
B. Lin and C. Guan Sustainable Production and Consumption 35 (2023) 365–375
367
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