CHEMICAL ENGINEERING TRANSACTIONS
VOL. 70, 2018
A publication of
The Italian Association
of Chemical Engineering
Online at www.aidic.it/cet
Guest Editors: Timothy G. Walmsley, Petar S. Varbanov, Rongxin Su, Jiří J. Klemeš
Copyright © 2018, AIDIC Servizi S.r.l.
ISBN 978-88-95608-67-9; ISSN 2283-9216
Towards a Sustainable Agri-Food System by an Energetic
and Environmental Efficiency Assessment
Isabel Garcia-Herreroa,*, María Margalloa, Jara Lasoa, Laura Batlle-Ballerb, Alba
Balab, Pere Fullanab, Ian Vazquez-Rowec, Maria Jesús Duráa, Carmen Sarabiaa,
Rebeca Abajasa, Francisco José Amo-Setiena, Ainhoa Quiñonesa, Angel Irabiena,
Ruben Aldacoa
aUniversidad de Cantabria, Avda. de los Castros s/n, 39005 Santander, Spain
bUNESCO Chair in Life Cycle and Climate Change, Escola Superior de Comerç International (ESCI), Universitat Pompeu
Fabra (UPF), Passeig Pujades 1, 08003 Barcelona, Spain
cPontificia Universidad Católica del Perú, Departamento de Ingeniería, Red Peruana Ciclo de Vida. Avenida Universitar
1801, San Miguel L0032, Lima, Perú
garciahmi@unican.es
Feeding the world’s population sustainably is a major challenge of our society and has been stated as one of
the key priorities for development cooperation by the EU policy framework on food security. However, the current
pattern of natural resources exploitation to meet humanity’s demand for food threatens long-term food security.
Food systems represent around 30 % of final energy use, generating up to 30 % of greenhouse gas (GHG)
emissions. Given the expected increase of global population (9 billion people by 2050) and the amount of food
losses and waste generated (a third of global food production), improving the efficiency of food systems along
the supply chain is essential to ensure food security. This work combines life cycle assessment (LCA) and data
envelopment analysis (DEA) to assess the efficiency of Spanish agri-food system and propose improvement
actions in order to reduce energy usage and GHG emissions. Results suggest that sweets and vegetable fats
categories provide the largest nutritional energy to consumer per unit of embedded energy in its production.
Around a 70 % average reduction target is estimated for the Spanish agri-food system to be efficient, with a
similar reduction in related greenhouse gas emissions.
1. Introduction
Food industry is the major manufacturing sector, representing 15 % of the sales in the European Union and
more than 1,200,000 million euros. However, despite food industry contributes significantly to a nation’s
economy, it is also responsible for a large consumption of natural resources and significant emissions to air,
water and land. A considerable use of energy can be attributed to agri-food systems, estimated at 30 % of final
energy consumption (Pimentel, 2008). Given the strong dependency on fossil fuels of our current energetic
system, such energy consumption is responsible for 20 – 30 % of total anthropogenic greenhouse gases (GHG).
Energy is required at every stage in food production including cultivation and harvesting of crops, animal
husbandry, transportation and distribution and food processing for consumption. This causes a low efficiency
for food systems, around 10 kcal of fossil fuel energy to produce 1 kcal of food (Pimentel, 2008). Some foods
are very efficient in their use of resources to produce a nutritious meal, such as fruits and vegetables, while
others are very inefficient, such as animal based products. In general, animal foods require 8 times more energy
than plant based commodities (Pelletier et al., 2011). In particular, livestock production shares around 18 % of
GHG, equivalent to industry and larger than the transport sector (Vittuari et al., 2016). The inefficiency problem
is wider when food losses and waste are taken into account. Around a third of all food produced for human
consumption is lost or wasted, c.a. 88 million tonnes of food in Europe. Since the embodied amount of energy
builds up along the chain, the latter the waste occurs, the higher the energy waste and the related GHG. On the
other hand, there is an expected trend of global population increase, for which a 60 % rise in food production is
DOI: 10.3303/CET1870136
Please cite this article as: Garcia-Herrero I., Margallo M., Laso J., Batlle-Baller L., Bala A., Fullana P., Vazquez-Rowe I., Dura M.J., Sarabia C.,
Abajas R., Amo-Setien F.J., Quinones A., Irabien A., Aldaco R., 2018, Towards a sustainable agri-food system by an energetic and
environmental efficiency assessment , Chemical Engineering Transactions, 70, 811-816 DOI:10.3303/CET1870136
811
forecasted for the year 2050, entailing a 50 % rise in global energy consumption (Vora et al., 2017). Therefore,
improving the food supply chain (FSC) efficiency has been identified as an essential means to ensure food
security, while reducing pressure on natural resources.
2. Methodology
The methodology described in this work attempts to assess the energetic and environmental efficiency of
Spanish agri-food system using a life cycle approach. First, a life cycle assessment (LCA) is conducted to
estimate the primary energy demand (PED) and related global warming potential (GWP). Second, the energy
return on investment (EROI) is estimated to describe the energy efficiency of food categories under study.
Finally, data envelopment analysis (DEA) is synergistically combined to LCA results to explore the operational
inefficiencies and the improvement actions.
2.1 Life cycle assessment (LCA)
LCA is a tool to assess the potential environmental impacts and resources used throughout a product´s life-
cycle (Margallo et al., 2016). In this regard, LCA has become one of the most relevant methodologies to help
organizations to perform their activities in the most environmental friendly way along the whole value chain.
In this work, LCA is conducted following the recommendations of ISO 14040 and ISO 14044 international
standards (ISO, 2006a; ISO, 2006b). The study is performed from cradle to consumer considering the phases
of agricultural production, processing & packaging, wholesale & retail and consumption (Figure 1). The
functional unit is described as the food basket with the representative food products consumed by a Spanish
citizen in a year, covering the daily energy requirement of 2,000 kcal of an adult.
Daily average consumption data for 48 representative commodities were sourced from the consumption
database of the Spanish Department of Agriculture and Fishery, Food and Environment (MAPAMA, 2017) for
the 2006-2016 period. Items were grouped into 11 food categories (eggs, meat, fish & seafood, dairy, cereals,
sweets, pulses, vegetable oils, vegetables, fruits and roots). Food losses and wastes (FLW), referring to a
decrease in food quantity along the food supply chain (FSC) were taken into account using the weight
percentages of Gustavsson et al. (2013). An extensive life cycle inventory (LCI) was built up using data from
the literature and GaBi software. Imported food products were taken into account based on trade statistics
compiled by the Spanish Tax Agency (Datacomex, 2018). For transportation, 400 km and 100 km distances to
wholesale and retailers were assumed. Electricity consumption for retail storage was considered, assuming 2
days storage for products requiring cooling conditions, and 15 days under freezing conditions. Transport to
home was estimated based on Milà i Fontanals et al. (2007). Regarding home storage, data from the LCA Food
Database was used (Nielsen et al., 2003). For cooking, energy factors of Foster et al. (2006) were taken. The
impact assessment method selected follows the ILCD/PEF recommendations v1.09 for determining the global
warming potential (GWP). The consumption of Primary energy resources (net calorific value, PED) is
determined according to PE International (2014) life cycle inventory. Once the PED and GWP embodied along
the agri-food supply chain are determined, the embodied energy and CO2 in FLW and can be determined using
FLW rates from Gustvasson et al. (2013) as indicated in Figure 1.
2.2 Energy return on investment (EROI)
EROI is the estimation of the quantity of energy delivered by a production technology relative to the quantity of
energy invested (Pelletier, 2010). Although this concept was initially devised to the assessment of energy
systems, the concept has been adapted to quantify ratios of food energy output relative to industrial energy
inputs. Pelletier et al. (2011) propose the application of three distinct EROI measures for studying energy
efficiency in food systems, although the most widely used is human-edible food energy return on industrially
energy investment, as indicated in Eq. (1). This provides an anthropocentric perspective on the nonrenewable
resource efficiencies of competing food production technologies. Nutritional data for the EROI estimation were
obtained from the food composition tables of the Institute for Education in Nutrition and Dietetics from Spain
(Farran et al., 2003). Such tables are registered in the FAO’s International Network of Food Data Systems (FAO-
INFOODS, 2018).
𝐸𝑅𝑂𝐼=
𝑁𝑢𝑡𝑟𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑑 𝑡𝑜 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟
𝑃𝑟𝑖𝑚𝑎𝑟𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑖𝑛𝑣𝑒𝑠𝑡𝑒𝑑
(1)
2.3 Data envelopment analysis (DEA)
DEA is a nonparametric tool that quantifies empirically the relative efficiency of multiple similar entities named
decision making units (DMUs) (Cooper et al. 2007). This linear programming method uses input and output data
from the different DMUs to formulate an optimization model, whose solution identifies the inefficient DMUs and
812
the target performance improvement values. Although DEA has been individually used for environmental and
eco-efficiency assessments, it can be synergistically nourished from life cycle inventory (LCI) data in order to
more effectively detect and remedy the technical inefficiencies that are sources of unnecessary environmental
impact (Vazquez-Rowe et al., 2010). The approach calculates the efficient frontiers for the DMUs under study:
the result for each DMU is an efficiency score and, for those DMU identified as inefficient, a target operating
point. The number of DMUs under study must satisfy the following equation:
𝑛≥𝑚𝑎𝑥 {𝑚 ·𝑠,3 · (𝑚+𝑠)} (2)
where m is the number of inputs used in the DEA study and s is the number of outputs involved.
In this work, the primary energy invested in food production and the global warming potential were considered
as inputs for the DMUs, while the nutritional energy supply was taken as outputs. Since there are 11 food
categories under study and each category corresponds to one DMU, the rule is satisfied. The DEA optimization
mode seeks to minimize inputs to produce the same output.
Figure 1: System boundaries of this work
3. Results
As shown in Table 1, the total energy invested in food production amounts daily to 19500 kcal for an average
Spanish citizen. This involves close to 4 kg of CO2 per day, being meat production the main responsible of such
emissions (42 %). Dairy and fish & seafood are the second main categories contributing to GWP, each sharing
a 12 % of the total emissions. Results suggest a high correlation between the investment of energy along the
FSC and the generation of greenhouse gas emissions, owing to the high dependency of the energetic model
on fossil fuels. Conversely, this is not directly related to the nutritional energy provided to consumer. As
displayed in Table 1, the major contributors to Spanish average diet are vegetable oils and cereals, being
responsible of nearly half of the energy supply. In fact, the contribution of meat to nutritional energy supply is
relegated to less than 12 %.
When considering FLW, it is observed that embodied energy waste duplicates the daily energy supplied to
consumer. The largest contributor to this fact is cereal category, accounting for 38%. This is directly related to
the unnecessary emission of CO2-eq., which also contributes 38% to the total GWP. It is followed by pulses
and fish & seafood categories, for which a 27 % embodied energy waste is estimated. The second largest
embodied CO2 waste is observed in pulses and fruits categories, estimated at 32 %.
Life cycle inventory(LCI)
Embodied Energy in FLW ( , )
Energetic-environmental efficiency
ECO-
LABEL
B
A
C
Embodied CO2 in FLW ( )
, = 𝑃𝐸 ,
, = 𝑃 ,
,
𝑃𝐸 , is the PED in stage j for food category i
, is the percentage of FLW in stage j
𝑃 , is the GWP of stage j for food category i
, is the percentage of FLW in stage j
Determination of food losses and waste (FLW)
Data collection of nutritional content
1) LIFE CYCLE ASSESSMENT (LCA)
Primary energy demand (PED) Global warming potential (PED)
3) DATA ENVELOPMENT ANALYSIS (DEA)
Operational target values
Inefficiency quantification
Energy return on investment (EROI)
D
e
fi
n
it
io
n
s
Collection of inputs and outputs of the system
Life cycle impact assessment (LCIA)
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Table 1: Energy invested from cradle to plate for the food categories under study and related CO2 emissions.
Nutritional energy provided to consumer is also displayed.
PED
(kcal/cap/d)
Energy
provided to
consumer
(kcal)
GWP
(g CO2/ cap/d)
Embodied
energy in FLW
(kcal/cap/d)
Embodied
GWP in FLW
(g/cap/d)
Eggs 1059 41 221 163 35
Meat 5465 216 1673 1162 378
Fish & seafood 3170 99 468 852 128
Dairy 1411 289 496 137 55
Cereals 2717 456 372 1042 143
Sweets 156 103 28 35 8
Pulses 490 58 85 142 27
Vegetable Oils 717 461 158 150 39
Vegetables 3297 72 261 745 70
Fruits 690 159 159 171 52
Roots 330 53 51 88 16
Total 19501 2000 3971 4685 951
These findings suggest the need of estimating the efficiency of the Spanish agri-food system. As a first
approach, the EROI indicator is used to estimate the energetic efficiency. As displayed in Table 2, the maximum
EROI is observed for sweets and vegetable oils, which means that such food categories are the most efficient
under the energetic point of view. These results also suggest the need of studying more in-depth nutrients to
encourage and to limit in order to provide a nutritional efficiency perspective. Results also show that animal
based products such as eggs, meat and fish present the lowest EROI (3.1 - 4.0 %), which agree with the study
of Pelletier et al. (2011), where a 8 times higher energy is reported for the production of animal products in
comparison to plant based products. An exception is observed in our results for vegetables. This is mainly due
to the inclusion of processed commodities in this category, in particular tomato sauce product, which exhibits a
1.2 % value for EROI.
Table 2: Energy return on investment (EROI).
Eggs Meat Fish &
seafood
Dairy Cereals Sweets Pulses Vegetable
Oils
Vegetables Fruits Roots
3.90 3.96 3.14 20.52 16.79 66.00 11.75 64.27 2.18 23.05 16.10
However, these efficiency results do not consider the environmental impacts of food production along the supply
chain. For this reason, DEA has been combined to LCA results, so that a wider scope of the efficiency of the
Spanish agri-food system can be provided.
Results provided by the DEA model for efficient and inefficient food categories are gathered in Figure 2. A food
category is deemed inefficient when efficiency score is lower than the unity, whereas relative efficiency equal to
the unity represents an efficient category. According to this, sweets are the sole efficient category, while a 97 %
efficiency is observed for vegetable oils. The less efficient DMU results again vegetables category, which is
mainly due to the consideration of tomato sauce, as above described. Results suggest that the planification of
citizen’s diet could affect to the quantity of embodied energy waste and related environmental impacts. For this
reason, specific strategies should be addressed to the categories revealing larger inefficiency scores, such as
meat, eggs, fish and vegetables, which agree to have better nutritional reputation than sweets or processed
cereals.
The previous findings agree with the pattern observed for EROI assessment, just based on energetic analysis.
While this can be taken as a verification of both approaches, DEA also provides a further exploration of the
operational inefficiencies and the improvement actions of the system under study. Hence, the DEA model was
also used to formulate new virtual and efficient values for the inputs of the inefficient DMUs, by projecting the
inefficient scores on the efficient targets established by the previously assessed as efficient food categories.
Both primary energy demand and global warming potential were subjected to minimisation while maintaining
the same nutritional supply.
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Figure 2: Relative efficiency of the different food categories under study.
As suggested by Figure 3a, significant reduction targets are needed for primary energy demand of inefficient
food categories, ranging from 96.7 to 2.8 %. This would reduce significant the environmental impact of such
categories, ranging from 96.4 % for meat to 19 % of vegetable fats (Figure 3b). Note that no improvement values
are suggested for sweets category, since it was designated as efficient DMU. In general, the methodological
approach suggests an average reduction target of 68 % for the primary energy demand, involving a similar
reduction in CO2-eq emissions. As previously suggested, reducing FLW has been identified as an essential
means of increasing efficiency along the supply chain, while reducing pressure on natural resources and related
environmental impacts. Currently the waste hierarchy, introduced by the Waste Framework Directive, has been
the rule followed to prioritize food waste prevention and management measures and to set reduction targets
according to the environmental criteria (Cristobal et al., 2018). The study of the correlation between food waste
generation and related embodied resources and environmental impacts enables the proposal of integrated
reduction targets. The competitiveness of the food sector can be improved in the various parts of the production
chain according to the circular economy concept. Currently, the environmental and sustainability vision of this
sector sets about ensuring more sustainable sourcing, resource efficiency, and sustainable consumption and
production towards the year 2030. A food eco-innovation strategic policy is necessary to support green
innovation whilst also improving the competitiveness, specifically for Small and Medium Enterprises (EASME).
Figure 3: a) Reduction targets for primary energy demand; b) Reduction targets for global warming potential.
4. Conclusions
Food systems are heavily reliant on energy resources, especially non-renewable resources. This causes
significant amounts of greenhouse gas emissions. In this study, the efficiency of the Spanish agri-food systems
is addressed from an energetic and environmental point of view. First, energy return on investment (EROI) is
used to perform an energetic efficiency assessment. Then, data envelopment analysis (DEA) is conducted to
include the environmental issue in the efficiency assessment. Both approaches provide similar results,
suggesting the high correlation between primary energy demand and CO2 emissions. Best results are obtained
for sweets and vegetable oils categories, while vegetables, fish, eggs and meat exhibit the lowest efficiency. As
expected, animal based products required more energy resources in their production. The unexpected results
0 0.2 0.4 0.6 0.8 1
Vegetables
Fish
Eggs
Meat
Pulses
Roots
Cereals
Dairy
Fruits
Vegetable fats
Sweets
Relative efficiency
65.90%
83.80%
94.80%
94.10%
72.70%
96.40%
81.20%
70.80%
0.00%
19.10%
92.40%
-0.5
-0.3
-0.1
0.1
0.3
0.5
0.7
0.9
0
400
800
1200
1600
2000
G
W
P
(
g
C
O
2
-e
q
/c
a
p
/d
a
y
)
Original DMU Virtual DMU
B)
74.60%
68.90%
94.10%
95.20%
66.30%
94.00%
82.20%
75.60%
0.00%
2.80%
96.70%
-0.5
-0.3
-0.1
0.1
0.3
0.5
0.7
0.9
0
1000
2000
3000
4000
5000
6000
P
ri
m
a
ry
e
n
e
rg
y
k
c
a
l/
c
a
p
/d
a
y
)
Original DMU Virtual DMU
A)
815
for vegetables categories are due to the inclusion of processed products in this category. Around a 70 % average
reduction target is estimated for the Spanish agri-food system to be efficient, with a similar reduction in related
greenhouse gas emissions. These results suggest the need of improving the efficiency of the supply chain by
introducing strategies of circular economy, such as stablishing appropriate food waste management measures
and the consequent reduction of food losses and waste. The introduction of nutritional and energetic criteria
besides the environmental one, provides an integrated framework to propose integrated reduction targets.
Acknowledgments
The authors are grateful for the funding of the Spanish Ministry of Economy and Competitiveness through the
Ceres-Procom: Food production and consumption strategies for climate change mitigation (CTM2016-76176-
C2-1-R) (AEI/FEDER, UE).
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http://www.lcafood.dk/