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The International Journal of Life Cycle Assessment (2023) 28:234–254
https://doi.org/10.1007/s11367-022-02128-8
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LCA FORENERGY SYSTEMS ANDFOOD PRODUCTS
Ex‑ante life cycle assessment ofcommercial‑scale cultivated meat
production in2030
PelleSinke1 · ElliotSwartz2· HermesSanctorum3· CoenvanderGiesen1· IngridOdegard1
Received: 1 July 2022 / Accepted: 14 December 2022 / Published online: 12 January 2023
© The Author(s) 2023, corrected publication 2023
Abstract
Purpose Cultivated meat (CM) is attracting increased attention as an environmentally sustainable and animal-friendly alterna-
tive to conventional meat. As the technology matures, more data are becoming available and uncertainties decline. The goal
of this ex-ante life cycle assessment (LCA) was to provide an outlook of the environmental performance of commercial-scale
CM production in 2030 and to compare this to conventional animal production in 2030, using recent and often primary data,
combined with scenario analysis.
Methods This comparative attributional ex-ante LCA used the ReCiPe Midpoint impact assessment method. System bounda-
ries were cradle-to-gate, and the functional unit was 1kg of meat. Data were collected from over 15 companies active in
CM production and its supply chain. Source data include lab-scale primary data from five CM producers, full-scale primary
data from processes in comparable manufacturing fields, data from computational models, and data from published litera-
ture. Important data have been cross-checked with additional experts. Scenarios were used to represent the variation in data
and to assess the influence of important choices such as energy mix. Ambitious benchmarks were made for conventional
beef, pork, and chicken production systems, which include efficient intensive European animal agriculture and incorporate
potential improvements for 2030.
Results and discussion CM is almost three times more efficient in turning crops into meat than chicken, the most efficient
animal, and therefore agricultural land use is low. Nitrogen-related and air pollution emissions of CM are also lower because
of this efficiency and because CM is produced in a contained system without manure. CM production is energy-intensive,
and therefore the energy mix used for production and in its supply chain is important. Using renewable energy, the carbon
footprint is lower than beef and pork and comparable to the ambitious benchmark of chicken. Greenhouse gas profiles are
different, being mostly CO2 for CM and more CH4 and N2O for conventional meats. Climate hotspots are energy used for
maintaining temperature in reactors and for biotechnological production of culture medium ingredients.
Conclusions CM has the potential to have a lower environmental impact than ambitious conventional meat benchmarks, for
most environmental indicators, most clearly agricultural land use, air pollution, and nitrogen-related emissions. The carbon
footprint is substantially lower than that of beef. How it compares to chicken and pork depends on energy mixes. While CM
production and its upstream supply chain are energy-intensive, using renewable energy can ensure that it is a sustainable
alternative to all conventional meats.
Recommendations CM producers should optimize energy efficiency and source additional renewable energy, leverage supply
chain collaborations to ensure sustainable feedstocks, and search for the environmental optimum of culture medium through
combining low-impact ingredients and high-performance medium formulation. Governments should consider this emerg-
ing industry’s increased renewable energy demand and the sustainability potential of freed-up agricultural land. Consumers
should consider CM not as an extra option on the menu, but as a substitute to higher-impact products.
Keywords Cultivated meat· Cultured meat· Comparative LCA· Alternative proteins· Renewable energy· Culture
medium· Carbon footprint· Land use
Responsible editor: Arnaud Hélias
Extended author information available on the last page of the article
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235The International Journal of Life Cycle Assessment (2023) 28:234–254
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1 Introduction
Cultivated meat (CM), also referred to as cultured or cell-
based meat, is genuine animal meat or seafood produced
by cultivating animal cells directly via modern biotech-
nological methods (Specht etal. 2018). CM is intended to
be interchangeable in diets, competing in the marketplace
on taste, nutrition, and other meat attributes. Currently,
over 100 companies across the globe have been founded
to bring CM to market; however, it has only been approved
for sale in Singapore (GFI 2022).
CM is a sub-discipline of cellular agriculture, which
broadly aims to substitute agricultural products derived
primarily from animals, including meat, seafood, milk,
materials such as leather and fur, and individual proteins
such as collagen or heme. Plant-derived products such as
cocoa, cotton, or palm oil can also be targeted through cel-
lular agriculture approaches. The potential benefits of cel-
lular agriculture lie in the removal of the animal or plant
middle-man, coincident with a reduction in any negative
externalities that contribute to climate change and envi-
ronmental degradation, risk of antibiotic resistance and
zoonotic disease, and animal welfare concerns (Stephens
etal. 2018). Furthermore, having more control over the
production process may lead to safer, more nutritious, and
tastier products than their conventionally produced coun-
terparts. Manufacturers of cellular agriculture products
assume that these features will make them attractive to
consumers and interchangeable in diets with little behav-
ioral change required.
1.1 Food system impacts, global picture
conventional animal agriculture, andfootprints
Although animal products contribute around 18% of calo-
ries and 37% of protein to the average global diet, the
impacts on the environment are disproportionately large
compared to non-animal products in diets (Poore and
Nemecek 2018). Estimates of global animal agriculture’s
contribution to environmental issues are as follows:
• Climate change: 16.5–19.4% contribution to total
anthropogenic greenhouse gas emissions, making ani-
mal production by far the highest contributor within
food system emissions, twice as large as plant-based
sources (Crippa etal. 2021; Twine 2021; Xu etal.
2021). The contribution of ruminants to total animal
agriculture emissions is significant due to their meth-
ane emissions, with enteric fermentation accounting
for 27% of global anthropogenic methane emissions
(Global Methane Initiative 2015; Grossi etal. 2019).
Without interventions food system emissions alone
could preclude Paris Agreement climate targets to limit
warming at 1.5°C by 2050 (Clark etal. 2020).
• Land use and land use change: 83% of global agricultural
land use, including pastures and cropland for feed (Poore
and Nemecek 2018), which in turn is the main driver
for global land use change (Poore and Nemecek 2018;
Pendrill etal. 2019; FAO 2022).
• Water use: 41% of green and blue water use combined,
although contribution to blue water use is around 6%
(Heinke etal. 2020).
• Nutrients: Over a third of anthropogenic nitrogen emis-
sions (Uwizeye etal. 2020) and a dominant driver of
disruption of natural nitrogen and phosphorus cycles
(Garske and Ekardt 2021).
• Biodiversity loss: All of the impacts mentioned above are
strong drivers for loss in biosphere integrity (Steffen etal.
2015). Current production of animal products has a dis-
proportionately large effect on biodiversity loss compared
to other food products (Benton etal. 2021).
Reducing the impact of meat production on the envi-
ronment can be achieved by improving animal agriculture
and reducing the amount of animal agriculture. Given that
animal meat consumption is projected to rise by more than
70% by the year 2050, compared to 2010 (FAO 2011), cel-
lular agriculture technologies that can ultimately reduce the
amount of animal agriculture are of paramount importance,
as any improvements to conventional animal agriculture may
be offset by anticipated growth.
1.2 Impacts ofCM andconventional meats
LCA studies up to date indicate that CM has the potential to
have lower carbon footprint, land use, water use, and eutroph-
ication effects than most conventional meats (Tuomisto and
Teixeira de Mattos 2011; Tuomisto etal. 2014, 2022; Mattick
etal. 2015). An overview of the main study characteristics
and results is provided in Appendix A. For land use, the dif-
ference is most striking, mirroring that CM is a more efficient
way of turning biotic resources into meat. Energy use of CM
however is higher, and therefore generally CO2-emissions are
expected to be higher. Because animal production systems
have greater emissions of strong greenhouse gasses (GHGs)
such as methane (CH4) and nitrous oxide (N2O), and have
higher land use change (LUC)-related emissions, aggregated
climate change effects of CM are generally found to be lower
than conventional meats. The most recent results for CM
from Mattick etal. (2015) and Tuomisto etal. (2022) show
that CM has the potential to have lower carbon footprints
than global averages for all animal meats but will likely have
higher carbon footprints than efficiently produced chicken
(Poore and Nemecek 2018).
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236 The International Journal of Life Cycle Assessment (2023) 28:234–254
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Because of the different GHG-emission profiles between
CM and conventional meats, Lynch and Pierrehumbert
(2019) modeled beef consumption scenarios over a time
period of 1000years, both conventional and CM beef. Their
conclusion is that while “GWP100a” carbon footprints of
CM may be lower than conventional beef, the overall long-
term effect on climate change may be higher, because CO2
from energy production in the CM system remains in the
atmosphere a long time, while CH4 from cows breaks down
into biogenic CO2. The study by Lynch and Pierrehumbert
(2019) however did not account for decarbonization of the
global energy mix. Only Tuomisto etal. (2022) accounted
for the increase in available decarbonized energy and
show that this can have significant influence on the results.
Because of the different GHG emission profiles between CM
and conventional meat, this is a crucial modeling choice and
therefore should be included in scenarios. In this study, this
was taken into account.
1.3 Ex‑ante LCA, upscaling, anduncertainty
CM is being developed with the promise of being more sus-
tainable. However, since the technology is still immature
and mostly on lab- or pilot-scale, this promise cannot yet
be tested on the (large) scale that will benefit most from
economies of scale. In contrast the conventional meat pro-
duction systems and supply chains are mature and efficiently
organized. Comparing a technology that is lab- or pilot-scale
to a mature technology that enjoys the high-efficiency ben-
efits of economies of scale yields an unrealistic picture of
how the new technology could perform. The difficulties in
comparisons notwithstanding, providing a picture of the
environmental impacts and hotspots of a future production
system can aid decision-making for increased sustainability
in the design stages of this system, when it is still relatively
inexpensive to change course of development (Villares etal.
2017; Cucurachi etal. 2019). Performing an LCA before
(ex-ante) a technology is fully developed can therefore
provide highly useful insights to deliver on the promise of
sustainability.
Tsoy etal. (2020) propose a framework for upscaling
of three consecutive steps. These are projected technology
scenario definition, preparation of a projected LCA flow-
chart, and projected data estimation. A few things should
be considered when developing future scenarios (Pesonen
etal. 2000). First, it is important to select a timeframe for the
scenarios. Secondly, there is the option to develop probable
or extreme scenarios. Experts can be used to determine or
describe realistic probable scenarios and future conditions
(Tsoy etal. 2020), while a bandwidth is created between
optimistic and pessimistic development trajectories for
extreme scenarios.
Heijungs and Huijbregts (2004) describe uncertainty
as “the problem of using information that is unavailable,
wrong, unreliable, or that show a certain degree of variabil-
ity.” A common challenge when conducting an ex-ante LCA
is the lack of representative data for the system assessed
which might introduce considerable uncertainty in the study
(van der Giesen etal. 2020). Voglhuber-Slavinsky etal.
(2022) proposed to explicitly acknowledge uncertainty and
use different scenario’s to address uncertainty. Where Tsoy
etal. (2019) states that the performance of ex-ante LCA
increasingly requires the involvement of stakeholders, the
necessary assumptions in defining these scenarios are based
on discussion with relevant stakeholders and “not on firm
statements that are gratuitously presented as correct” (Ott
etal. 2022). Similar to Ott etal. (2022), this study uses sce-
nario and sensitivity analysis to deal with the encountered
uncertainty. For further discussion of ex-ante LCA literature,
see Appendix H.
2 Methods
2.1 Methods andmaterials
2.1.1 General method
This study was a comparative (ex-ante) attributional LCA fol-
lowing general guidelines (ISO14044). Various types of cul-
tivated and conventionally produced meat were assessed and
compared. The impact assessment methods used are ReCiPe
2016 Midpoint v1.1 (Hierarchist perspective) (Huijbregts etal.
2017) and cumulative energy demand (CED) (Frischknecht
etal. 2007), with characterization factors adapted for lower
heating values (LHV), as is included in the used Simapro ver-
sion 9.2.0.2. Six indicators are included in the main paper in
order to limit the number of figures. These are selected both
based on perceived relevance to conventional meat and CM
production systems and on robustness to minor changes in
the LCA models. All ReCiPe 2016 indicators are reported in
Appendix B, and the most important findings for these indica-
tors are discussed in the main paper. Four indicators that are
generally used in LCA of food products (Poore and Nemecek
2018) are included: carbon footprint (climate change), land
use, blue water use, and terrestrial acidification. In addition,
fine particulate matter (FPM) formation, CED, and feed con-
version ratio (FCR) are included. FPM formation is relevant
because this has significant effects on human health (Huijbregts
etal. 2017) and both animal agriculture and industrial supply
chains (such as in CM) have strong contributions to total emis-
sions (Weagle etal. 2018; Wyer etal. 2022). FCR and CED are
included because together they illustrate a fundamental differ-
ence between conventional meat and CM. CM is reportedly a
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237The International Journal of Life Cycle Assessment (2023) 28:234–254
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more efficient way to convert feed into meat, without the need
for growing the whole animal, but with added industrial energy
to fulfill certain functions of living organisms. There are differ-
ent approaches to the indicator CED, and as of yet, there is no
harmonized methodology (Frischknecht etal. 2015). Acknowl-
edging the different approaches to CED, it is useful as a screen-
ing indicator and presented alongside the carbon footprint in
this study for improved interpretation (Huijbregts etal. 2006).
The FCR shows the amount of dry matter (dm) ingredi-
ents needed for the output of 1kg of fresh meat (assumed
dm content between 20 and 30% and protein content between
18 and 25%, see Appendix E). Differentiation is made for
primary products, by-products, and biotic and mineral
resources. The inputs are counted on a crop level. For exam-
ple, to produce 1kg of glucose, more than 1kg (dm) of
sugar crop is needed, because a sugar beet consists of more
than glucose alone. The amount of ingredients needed for
producing 1kg of CM is therefore higher than the sum of
the ingredients in the medium. Whether a by-product can
be used as feed (for animals or CM) is an important sustain-
ability characteristic of a feed regime. It is important to note
here that soybean meal is also classified as a by-product in
the frame used, although this is more a coproduct (Walker
2000). For CM, it is relatively uncertain to what extent by-
products can be used as feed, and therefore only the soy
hydrolysate is assumed to be derived from soybean meal.
2.1.2 Data collection andhandling
Primary data were collected from over 15 companies and
research institutes, both active in CM production and in
the CM supply chain, supplemented by literature and theo-
retical modeling. For an overview of the organizations that
provided data, see Appendix C. Important primary data
points and ranges were checked by independent experts
and/or by mass and energy balancing. Appendix D provides
an overview of the foreground data used, the data quality
(representativeness), number of sources used. and whether
an independent cross-check has been made. Datasets from
individual organizations are confidential, but derived param-
eters and scenarios are included in the appendix. This study
uses sensitivity and scenario analyses to treat uncertainty
and variability in the dataset. For upscaling we considered
the framework presented in Tsoy etal. (2020). We defined
scenarios (with the help of questionnaires and conversations
with stakeholders), prepared an LCA flowchart of what an
average future facility might look like (Figs.1 and 2), and
created the data inventory. Data handling for upscaling is
discussed in Sect.2.3 when applicable.
Data collection took place over the period 2019–2022.
In preliminary conversations with the organizations, a prob-
able timeline for commercial-scale CM production was estab-
lished, resulting in the target year 2030 and a facility size of
10 ktonne/year. This is not to imply that CM products will be
cost-competitive with conventional meat by that time but that
some products such as minced meat could be produced and
sold at ktonne scales. Subsequently, data were requested from
the organizations for their current situation and expectations
for 2030, including technology improvements. The latter data
points were cross-checked and used as much as possible. If
only current data were available, future expectations were
either extrapolated with the help of experts or used as-is.
Clear outliers were removed from the dataset. For impor-
tant and variable data, such as culture media composition
and quantity used, and energy mixes, various scenarios were
developed, and a baseline model was created with representa-
tive values from the dataset (see Sect.2.3). These values were
determined based on data spread, for example, mean, mode,
or geometric mean. The baseline model is not representa-
tive of any single cell type or technology, and the values can
therefore not be interpreted as such.
For CM production, data has been collected and mod-
eled in as much detail as possible, but since there are still
unknowns (and unknown unknowns), data gaps cannot be
avoided. To balance this, conservative estimates were used
and extreme, worst-case scenarios were included.
Data from both land and aquatic animal cell cultures were
collected. CM production for these different species follows
similar practices. However, there are important differences
related to heating and cooling demand, where aquatic cells
in many cases require lower temperatures for growth and
are more tolerant to fluctuations in temperature (Krueger
etal. 2019). Therefore, the worst case is used (land animals,
cultivated around 37°C). These study results are not directly
representative of aquatic CM products, but they can help
shed light on general hotspots when taking these differences
into consideration during the interpretation of the results.
The differences in culture medium use efficiency between
land and aquatic species have not yet been robustly assessed.
Data handling for conventional meat production is dis-
cussed in paragraph 2.3.4.
2.1.3 Background data, software, andallocation
procedures
Background data were taken from Ecoinvent 3.7.1. (system
model: allocation, cut-off by classification) (Wernet etal.
2016) and Agri-Footprint 5.0 (system model: economic
allocation) (van Paassen etal. 2019). The software used is
Simapro 9.2.0.2. Allocation was done using economic prin-
ciples, as the available data best suited this form of alloca-
tion, and it is in line with the Ecoinvent database methodol-
ogy (Wernet etal. 2016). Although it is regularly done in
LCA research, one should be aware of potential problems
when combining two datasets from different sources, such as
Agri-Footprint and Ecoinvent. Problems could be differences
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238 The International Journal of Life Cycle Assessment (2023) 28:234–254
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in methodologies and modeling, selection criteria for data,
and different naming of environmental interventions (emis-
sions and extractions) which can cause problems in impact
assessment. Agri-Footprint has indicated specifically to
this point that modeling of field emissions is mostly done
using similar methodologies as Ecoinvent, and the software
developer of SimaPro, PRé, has ensured that environmen-
tal interventions have been mapped to be compatible with
ReCiPe for both databases (Blonk Sustainability n.d.). Addi-
tionally, we have ensured consistency by using main inputs
from agro-food supply chains from Agri-Footprint and using
transport, energy, synthetic chemicals, and other materials
from Ecoinvent. In order to make the datasets used consist-
ent with the 2030 time horizon used in the study, we have
created foreground processes for the most important inputs
(including energy mixes for 2030, which is also used as an
input into other foreground processes). Further data handling
is described in Sect.2.3. Adapting background databases
in order to be representative of 2030 was not feasible. This
may cause a slight overestimation of environmental impacts
throughout all LCA models presented in this study (both
CM and conventional meats), assuming globally technolo-
gies only become more sustainable over time.
2.2 Goal andscope
2.2.1 Goal
The goal of this study was to compare commercial-scale
CM production in 2030 to conventional meat production to
gain insights into the comparative environmental impacts of
different meats and identify hotspots in CM production. Sce-
narios and sensitivity analyses were used to further explore
the effects of developments internal and external to the CM
product system on the comparison and hotspots.
Fig. 1 Simplified LCA flowchart of cultivated meat (CM) production
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239The International Journal of Life Cycle Assessment (2023) 28:234–254
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2.2.2 Scope, system boundaries, andflowchart
System boundaries were cradle-to-gate, excluding packag-
ing (as we assume this to be identical for all products). In
the case of CM, this means after harvesting but before leav-
ing the facility. For conventional meat, this means at the
slaughterhouse gate. All upstream production processes and
transport were included in the scope. All impacts associated
with the product system up to the economy-environment
boundary were included (resource extraction, land use, or
physical emissions). Buildings were excluded for both con-
ventional meats and CM. For CM, bioreactors and culture
medium storage and mixing tanks were included, as these
are inherent to this different technology for meat produc-
tion, and actually can be considered the replacement of the
animal’s body in the CM product system.
The CM system flowchart is provided in Fig.1.
2.2.3 Functional unit
The functional unit was 1kg of land animal meat produced
in 2030. Meat types compared were CM, beef (beef herd),
beef (dairy herd), pork, and chicken. The CM cell type was
non-specific, cultivated around 37°C. CM was produced
using a 10% edible scaffold. To make a conservative com-
parison, the reference product for conventional meats was
1kg of meat, and for CM is 1.1kg product, which includes
1kg meat cells and 0.1 kg edible scaffold. The average
(macro)nutritional composition of the meats under study is
provided in Appendix E.
2.3 Product systems understudy andbaseline
scenario data
2.3.1 CM facility andproduction line design
A CM production facility producing 10 ktonne per year was
modeled. The general product system is shown in Fig.1.
Production lines in the facility operate in parallel and on
staggered schedules. One production line is shown in Fig.2.
Its design was based on Specht (2020) and was adapted in
some aspects based on input from CM companies, such as
the size of the largest proliferation vessel.
Data collection and handling are described in Sect.2.1.2.
The baseline model parameters are provided in Appendix
D. Scenarios and sensitivity analyses were performed to
account for variation and other types of uncertainty.
The baseline production process is semi-continuous
with three harvests from the largest proliferation vessel
(see Fig.2). The same cell culture medium was assumed
to be used throughout the process. Cell proliferation takes
place in a seed train until the largest stirred-tank reactor
(STR) (working volume 10,000 L) is filled to maximum
cell density. At this point, 50% of the cells are harvested,
culture medium replenished, and cells further proliferate
until maximum cell density is reached. As cell doubling
Fig. 2 Design of 1 semi-continuous production line with 3 harvests from the largest proliferation vessel
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240 The International Journal of Life Cycle Assessment (2023) 28:234–254
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is exponential, most cell production takes place in this last
phase of proliferation. A targeted feeding regime allows
ingredients to be balanced, and water is expended and recy-
cled throughout the process. There are three intermediate
harvests, in total resulting in 200% of cells, relative to the
maximum number of cells in the largest proliferation reactor
(50% + 50% + 100%). Harvested cells are seeded onto edible
scaffolds for further differentiation and maturation, which
occurs in 4 perfusion reactors (PR, working volume 2000
L) operating in parallel. Conservatively, it was assumed that
no further cell growth takes place in this phase even though
some data suggest mass increase during differentiation can
be more than 100% (Tuomisto etal. 2022). Additional equip-
ment included were medium storage and mixing tanks, a cell
banking system, and centrifuges.
After each production run (12days for the 10,000 L STR
and 10days for the PR), the reactors are cleaned using a
clean-in-place and steam-in-place (CIP/SIP) system. The
total production run time from cell vial to final harvest
from the PR is 42days. For 10 kton/year production, it was
assumed that the facility needs around 130 production lines
to be operating in parallel on a staggered production sched-
ule, which includes accounting for 15–20% downtime for
cleaning and maintenance.
2.3.2 Process parameters
The main parameters regarding the cell culture process and
system design are provided in Appendix D.
2.3.3 Material andenergy inputs andtheir scenarios
For an overview of the sources, data quality, amount of data
points, and whether there has been an independent cross-
check of the data, see Appendix D. The most important data
choices and assumptions are discussed below.
Culture medium Data about culture medium use were pro-
vided by nearly all CM producers involved in this study.
Data was asked for both the current situation and 10years
from now (see Appendix D for more information about
data collection for culture medium). How lab-scale meas-
ured medium efficiency relates to upscaled performances is
uncertain. Some indicated that medium efficiency will go
down with larger scales, and some indicated that there is
no reason to think this will change much or that technology
development will counter any negative effects. The proposed
high medium scenario amply covers the maximum efficiency
decrease mentioned, compared to the baseline (mid) medium
scenario (−20%).
Data were aggregated to the ingredient group level to ensure
confidentiality. Variation in the dataset indicated different
process characteristics and expectations of developments. For
the “low-medium” scenario, data assumptions for enhanced
catabolism cell types from Humbird (2021) were used as an
additional data source for amino acid and sugar consumption.
Lower amino acid and sugar quantities were received from CM
companies but could not be verified by independent experts
and were excluded from this study. Three scenarios were devel-
oped, including two extreme scenarios reflecting the upper and
lower data boundary, in addition to a probable baseline sce-
nario. The baseline scenario values for the ingredient groups
were based on the mean of the dataset, in some cases adjusted
up- or downward if most data points were clustered around a
certain value or if conversation with experts suggested some
data points were more robust than others. The scenarios are
shown in Table1. The values correspond to the amount of
medium ingredients needed for the production of 1kg of meat.
Compared to standard DMEM/F12, the medium formulations
in this study contain relatively high amounts of amino acids.
This could be explained by the fact that when hydrolysates
are used as a (partial) source of amino acids, current evidence
shows that more of them are needed than when single amino
acids are used, because the composition is not defined and not
optimal. Despite this, CM companies are likely to make this
tradeoff due to cost (Humbird 2021) and environmental impact
(Tuomisto etal. 2022) savings that can be expected with the
use of hydrolysates.
The ingredients modeled were feed- or food-grade, with
the exception of the recombinant proteins, for which only
pharma grade data was available. The medium is sterilized
using heat and microfiltration (for heat-sensitive substances).
Pharma-grade microfiltration cartridges were modeled based
on confidential company data.
The main energy, carbon, and nitrogen sources for CM
are glucose and amino acids. Glucose is supplied as con-
ventional food-grade maize glucose. Seventy-five percent
of amino acids are supplied from soy hydrolysate, and the
remaining 25% are single product amino acids from either
microbial or chemical production. The soy used is assumed
to be deforestation-free, just as for the animal feed in this
study. Involved organizations indicated that it is plausible
that hydrolysates can be used to supply amino acids in the
culture medium, as long as the composition is well defined
to enable targeted supplements (Ho etal. 2021). Addition-
ally, soy was selected based on primary data, as it has an
essential amino acid profile that roughly matches that of the
popular basal medium formulation DMEM/F12 (Humbird
2021). L-Glutamine is the most abundant amino acid supple-
mented to the culture medium and is produced microbially.
L-Glutamine is not available in Agri-Footprint, and therefore
an average of three microbially produced feed-grade amino
acids (lysine, threonine, and methionine) is used as a proxy
(Marinussen and Kool 2010). If only single amino acids
were used instead of hydrolysates, it is possible that less
amino acids would be needed than when using hydrolysates.
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241The International Journal of Life Cycle Assessment (2023) 28:234–254
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Recombinant proteins have important functions in the
medium, mainly as growth factors that play a broad role in
the control of various cellular pathways or as proteins that can
transport and deliver nutrients and other macromolecules. It
was assumed that these proteins are produced recombinantly
using microbial fermentation, but little environmental data
are available in the public literature. In this study, confi-
dential company data is used for albumin, transferrin, and
insulin-like growth factor (IGF). These substances are used
in different concentrations in the medium, of which albumin
has by far the highest concentration (if used) and IGF the
lowest. Regarding albumin, companies have widely differ-
ing opinions as to what concentration is needed, or whether
this will be included at all, and therefore the scenarios reflect
this, with albumin present in both the mid- and high-medium
scenarios. IGF production is currently at the smallest scale
and has the highest environmental impact and therefore was
used as a proxy for growth factors, for which production will
likely be at small scales for a long time.
The pH buffering system used is largely bicarbonate in
the low- and mid-medium scenarios. HEPES is used as an
additional buffer, as this is currently widely used. In the
low-medium scenario, HEPES is strongly reduced. HEPES
is a zwitterionic sulfonic acid (C-ring with 4C and 2N),
and therefore naphthalene sulfonic acid, also zwitterionic,
is selected as a proxy.
Edible scaffold Most companies indicated that differentiation
will take place on an edible scaffold. Some options for this are
hydrogels, electrospun collagen mesh, and textured vegetable
protein (TVP), which in that order increasingly support tex-
tured final products (Bomkamp etal. 2022; Wollschlaeger
etal. 2022; Seah etal. 2022). As the final product in this study
was a minced-meat-like product, a starch hydrogel scaffold is
modeled, based on De Marco etal. (2017).
Energy Two energy mixes were modeled for this study.
The conventional energy mix was based on a global aver-
age stated policies scenario for 2030 in the World Energy
Outlook (IEA 2019) (for composition see Appendix D), and
heat is generated using natural gas (European market mix for
industrial heat production from Ecoinvent). The sustainable
energy mix was based on on-shore wind and solar PV elec-
tricity (both 50%) and heat from geothermal sources. Three
energy scenarios were defined, using the energy mixes as an
input into different parts of the model, delineated by scope
1, 2, and 3 as defined in the Greenhouse Gas Protocol (GHG
Protocol 2011):
• Ambitious Benchmark 2030: Renewable energy for scope
1, 2, and 3(scope 3 modelingonly for culture medium
ingredients, scaffold, filters, and water purification)
• Renewable scope 1 and 2: Renewable energy for scope 1
and 2 (at the facility), average mix for scope 3 (upstream)
• Global average energy: Global average energy mix for
scope 1, 2, and 3*
Energy demand for the CM facility and upstream materi-
als and ingredients production was based on primary data
(upstream materials) and computational models for similar
Table 1 Culture medium scenarios (total in g ingredients per kg of cultivated meat cells, therefore excluding any scaffolding material)
Components Low-medium
scenario (g) Baseline scenario
(g) High-medium sce-
nario (g) Main ingredients
Amino acids (total), of which: 200 283 400 L-glutamine, L-Arginine hydrochloride,
multitude of other amino acids
Amino acids from hydrolysate 150 212 300
Amino acids from conventional
production 50 71 100
Sugars (total), of which: 320 400 500 Glucose, pyruvate
Sugars: Glucose 319 398 396
Sugars: Pyruvate 1 2 4
Recombinant proteins 0.2 3 50 Albumin (dominant in the mid- and high
medium scenarios), insulin, transferrin
Salts 100 224 500 Sodium chloride, sodium bicarbonate
Buffering agent 2 26 350 HEPES
Vitamins 0.2 2 20 i-Inositol, Choline chloride
Growth factors << 1 << 1 << 1
Water 20,000 44,721 100,000 Ultrapure water
Total (g) 21,142 46,342 102,620
Total (L) 21 47 103
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242 The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
large-scale cell culture processes, using the assumptions and
process parameters provided in Appendix D. The energy use
was estimated for one production line as under study here
(Fig.2), and this was multiplied by the amount of production
lines present at the facility. See Appendix D for further infor-
mation about data collection, assumptions, and handling for
facility energy use. For the upstream materials production,
we asked for expected energy use at production scales that
the producers expected to attain in 2030. The energy use
differs per ingredient, as, for example, the technology and
production scales for albumin are already more advanced
than for growth factors, which will be expected to stay at
small scales for a long time due to more limited demand.
Therefore, implicitly, expected status of technology and mar-
ket size is also included for some upstream products.
Water use Water is used in the process for cell culture medium,
CIP, and washing after harvesting. For cell culture medium
water use, see Table1. For the CIP/SIP step, every cleaning
cycle consumes 25%v of the bioreactor working volumes. The
meat is washed after harvesting, for which 2 L water per kg of
meat was assumed. All water was modeled as ultrapure water,
for which production was modeled based on confidential pri-
mary data. Internal water recycling was included and assumed
to be at 75% efficiency, as is demonstrated in full-scale (algal)
cell cultures (Yang etal. 2011; Wang etal. 2012).
Cleaning (CIP/SIP) Energy and water use for equipment
cleaning and sterilization is reported above. Additionally,
an alkaline cleaning agent (NaOH) is used in the CIP step at
a concentration of 0.05% v/v.
Wastewater Waste metabolites produced were calculated,
based on mass balancing of inputs and outputs and includ-
ing the metabolism of C and N sources (see Appendix D)
(Mattick etal. 2015). Wastewater could be treated on- or
off-site, and valuable substances could be separated, reused,
or sold as feedstock to third parties, potentially resulting in
an overall reduced environmental burden. For this study,
wastewater treatment is modeled (proxy: from potato starch
production), and no recycling of medium components except
water was assumed. While companies indicated that this is
an objective, with the provided data, it was not possible to
calculate environmental tradeoffs with regard to increased
energy use and using recycling-grade (ultra)filtration filters.
Equipment Production of bioreactors and storage and mix-
ing tanks for culture medium was modeled (see Appendix
D). Assumptions for calculations for steel and glass wool
insulation were based on Tuomisto etal. (2014), with added
10% mass for piping, heat exchangers, and maintenance in
all equipment. Additional PVC tubing and electronic control
panels were included. The average lifetime of equipment was
20years, with materials recycled at end-of-life.
2.3.4 Conventional meat production anddetermination
ofambitious benchmarks
In this study, we include ambitious benchmarks for meats
from intensive, Western European animal agriculture. Ambi-
tious benchmarks were used to ensure that no unfair advan-
tage is given to CM. CM is often presented as an environ-
mental solution, but in order for that to be true on a product
level, it needs to be able to compete environmentally with
conventional meat products from efficient and sustainable
production systems. The comparison made in this study
shows minimum expected benefits from CM. Current global
average production of conventional meat has 2x – 4 × higher
footprints than the ambitious benchmarks (Poore and
Nemecek 2018).
The ambitious benchmarks for conventional meats are
based on LCA models from Agri-Footprint LCA database
(van Paassen etal. 2019), which represent intensive, efficient
production systems located in the Netherlands (chicken,
pork, dairy cattle) and Ireland (beef cattle). These models
are extended with technological and supply chain improve-
ments. History learns that adoption of sustainability inno-
vations in (animal) agriculture systems is challenging and
actual effects on sustainability are uncertain (OECD 2001).
Additionally, there seems to be an increasing demand for
products with higher animal welfare standards (Scherer etal.
2019), which make certain innovations that could decrease
product carbon footprints unlikely (e.g., slaughtering at
younger ages or keeping livestock fully indoors to capture
methane). The ambitious benchmarks focus on a selection of
improvements that are proven to be feasible and are likely to
be implemented at larger scales by 2030. This does not mean
that these are expected to disseminate widely or globally,
but there is a high likelihood that these kinds of production
systems will exist.
The improvements included for the ambitious bench-
marks are the following (see Appendix F for substantiation):
• Reduced methane emissions from cattle (− 15%) through
the use of enzymes
• Reduced ammonia emissions from cattle (− 5.4%)
through increased outdoor grazing
• Renewable energy at farm and feed facilities
• No LUC and associated GHG emissions related to soy-
bean production
Beef produced in grazing systems on marginal lands was
excluded from the comparison. It should be acknowledged
that animal production systems can be very different and are
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243The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
often interlinked. This adds complexity to an LCA, among
others with regard to allocation and land use comparisons.
For example, meat from dairy systems (cows, calves, and
bulls) provides more than half of global cattle meat (Vellinga
etal. 2010). This meat has a lower impact because most of the
emissions are allocated to the milk produced in this system.
Meat from dairy systems was also included in the comparison.
The ambitious benchmark of CM consists of the baseline
scenario with renewable energy for scope 1, 2, and partially
scope 3. All scenarios for CM also include LUC-free soy
for soy hydrolysate.
2.4 Sensitivity analyses
There are many process parameters that influence the envi-
ronmental impact and could be further optimized by CM
companies. In this study, six sensitivity analyses were per-
formed for key process parameters that were found variable
in the primary data collected or that were identified in earlier
model iterations as influential for the environmental perfor-
mance of the process (or both):
(A) Cell culture medium composition and efficiency
(B) Maximum cell density
(C) Production run time
(D) Cell volume
(E) Amount of harvests from the largest proliferation vessel
(F) Partially passive cooling
These parameters influence, among others, the number
of bioreactors and production runs, culture medium quan-
tity used, and energy and water demand. For the medium
scenarios, see Table1. For elaboration on the influence on
process dynamics and parameters used for the other sensitiv-
ity analyses, see Appendix G.
3 Results
3.1 Carbon footprints andgreenhouse gas profiles
The carbon footprint results for the baseline production sce-
nario are shown in Fig.3. The baseline scenario is described
in Sect.2.3. Results are shown for different energy mixes,
which are repeated here for clarity (for further info, see
Sect.2.3):
• Ambitious benchmark: Renewable energy for scope 1, 2,
and 3*
• Renewable scope 1 and 2: Renewable energy for scope 1
and 2 (at the facility), average mix for scope 3 (upstream)
• Global average energy: Global average energy mix for
scope 1, 2, and 3
*Scope 3 modeling: only for culture medium ingredients,
scaffold, filters, and water purification
The carbon footprint of cultivated meat is sensitive to
selection of energy mix. In the global average energy sce-
nario, the carbon footprint is over 14kg CO2-eq./kg meat,
while the ambitious benchmark has a carbon footprint of
less than 3kg CO2-eq./kg meat. In the renewable scope 1
and 2 scenario, the carbon footprint is around 4kg CO2-eq./
kg meat. The hotspot analysis shows that the carbon foot-
print is mainly driven by energy use at the facility (scope 1
and 2) and energy use in production of medium ingredients.
Depending on what electricity mix is used, and in which
scopes, either facility energy use or medium ingredient pro-
duction is the main hotspot.
Energy use at the facility is mainly driven by energy use
of the heat exchanger (cooling energy, ~ 75%), followed by
heating the culture medium (~ 10%), aeration, agitation, CIP/
SIP, and HVAC (all < 5%, see Appendix D). While differ-
ent estimates show slightly different hotspots regarding this
Fig. 3 Carbon footprint of cul-
tivated meat in 2030, baseline
scenario with different energy
mixes
0
2
4
6
8
10
12
14
16
Ambitious
benchmark
Renewable
Scope 1&2
Global average
energy
kg CO
2
-eq./kg meat
Scaffold, equipment & othe
r
Energy at facility: other
Energy at facility: cultivatio
n
Culture medium
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244 The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
energy use, it is clear that in-facility energy demand is high
and current models indicate that cooling energy is one of the
main contributors to be expected. The carbon footprint of
the culture medium is mainly driven by production of amino
acids from microbial or chemical production (29–37%,
depending on the energy mix during production), followed
by recombinant proteins (8–29%), glucose (22–29%), and
soy hydrolysate (12–16%). In the high-medium scenario,
albumin and HEPES both contribute significantly as they
are used in higher concentrations and have a high associ-
ated footprint. On a per kg ingredient basis, the recombinant
proteins have the highest carbon footprint by far, followed by
amino acids from microbial or chemical production. These
are energy-intensive biochemical processes that are currently
mostly not produced at large scales (except for some amino
acids commonly used as food and feed additives), and there-
fore do not enjoy the benefits of economies of scale. The
data for these processes is also rather uncertain, but it seems
clear that most currently used (fermentation) technologies
have high energy use for some time to come, until the indus-
try is fully mature.
The production of the scaffold has a minor contribution
but is also only used at small mass percentages (10% of the
final product) and is made from relatively low-impact mate-
rials. Other (minor) drivers for the carbon footprint are CIP/
SIP and recycling of water. Equipment has a relatively low
contribution to the carbon footprint, at the lifetimes assumed
in the baseline scenario (20years).
Compared to conventional meat, the greenhouse gas
emissions profile is different. In CM production, the main
contributor is CO2, directly or indirectly from energy con-
sumption (also production of raw materials and upstream
industrial processes). In conventional meats, this is more
CH4 and N2O (Table2).
3.2 Comparison ofambitious benchmarks
Figure4 shows the comparison of the ambitious benchmarks
of cultivated and conventional meats for 2030, for a selection
of environmental impacts. Other environmental indicators
are included in Appendix B. CM has a carbon footprint com-
parable to chicken and lower than pork and beef. Beef from
beef cattle has the highest environmental impact for most
indicators. This is largely driven by the production of the
strong greenhouse gas methane, in addition to the relatively
Table 2 Comparison of carbon footprint and greenhouse gas emission profiles of CM and conventional meats
a Scope 3 processes that use renewable energy are the (bio)chemical production of medium ingredients (not the agricultural feedstock produc-
tion), scaffolds, and microfiltration filters
b Percentages may not add up to 100% due to rounding
Meat System Total Contribution of GHG to
carbon footprintbSource
kg CO2-eq CO2CH4N2O dLUC Other
Cultivated meat 2030
Baseline model + energy
scenarios
2030 ambitious benchmark 2.8 84% 10% 5% 0% 1% This study
Renewable scope 1 and 2 4.0 86% 9% 4% 0% 1% This study
Global average energy 14.3 91% 7% 2% 0% 0% This study
Cultivated meat 2030
Sensitivity analyses best and
worst case
Sensitivity analysis best case
2030 ambitious
benchmark + passive cooling
2.2 83% 10% 6% 0% 1% This study
Sensitivity analysis worst case
Global average energy + high
medium scenario
24.8 90% 8% 2% 0% 0% This study
Chicken 2030 ambitious benchmark 2.7 58% 9% 21% 13% 0% This study
Current ambitious benchmark 6.0 34% 4% 9% 52% 0% Agri-Footprint 5.0
2018 global average 9.0 n.a n.a n.a n.a n.a Poore and Nemecek (2018)
Pork 2030 ambitious benchmark 5.1 35% 31% 23% 11% 0% This study
Current ambitious benchmark 6.9 34% 23% 17% 26% 0% Agri-Footprint 5.0
2018 global average 11.4 n.a n.a n.a n.a n.a Poore and Nemecek (2018)
Beef (dairy cattle) 2030 ambitious benchmark 8.8 16% 54% 27% 2% 0% This study
Current ambitious benchmark 11.0 18% 49% 22% 11% 0% Agri-Footprint 5.0
2018 global average 32.4 n.a n.a n.a n.a n.a Poore and Nemecek (2018)
Beef (beef cattle) 2030 ambitious benchmark 34.9 16% 46% 37% 1% 0% This study
Current ambitious benchmark 39.8 17% 46% 32% 5% 0% Agri-Footprint 5.0
2018 global average 98.6 n.a n.a n.a n.a n.a Poore and Nemecek (2018)
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245The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
high feed conversion ratio (see Table3), due to which a lot
of land and agricultural inputs are needed.
Cumulative energy demand is higher than in most con-
ventional meat systems, and this is driven by energy use
within the facility (> 70%), followed by energy use for (bio)
0
5
10
15
20
25
30
35
40
CM ChickenPorkBeef
(dairy
cattle)
Beef
(beef
cattle)
2030 ambitious benchmarks
kg CO
2
-eq./kg meat
Carbon footprint
0
20
40
60
80
100
120
140
160
180
CM ChickenPorkBeef
(dairy
cattle)
Beef
(beef
cattle)
2030 ambitious benchmarks
MJ/kg meat
Cumulative energy demand
0
5
10
15
20
25
30
CM ChickenPorkBeef
(dairy
cattle)
Beef
(beef
cattle)
2030 ambitious benchmarks
m
2
a crop-eq./kg meat
Land use
0
50
100
150
200
250
300
CM ChickenPorkBeef
(dairy
cattle)
Beef
(beef
cattle)
2030 ambitious benchmarks
l/kg meat
Blue water use
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
CM ChickenPorkBeef
(dairy
cattle)
Beef
(beef
cattle)
2030 ambitious benchmarks
kg SO
2
-eq./kg meat
Acidification
0.00
0.02
0.04
0.06
0.08
0.10
0.12
CM ChickenPorkBeef
(dairy
cattle)
Beef
(beef
cattle)
2030 ambitious benchmarks
kg PM
2.5
-eq./kg meat
Fine particulate matter
Fig. 4 Comparison of ambitious benchmarks of cultivated meat and conventional meats for 2030
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246 The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
chemical production of medium ingredients (~ 25%). If this
energy comes from renewable sources, it can be largely
decoupled from the carbon footprint, as is seen in this com-
parison of ambitious benchmarks.
Land use for CM is lower than for all conventional meats,
which is a robust conclusion that is explained by the more
efficient conversion of crops into final product, and there-
fore lower agricultural land use. Land occupied for renew-
able energy production (solar and wind) contributes around
10–20% to total land use, highlighting a tradeoff in land use for
CM, but overall the reduced land use for crops far outweighs
the increased land use for renewable energy production.
Blue water use (surface and groundwater) in CM produc-
tion is higher for chicken, pork, and beef from dairy cattle,
and lower for beef from beef cattle. This result is sensitive
to internal water recycling at the facility (in this ambitious
benchmark, the recycling percentage is 75%).Around half of
the water use is at the facility itself (mostly for use in culture
media), and half in the supply chain, mainly for the (bio)
chemical production of medium ingredients and in the pro-
duction of the renewable energy materials and infrastructure.
Fine particulate matter and acidification results for CM are
lower than those of all conventional meats, and these results are
relatively insensitive to changes in the model. The main reason
for this is that ammonia emissions for CM are lower than in
the animal systems, because there is no manure and CM needs
less crops and therefore less fertilizer. Whereas ammonia is the
dominant driver for fine particulate matter and acidification for
the conventional meat systems, sulfur dioxide and NOx are the
main drivers in the CM system. These are linked to the indus-
trial upstream processes, mainly the production of chemicals for
medium ingredient production and the mining and processing
of materials for the renewable energy infrastructure.
Other impact categories are provided in Appendix B,
some of which are discussed here. Marine eutrophica-
tion shows similarities to acidification results, because
nitrogen-related emissions (importantly ammonia) are
dominant in both indicators. Freshwater eutrophication
results are potentially relatively high for CM (higher than
for chicken and pork) but are sensitive to the configuration
of wastewater treatment processes and upstream industrial
chemical processes and their treatment and therefore also
relatively uncertain. Real-world measurements should pro-
vide a better idea of what type of wastewater treatment is
needed. Toxicity impact categories show variation. While
for the water-related toxicity impact categories conventional
meat production has higher scores, terrestrial ecotoxicity and
human non-carcinogenic toxicity are potentially indicators
where CM performs worse than conventional meat. Similar
to freshwater eutrophication, this is due to upstream pro-
duction of raw materials for the industrialized and energy-
intensive supply chain, highlighting the need for transparent
supply chains and responsible sourcing.
The feed conversion ratio (FCR) for the ambitious bench-
marks is shown in Table3. CM feed conversion ratio is
lower than all conventional meats, which means it is a more
efficient way of turning crops into meat. This explains why
the agricultural land use of CM is low compared to conven-
tional meats. The low FCR is also linked to the relatively
high energy use in CM production, as part of the energy
needed by animals to keep their biological processes going
are now supplied via external (electrical or other) energy.
When looking at the biotic FCR, CM is almost three
times more efficient than chicken, which has the most effi-
cient feed conversion of the conventional meats. Mineral
feed use for CM is relatively high, while for conventional
animal production, this is negligible. In CM production, this
mainly concerns direct use of salts in the culture medium
and indirect use for microbial production of the amino acids
and recombinant proteins. Conventional animals have a rela-
tively large share of by-products in their feed, compared to
CM. However, as the FCR for CM is lower, primary feed use
is still lowest for CM, almost twice as low as for chicken.
3.3 Sensitivity analyses
The dataset used showed variation in some aspects, high-
lighting the different approaches to producing CM and the
uncertainties at this stage of technology development. To
account for this, sensitivity analyses were made, for which
Table 3 Feed conversion ratio (FCR) of the ambitious benchmarks, dm in:fresh meat out
a Intensive, Western European production
Resource type Description Cultivated meat ChickenaPorkaBeef (dairy cattle)aBeef (beef cattle)a
Biotic Primary feed 0.8 1.5 3.1 3.7 4.6
By-product feed 0.2 1.3 1.5 2.1 1.1
Grass 7.5 31.6
Mineral Salts and other 0.2
Total biotic + mineral (incl. grass) 1.3 2.8 4.6 13.4 37.3
Total biotic + mineral (excl. grass) 1.3 2.8 4.6 5.8 5.7
Total biotic (excl. grass) 1.0 2.8 4.6 5.8 5.7
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247The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
the carbon footprint results are shown in Table4. Full ReC-
iPe indicator results and further description of the scenarios
are provided in Appendix G.
The most promising routes for lowering the carbon foot-
print of CM production are applying smart cooling (combin-
ing and optimizing passive and active cooling), increasing
the amount of harvests from the largest proliferation ves-
sel (moving towards continuous production), improving
medium efficiency (especially optimizing use of recombi-
nant proteins, amino acids, and pH buffer system), and short-
ening overall production time. These improvements lead to a
reduced energy demand, either in the upstream supply chain
or during production.
Conversely, energy demand and thereby the carbon foot-
print are increased by inefficient medium use, less harvests
(moving towards batch processes), and attaining lower maxi-
mum cell densities or working with smaller cells (at constant
cell densities).
4 Discussion
4.1 Comparison ofcultivated andconventional
meats
CM and conventional meat are fundamentally different tech-
nologies for producing the same product: meat. Compared to
conventional meats, CM is produced in a closed environment
that enables higher degrees of control. This comes at the
expense of requiring more energy but with the advantage of
requiring different and less feed. It is therefore not surpris-
ing that the environmental profiles of CM and conventional
meats are different. This study includes the latest estimates
from industry and experts. While many uncertainties still
exist, public and private research and commercial develop-
ments are accelerating, and environmental data is coming
available at a fast pace. The use of scenarios informs the
high-level conclusions that can already be drawn at this stage
of technology development.
More efficient feed use for CM translates itself directly
into lower agricultural land use and good performance on
other environmental indicators that are strongly linked to
crop production. Indirectly, it also causes CM to have higher
energy use, because part of the energy (calories) used for
biological processes in animals (such as maintaining body
temperature) is replaced by electricity and heat. The impor-
tant distinction between those two types of energy is that
electricity and heat can be produced sustainably, while the
sustainability improvement potential for animal feed is more
limited and less scalable.
The controlled environment, direct metabolism, and
absence of manure in CM production ensure limited emis-
sions from the production process itself. Importantly, ammo-
nia and the strong greenhouse gasses methane and nitrous
oxide are avoided or can be mitigated during wastewater
treatment or spent media recycling, the latter of which was
not yet included in this study. This is in contrast to conven-
tional meat production, where these emissions are harder to
mitigate, because these are inherent to biological processes
that happen in a less controlled environment. CM therefore
performs relatively well on environmental indicators that
are strongly linked to ammonia, such as acidification, fine
particulate matter formation, and marine eutrophication.
For climate change, the sustainability potential of CM is
Table 4 Sensitivity analyses for CM production
Scenario Carbon footprint (CO2-eq./kg meat)
2030
ambitious
benchmark
Renewable
scope 1 and 2 Global
average
energy
Baseline scenario (reference) 2.8 ref. 4.0 ref. 14.3 ref.
A1: Shorter production run time (− 25%: 32days, 3 harvests) 2.6 − 7% 3.7 − 8% 13.6 − 5%
A2: Longer production run time (+ 25%: 52days, 3 harvests) 3.0 6% 4.3 7% 15.0 4%
B1: Higher cell density (x1.4: 7.1E7 cells/ml) 2.8 − 1% 4.0 − 1% 14.0 − 2%
B2: Lower cell density (× 10: 5E6 cells/ml) 3.9 37% 5.3 30% 20.9 46%
C1: Larger cell volume (5000 µm3) 2.8 − 1% 4.0 − 1% 14.1 − 2%
C2: Smaller cell volume (500 µm3) 3.6 27% 5.0 23% 18.8 31%
D1: Low medium (more efficient medium usage, removal of albumin, largely reduced HEPES use) 2.3 − 17% 2.9 − 28% 12.9 − 10%
D2: High medium (less efficient medium usage, full use of albumin and HEPES) 5.0 78% 13.8 241% 24.8 73%
E1: More harvests from proliferation vessel (5 harvests) 2.5 − 10% 3.8 − 7% 12.2 − 15%
E2: Less harvests from proliferation vessel (1 harvest—batch process) 3.6 28% 4.8 20% 20.4 43%
E3: More harvests from proliferation vessel (10 harvest—going towards continuous process) 2.3 − 18% 3.5 − 13% 10.4 − 28%
F1: Smart cooling (active + passive, 50% electricity reduction for cooling) 2.2 − 21% 3.4 − 15% 9.6 − 33%
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248 The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
high, because it is mostly CO2 driving its carbon footprint,
and this emission can be reduced by using decarbonized
technologies like renewable energy. As the global energy
system continues to decarbonize, the average footprint of
CM will continue to decline more strongly than that of con-
ventional meats. Methane and nitrous oxide emissions in
conventional meat production are more difficult to reduce
(Eckard etal. 2010; Höglund-Isaksson and Gómez-Sanabria
2020). If renewable energy is used, the carbon footprint of
CM can be low and comparable to chicken, but if this is not
the case, its carbon footprint may be higher than pork. With
high certainty, the carbon footprint of CM will be lower than
beef from beef cattle.
The bioreactor-based production of CM and the biochemi-
cal production of medium ingredients in the upstream supply
chain have a few important characteristics. Besides the afore-
mentioned high energy use, there is also relatively high blue
water use (surface- and groundwater) and mineral resource
use. Blue water use in industrial bioprocessing technologies
is high. While conventional meats are known for high water
use, the majority of this is green water (rainwater), which is
easily replenishable. Looking at blue water use alone, CM
scores higher than chicken, pork and beef from dairy cattle,
when 75% water is recycled at the facility. Further reduction
of the blue water footprint of CM is possible through further
increasing recycling at the facility (which is in theory well
possible within a controlled environment (Yang etal. 2011;
Wang etal. 2012), and efforts in the supply chain, for exam-
ple by reducing water use for production of culture medium
ingredients.Mineral resource use in CM production in feed
is mostly due to salts in the culture medium and upstream
microbial production processes of medium ingredients. These
salts have relatively low associated impacts per kg, but the
amounts add up when used in significant amounts. Recy-
cling of salts was not considered in this LCA but could be
an important avenue for improving total resource use and
associated environmental indicators. The bioreactors and
other equipment needed for CM production use substantial
amounts of steel and other materials. However, this does not
come up as a hotspot in the carbon footprint, as the opera-
tional impacts (of energy and resources) dwarf the impacts
of the bioreactors over their lifetime (assumed to be 20years
in this study).
Implicit in the comparison of CM and conventional meats
is that the products are equal, or in LCA terminology, the
function is the same. CM uses the same biological processes
to produce the same meat cells; therefore, its function is
arguably the same as conventional meats. However, there
are more ways to look at the function of foods within an
LCA framework, for example, by taking a diet- or product-
based perspective or by focusing on specific nutritional qual-
ity (McAuliffe etal. 2020). As health effects and consumer
perception and behavior cannot yet be studied in relation to
CM, these factors cannot yet be considered. Another factor
influencing the functional unit is the inclusion of the (edible)
scaffold in the final product. While potentially optional for
some CM products, scaffolding materials permit cell adher-
ence and mimic the extracellular matrix of the cells, which
can allow for greater control over the final product’s tex-
ture. Many options for scaffolding material exist (Seah etal.
2022). In this study, the scaffolding material was not con-
sidered meat, and therefore (conservatively) the reference
product of CM is actually ~ 1.1kg of final product, includ-
ing 1kg meat cells and ~ 0.1kg scaffolding material. If the
nutritional quality and consumer perception of the product
including scaffold are comparable to meat, this correction
to the functional unit arguably does not need to be made.
Some effects that are relevant when comparing two fun-
damentally different technologies were not captured in this
LCA. Examples of topics often mentioned in relation to alter-
native proteins such as CM are animal welfare, ecosystem
functions of livestock systems, biodiversity and ecosystem
impacts (especially in aquatic environments), zoonotic dis-
ease and antimicrobial resistance risk, odor and other aspects
affecting quality of living, food security and food sovereignty,
the resilience of and distribution of power in supply chains,
and consumer perception and behavior. These are not cap-
tured in an LCA but arguably part of a broader definition
of sustainability, and therefore there are attempts to include
these within the LCA framework (for animal welfare, see,
e.g., Scherer etal. 2018). Also, a greater debate about the
role of animals in the agricultural and food systems is inad-
equately captured by using a product-based comparison.
An example is the function of animals to produce food on
marginal lands or in high-quality recycling of waste streams.
There is a lot of variation in the environmental impacts or
values that meat production systems have, both for conven-
tional meats and CM. By zooming in on ambitious bench-
marks, this variation is ignored. This study therefore provides
a relevant dataset for the greater discussion on CM, specifi-
cally regarding its product environmental footprint, but many
other factors are important and should be considered.
4.2 Insights from10years ofcultivated meat LCAs
A little over 10years ago, Tuomisto and Teixeira de Mattos
(2011) published the first LCA of CM. That study was fol-
lowed by Tuomisto etal. (2014) and Mattick etal. (2015), and
the results from those early studies were incorporated in addi-
tional assessments by Smetana etal. (2015, 2018) and Lynch
and Pierrehumbert (2019). The most recent addition is an
LCA based on experimental bench-scale data from Tuomisto
etal. (2022). A comparison of these studies, including study
design and main environmental indicators, are presented
in Appendix A and compared to this study. Over the years,
insights have progressed, uncertainties have decreased, and
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249The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
environmental benefits and hotspots have become clearer. In
all studies, biotic resource use is relatively low compared to
conventional meats, but there is variation in the type of feed-
stocks used and the extent to which hydrolysates can be incor-
porated. These factors can significantly influence environmen-
tal performance. In this study, the effects of more sustainable
feedstocks were not included, but previous assessments,
such as those that modeled hydrolysates from cyanobacte-
ria (Tuomisto and Teixeira de Mattos 2011; Tuomisto etal.
2014), show that there may be opportunities here. The con-
trolled environment and efficient resource use of CM show
sustainability potential with regard to direct farm/facility-level
emissions and associated disruption of nutrient cycles, which
are serious and hard to mitigate environmental concerns in
conventional animal agriculture. Estimates of energy use have
increased over the years, highlighting the relative uncertainty
in this regard and the importance of decarbonizing the energy
sources. As energy estimates are shown to be uncertain, in
this study, a conservative approach has been taken, in which
importantly the cooling load is supplied by active cooling. It
may well be that energy use at large scales is lower than the
current estimate, as is shown by the sensitivity analysis on
smart cooling.
4.3 Environmental hotspots incultivated meat
production andtechnology development
The main environmental hotspots of CM production are the
facility energy use and culture medium ingredient produc-
tion, in which energy use also plays an important role. The
impacts of CM can therefore be greatly reduced by using
renewable energy in both the facility (scope 1 and 2) and the
supply chain, mainly for production of medium ingredients.
4.3.1 Energy
Facility energy use is directly within the influence of the CM
producers. As far as possible, energy efficiency should be
optimized. In this study, a conservative estimate of energy
use is taken, mainly regarding the need for a large active
cooling load, which accounts for ~ 75% of energy use. The
need for the cooling load as modeled here is uncertain.
In comparison, Tuomisto etal. (2022) model lower facil-
ity energy demand (according to our calculations 2.8–9.6
kWh/kg meat cells, compared to 22.3 kWh/kg meat cells
in the baseline scenario in this study). As this study and
Tuomisto etal. (2022) assess different bioreactor systems (a
combination of STRs and perfusion reactors and a hollow
fiber bioreactor, respectively), different production scales
(commercial and bench-scale, respectively) have different
process characteristics; these numbers cannot directly be
compared, and therefore these differences cannot easily be
explained. An important reason could be that the approach
used to calculate facility energy demand in this study differs
from previous studies (Mattick etal. 2015; Tuomisto etal.
2022) in the sense that data from computational models of
similar processes at large scales were used, instead of using
thermodynamic calculations for specific processes at the cell
level. This results in higher estimates for overall energy use
in this study and specifically for necessary cooling load. It
is therefore a conservative approach. It is also possible that
there are differences in heating and cooling efficiency due
to differences in bioreactor sizes, overall water volumes,
cooling jacket design, and vessel geometry. The topic of
heating and cooling balance is an area of ongoing research
and development in the field, because large-scale empirical
data for CM production does not exist yet. While small-scale
cultures generally do not produce much heat, heat dynamics
may change at larger scales, resulting in temperature hot-
spots that need to be cooled quickly (Li etal. 2020). The
demand for heating and cooling will depend on multiple
factors such as reactor design, cell densities, oxygen uptake
rates (OUR), and glucose consumption. Further modeling of
real-world pilot CM processes, and preferably experimental
data, is needed to provide more accurate estimates of heating
and cooling demand at scale.
Environmental impacts of cooling could also be mitigated
by applying partially passive cooling. Whether this is pos-
sible will depend on the environmental conditions of the
facility location and ambient temperatures. The most univer-
sally applicable cooling systems are based on active cooling,
using vapor-compression refrigeration or cooling water, both
of which ultimately reject their heat to the ambient air. The
economic optimum temperature differential in the cooling
system will be around 10°C. The production process consid-
ered in this study has to be kept around 37°C, so the cooling
fluid will have a maximum temperature of around 27°C.
Another 10°C temperature differential is required to reject
the heat to the ambient air. Roughly speaking, there are four
options for cooling, ranging from active to fully passive
cooling, that are suitable for different ambient temperatures:
• Using a refrigeration cycle, for ambient temperatures up
to 27°C
• Using cooling water and an evaporating cooling tower,
for ambient temperatures up to 22°C
• Using cooling water and an air fin cooler with mechanical
air circulation, for ambient temperatures up to 17°C
• Using cooling water and an air fin cooler without
mechanical air circulation, for ambient temperatures up
to 12°C
Depending on year-round ambient temperature condi-
tions, there will be a need for active or passive cooling, or
likely a combination of both, as many locations on Earth
have strong seasonal temperature fluctuations. In the
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250 The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
baseline scenario, year-round use of the refrigeration cycle
was assumed as this is suitable for any location at any time,
but this is a conservative approach.
The cooling electricity demand can be reduced by opti-
mizing the cooling system to the geographical location. The
sensitivity analysis on smart cooling (cutting down the elec-
tricity demand by 50%) shows that this can have a significant
effect on the carbon footprint (− 33% in the global average
energy scenario and − 21% in the ambitious benchmark sce-
nario). This could be extended to the biochemical production
processes in the upstream supply chain, but this was not
considered in this study. Also not considered in this study
is that the carbon footprint of electricity will on average be
lower when active cooling load is most needed, as this is
when ambient temperatures are highest and there is more
solar electricity available. This has a damping effect on the
carbon footprint of products that use a lot of cooling energy.
Cooling system design will always be strongly related to
location-specific opportunities. Cold-water sources (e.g.,
oceans or rivers) or residual heat from industry (for absorp-
tion cooling) might be viable options, but this depends on
geographical, legal, and economic context.
Whatever the exact assumptions for facility energy
demand, it is clear that energy sourcing is an important
lever for sustainability. Lower energy use results in lower
carbon footprints and the conservative energy assumptions
in this study therefore possibly result in conservative carbon
footprints estimates. CM companies should look for suitable
locations where renewable energy, especially electricity, is
abundant. There is evidence that this is already occurring in
the CM industry.
CM is a new and potentially large sector with substantial
energy demand, and thus its role in the energy transition
must be considered. On the one hand, it may increase pres-
sure on an energy transition that is already experiencing dif-
ficulty meeting global climate goals (Gielen etal. 2021). On
the other hand, it is potentially disruptive to the food system,
providing meat with a significantly smaller land footprint,
which provides opportunities to use this land for additional
carbon storage or renewable energy production while reduc-
ing deforestation pressure. Additional studies are needed to
further understand this carbon opportunity cost, and robust
policies will be needed to realize these opportunities.
4.3.2 Culture medium
Culture medium ingredient production is the second envi-
ronmental hotspot, which becomes important (even domi-
nant) when renewable energy is used for the facility. These
impacts are mostly driven by the energy-intensive produc-
tion of recombinant proteins and single amino acids pro-
duced microbially or chemically, followed by glucose, soy
hydrolysate, and HEPES (if used). When albumin is used in
the medium, this dwarfs the impact of the other ingredients,
as it is needed in up to 100,000 times higher concentra-
tions than growth factors. The estimated carbon footprint of
microbially produced amino acids is 5x – 10 × higher than
amino acids from hydrolysates. If production with hydro-
lysates is not realized, and companies continue to rely on
single amino acids, the impacts of culture medium are likely
to be higher, even though less amino acids would be needed
as the medium would be fully defined. This is illustrated in
Tuomisto etal. (2022), where the amino acids are the domi-
nant driver for the life cycle carbon footprint of CM. The
industries supplying the recombinant proteins and amino
acids necessary for CM production are not yet always at
scale, and therefore this study uses ex-ante estimates for
these products for a large part. Currently, these ingredi-
ents come from the pharma sector, where price pressure is
low, and therefore energy efficiency is not the main prior-
ity. As the CM industry matures, the recombinant protein
and amino acid industries will also mature and be incentiv-
ized towards more sustainable practices given the impor-
tance of scope 3 emissions. A dialogue between the CM
companies and their suppliers is needed to implement these
practices, guided by LCAs that model medium composi-
tion and efficiency changes. The majority of recombinant
proteins are currently supplied by microbial fermentation;
however, other methods such as expression in plants, insects,
or cell-free systems may offer advantageous sustainability
characteristics (Tripathi and Shrivastava 2019; GFI 2021).
Fermentation using filamentous fungi currently looks highly
promising from an environmental perspective (Järviö etal.
2021). Future research is needed to clarify the most suitable
production platforms that balance quality, cost, sustainabil-
ity, and other factors that may be important for regulation
and consumer acceptance.
The exact composition and efficiency of the culture
medium show variation between technologies and cell types
(O’Neill etal. 2022). In this study, the various medium sce-
narios reflect realistic future scenarios, to current knowl-
edge, but not per se the absolute upper or lower bounds of
medium efficiency. Lower numbers than those modeled in
the low-medium scenario have been reported, but could
not be generalized across products and technologies at this
point, and were therefore not included. Future research and
experimental data will have to provide additional insights
on this topic.
It is uncertain to what extent CM lends itself to convert-
ing waste- or by-products into edible meat. This could be
an avenue through which the footprint of culture medium
can be reduced. For example, it is possible that the glucose,
both for direct use in the culture medium and for indirect
use for biochemical fermentation processes in the supply
chain, can be sourced from waste- or by-products such as
lignocellulosic biomass. Conventional animal production
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251The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
lends itself well to the conversion of by-products into meat,
but the currently optimized production systems, including
the ambitious benchmarks in this study, are not representa-
tive of such a system, and there are limits to the scale of
global animal production when increasing the amount of
by-products (Mottet etal. 2017). Also, the ambiguous termi-
nology around waste-, co-, and by-products casts confusion
on the debate, most clearly seen in the discussion regarding
soybean meal. Perhaps more useful indicators focus on the
percentage that is “human-edible” in an ingredient, which in
the case of soybean meal is 80% (Wilkinson 2011), or on the
share of land that could otherwise be used for human-edible
food production (van Zanten etal. 2016).
Offsetting the environmental impact of the culture
medium could also be accomplished by harvesting co-
products from a CM process. This is not considered in this
study because of lacking data regarding separation, capture,
and recycling at scale, but Tuomisto etal. (2022) show that
this could have significant effects on overall system perfor-
mance. This is an interesting area for future research. It is
estimated that under baseline assumptions ~ 3200 tonnes of
lactate, 16 tonnes of ammonia, and 1 tonne of alanine will
be produced as waste in a year of operation in this model
facility (Appendix D). Capture and recycling of these co-
products are possible and could valorize them as inputs into
downstream applications for bioplastics, fertilizers, or other
feedstocks (Nahmias and Wissotsky 2021). The environ-
mental benefits of this could be substantial. For example,
the carbon footprint of biobased lactic acid ranges from 1.6
(Morão and de Bie 2019) to 11kg CO2-eq./kg (Parajuli etal.
2017), depending on the feedstock and production process.
Avoided production of virgin lactic acid production could
be partially counted as a reduction in the impacts of CM
production. Additional studies are needed to understand the
techno-economics of such recycling approaches as well as
potential sustainability tradeoffs if recycling systems require
high amounts of energy to operate.
4.3.3 Other technological innovations
In order to realize the production system as modeled in this
study, technological innovations are needed. Importantly,
this relates to the bioreactor platforms. Perfusion systems
modeled in this study are not yet available for meat produc-
tion. Cost-effective systems will have to be developed over
the next decade. Important characteristics relate to auto-
mated feeding (e.g., nutrients and oxygen), perfusion and
removal of unwanted substances (e.g., ammonia and lactate),
and incorporation of scaffolding. At the facility scale, these
relate to harvesting of cells and recycling of nutrients and
other medium ingredients. In this study, only water recy-
cling was included (at 75% efficiency). When recycling solid
ingredients, additional separation steps are needed, which
can be costly and will likely have a tradeoff with added
energy and material use. Future research will have to shed
light on these costs and environmental tradeoffs.
4.3.4 Product development andtargeted diet substitution
It is important to see the potential of CM in the context of
diets. From an environmental perspective, the largest gains
are by substituting the highest impact conventional meat
products by CM on the plate of the consumer, being beef
from beef cattle. Of course other factors besides those cap-
tured in an LCA play a role and regional context matters, but
the message is that CM can be seen as a tool to reduce the
downsides of current global demand for conventional animal
products. In this sense, it can be an attractive part of a mix
of sustainable protein sources in a healthy diet, which also
includes increased amounts of fully plant-based options, still
the most direct way to consume proteins while having the
lowest associated impacts (Poore and Nemecek 2018). Lastly,
hybrid products (partly plant-based, partly CM) can be made
to increase the amount of sustainable proteins in diets while
also optimizing for efficiency, sustainability, and costs.
5 Conclusions
CM has the potential to be a sustainable source of animal
protein. How it compares to conventional meats depends
on various factors, most importantly the sources of energy
used for the facility and the production of medium ingre-
dients. When fully renewable energy is used in these areas,
its carbon footprint can compete with ambitious bench-
marks of chicken and is lower than that of the other con-
ventional meats. Land use of CM is significantly lower than
all conventional meats, resulting from the more efficient
conversion of crops into meat. When CM replaces con-
ventional meats in diets, this means that land is freed up.
This land could be used to mitigate climate change, support
biodiversity, or provide other societal and environmental
benefits, but robust policies are needed to realize this.
CM companies should invest in strong supply chain col-
laborations to drive down the carbon footprint in all parts of
the supply chain. Strong climate goals can be set and real-
ized by continuously conducting LCAs to support decision-
making and guide technology development.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s11367- 022- 02128-8.
Acknowledgements GAIA and the Good Food Institute funded initial
screening economic and environmental assessments, which were pub-
lished by CE Delft. Follow-up study resulting in this publication was co-
funded by GAIA, the Good Food Institute, and CE Delft. Organizations
that contributed by providing data or performing cross-checks of data
ranges, and which agreed to be acknowledged, are listed in Appendix C.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
252 The International Journal of Life Cycle Assessment (2023) 28:234–254
1 3
Author contribution Pelle Sinke (PS), Ingrid Odegard (IO), Elliot
Swartz (ES), and Hermes Sanctorum (HS) contributed to the study
conception. PS and IO made the study design. Contacts with data part-
ners were established via the authors and the extended network of the
Good Food Institute. Material preparation was performed by PS and
IO and reviewed by ES. Data collection and analysis were performed
by PS and IO. Interpretation of modeling results was performed by PS,
IO, ES, and HS. Design of follow-up questions was performed by PS,
IO, ES, and Coen van der Giesen (CG). The first draft of the manuscript
was written by PS and reviewed by ES, HS, and CG. All authors com-
mented on previous versions of the manuscript. All authors read and
approved the final manuscript.
Funding The study was funded by GAIA, The Good Food Institute, a
donor-backed 501(c)3 nonprofit, and CE Delft.
Data availability Most data generated or analyzed during this study
are included in this published article and its supplementary materials.
In some cases, these are aggregated data, summarized as averages or
scenarios, in order to account for variation and to ensure data confi-
dentiality. In a few cases, confidential company data that could not
be aggregated are used. In these cases, it is mentioned in-text that
confidential data are used and these are not included in the article or
supplementary materials.
Declarations
Conflict of interest Elliot Swartz works at the Good Food Institute.
Hermes Sanctorum works as a private consultant for GAIA. Pelle
Sinke, Ingrid Odegard, and Coen van der Giesen work at CE Delft.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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Authors and Affiliations
PelleSinke1 · ElliotSwartz2· HermesSanctorum3· CoenvanderGiesen1· IngridOdegard1
* Pelle Sinke
sinke@ce.nl
1 LCA Department - Food Chains Team, CE Delft, Oude Delft
180, Delft2611HH, theNetherlands
2 Science & Technology Department, The Good Food Institute
(GFI), 1380 Monroe St. NW #229, Washington, DC20010,
USA
3 GAIA, Hopstraat 43, 1000Brussels, Belgium
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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