Analysis and valuation of the health and climate
change cobenefits of dietary change
, H. Charles J. Godfray
, Mike Rayner
, and Peter Scarborough
Oxford Martin Programme on the Future of Food, Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom;
Foundation Centre on Population Approaches for Non-Communicable Disease Prevention, Nuffield Department of Population Health, University of Oxford,
Headington, Oxford OX3 7LF, United Kingdom; and
Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom
Edited by David Tilman, University of Minnesota, St. Paul, MN, and approved February 9, 2016 (received for review November 22, 2015)
What we eat greatly influences our personal health and the environ-
ment we all share. Recent analyses have highlighted the likely dual
health and environmental benefits of reducing the fraction of animal-
sourced foods in our diets. Here, we couple for the first time, to our
knowledge, a region-specific global health model based on dietary and
weight-related risk factors with emissions accounting and economic
valuation modules to quantify the linked health and environmental
consequences of dietary changes. We find that the impacts of dietary
changes toward less meat and more plant-based diets vary greatly
among regions. The largest absolute environmental and health
benefits result from diet shifts in developing countries whereas
Western high-income and middle-income countries gain most in per
capita terms. Transitioning toward more plant-based diets that are in
line with standard dietary guidelines could reduce global mortality by
6–10% and food-related greenhouse gas emissions by 29–70% com-
pared with a reference scenario in 2050. We find that the monetized
value of the improvements in health would be comparable with, or
exceed, the value of the environmental benefits although the exact
valuation method used considerably affects the estimated amounts.
Overall, we estimate the economic benefits of improving diets to be
1–31 trillion US dollars, which is equivalent to 0.4–13% of global gross
domestic product (GDP) in 2050. However, significant changes in the
global food system would be necessary for regional diets to match the
dietary patterns studied here.
greenhouse gas emissions
The choices we make about the food we eat affect our health
and have major ramifications for the state of the environment.
The food system is responsible for more than a quarter of all
greenhouse gas (GHG) emissions (1), of which up to 80% are
associated with livestock production (2, 3). The aggregate dietary
decisions we make thus have a large influence on climate change.
High consumption of red and processed meat and low consump-
tion of fruits and vegetables are important diet-related risk factors
contributing to substantial early mortality in most regions while
over a billion people are overweight or obese (4). Without targeted
dietary changes, the situation isexpectedtoworsenasagrowing
and more wealthy global population adopts diets resulting in more
GHG emissions (5) and that increase the health burden from
chronic, noncommunicable diseases (NCDs) associated with high
body weight and unhealthy diets (6).
Recent analyses have highlighted the environmental benefits of
reducing the fraction of animal-sourced foods in our diets and have
also suggested that such dietary changes could lead to improved
health (7–14). They have shown that reductions in meat con-
sumption and other dietary changes would ease pressure on land
use (11, 12) and reduce GHG emissions (7, 11–14). Changing diets
may be more effective than technological mitigation options for
avoiding climate change (14) and may be essential to avoid nega-
tive environmental impacts such as major agricultural expansion
(7) and global warming of more than 2 °C (13) while ensuring
access to safe and affordable food for an increasing global pop-
ulation (8, 15).
The diets investigated in these studies include diets with a pro
rata reduction in animal products (ruminant meat, total meat,
dairy) (11, 13, 14), specific dietary patterns that include reduced or
no meat (such as Mediterranean, “pescatarian,”and vegetarian
diets) (11, 12), and diets based on recommendations about healthy
eating (7, 11). The health consequences of adopting these diets
have not been explicitly modeled or quantitatively analyzed, but
instead inferences have been drawn from information available in
the epidemiological literature (16). In the most comprehensive
study to date, Tilman and Clark (12) analyzed the GHG emissions
of a series of diets that differed in their animal-sourced food con-
tent and presented their results alongside a series of observational
studies of the health consequences of adopting the different diets.
Here, we use a region-specific global health model to link the
health and environmental consequences of changing diets. We also
make a first attempt, to our knowledge, to estimate the economic
value of different dietary choices through their effects on health
and the environment. For the health analysis, we built a compar-
ative risk assessment model to estimate age and region-specific
mortality associated with changes in dietary and weight-related risk
factors (4, 17). The specific risk factors influence mortality through
dose–response relationships, which allow us to compare different
dietary scenarios based on their exposure to those risk factors.
Given the availability of consistent epidemiological data, we fo-
cused on changes in the consumption of red meat, and of fruits and
vegetables, which together accounted for more than half of diet-
related deaths in 2010 (4), and also on the fraction of people who
are overweight or obese through excess calorie consumption, which
too is associated strongly with chronic disease mortality (18, 19).
The food system is responsible for more than a quarter of all
greenhouse gas emissions while unhealthy diets and high body
weight are among the greatest contributors to premature
mortality. Our study provides a comparative analysis of the
health and climate change benefits of global dietary changes
for all major world regions. We project that health and climate
change benefits will both be greater the lower the fraction of
animal-sourced foods in our diets. Three quarters of all benefits
occur in developing countries although the per capita impacts
of dietary change would be greatest in developed countries.
The monetized value of health improvements could be com-
parable with, and possibly larger than, the environmental
benefits of the avoided damages from climate change.
Author contributions: M.S., H.C.J.G., M.R., and P.S. designed research; M.S. performed re-
search; M.S., H.C.J.G., M.R., and P.S. analyzed data; and M.S. and H.C.J.G. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Data deposition: The region-specific results of the health, environmental, and economic
valuation analyses have been deposited in the Oxford University Research Archive (ORA),
ora.ox.ac.uk/ (doi: 10.5287/bodleian:XObxm2ebO).
To whom correspondence should be addressed. Email: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1523119113 PNAS Early Edition
The disease states included were coronary heart disease (CHD),
stroke, type 2 diabetes (T2DM), and cancer that is an aggregate of
site-specific cancers. These four disease states accounted for about
60% of NCD deaths and for about 40% of deaths globally in 2010
(6). Given that dietary and weight-related risk factors are pre-
dominantly associated with chronic disease mortality, we focused
on the health implications of changes in those risk factors for adults
For the environmental analysis, we linked regional and scenario-
specific food type consumption levels to GHG emissions using
Tilman and Clark’s metaanalysis of life cycle studies (12) although
we adjusted for likely future productivity improvements (3). In the
economic analysis, we placed a monetary value on changes in
GHG emissions by using estimates of the social cost of carbon (20)
and explored monetizing the health consequences using the value
of statistical life (21, 22) and projections of health-care expenditure
by cause of death (23–25). We stress from the outset that we
consider the economic valuation to be a first step and that the
estimates are not exactly comparable nor do they include all
consequences of dietary changes.
We used this coupled modeling framework to analyze the en-
vironmental and health impacts of four dietary scenarios in the
year 2050 (SI Appendix,TableS1)(7,9–13). The first (referred to
belowasREF)isareferencescenario based on projections from
the Food and Agriculture Organization of the United Nations
(FAO), with adjustments to take into account the fraction of
nonedible and wasted food (26, 27). The second scenario [healthy
global diets (HGD)] assumes the implementation of global dietary
guidelines on healthy eating (16, 28) and that people consume just
enough calories to maintain a healthy body weight (29). The last
two scenarios also assume a healthy energy intake but based on
observed vegetarian diets (30, 31), either including eggs and dairy
[lacto-ovo vegetarian (VGT)] or completely plant-based [vegan
(VGN)]. The three nonreference scenarios are not intended to be
realizable dietary outcomes on a global level but are designed to
explore the range of possible environmental and health outcomes
of progressively excluding more animal-sourced foods from human
diets (7, 9–13).
The different diet scenarios were implemented by adjusting the
region-specific diets described in the REF scenario, which main-
tained the regional character of food consumption (SI Appendix,
section SI.1). The HGD diet included (per day) a minimum of five
portions of fruits and vegetables (16), fewer than 50 g of sugar (16),
a maximum of 43 g of red meat (28), and an energy content of
2,200–2,300 kcal, depending on the age and sex composition of the
population (29). The VGT and VGN diets differed from the HGD
etables (30, 31) and one portion of pulses (30, 31), with no red meat,
poultry, or fish, and in the VGN diet no dairy or eggs. Energy intake
was adjusted to the target levels by varying the proportion of staple
foods in the diet, but preserving their region-specific composition.
Less than half of all regions meet, or are projected to meet, dietary
recommendations for the consumption of fruit, vegetables, and red
meat, and also exceed the optimal total energy intake (SI Appendix,
Fig. S1). As a consequence, large changes in the food system would
be necessary to achieve the dietary patterns considered here (SI
Appendix,TableS7). In the HGD scenario, the changes include
increasing global fruit and vegetable consumption by 25% (99 g·d
and by more in Sub-Saharan Africa (190%, 323 g·d
), South Asia
(101%, 248 g·d
), and Latin America (39%, 138 g·d
creasing global red meat consumption by 56% (42 g·d
), and by more
in Western high-income and middle-income countries (78%, 113 g·d
and 69%, 72 g·d
, respectively), East Asia (74%, 93 g·d
Latin America (72%, 83 g·d
). The nonmeat diets require greater
increases in the consumption of fruits and vegetables (VGT, 39%,
; VEG, 54%, 212 g·d
), and of pulses (324%, 61 g·d
each). Compared with the reference scenario, the alternative diets
require 15% less total energy intake.
Health Impacts. Moving to diets with fewer animal-sourced foods
would have major health benefits (Fig. 1A). Compared with the
reference scenario, we project that adoption of global dietary
guidelines (HGD) would result in 5.1 million avoided deaths per
year [95% confidence interval (CI), 4.8–5.5 million] and 79
million years of life saved (CI, 75–83 million) (Fig. 1Aand SI
Appendix, Fig. S2). The equivalent figures for the vegetarian
(VGT) diet are 7.3 million avoided deaths (CI, 7.0–7.6 million)
and 114 million life years saved (CI, 111–118 million) and for the
vegan (VGN) diet 8.1 million avoided deaths (CI, 7.8–8.5 mil-
lion) and 129 million life years saved (CI, 125–133 million).
Differentiated by risk factor, more than half of avoided deaths
(51–57% across the three scenarios) were due to decreased red meat
consumption, 24–35% to increased fruit and vegetable consumption,
and 19–30% to a lower prevalence of being overweight and obese
associated with limiting excessive energy intake. The reduced mor-
tality in the VGT and VGN scenarios compared with the HGD
scenario was due to lower red meat consumption (1.7 million ad-
ditional avoided deaths in each) and higher fruit and vegetable
consumption (VGT, 0.8 million; VGN, 1.8 million additional avoi-
ded deaths). Across the three nonreference scenarios, about 45–47%
of all avoided deaths were from reduced coronary heart disease
(CHD), 26% from stroke, 16–18% from cancer, and 10–12% from
type 2 diabetes mellitus (T2DM) (SI Appendix,Fig.S3). Adopting
the nonreference diets reduced the combined number of deaths
per year from CHD, stroke, cancer, and T2DM in 2050 by 12%
(HGD), 17% (VGT), and 19% (VEG) and the overall number of
deaths from all causes by 6% (HGD), 9% (VGT), and 10% (VEG)
Our analysis allows a regional breakdown of the health ben-
efits of dietary change. The greatest number of avoided deaths
(∼72% across the three nonreference scenarios) occurred in
developing countries, in particular in East Asia (31–35%) and
South Asia (15–19%) (Fig. 1A). Reducing red meat consumption
was the risk factor that had the most positive effect on health in
East Asia (78–82%), Western high- and middle-income countries
(64–71%; 58–65%), and Latin America (42–48%). Increasing fruit
and vegetable consumption was responsible for the majority of
avoided deaths in the least developed regions (South Asia, 75–
83%; Sub-Saharan Africa, 72–84%). Reduced energy intake and
the consequent fewer people overweight and obese were particu-
larly important in the Eastern Mediterranean (41–79%), Latin
America (32–48%), and Western high- and middle-income coun-
tries (29–40%; 20–33%). The model results can also be expressed
as avoided deaths per capita, a measure of personal risk (SI Ap-
pendix,FigsS5–S7). By this measure the greatest benefits of dietary
change occurred in developed countries due to the relatively larger
per capita reductions in red meat consumption and total energy
intake that are necessary to meet dietary guidelines (HGD) or a
vegetarian diet (VGT, VGN) (SI Appendix,TableS7).
Emissions Impacts. In line with other studies (7, 12, 13), we find that
dietary changes toward less animal-sourced foods can help mitigate
an expected growth in food-related GHG emissions. Under our
reference scenario, we project GHG emissions associated with food
consumption to increase by 51%, from 7.6 ±0.1 giga tonnes (Gt)·y
(measured in CO
equivalents) in 2005/2007 to 11.4 ±0.2 Gt·y
in 2050 (SI Appendix,Fig.S8). Food-related GHG emissions in the
HGD scenario were 8.1 ±0.1 Gt·y
, which is 29% less than REF
emissions in 2050 and 7% greater than emissions in 2005/2007. The
two vegetarian diets resulted in food-related GHG emissions at
midcentury (VGT, 4.2 ±0.1 Gt·y
were 45–55% lower than the 2005/2007 levels and 63–70% lower
than REF emissions. Emissions reductions in the HGD scenario
were largely attributable to reduced red meat consumption (3.2 ±
, 97%) whereas reductions in red meat (6.1 ±0.1
, 85%) and poultry (1.08 ±0.01 GtCO
, 15%) were re-
sponsible for lower VGT emissions, and lower consumption of red
meat (76%), poultry (13%), and eggs and dairy (1.2 ±0.03 GtCO
15%) for lower VGN emissions (Fig. 1B). In relation to an emis-
sions pathway that is believed to be likely to limit global temperature
www.pnas.org/cgi/doi/10.1073/pnas.1523119113 Springmann et al.
increase to below 2 °C (32), we project that the ratio of food-related
GHG emissions to GHG emissions from all sources increases from
16% in 2005/2007 to 52%, 37%, 19%, and 15% in 2050 in the REF,
HGD, VGT, and VGN scenarios, respectively (SI Appendix,Fig.S6
and section SI.3).
We can identify where changes to region-specific diets contrib-
ute the most to reduced GHG emissions. About three-quarters of
the total reductions (72–76% across the nonreference scenarios)
occurred in developing countries, in particular in East Asia (HGD,
55%; VGT, 41%; VEG, 38%) and Latin America (13–15%) (Fig.
1B). In contrast, food-related GHG emissions per capita fell twice
as much in developed compared with developing countries across
all three nonreference scenarios (SI Appendix,Fig.S10), driven
mainly by reductions in red meat consumption (SI Appendix,Table
S7). As a result, the difference in food-related per capita GHG
emissions between developed and developing countries narrowed
(SI Appendix,Fig.S9). The average per capita GHG emissions
from someone in a developing country was 53% that of a person
from a developed country in the REF scenario but only 26% and
20% in the HGD and VGT scenarios, respectively. In the VGN
scenario, food-related GHG emissions per capita were 4% lower in
developed countries than in developing ones, which was due to
higher fruit and vegetable consumption in some developing coun-
tries (exceeding adjusted values in the baseline) (SI Appendix,
Table S8). On a country level, 77 out of the 105 regions in the
environmental analysis reduced their food-related GHG emissions
per capita in the HGD scenario whereas an increase occurred in 28
(SI Appendix,Fig.S11). These increases in emissions were rela-
tively minor (together they made up about 2% of the total changes
in food-related GHG emissions) and were primarily due to increasing
energy intake in regions with extensive current undernourishment, in
particular in Africa. In the VGT and VGN scenarios, the number of
regions where per capita food-related GHG emissions increased was
reduced from 28 to 1 (the Democratic Republic of the Congo).
Economic Valuation. We used two complementary approaches to
assess the economic value of the health benefits associated with
dietary change. First, using “cost-of-illness”techniques (23, 25),
we calculated the direct health-care costs and the indirect costs
of informal care and lost work days that are associated with
deaths from specific diseases. Second, we used region-specific
data on the willingness of individuals to pay for incremental
mortality reductions, the “value of statistical life”(VSL) (21, 22), to
obtain an estimate of the cost of the lives (and life-years) saved
under each dietary scenario. The two approaches span the range of
potential valuation methods (33, 34); the VSL approach is com-
monly used in cost-benefit analysis (22) to indicate societal pref-
erences whereas the cost-of-illness approach, in particular its direct
cost component, highlights the economic impact on the health-care
Using the cost-of-illness approach, we estimate that the health-
related cost savings of moving to the diets based on dietary
guidelines (HGD) from that assumed in the REF scenario will be
735 billion US dollars per year ($735 billion·y
in the range [based on uncertainties in the cost transfer method
(Methods)] $482–987 billion·y
(Fig. 2). Greater savings occur with
the adoption of vegetarian diets (VGT, $973 billion·y
) and vegan diets ($1,067 billion·y
). As a percentage of expected world gross
domestic product (GDP) in 2050, these savings amount to 2.3%
(1.5–3.1%) for HGD diets, 3.0% (2.0–4.0%) for VGT diets, and
3.3% (2.2–4.4%) for VGN diets. About two thirds of the savings
(64–66% across the nonreference scenarios) were due to reduc-
tions in direct health care-related costs, a third (31–33%) to less
need for unpaid informal care (although this figure is an un-
derestimate because we were not able to obtain estimates of the
indirect costs of diabetes), and a small fraction (3–4%) to reduced
productivity from lost labor time (SI Appendix,Fig.S12). Although
more than twice as many deaths were avoided in developing
countries than in developed ones, more than half of all cost savings
(54–56%) occurred in developed countries due to their higher
health expenditure and income (SI Appendix,Fig.S12and Fig. 1A).
The value-of-statistical-life approach led to much higher esti-
mates of the economic benefits associated with dietary change (Fig.
2). For the HGD scenario, we estimate that the monetized value
associated with diet-related changes in mortality amount to
21 trillion (or 10
) US dollars per year ($21 trillion·y
with a range (again reflecting uncertainties in the methodology)
of $10–31 trillion·y
. The values we obtain for the VGT diet are
), and for the VGN diet $30
). In terms of percentage of expected
global GDP in 2050, these values amount to 9% (4–14%) for HGD
diets, 12% (6–18%) for VGT diets, and 13% (6–20%) for VGN
diets (Fig. 2). A criticism of the VSL approach, which treats each
avoided death as equally valuable, is that most of the avoided
deaths occur late in life (SI Appendix,Fig.S4). Recalculating the
estimates by monetizing the years of life saved reduces them by
approximately one half (Fig. 2). The regional distribution of the
monetized economic benefits broadly corresponds to the distribu-
tion of changes in mortality despite regional variation in the value
of statistical life (SI Appendix,Fig.S13).
To explore the economic benefits of reduced GHG emissions,
we used estimates of the social cost of carbon (20) for the year
2050 and calculated the value of avoided harm due to less
in the atmosphere (Fig. 2). We found that adoption of
diets meeting dietary guidelines (HGD) would have monetized
Fig. 1. Health and environmental analysis of dietary change for the year 2050. The diet scenarios include a reference scenario based on FAO projections
(REF), a scenario based on global guidelines on healthy eating and energy intake (HGD), and scenarios based on vegetarian (VGT) and vegan (VGN) dietary
patterns. (A) Number of avoided deaths in the dietary scenarios relative to the reference scenario in 2050 by risk factor and region. Risk factors include
changes in the consumption of fruits and vegetables [ΔC(fruit&veg)] and red meat [ΔC(red meat)], combined changes in overweight and obesity (Δweight),
and all risk factors combined (Total). The regional aggregation is detailed in SI Appendix, Table S3 and section SI.1). (B) Changes in food-related greenhouse
gas (GHG) emissions in the dietary scenarios relative to the reference scenario in 2050 by food group and region.
Springmann et al. PNAS Early Edition
environmental benefits of $234 billion·y
, with values in the
range $89–729 billion·y
for different assumptions about dis-
count rates (Methods). The benefits were greater for diets with
fewer animal-sourced foods: for VGT, $511 billion·y
) and, for VGN, $570 billion·y
). As a percentage of expected world GDP in 2050, the
benefits amounted to 0.10% (0.04–0.32%) for HGD diets, 0.22%
(0.08–0.69%) for VGT diets, and 0.25% (0.09–0.77%) for VGN
diets. The regional distribution of the monetized environmental
benefits largely reflects the changes in GHG emissions (SI Ap-
pendix, Fig. S14 and Fig. 1B).
Our analysis indicates that dietary changes toward fewer animal
and more plant-based foods are associated with significant ben-
efits due to reductions in diet-related mortality and GHG emis-
sions. Changes in the consumption of red meat, fruits, and
vegetables and in total energy intake could result in reductions
in total mortality of 6–10%, compared with a reference diet in
2050. This estimate is likely an underestimate of the total impact
that the dietary patterns studied here could have on diet-related
mortality because we were not able to model the health conse-
quences of changes in the consumption of all food groups. For
example, diets with fewer animal-sourced foods typically include
more nuts and whole grains (30, 31), which evidence suggests
have health benefits and are likely to increase the number of
avoided deaths (4). Similarly, it is known that salt and sugar
ingested in sugary drinks affect health (4), but comparative in-
ternational data on their effects is insufficient to include in our
models whereas the health impacts of other food groups (for
example dairy) is inconclusive (35). Wherever possible, we have
placed confidence estimates around our results, but we are aware
that other sources of uncertainty exist that we have not been able
to treat. Those uncertainties include food demand and mortality
projections, possible deviations from the linear dose–response
relationships linking risk factors and mortality, and our inability
to remove all possible confounding effects when deriving relative
Our health estimates are in line with current epidemiological
evidence. Tilman and Clark (12) reported results from a meta-
analysis that indicated that adopting vegetarian, pescatarian, and
Mediterranean dietary patterns could reduce overall mortality by
0–18%. Orlich et al. (36) reported results from a prospective
cohort study, focused on vegetarian dietary patterns, that in-
dicated reductions in mortality from all causes in vegetarians and
vegans compared with nonvegetarians of 9% and 15%, re-
spectively; and, in combining those results with two preceding
prospective cohort studies, Le and Sabaté (37) reported reduc-
tions in mortality in vegetarians compared with nonvegetarians
living in the United States of 12–20%. However, a prospective
cohort study focused on vegetarians living in the United King-
dom found no statistically significant reduction in mortality
compared with nonvegetarians (38), the reasons for which are
debated (37). In general, it should be noted that inferring the
health impacts of dietary patterns from observational studies is
complicated by the potential presence of multiple confounding
factors (even if some are controlled for).
The strength of our health analysis is that we used dose–response
relationships of dietary and weight-related risk factors, such as
changes in red meat consumption and overweight, that are epi-
demiologically more robust than the association of mortality with
complete diets. With this approach, we were able to analyze dif-
ferences in mortality caused by changes in consumption of specific
food groups in individual regions. We found that about half of the
global avoided deaths occurred because of the consumption of less
red meat and that the other half was due to a combination of in-
creased fruit and vegetable consumption and reductions in total
energy intake (and the associated decreases in the fraction of
people overweight and obese). However, there were marked re-
gional variations. For example, the two areas with the greatest
number of avoided deaths were East Asia and South Asia, in the
former primarily driven by reduced red meat consumption and in
the latter by increased fruit and vegetable consumption. Regions
also differed in whether the net sum of avoided deaths was due to
a modest reduction in the risk of mortality of many people or a
larger reduction in the risks to a smaller population. The greatest
improvement in per capita risk reductions occurred in Western
high- and middle-income countries due to reduced red meat con-
sumption and lower energy intakes.
In our environmental analysis, we project reference emissions
to increase by 51% between 2005/20007 and 2050 (from 7.6
-eq to 11.4 GtCO
-eq) and dietary changes to decrease
the reference emissions by 29–70% (3.3–8.0 GtCO
latter is likely to be a conservative estimate because we did not
account for the beneficial impacts of dietary change on land use
through avoided deforestation. Other studies have estimated
that the associated emissions reductions could amount to 2.1–2.8
-eq per year between 2010 and 2050 (7, 12). We also did
not take into account emissions feedbacks from increased life
expectancy in the dietary-change scenarios. However, such ef-
fects are likely to be small for the health impacts estimated here
(SI Appendix, section SI.9).
In aggregate, our results are consistent with previous studies of
the environmental consequences of dietary change. Hedenus et al.
(13) projected that dietary changes (ranging from the partial re-
placements of ruminant meats with other meats, and of animal
products with pulses and cereals) could reduce food-related GHG
emissions in 2050 by 3.4–5.2 GtCO
-eq and that technical mitiga-
tion in the agricultural sector and increased productivity could lead
to additional reductions of 1.7–2GtCO
-eq each. Tilman and
Fig. 2. Economic valuation of the health and en-
vironmental benefits of dietary change compared
with a reference scenario for the year 2050. The
three nonreference scenarios are as follows: one
based on global guidelines on healthy eating and
energy intake (HGD) and two based on vegetarian
and vegan dietary patterns (VGT and VGN). (Left)
The value of environmental benefits derived from
estimates of the social cost of carbon (SCC) and the
value of healthcare benefits based on estimates of
the costs of illness (CoI), including direct healthcare
costs and total costs, which also include indirect
costs associated with unpaid informal care and
productivity losses from lost labor time. (Right)The
value of health benefits associated with the will-
ingness to pay for mortality reductions based on
the value of statistical life and life-year (VSL and
VSLY). The uncertainty intervals for the environ-
mental valuation stem from different SCC values in 2050 [71 US dollars per ton of CO
); 27–221 USD/tCO
], and the uncertainty intervals for
the health valuation stem from high and low values of the costs of illness (±30%) and the VSL (±50%).
www.pnas.org/cgi/doi/10.1073/pnas.1523119113 Springmann et al.
Clark (12) projected that adopting Mediterranean, pescatarian,
and vegetarian diets would reduce food-related GHG emissions in
2050 by 4.2–8.4 GtCO
-eq, and Baj
zelj et al. (7) projected reduc-
tions of 5.8–6.4 GtCO
-eq in 2050 if dietary recommendations were
globally adopted. In contrast to our study, Baj
zelj et al. (7) included
land-use emissions, and their dietary scenario is largely based on
national health guidelines, which are more stringent than the global
ones we used in our HGD scenario. Although we adopted the
same baseline GHG emissions factors as Tilman and Clark (12),
our reference estimates are slightly lower than theirs (SI Appendix,
section SI.10) because we accounted for output-based productivity
improvements in agriculture (which lower emissions intensities),
and we did not account for the GHG emissions associated with the
consumption of fish and seafood. Another difference is that we
used food demand projections produced by FAO whereas Tilman
and Clark generated their own income-dependent ones.
The strength of our environmental analysis is that we were able
to explore regional details. For example, we found that some in-
creases in food consumption-related GHG emissions would be
necessary to achieve global dietary recommendations in Sub-
Saharan Africa but that, overall, adopting global dietary recom-
mendations would reduce the food-related per capita emissions
gap between developing and developed countries (and close the
gap completely if purely plant-based diets were adopted). Our
analysis also indicated that adopting global dietary guidelines
would not be enough to reduce food-related GHG emissions to the
same extent that total GHG emissions will need to fall to achieve a
climate stabilization pathway that would have a high probability of
limiting global temperature increases to below 2 °C (32). For
managing food demand (including efficiency improvements in line
with current trends) to make its prorated contribution, reductions
in animal-based foods of the degree found only in the VGN sce-
nario would be required. Given that such reductions would be
hard to achieve, our analysis suggests that, to achieve climate sta-
bilization, a balance will need to be struck between the degree of
adoption of plant-based diets, advances in mitigation technologies
of the food sector, and disproportionate reductions in non–food-
related GHG emissions.
In our economic analysis, we found that the economic value of the
health benefits associated with more plant-based diets is comparable
with, or exceeds, the value of the environmental benefits (depending
on the valuation method used). However, although these valuation
techniques are routinely used in cost-benefit analyses (20, 22), they
are not strictly comparable. The value of environmental benefits
represents the value of avoided damages, the health benefits based
on cost-of-illness estimates capture the direct and some of the in-
direct healthcare costs avoided, and the health benefits based on
value-of-statistical-life estimates can be interpreted as the aggregate
value that individuals in society would be willing to pay for the re-
ductions in mortality associated with the different dietary patterns. In
assessing the worth of public programs aimed to achieve healthier
and more environmentally sustainable diets, the use of measures based
on avoided costs provides a narrow estimate of cost-effectiveness
whereas the use of the value-of-statistical-life approach can be
seen as providing a broader estimate of net societal benefits.
We are not aware of other studies that contrasted the value of
environmental and health benefits (SI Appendix, section SI.11), and
we repeat the caveat that the valuation techniques we used are
subject to significant uncertainties. The most important source of
uncertainty for the environmental valuation is the discount rate
used to calculate the net present value of the future harm caused by
climate change. For example, changing the discount rate from five
to a measure that assumes higher than expected impacts of tem-
peratures in the upper tails of the modeled distribution (Methods)
increases the value of the environmental benefits of the HGD diet
scenario from $89 billion to $729 billion. The main source of un-
certainty in the health valuation involves the benefit transfer tech-
nique (Methods)usedtoobtainregion-specific costs-of-illness (CoI)
and value-of-statistical-life (VSL) estimates. Ideally, we would have
used values that were specifically estimated for the regions used.
However, such data do not exist for all of the regions included in
this study, so instead we used a comprehensive and quality-screened
database of VSL estimates (21, 22) and a regional set of comparable
CoI estimates (23–25). The valuation based on CoI estimates might
be further improved by the inclusion of comorbidities that can affect
the costs attributable to specific disease, and by breaking down
aggregate cancer costs into site-specific costs. Sufficient data already
exist in some regions to explore the latter, but not enough for a
global analysis (34). Finally, we note that we did not assess the
market responses associated with dietary changes: e.g., due price
changes, which remain an important area for future research.
There is a general consensus that dietary change across the globe
can have multiple health, environmental, and economic benefits
(12). Our analysis confirms this view and takes a step forward in
providing better estimates of the magnitude of the possible benefits
and how they are distributed across different regions. It introduces
a framework to analyze multiple costs and benefits across different
sectors simultaneously. The size of the projected benefits, even
taking into account all of the caveats about the unavoidable sources
of uncertainty in our work, should encourage researchers and
policy makers to act to improve consumption patterns. But we also
show the magnitude of the task. To achieve the HGD diet that
embodies a (minimal) global consensus on the consumption of a
few major food groups would require a 25% increase in the
number of fruits and vegetables eaten globally and a 56% re-
duction in red meat whereas, overall, the human species would
need to consume 15% fewer calories. We hope our work will help
identify the targeted, region-specific interventions (8, 39) that will
be needed on both the production and consumption sides of the
food system to achieve these benefits.
In the health analysis, we estimated the mortality and disease burden attrib-
utable to dietary and weight-related risk factors by calculating “population
attributable fractions”(PAFs). PAFs describe the proportions of disease cases
that would be avoided were the risk exposure changed from a baseline to a
counterfactual (4, 17). We assumed that changes in relative risks follow a dose–
response relationship (4) and that PAFs combine multiplicatively (4, 40).
Changes in mortality were calculated by multiplying region- and disease-specific
PAFs by region, disease, and age-specific death rates and population numbers
(SI Appendix,sectionSI.2). In addition to changes in mortality, we also calcu-
lated the years of life lost (YLL) due to a change in dietary and weight-related
risk factors. We did this calculation by multiplying each age-specific death by
the life expectancy at that age using the Global Burden of Disease standard
abridged life table (40).
We used publically available data sources to parameterize the comparative risk
analysis. Population and mortality projections for the year 2050 were adapted from
the United Nations Population Division and the World Health Organization (WHO),
respectively. The diet and weight-related relative risk parameters (SI Appendix,
Table S4) were taken from pooled analyses of prospective cohort studies (18, 19)
and from metaanalyses of prospective cohort and case-control studies (28, 41–46).
The cancer associations have been judged as probable or convincing by the World
Cancer Research Fund, and, in each case, a dose–response relationship had been
identified and there was consistent evidence suggesting a plausible mechanism
(28). For the weight-related risk assessment, we used the scenario estimates of
total energy intake to estimate changes in the prevalence of being overweight
and obese based on historical relationships between weight categories and caloric
availability using data from the WHO and the FAO (SI Appendix,sectionSI.2).
In the environmental analysis, we calculated the environmental impacts
associated with the different dietary scenarios by using commodity-specific
GHG emissions factors. The emissions factors are adopted from a recent
metaanalysis of life cycle analyses (LCAs) that estimated the “cradle to farm
gate”emissions of different food items (12), with adjustments to account for
likely productivity improvements that would reduce GHG intensity over time
(3) (SI Appendix, section SI.3). The factors exclude emissions from land-use
change and post–farm-gate activities, such as processing, packaging, and
transportation to households. We did not include GHG emissions related to
the consumption of fish and seafood because those food groups are not
resolved in the projections of food demand used in this study (26).
To estimate the economic consequences of the health impacts, we used
two complementary costing methods (33, 34): the value-of-statistical-life
(VSL) approach (22) and the cost-of-illness (CoI) approach (47). We based our
VSL valuation on a comprehensive global metaanalysis of stated prefer-
ence surveys of mortality risk valuation undertaken for the Organization for
Springmann et al. PNAS Early Edition
Economic Co-operation and Development (OECD) (21). Following OECD rec-
ommendations, we adopted a VSL base value for the European Union (EU)
of 3.5 million US dollars (1.75–5.25 million US dollars) and used the benefit-
transfer method to calculate VSLs in other regions (22), taking into account
differences in income expressed as GDP per capita adjusted for purchasing
power parity (PPP) and projected to 2050 (SI Appendix, section SI.4). We also
monetized the health impact in terms of years of life lost (YLL) by using the
value of statistical life year (VSLY). We calculated the VSLY for each region
by expressing the VSL as the discounted net present value of the VSLY
throughout a lifetime, adopting a discount rate of 3% and a maximum age
of 86 adapted from the Global Burden of Disease standard life table. We
used nonlinear programming (GAMS, NLP solver) to numerically solve for
the VSLYs per region (SI Appendix, section SI.4).
To implement the CoI approach, we used a cost transfer method to estimate
the costs of illness in different parts of the world. This technique is similar to
the benefit transfer method described above, and it has been used in other
global assessments (34). We based our cost-of-illness estimates on a comparative
assessment of the economic burden of cardiovascular diseases (23, 24) and
cancer (25) across the EU. We adopted the total cost estimate associated with
CHD, stroke, and cancer for the EU in 2009, which included direct costs
(healthcare expenditure, health service utilization, expenditure on medication)
and indirect costs (opportunity costs of informal care, productivity costs due to
mortality and morbidity), calculated costs per death based on mortality statistics
(24), and estimated the costs per death by disease in the EU and other regions
in 2050 by scaling the base values by the ratio of health expenditure per
capita for direct costs and by the ratio of GDP per capita (adjusted for pur-
chasing power parity) for indirect costs (SI Appendix,sectionSI.4). Productivity
losses due to morbidity and mortality, which are a part of the indirect costs,
were included only for deaths occurring among adults of working age (<65 y
old). For the CoI analysis related to diabetes (SI Appendix, section SI.4), we
adopted country-specific cost estimates (48) and, to avoid double-counting of
cardio vascular disease-related complications, adjusted those estimates for the
incremental cost component specifically attributable to diabetes (49, 50).
In the economic valuation of the environmental effects of dietary change,
we estimated the monetary value of changes in GHG emissions. We used
estimates of the social cost of carbon (SCC), which represents the monetized
damages associated with an incremental increase in carbon emissions. The
values adopted are based on a comprehensive integrated-assessment mod-
eling exercise facilitated by technical experts from several US agencies (20).
For the year 2050, the SCC estimates are 27, 71, 98, and 221 US dollars·ton
for discount rates of 5%, 3%, and 2.5%, and the 95th percentile at a
3% discount rate. The last value is designed to represent the possible higher
than expected economic impacts from climate change further out in the tails
of the SCC distribution (20).
ACKNOWLEDGMENTS. We thank Aikaterini Kavallari (FAO) for data support
and valuable comments and Alastair Gray (HERC, University of Oxford) for
1. Vermeulen SJ, Campbell BM, Ingram JSI (2012) Climate change and food systems.
Annu Rev Environ Resour 37(1):195–222.
2. Steinfeld H, et al. (2006) Livestock’s Long Shadow (FAO, Rome).
3. Tubiello FN, et al. (2014) Agriculture, Forestry and Other Land Use Emissions by
Sources and Removals by Sinks: 1990–2011 Analysis (FAO Statistics Division, Rome).
4. Lim SS, et al. (2012) A comparative risk assessment of burden of disease and injury at-
tributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic
analysis for the Global Burden of Disease Study 2010. Lancet 380(9859):2224–2260.
5. Popkin BM (2006) Global nutrition dynamics: The world is shifting rapidly toward a
diet linked with noncommunicable diseases. Am J Clin Nutr 84(2):289–298.
6. Lozano R, et al. (2012) Global and regional mortality from 235 causes of death for 20
age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease
Study 2010. Lancet 380(9859):2095–2128.
zelj B, et al. (2014) Importance of food-demand management for climate mitiga-
tion. Nat Clim Chang 4(10):924–929.
8. Godfray HCJ, et al. (2010) Food security: The challenge of feeding 9 billion people.
9. Hallström E, Carlsson-Kanyama A, Börjesson P (2015) Environmental impact of dietary
change: A systematic review. J Clean Prod 91:1–11.
10. Ripple WJ, et al. (2014) Ruminants, climate change and climate policy. Nat Clim Chang
11. Stehfest E, et al. (2009) Climate benefits of changing diet. Clim Change 95(1):83–102.
12. Tilman D, Clark M (2014) Global diets link environmental sustainability and human
health. Nature 515(7528):518–522.
13. Hedenus F, Wirsenius S, Johansson DJA (2014) The importance of reduced meat and dairy
consumption for meeting stringent climate change targets. Clim Change 124(1-2):79–91.
14. Popp A, Lotze-Campen H, Bodirsky B (2010) Food consumption, diet shifts and associated non-
CO2 greenhouse gases from agricultural production. Glob Environ Change 20(3):451–462.
15. Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double
global crop production by 2050. PLoS One 8(6):e66428.
16. WHO (2003) Diet, Nutrition and the Prevention of Chronic Diseases: Report of the
Joint WHO/FAO Expert Consultation (WHO, Geneva).
17. Murray CJ, Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S (2003) Comparative
quantification of health risks conceptual framework and methodological issues.
Popul Health Metr 1(1):1.
18. Berrington de Gonzalez A, et al. (2010) Body-mass index and mortality among 1.46
million white adults. N Engl J Med 363(23):2211–2219.
19. Whitlock G, et al.; Prospective Studies Collaboration (2009) Body-mass index and
cause-specific mortality in 900 000 adults: Collaborative analyses of 57 prospective
studies. Lancet 373(9669):1083–1096.
20. Interagency Working Group (2013) Technical Update on the Social Cost of Carbon for
Regulatory Impact Analysis-Under Executive Order 12866 (Office of Management and
Budget, Washington, DC).
21. Lindhjem H, Navrud S, Braathen NA, Biausque V (2011) Valuing mortality risk re-
ductions from environmental, transport, and health policies: A global meta-analysis
of stated preference studies. Risk Anal 31(9):1381–1407.
22. OECD (2012) Mortality Risk Valuation in Environment, Health and Transport Policies
23. Leal J, Luengo-Fernández R, Gray A, Petersen S, Rayner M (2006) Economic burden of
cardiovascular diseases in the enlarged European Union. Eur Heart J 27(13):1610–1619.
24. Nichols M, Townsend N, Scarborough P, Rayner M (2012) European Cardiovascular
Disease Statistics (European Heart Network AISBL, Brussels).
25. Luengo-Fernandez R, Leal J, Gray A, Sullivan R (2013) Economic burden of cancer across
the European Union: A population-based cost analysis. Lancet Oncol 14(12):1165–1174.
26. Alexandratos N, Bruinsma J (2012) World Agriculture Towards 2030/2050: The 2012
Revision (FAO, Rome).
27. Gustavsson J, Cederberg C, Sonesson U, Van Otterdijk R, Meybeck A (2011) Global
Food Losses and Food Waste: Extent, Causes and Prevention (FAO, Rome).
28. WCRF/AICR (2007) Food, Nutrition, Physical Activity, and the Prevention of Cancer: A
Global Perspective (AICR, Washington, DC).
29. WHO (2004) Human Energy Requirements: Report of a Joint FAO/WHO/UNU Expert
Consultation, Rome, Italy, 17–24 October 2001 (WHO, Geneva).
30. Haddad EH, Tanzman JS (2003) What do vegetarians in the United States eat? Am J
Clin Nutr 78(3, Suppl):626S–632S.
31. Scarborough P, et al. (2014) Dietary greenhouse gas emissions of meat-eaters, fish-
eaters, vegetarians and vegans in the UK. Clim Change 125(2):179–192.
32. UNEP (2014) The Emissions Gap Report 2014 (United Nations Environment Pro-
33. WHO (2009) WHO Guide to Identifying the Economic Consequences of Disease and
Injury (WHO, Geneva).
34. Bloom DE, et al. (2011) The Global Economic Burden of Noncommunicable Diseases
(World Economic Forum, Geneva).
35. Willett WC, Stampfer MJ (2013) Current evidence on healthy eating. Annu Rev Public
36. Orlich MJ, et al. (2013) Vegetarian dietary patterns and mortality in Adventist Health
Study 2. JAMA Intern Med 173(13):1230–1238.
37. Le LT, Sabaté J (2014) Beyond meatless, the health effects of vegan diets: Findings
from the Adventist cohorts. Nutrients 6(6):2131–2147.
38. Key TJ, et al. (2009) Mortality in British vegetarians: Results from the European Prospective
Investigation into Cancer and Nutrition (EPIC-Oxford). Am J Clin Nutr 89(5):1613S–1619S.
39. Garnett T, Mathewson S, Angelidis P, Borthwick F (2015) Policies and Actions to Shift
Eating Patterns: What Works? A Review of the Evidence of the Effectiveness of
Interventions Aimed at Shifting Diets in More Sustainable and Healthy Directions
(Food Climate Research Network, Oxford).
40. Murray CJL, et al. (2012) GBD 2010: Design, definitions, and metrics. Lancet 380(9859):
41. Micha R, Wallace SK, Mozaffarian D (2010) Red and processed meat consumption and
risk of incident coronary heart disease, stroke, and diabetes mellitus: A systematic
review and meta-analysis. Circulation 121(21):2271–2283.
42. Chen G-C, Lv D-B, Pang Z, Liu Q-F (2013) Red and processed meat consumption and
risk of stroke: A meta-analysis of prospective cohort studies. Eur J Clin Nutr 67(1):
43. Dauchet L, Amouyel P, Dallongeville J (2005) Fruit and vegetable consumption and
risk of stroke: A meta-analysis of cohort studies. Neurology 65(8):1193–1197.
44. Dauchet L, Amouyel P, Hercberg S, Dallongeville J (2006) Fruit and vegetable con-
sumption and risk of coronary heart disease: A meta-analysis of cohort studies. J Nutr
45. Li M, Fan Y, Zhang X, Hou W, Tang Z (2014) Fruit and vegetable intake and risk of
type 2 diabetes mellitus: Meta-analysis of prospective cohort studies. BMJ Open 4(11):
46. Feskens EJM, Sluik D, van Woudenbergh GJ (2013) Meat consumption, diabetes, and
its complications. Curr Diab Rep 13(2):298–306.
47. Akobundu E, Ju J, Blatt L, Mullins CD (2006) Cost-of-illness studies: A review of current
methods. Pharmacoeconomics 24(9):869–890.
48. Zhang P, et al. (2010) Global healthcare expenditure on diabetes for 2010 and 2030.
Diabetes Res Clin Pract 87(3):293–301.
49. American Diabetes Association (2013) Economic costs of diabetes in the U.S. in 2012.
Diabetes Care 36(4):1033–1046.
50. Köster I, Huppertz E, Hauner H, Schubert I (2011) Direct costs of diabetes mellitus in
Germany: CoDiM 2000-2007. Exp Clin Endocrinol Diabetes 119(6):377–385.
www.pnas.org/cgi/doi/10.1073/pnas.1523119113 Springmann et al.