Abstract
The adoption of healthy diets with low environmental impact has been widely promoted as an important climate change mitigation strategy. Typically, these diets are high in plant-sourced and low in animal-sourced and processed foods. Despite the fact that their environmental impacts vary, they are often referred to as 'sustainable diets'. Here we systematically review the available published evidence on the effect of 'sustainable diets' on environmental footprints and human health. Eight databases (OvidSP-Medline, OvidSP-Embase, EBSCO-GreenFILE, Web of Science Core Collection, Scopus, OvidSP-CAB-Abstracts, OvidSP-AGRIS, and OvidSP-Global Health) were searched to identify literature (published 1999–2019) reporting health effects and environmental footprints of 'sustainable diets'. Available evidence was mapped and pooled analysis was conducted by unique combinations of diet pattern, health and environmental outcome. Eighteen studies (412 measurements) met our inclusion criteria, distinguishing twelve non-mutually exclusive sustainable diet patterns, six environmental outcomes, and seven health outcomes. In 87% of measurements (n = 151) positive health outcomes were reported from 'sustainable diets' (average relative health improvement: 4.09% [95% CI −0.10–8.29]) when comparing 'sustainable diets' to current/baseline consumption patterns. Greenhouse gas emissions associated with 'sustainable diets' were on average 25.8%[95%CI −27.0 to −14.6] lower than current/baseline consumption patterns, with vegan diets reporting the largest reduction in GHG-emissions (−70.3% [95% CI: −90.2 to −50.4]), however, water use was frequently reported to be higher than current/baseline diets. Multiple benefits for both health and the environment were reported in the majority (n = 315[76%]) of measurements. We identified consistent evidence of both positive health effects and reduced environmental footprints accruing from 'sustainable diets'. The notable exception of increased water use associated with 'sustainable diets' identifies that co-benefits are not universal and some trade-offs are likely. When carefully designed, evidence-based, and adapted to contextual factors, dietary change could play a pivotal role in climate change mitigation, sustainable food systems, and future population health.

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1. Background
Major food system transformations are required as part of an integrated set of global actions to meet the Paris Agreement on climate [1] and multiple United Nations sustainable development goals (SDGs), including those on hunger (SDG 2), health (SDG 3), responsible production and consumption (SDG 12) and climate action (SDG 13) [2]. Globally 22% of children are stunted, 39% of adults overweight and 2 billion people anaemic (mainly due to iron deficiency) [3], and major transformations to the food system are needed to improve global health and ensure a sufficient supply of nutritious foods for all in the future. The global food system also has a major environmental footprint; it contributes 21%–37% of global greenhouse gas (GHG) emissions, and has a significant impact on land and water use and biodiversity [1, 4–6]. While technological advances have increased agricultural efficiency and reduced environmental footprints [7], the impact of food systems on the environment are expected to increase substantially by 2050, largely due to population growth and dietary change, particularly in rapidly transitioning economies [8, 9].
Multiple recent reports have promoted the adoption of diets with low environmental impact (or diets through which people aspire to consume more sustainably) as an important climate change mitigation strategy. Typically, these diets are high in plant-sourced foods and low in animal-sourced and processed foods. Despite the fact that their environmental impact—with respect to planetary boundaries—varies greatly, they are often referred to as 'sustainable diets' [9, 10]. The focus has been on adult diets because of the specific nutritional requirements for children. Analyses have typically highlighted the so-called 'co-benefits' for both population health and the environment of reduced consumption of animal-sourced food products (mainly red and processed meats and dairy) and increased consumption of plant-sourced foods [11, 12]. Reducing greenhouse gas emissions of food systems, along with other actions including major efforts to minimize food loss and waste, are also being widely promoted as likely to improve global health and also potentially result in economic benefits [13, 14]. The complexities of sustainable and healthy food systems reach far beyond these two dimensions, and include working conditions in the agricultural sector and animal welfare as well as cultural and socio-economic aspects of diets. The relationship between food systems and climate change is bi-directional, and climate change is currently affecting yields of crops and livestock products and is projected to continue to do so in the future [15, 16].
The evidence base on the health effects and environmental footprints of sustainable diets has grown rapidly over the past decade. Previous reviews have assessed the nutritional content of 'sustainable diets' [12, 17], their health effects or their environmental footprints [11, 18, 19]. Here we address a gap in evidence by systematically reviewing the published literature from empirical and modelling studies that assess both the environmental footprints and human health effects of 'sustainable diets'—predominantly diets high in plant-sourced and low animal-sourced foods. We identify both co-benefits and co-harms that accrue from the numerous pathways through which diets affect health and impact the environment, to enable potential trade-offs to be considered and addressed in the decision-making processes. This review aims to assess the multiple-impacts on the environment and health of several forms of 'sustainable diets' in order to support the design of evidence-based climate change mitigation policy.
2. Methods
This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [20] and presents the ROSES flow-chart [21]. We searched eight literature databases (OvidSP Medline, OvidSP Embase, EBSCO GreenFILE, Web of Science Core Collection, Scopus, OvidSP CAB Abstracts, OvidSP AGRIS, and OvidSP Global Health) using the London School of Hygiene & Tropical Medicine institutional access and optimized our search strategy to identify studies that reported on both environmental and health outcomes of 'sustainable diets'. Experts from the London School of Hygiene & Tropical Medicine library services were consulted and reviewed drafts of the search strings. We published our protocol and received peer-reviewed comments prior to initiating the database search [22]. Our search included terms to identify dietary change towards 'sustainable diets' (e.g. dietary shift, dietary change, sustainable consumption). Eight sources of grey literature were explored—through websites, reports and data repositories of CGIAR, Potsdam Institute for Climate Impact Research (PIK), Stockholm Environment Institute (SEI), FOLU-Systemiq, United Nations Social Development Network (UNSDN), International Institute for Applied Systems Analysis (IIASA), World Resource Institute (WRI), Food and Agricultural Organization of the United Nations (FAO)—to identify the need for an additional systematic search in grey literature. Our primary outcomes were changes in health including all-cause mortality and incidence of cancers, cardiovascular disease and diabetes, and the environment footprints of diets including GHG emissions, water footprints and land use. Search terms were first developed for the Web of Science Core Collection (appendix
2.1. Selection criteria
We included published empirical and modelling studies that reported the effect of dietary shifts, or comparison of different dietary patterns, and reported both health outcomes and environmental footprints. As scientific knowledge and the experimental and modelling rigour of environmental and health impact assessment has recently increased substantially, we only included evidence published over the past 20 years (between January 1999 and October 2019). We excluded review articles, articles with no quantitative outcomes and articles that did not meet quality criteria (see published protocol [22]). Study bias was assessed using criteria adapted from the Van Voorn checklist for modelling studies [23] and the CASP randomized control trial checklist [24].
2.2. Data analysis
Data were extracted, mapped and summarized in aggregates of dietary profile, health outcomes and environmental footprints. The dietary profile aggregate selection emerged from the identified studies and their respective author definitions. Geographic location of study setting was extracted and labelled as high-, middle-, or low-income country (HIC, MIC, LIC), using the World Bank classification [25]. Studies typically reported multiple 'measurements' with distinct combinations of exposures (diet), health effects and environmental footprints. We used these individual measurements reported within studies as the unit of analysis. We removed measurements that were duplicated within a single study or across multiple studies, with the exception of baseline measurements used to compare against several alternative diets. Measurements in which the exposure was 'set' (for example studies trying to identify what dietary change would be required for a certain % reduction in environmental footprint) were removed, as they performed post hoc modelling of diets under set conditions, rather than modelling environmental and health impacts of dietary change. Location and population age were recorded to assess potential differences by national income category (low, medium, high) and age (children, adults, older people).
For studies reporting data on 'baseline', 'average' or 'business-as-usual' health effects and environmental footprints (5 studies), these data were used as baseline values against which the health effects and environmental footprints of alternative diets were compared. For studies without baseline data, but for which relevant matched data on health and/or environmental footprints could be identified (e.g. reported by WHO or World Resources Institute) (13 Studies), the publicly available data were used as the comparator (appendix
The direction and relative difference (percentage change) in health and environmental outcomes were extracted for each individual measurement, comparing 'sustainable diets' to baseline diets. Outcome data were pooled, where a minimum of three studies—reporting on identical combinations of dietary shift, environmental outcome and health outcome—were available. Pooled analyses were adjusted for the nested nature of measurements within studies. For empirical studies, confidence intervals for pooled results were calculated, if sufficient studies (n = 3) reported on their individual confidence limits. For modelling studies, uncertainty estimates around pooled results were derived based on individually reported values, assuming additive uncertainty. When less than the required number of studies reported on a specific outcome uncertainty limits were still reported (for the reader's information), but appear in italics in tables and figures. Sensitivity analysis was performed excluding studies that included adults >60 years of age and children as part of the study population, and excluding studies in low-income settings.
3. Results
3.1. Systematic search results
Our initial search identified 3203 unique papers for title and abstract screening, and resulted in 144 articles eligible for full-text screening. Of these, 18 articles (13 modelling studies and 5 empirical studies) reporting 412 measurements, met our inclusion criteria (figure 1, figure 2, and appendix
Figure 1. ROSES flow chart of searching, screening and inclusion of papers for systematic review on health effects and environmental footprints of 'sustainable diets'.
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Standard image High-resolution imageFigure 2. Heat map of health and environmental outcome combinations reported in 18 studies included in systematic review (values are number of measurements; (x) = number of studies); CVD = cardiovascular disease; 'Diet-related Chronic Disease' = Morbidity and/or mortality of combined nutrition related chronic diseases. GHG = Greenhouse Gas Emissions; LU = Land Use; WU = Water Use; NU = Nitrogen Use; PU = Phosphorus Use; Other includes acidification, biodiversity loss, and environmental footprint index.
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Standard image High-resolution imageIncluded studies examined a variety of overlapping dietary patterns (table 1) all of which incorporated a reduction in animal-sourced foods, particularly red and processed meats and dairy, and an increase in plant-sourced foods. For the purposes of further analyses the author-defined dietary patterns were categorised into 12 groups (table 1).
Table 1. Author-defined dietary descriptions in 18 studies included in systematic review and combined categories used in analyses (n = number of measurements; DHD = Dutch Healthy Diet index; RSN = Swiss Society for Nutrition; GDG = Global Dietary Guidelines; HDI = Healthy Diet Indicator; DASH = Dietary Approaches to Stop Hypertension; GBD = Global Burden of Disease; ASF = animal source foods; PSF = plant source foods; SS = starchy staples); GHG = greenhouse gas.
Author definition of diet | Dietary label in this review | Reference | N |
---|---|---|---|
'Sustainable Diet' | Low GHG Emission | Biesbroek 2014 [26] Irz 2016 [27] | 10 |
Adherence to dietary guidelines: DHD, RSN, GDG, HDI, DASH and GBD | Dietary Guidelines | Biesbroek 2017 [28] Chen 2019 [29] Springmann 2016 [30] | 26 |
'Flexitarian' | Flexitarian | Chen 2019 [29] Springmann 2018 a [31] | 7 |
Increased consumption of PSF | Increase PSF | Springmann 2018b [32] | 1 |
'Mediterranean diet' | Mediterranean | Farchi 2017[33] Fresan 2018 [34] | 6 |
Pescatarian OR increase in fish consumption | Pescatarian/increase fish | Chen 2019 [29] Irz 2017 [35] Salazar 2019 [36] Springmann 2018 a [31] | 8 |
Reduction of meat or other ASF, no substitution | Reduce ASF no substitute | Aston 2012 [37] Biesbroek 2014 [26] Hobbs 2019 [38] Irz 2016 [27] Irz 2017 [35] Springmann 2018b [32] | 15 |
Reduction of ASF with substitution with PSF | Substitute ASF with PSF | Biesbroek 2014 [26] Cobiac 2019 [39] Irz 2016 [27] Irz 2017 [35] Milner 2015 [40] Scarborough 2012 [41] Soret 2014 [42] Springmann 2018 a [31] Visecchia 2012 [43] | 61 |
Reduction of ASF with substitution with SS | Substitute ASF with SS | Biesbroek 2014 [26] | 2 |
Reduction of meat with substitution with other ASF | Substitute meat with ASF | Biesbroek 2014 [26] Scarborough 2012 [41] | 6 |
'Vegan' | Vegan | Chen 2019 [29] Rosi 2017 [44] Springmann 2016 [30] Springmann 2018 a [31] | 18 |
'Vegetarian' | Vegetarian | Chen 2019 [29] Fresan 2018 [34] Rosi 2017 [44] Soret 2014 [42] Springmann 2016 [30] Springmann 2018 a [31] | 23 |
Seventeen studies (13 modelling studies; 4 empirical studies) reported estimated reductions in greenhouse gas emissions (in kg CO2 equivalents) associated with 'sustainable diets'. Land, water, phosphorus and nitrogen use were also regularly reported (table 2), bringing the total number of identified environmental outcomes studied in the included papers to five. All-cause mortality, and combined mortality or morbidity of nutrition related chronic diseases (in DALYs, deaths averted, etc) were the most commonly reported health outcomes (8 modelling studies, 4 empirical studies), cardiovascular disease (CVD) (x = 7) and cancer (x = 7) were also frequently reported (table 2), totalling seven different identified health outcome aggregates.
Table 2. Relative effect (%) on health outcomes by dietary category [exposure versus baseline] in 18 studies included in systematic review (n = number of measurements; ASF = Animal Sourced Foods; PSF = Plant Sourced Foods; SS = Starchy Staples; * = single study; ¥ = duplicates removed (multiple environmental outcomes)) and Relative effect (%) on environmental footprint by dietary category [exposure versus baseline] in 18 studies included in systematic review (n = number of measurements; ASF = Animal Sourced Foods; PSF = Plant Sourced Foods; SS = Starchy Staples; * = single study; ¥ = duplicates removed (multiple health outcomes)).
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3.2. Health impacts of 'sustainable diets'
'Sustainable diets' were reported to improve health outcomes in 87% (n = 151) of the included measurements. In modelling studies, vegan diets and diets in which there was a degree of replacement of ASF with PSF had the largest effect on combined health outcomes (−13.9% [95% CI −22.7 to −5.2] and −7.3% [95% CI −11.1 to −3.6 respectively] that was broadly consistent across individual health outcomes. Author-defined 'sustainable diets' (labelled as 'low GHG emission diet') was the only category for which a significant worsening of health outcomes was identified (+12.8% [95% CI +8.78 to +16.9]), however this was based on a very small number (x = 2) of papers. Findings from modelling and empirical studies were typically not concordant.
3.3. Environmental impacts of 'sustainable diets'
Studies evaluated a range of six different environmental outcomes associated with 'sustainable diets' (table 2 and appendix
3.4. Health effects and environmental footprints of 'sustainable diets'
Compared with baseline diets, 'sustainable diets' were associated with both positive health effects and reduced GHG emissions in the majority of reported measurements (n = 151[87%]) (figure 3); the remaining measurements reported increased GHG emissions (n = 1), negative health effects (n = 18), or both (n = 4). Vegan, flexitarian, pescatarian, and diets in which meat was substituted with other animal source foods consistently found positive health effects and reduced GHG emissions compared to baseline diets (figure 3(a)).
Figure 3. Reductions in (A) greenhouse gas emissions; (B) land use; (C) water use; and (4) nitrogen use; and their associated combined health outcomes for the eight most common dietary category reported in measurements included in systematic review. (GHGe = Greenhouse Gas Emissions; ASF = animal sourced foods; PSF = plant sourced foods.
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Standard image High-resolution imageSensitivity analysis was performed excluding studies with older adults (65+ years of age) or children (0–18 years of age) in their study populations and studies performed in low-income countries. These exclusions did not change the direction and scale of the relationship between dietary change and health and environmental outcomes, with a few differences in statistical significance of pooled findings. It should be noted that the available evidence on the excluded age groups and low-income settings was relatively sparse. Age-specific results can be found in the appendix
When looking at land and nitrogen use we see similar trends reported in the included studies: compared with baseline diets, shifts towards 'sustainable diets' were reported to reduce land use and improve health in the majority of measurements (n = 45 [61%]), with 8 measurements (11%) reporting health, but no land use benefits, 17 measurements reporting land use but no health benefits, and 4 (5.4%) reporting on harms for both health and land use. The later were all evaluating shifts to 'guideline diets' (figure 3(b)). Nitrogen use was reported to be reduced in 44 (92%) experiments of which 39 (81% of all experiments) also showed an improvement in health; four experiments (8.3%), all evaluating shifts towards pescatarian diets, were found to increase nitrogen use (figure 3(d)), whilst 5 experiments (10.4%) showed a decrease in nitrogen use, but a detrimental health impact of shifts towards 'sustainable diets'.
The reported change in combined water use and health outcomes of shifts toward 'sustainable diets' showed a different picture: only 27% of all measurements (n = 13) report both health and water use benefits of shifts towards 'sustainable diets'. Throughout all dietary change aggregates, water use is reported in 67% of the measurements (n = 32) to increase when shifting from baseline to 'sustainable diets'. Three (out of 10) measurements evaluating shifts from baseline to dietary guidelines reported a reduction in water use, but also poorer health outcomes (figure 3(c)).
4. Discussion
4.1. Research findings
This systematic review identified consistent evidence across a large spectrum of modelling and empirical studies of both positive health effects and reduced environmental footprints (especially lower GHG emissions and nitrogen use) accruing from diets with low environmental impact (or diets through which people aspire to consume more sustainably). The notable exception of increased water use—and to a lesser extent land use—associated with 'sustainable diets' identifies that co-benefits are not universal and some trade-offs are likely. 25% of the world's population currently live in countries that face 'extremely high' levels of water stress [45], and it is therefore crucial for sustainable food system planning to identify these and other trade-offs at national and/or sub-national level. Evidence for some dietary environmental footprints (such as phosphorus use, loss of biodiversity, acidification) was scarce. A small number of studies showed adverse impacts on health and environmental outcomes from 'sustainable diets', suggesting a major role for local contextual factors including agricultural practices, trade strategies and specific foods consumed. The vast majority of identified studies were conducted in high- and middle- income countries, and a clear gap in evidence from low-income settings was revealed by this review.
The review identified many definitions of 'sustainable diets', most of which showed benefits for both health and the environment. Given that population shifts in diets require large scale behaviour change, this flexibility in definition may be crucial as it creates the opportunity to develop bespoke dietary guidelines and recommendations that could represent more realistic changes from current diets and would thereby ensure a good fit for the contextual underlying population diet and health profile. There were notable differences in the results reported in modelling studies and the relatively small number of empirical studies. Whilst a few of the small number of empirical studies—included in this review—report on negative health impacts, a recent empirical study from the UK (including over 500 000 adults) showed that adherence to national dietary guidelines is associated with a statistically significant reduction in mortality and dietary GHG-emission as compared to average diets, whilst dietary water footprints were similar across aggregates of varying levels of adherence to the guidelines ([46]—this study was published after completion of the systematic database search). This underlines that real-world and context-specific validation of the health, environmental and broader effects of 'sustainable diets' remains important.
4.2. Interpretation & context
Whilst we consider two impact 'dimensions'—health and environmental footprints—in this review, there are several other important dimensions to consider. For example, large-scale shifts away from animal sourced foods could ultimately substantially change (global) food prices with substantial impacts on animal welfare and the livelihoods of producers. In addition, there are important trade-offs between different environmental impacts for example substantially increased production and supply of plant-based foods could increase pressure on scarce land and water resources especially in major producing areas typically in low and middle-income countries. Further development of context-specific 'Planetary Health Diets' that would further contribute to efforts to stay within all nine planetary boundaries [47], would be crucial for protecting human health and the natural systems on which this depends.
Behavioural interventions to improve health and/or environmental sustainability often have the potential for substantial impact, however they have proved difficult to achieve, especially when immediate benefits, for health, well-being or economic situation, are not directly experienced (e.g. [48].). A strong policy framework that that supports dietary choices improves health and the environment would facilitate behaviour change at scale [49]. Such a framework should include an evidence-based selection of components, such as (and not restricted to) regulations for mandatory and enhanced food labelling to inform consumers on the footprints of purchased products, an extended curriculum on sustainable diets in secondary education, or increased budget allocation for sustainable cooking classes.
Diets that were vegan, vegetarian, pescatarian or followed national dietary guidelines typically showed the most profound impacts on both health outcomes and environmental footprints. Up to 19.3% reductions were reported for health outcomes such as diabetes (average effect − 4% for all health outcomes), and large average reductions reported for food system greenhouse gas emissions and land use (−24% and −9% respectively) and extreme greenhouse gas reductions of up to 80% associated with vegan diets. Comparing this to other interventions related to behaviour change—such as active commutes to work—shifts towards more 'sustainable diets' show similar positive health and environmental impacts: active commuting was, for example reported to lower risk of all-cause mortality by 8% and diabetes by 30% [50], whilst a study from Stockholm [51] reported a 7% reduction of nitrous oxides and black carbon in the most densely populated inner-city areas as result of active commuting interventions.
4.3. Strengths and limitations
To our knowledge this is the first systematic review and pooled analysis evaluating the published peer-reviewed evidence on coexisting health and environmental benefits from adoption of diets with a low environmental impact or through which people aspire to consume more sustainably. We used comprehensive and rigorous search strings to identify the largest possible number of peer-reviewed papers to systematically present the totality of the available evidence base. Our study was subject to a number of limitations. First, relying on literature databases of published papers will inevitably introduce reporting and publication bias. It is not possible to assess the scale and impact of these biases on the overall findings of this study, however—after exploring eight highly relevant sources of grey literature and finding no additional data or data sources for this review—we assume to have missed only a small volume of additional findings that would possibly have been identified if a systematic grey literature review would have been performed. Furthermore, heterogeneity of study designs, definitions of 'sustainable diets'—even within our defined dietary categories, and study setting, limited the possibilities to pool results. Some of the dietary categories (partly) overlap and author definitions were sometimes poorly described, which further challenged the labelling of sustainable diet groups. Nonetheless, direction and scale of evidence on health and environmental impact of dietary shifts was relatively consistent across studies with various definitions of 'sustainable diets'. Dietary environmental footprints were predominantly measured using life cycle assessment (LCA) methods: whilst this is generally regarded as the 'gold standard' for this type of estimation, the method comes with its limitations and uncertainties around the estimates it produces. Finally, the restricted geographical spread of study settings, and the low number of studies in low- and middle-income settings limited the possibilities of geographical analysis, and exploration of socio-economic and contextual differences in health and environmental impacts of similar dietary shifts.
4.4. Policy implications
Our study results suggest substantial co-benefits to both health and the environment accruing from the adoption of 'sustainable diets' and support recommendations from the UNFCCC and others (e.g. [8, 9, 43, 52].) that the adoption of 'sustainable diets' is a powerful climate change mitigation strategy. Given the large spectrum of different dietary shifts that could ultimately lead to health and environmental benefits, and the various trade-offs of each of them, it is extremely important to further translate sustainable dietary recommendations into national and sub-national food strategies—including food based dietary guidelines. It will be crucial to identify, acknowledge and adequately address those that will be advantaged and disadvantaged by such guidelines particularly if the dietary changes are unaffordable for the poor, as successful climate change mitigation through diets will require transformational societal change.
4.5. Conclusions
Widescale adoption of diets with a low environmental impact offer an important opportunity for both climate change mitigation and health benefits through the food system. There are many different ways that such shifts could be shaped: contextualization and exploring realistic behaviour change options will be crucial in developing and implementing impactful recommendations around 'sustainable diets'. Trade-offs beyond health and environmental impact should be assessed and need to be studied in more depth, notably the water requirements of major shifts to more plant-based food consumption. It will be important to avoid unintended consequences of such dietary shifts, notably due to (increased) international trade, which could alter availability of certain foods in exporting countries. When carefully designed, based on the latest evidence, and adopted to contextual factors, true 'Planetary Health Diets' could play a pivotal role in climate change mitigation, sustainable food systems, and population health in the future.
Data availability statement
The data that support the findings of this study are available upon reasonable request from the authors.
Funding
Children's Investment Fund Foundation; Wellcome Trust [Grant Code: 205200/z/16/z.]
Appendix A
Table A1. Search strategy for web of science core collection.
Search # | Search term |
---|---|
#20 | #13 AND #14 AND #19 |
#19 | #15 OR #16 OR #17 OR #18 |
#18 | TS = ((diet* OR consum* OR 'eating pattern' OR meal* OR nourish*) near/3 (current OR average* OR change* OR shift* OR choice* OR scenario* OR habit* OR sustain*)) |
#17 | TS = ((plant-based OR fruit* OR vegetable* OR legume* OR nut* OR pulse*) near/3 (iodiver* OR higher)) |
#16 | TS = ((meat OR animal-sourced OR dairy OR ultra-processed OR UPF) near/3 (reduc* OR decreas* OR free)) |
#15 | TS = (vegan* OR vegetarian* OR flexitarian* OR pescatarian* OR sea-food OR seafood OR fish*) |
#14 | TS = ((climate OR environment*) near/5 (friendly OR footprint OR foot-print OR 'foot print' OR impact* OR damage* OR greenhouse OR land* OR 'land use' OR water* OR use* OR benefit* OR implication* OR carbon* OR sustain* OR iodiverse* OR nitrogen)) |
#13 | #11 AND #12 |
#12 | #3 OR #4 OR #5 OR #6 OR #7 OR #8 OR #9 OR #10 |
#11 | #1 OR #2 |
#10 | TS = (CKD OR cardiovascular OR cardio-vascular OR cancer OR BP) |
#9 | TS = 'kidney disease' |
#8 | TS = 'heart disease' |
# 7 | TS = (hypertension OR stroke OR diabetes OR ICH OR chronic) |
# 6 | TS = 'blood pressure' |
#5 | TS = (anemia OR anaemia) |
# 4 | TS = ((nutrient OR iron OR iodine OR 'vitamin D' OR 'vitamin B12' OR calcium OR 'Vitamin A' OR zinc OR magnesium) near/2 (deficien* OR shortage* OR value*)) |
# 3 | TS = (obesity OR overweight OR over-weight OR underweight OR under-weight OR malnutrition OR malnour*) |
# 2 | TS = (prevalence OR incidence OR risk OR rate OR mortality or morbidity) |
# 1 | TS = (health* OR wellbeing OR well-being) |
Appendix B. Sensitivity analysis health outcomes
Adults aged 25–70 only.
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High- and middle-income countries only.
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Appendix C. Origin of baseline data
Figure D1. (a) Relative difference in GHG emissions when shifting from current to sustainable diets for all modelling studies and (b) Relative difference in GHG emissions when shifting from current to sustainable diets for middle- and high-income countries only.
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Standard image High-resolution imageOverview of source of baseline data of studies included in the review.
Author | Year of study | Baseline outcome source |
---|---|---|
Aston | 2012 | Paper |
Biesbroek | 2014 | Paper |
Biesbroek | 2017 | Paper |
Chen | 2019 | Global burden of Disease |
Cobiac | 2019 | Global burden of Disease |
Farchi | 2017 | Paper |
Hobbs | 2019 | Paper |
Irz | 2016 | Paper |
Irz | 2017 | Paper |
Irz | 2017 | Paper |
Milner | 2015 | Contacted Author |
Rosi | 2017 | Paper |
Scarborough | 2012 | Paper |
Soret | 2014 | Paper |
Springmann | 2016 | Paper |
Springmann | 2018a | Global burden of Disease |
Springmann | 2018b | Global Burden of Disease |
Visecchia | 2012 | Paper |
Figure D2. (a) Relative difference in Land Use when shifting from current to sustainable diets for all studies and (b) Relative difference in Land Use when shifting from current to sustainable diets for middle- and high-income countries only.
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Standard image High-resolution imageAppendix D.
Figures D1(a) and D2(a) depict the range of relative difference in GHG emissions and land use reported in each scenario when shifting from baseline consumption patterns to sustainable diets. GHG emissions were consistently found to be inversely associated with shifts towards more sustainable diets in each study, while the association with land use projections were inconclusive.
Sensitivity analysis excluding results from low-income countries (two studies) (figures D1(b) and D2(b)) show a reduction in both GHG and land use from shifts toward sustainable diets in middle- and high-income countries.
The largest reduction in GHG emissions were reported in studies assessing shifts to vegan diets (−70.3% [95% CI: −90.2 to −50.4]), vegetarian diets (−59.3% [95% CI: −76.0 to −42.5]), pescatarian diets (−46.4 [95% CI: −83.4 to −9.49], flexitarian diets (−46.0 [95% CI −49.6 to −42.4], and diets where animal source foods were replace by plant source foods (−25.5% [95% CI: −36.1 to −13.0]). Shift to dietary guidelines—that were not specifically aiming at a reduction in environmental footprints—showed to reduce GHG emissions on average by 24.1% (95% CI −48.0 to −0.15).
From a land use perspective, substituting animal sourced foods with plant sourced foods, shifting to 'Sustainable Diets', and adherence to various dietary guidelines were reported to be associated with the largest average reduction in land use (−23.7% [95% CI: −44.9 to −2.45]; −18.0% [95% CI: −24.5 to −11.5]; and −13.7% [95% CI: −35.3–7.97] respectively). Sensitivity analysis—excluding studies from low-income countries—did not significantly alter the results of the core analysis.
Combining evidence on water use of dietary shift showed statistically significant impacts for shifts towards two dietary patterns: shifting from current to dietary guidelines showed to reduce water footprints by 29.2% [95% CI −31.1 to −27.3], whilst dietary shifts whereby by animals sourced foods are replaced by plant sourced foods showed—on average an increase of 13.8% [95% CI 8.72–18.92], however both estimates were based on multiple measurements in a single study. All other dietary shifts did not show statistically significant impacts on water use.
Results on other environmental parameters—including change in use of phosphorus and nitrogen due to dietary shifts—can be found in appendix E.
Appendix E. Data included studies
Study number | Type of study | First author | Year of publication | Region | Final diet category | Health category | Health unit | Health—absolute difference | Health—relative difference | Env outcome | Env unit | Environment—absolute difference | Environment—relative difference |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Modelling | Aston | 2012 | HIC/MIC | Reduce ASF no substitute | Cancer | % risk change | −12.20 | −12.20 | GHG | kg CO2e | −0.62 | −13.48 |
1 | Modelling | Aston | 2012 | HIC/MIC | Reduce ASF no substitute | Cancer | % risk change | −7.70 | −7.70 | GHG | kg CO2e | −0.43 | −12.94 |
1 | Modelling | Aston | 2012 | HIC/MIC | Reduce ASF no substitute | CVD | % risk change | −9.70 | −9.70 | GHG | kg CO2e | −0.62 | −13.48 |
1 | Modelling | Aston | 2012 | HIC/MIC | Reduce ASF no substitute | CVD | % risk change | −6.40 | −6.40 | GHG | kg CO2e | −0.43 | −12.94 |
1 | Modelling | Aston | 2012 | HIC/MIC | Reduce ASF no substitute | Diabetes | % risk change | −12.00 | −12.00 | GHG | kg CO2e | −0.62 | −13.48 |
1 | Modelling | Aston | 2012 | HIC/MIC | Reduce ASF no substitute | Diabetes | % risk change | −7.50 | −7.50 | GHG | kg CO2e | −0.43 | −12.94 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | All-cause Mortality/Morbidity | deaths per 100000PY | 36.93 | 8.31 | GHG | kgCO2e/d | −0.30 | −7.75 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | All-cause Mortality/Morbidity | deaths per 100000PY | 86.83 | 19.54 | GHG | kgCO2e/d | −1.01 | −26.10 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | Cancer | deaths per 100000PY | 18.15 | 8.47 | GHG | kgCO2e/d | −0.30 | −7.75 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | Cancer | deaths per 100000PY | 21.76 | 10.16 | GHG | kgCO2e/d | −1.01 | −26.10 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | CVD | deaths per 100000PY | 6.85 | 7.00 | GHG | kgCO2e/d | −0.30 | −7.75 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | CVD | deaths per 100000PY | 20.49 | 20.93 | GHG | kgCO2e/d | −1.01 | −26.10 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | RD | deaths per 100000PY | 1.94 | 7.87 | GHG | kgCO2e/d | −0.30 | −7.75 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | RD | deaths per 100000PY | 4.99 | 20.27 | GHG | kgCO2e/d | −1.01 | −26.10 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | All-cause Mortality/Morbidity | deaths per 100000PY | 36.93 | 8.31 | LU | m2*year/d | −0.31 | −8.59 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | All-cause Mortality/Morbidity | deaths per 100000PY | 86.83 | 19.54 | LU | m2*year/d | −0.99 | −27.42 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | Cancer | deaths per 100000PY | 13.08 | 6.10 | LU | m2*year/d | −0.31 | −8.59 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | Cancer | deaths per 100000PY | 21.06 | 9.83 | LU | m2*year/d | −0.99 | −27.42 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | CVD | deaths per 100000PY | 10.41 | 10.64 | LU | m2*year/d | −0.31 | −8.59 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | CVD | deaths per 100000PY | 20.50 | 20.95 | LU | m2*year/d | −0.99 | −27.42 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | RD | deaths per 100000PY | −3.09 | −12.56 | LU | m2*year/d | −0.31 | −8.59 |
2 | Empirical | Biesbroek | 2014 | HIC/MIC | 'Sustainable Diet' | RD | deaths per 100000PY | 7.16 | 29.09 | LU | m2*year/d | −0.99 | −27.42 |
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Reduce ASF no substitute | All-cause Mortality/Morbidity | % Reduction | −4.00 | GHG | % reduction | −11.50 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | % Reduction | −9.00 | GHG | % reduction | −10.00 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | % Reduction | −6.00 | GHG | % reduction | −10.00 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with starchy staples | All-cause Mortality/Morbidity | % Reduction | −0.50 | GHG | % reduction | −10.80 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with starchy staples | All-cause Mortality/Morbidity | % Reduction | −11.00 | GHG | % reduction | −10.10 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute meat with other ASF | All-cause Mortality/Morbidity | % Reduction | −4.00 | GHG | % reduction | −10.00 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute meat with other ASF | All-cause Mortality/Morbidity | % Reduction | −19.00 | GHG | % reduction | −4.50 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute meat with other ASF | All-cause Mortality/Morbidity | % Reduction | −6.00 | GHG | % reduction | −0.60 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with starchy staples | All-cause Mortality/Morbidity | % Reduction | −0.50 | LU | % reduction | −11.30 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with starchy staples | All-cause Mortality/Morbidity | % Reduction | −11.00 | LU | % reduction | −9.70 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | % Reduction | −9.00 | LU | % reduction | −10.80 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | % Reduction | −6.00 | LU | % reduction | −10.30 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute meat with other ASF | All-cause Mortality/Morbidity | % Reduction | −4.00 | LU | % reduction | −10.90 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute meat with other ASF | All-cause Mortality/Morbidity | % Reduction | −19.00 | LU | % reduction | −9.80 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Substitute meat with other ASF | All-cause Mortality/Morbidity | % Reduction | −6.00 | LU | % reduction | −4.50 | ||
2 | Modelling | Biesbroek | 2014 | HIC/MIC | Reduce ASF no substitute | All-cause Mortality/Morbidity | % Reduction | −4.00 | LU | % reduction | −11.70 | ||
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 138.62 | 27.54 | GHG | kgCO2e/d | 0.21 | 4.49 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | −12.20 | −2.42 | GHG | kgCO2e/d | −0.05 | −1.09 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 20.91 | 4.40 | GHG | kgCO2e/d | 0.09 | 2.04 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 114.60 | 19.21 | GHG | kgCO2e/d | 0.07 | 1.94 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | −77.40 | −12.98 | GHG | kgCO2e/d | −0.10 | −2.53 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 5.73 | 0.96 | GHG | kgCO2e/d | 0.06 | 1.73 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 138.62 | 27.54 | LU | m2*year/d | 0.14 | 3.09 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | −12.20 | −2.42 | LU | m2*year/d | 0.03 | 0.70 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 20.91 | 4.40 | LU | m2*year/d | 0.12 | 2.77 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 114.60 | 19.21 | LU | m2*year/d | 0.03 | 0.81 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | −77.40 | −12.98 | LU | m2*year/d | 0.00 | 0.00 |
3 | Empirical | Biesbroek | 2017 | HIC/MIC | Dietary Guidelines | All-cause Mortality/Morbidity | deaths per 100000PY | 5.73 | 0.96 | LU | m2*year/d | 0.15 | 4.38 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −953.00 | −0.16 | GHG | kgCO2e/d | −1.06 | −46.98 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −15 756.00 | −2.67 | GHG | kgCO2e/d | −1.23 | −54.30 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 419.29 | 0.33 | GHG | kgCO2e/d | −1.06 | −46.98 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −5424.19 | −4.33 | GHG | kgCO2e/d | −1.23 | −54.30 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 531.41 | 0.90 | GHG | kgCO2e/d | −1.06 | −46.98 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −2605.05 | −4.41 | GHG | kgCO2e/d | −1.23 | −54.30 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −953.00 | −0.16 | WU | m3 | −0.19 | −32.33 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −15 756.00 | −2.67 | WU | m3 | −0.15 | −26.10 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | DRCD | DALYs | −20 986.00 | −3.55 | WU | m3 | 0.01 | 2.34 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | DRCD | DALYs | −8049.00 | −1.36 | WU | m3 | 0.02 | 3.51 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | DRCD | DALYs | −10 679.00 | −1.81 | WU | m3 | 0.02 | 2.90 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | DRCD | DALYs | −5259.00 | −0.89 | WU | m3 | 0.00 | 0.02 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −953.00 | −0.16 | LU | m2 | −1.15 | −26.15 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −15 756.00 | −2.67 | LU | m2 | −1.42 | −32.41 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | DRCD | DALYs | −20 986.00 | −3.55 | LU | m2 | −0.31 | −6.97 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | DRCD | DALYs | −8049.00 | −1.36 | LU | m2 | −0.12 | −2.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | DRCD | DALYs | −10 679.00 | −1.81 | LU | m2 | −0.17 | −3.87 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | DRCD | DALYs | −5259.00 | −0.89 | LU | m2 | −0.22 | −4.96 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −953.00 | −0.16 | NU | g/d | −8.03 | −27.67 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −15 756.00 | −2.67 | NU | g/d | −9.72 | −33.48 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | DRCD | DALYs | −20 986.00 | −3.55 | NU | g/d | −5.47 | −18.83 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | DRCD | DALYs | −8049.00 | −1.36 | NU | g/d | −3.36 | −11.57 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | DRCD | DALYs | −10 679.00 | −1.81 | NU | g/d | −3.33 | −11.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | DRCD | DALYs | −5259.00 | −0.89 | NU | g/d | −3.00 | −10.35 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −953.00 | −0.16 | PU | g/d | −1.49 | −28.40 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | DRCD | DALYs | −15 756.00 | −2.67 | PU | g/d | −1.79 | −34.12 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | DRCD | DALYs | −20 986.00 | −3.55 | PU | g/d | −0.90 | −17.29 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | DRCD | DALYs | −8049.00 | −1.36 | PU | g/d | −0.62 | −11.91 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | DRCD | DALYs | −10 679.00 | −1.81 | PU | g/d | −0.61 | −11.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | DRCD | DALYs | −5259.00 | −0.89 | PU | g/d | −0.55 | −10.42 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2341.99 | −3.41 | GHG | kgCO2e/d | −1.06 | −46.98 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2617.28 | −3.81 | GHG | kgCO2e/d | −1.23 | −54.30 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | 438.58 | 0.12 | GHG | kgCO2e/d | −1.06 | −46.98 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | −5109.90 | −1.43 | GHG | kgCO2e/d | −1.23 | −54.30 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | DRCD | DALYs | −5259.00 | −0.89 | GHG | kgCO2e/d | −1.03 | −45.37 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1369.39 | −1.09 | GHG | kgCO2e/d | −1.03 | −45.37 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 419.29 | 0.33 | WU | m3 | −0.19 | −32.33 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −5424.19 | −4.33 | WU | m3 | −0.15 | −26.10 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −9785.20 | −7.82 | WU | m3 | 0.01 | 2.34 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −1967.18 | −1.57 | WU | m3 | 0.02 | 3.51 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −4473.01 | −3.57 | WU | m3 | 0.02 | 2.90 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1369.39 | −1.09 | WU | m3 | 0.00 | 0.02 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 419.29 | 0.33 | LU | m2 | −1.15 | −26.15 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −5424.19 | −4.33 | LU | m2 | −1.42 | −32.41 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −9785.20 | −7.82 | LU | m2 | −0.31 | −6.97 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −1967.18 | −1.57 | LU | m2 | −0.12 | −2.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −4473.01 | −3.57 | LU | m2 | −0.17 | −3.87 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1369.39 | −1.09 | LU | m2 | −0.22 | −4.96 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 419.29 | 0.33 | NU | g/d | −8.03 | −27.67 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −5424.19 | −4.33 | NU | g/d | −9.72 | −33.48 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −9785.20 | −7.82 | NU | g/d | −5.47 | −18.83 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −1967.18 | −1.57 | NU | g/d | −3.36 | −11.57 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −4473.01 | −3.57 | NU | g/d | −3.33 | −11.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1369.39 | −1.09 | NU | g/d | −3.00 | −10.35 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 419.29 | 0.33 | PU | g/d | −1.49 | −28.40 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −5424.19 | −4.33 | PU | g/d | −1.79 | −34.12 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −9785.20 | −7.82 | PU | g/d | −0.90 | −17.29 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −1967.18 | −1.57 | PU | g/d | −0.62 | −11.91 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −4473.01 | −3.57 | PU | g/d | −0.61 | −11.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1369.39 | −1.09 | PU | g/d | −0.55 | −10.42 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1417.66 | −2.40 | GHG | kgCO2e/d | −1.03 | −45.37 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Diabetes | DALYs | −625.75 | −0.91 | GHG | kgCO2e/d | −1.03 | −45.37 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Cancer | DALYs | −1846.29 | −0.52 | GHG | kgCO2e/d | −1.03 | −45.37 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | DRCD | DALYs | −10 679.00 | −1.81 | GHG | kgCO2e/d | −1.49 | −65.65 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −4473.01 | −3.57 | GHG | kgCO2e/d | −1.49 | −65.65 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −2394.50 | −4.06 | GHG | kgCO2e/d | −1.49 | −65.65 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 531.41 | 0.90 | WU | m3 | −0.19 | −32.33 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −2605.05 | −4.41 | WU | m3 | −0.15 | −26.10 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −3033.43 | −5.14 | WU | m3 | 0.01 | 2.34 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −2382.34 | −4.03 | WU | m3 | 0.02 | 3.51 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −2394.50 | −4.06 | WU | m3 | 0.02 | 2.90 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1417.66 | −2.40 | WU | m3 | 0.00 | 0.02 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 531.41 | 0.90 | LU | m2 | −1.15 | −26.15 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −2605.05 | −4.41 | LU | m2 | −1.42 | −32.41 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −3033.43 | −5.14 | LU | m2 | −0.31 | −6.97 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −2382.34 | −4.03 | LU | m2 | −0.12 | −2.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −2394.50 | −4.06 | LU | m2 | −0.17 | −3.87 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1417.66 | −2.40 | LU | m2 | −0.22 | −4.96 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 531.41 | 0.90 | NU | g/d | −8.03 | −27.67 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −2605.05 | −4.41 | NU | g/d | −9.72 | −33.48 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −3033.43 | −5.14 | NU | g/d | −5.47 | −18.83 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −2382.34 | −4.03 | NU | g/d | −3.36 | −11.57 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −2394.50 | −4.06 | NU | g/d | −3.33 | −11.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1417.66 | −2.40 | NU | g/d | −3.00 | −10.35 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | 531.41 | 0.90 | PU | g/d | −1.49 | −28.40 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | CVD | DALYs | −2605.05 | −4.41 | PU | g/d | −1.79 | −34.12 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −3033.43 | −5.14 | PU | g/d | −0.90 | −17.29 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −2382.34 | −4.03 | PU | g/d | −0.62 | −11.91 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | CVD | DALYs | −2394.50 | −4.06 | PU | g/d | −0.61 | −11.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | CVD | DALYs | −1417.66 | −2.40 | PU | g/d | −0.55 | −10.42 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Diabetes | DALYs | −605.54 | −0.88 | GHG | kgCO2e/d | −1.49 | −65.65 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Cancer | DALYs | −3206.16 | −0.90 | GHG | kgCO2e/d | −1.49 | −65.65 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | DRCD | DALYs | −20 986.00 | −3.55 | GHG | kgCO2e/d | −1.89 | −83.39 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −9785.20 | −7.82 | GHG | kgCO2e/d | −1.89 | −83.39 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | CVD | DALYs | −3033.43 | −5.14 | GHG | kgCO2e/d | −1.89 | −83.39 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Diabetes | DALYs | −1150.42 | −1.67 | GHG | kgCO2e/d | −1.89 | −83.39 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2341.99 | −3.41 | WU | m3 | −0.19 | −32.33 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2617.28 | −3.81 | WU | m3 | −0.15 | −26.10 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Diabetes | DALYs | −1150.42 | −1.67 | WU | m3 | 0.01 | 2.34 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Diabetes | DALYs | −550.66 | −0.80 | WU | m3 | 0.02 | 3.51 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Diabetes | DALYs | −605.54 | −0.88 | WU | m3 | 0.02 | 2.90 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Diabetes | DALYs | −625.75 | −0.91 | WU | m3 | 0.00 | 0.02 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2341.99 | −3.41 | LU | m2 | −1.15 | −26.15 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2617.28 | −3.81 | LU | m2 | −1.42 | −32.41 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Diabetes | DALYs | −1150.42 | −1.67 | LU | m2 | −0.31 | −6.97 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Diabetes | DALYs | −550.66 | −0.80 | LU | m2 | −0.12 | −2.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Diabetes | DALYs | −605.54 | −0.88 | LU | m2 | −0.17 | −3.87 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Diabetes | DALYs | −625.75 | −0.91 | LU | m2 | −0.22 | −4.96 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2341.99 | −3.41 | NU | g/d | −8.03 | −27.67 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2617.28 | −3.81 | NU | g/d | −9.72 | −33.48 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Diabetes | DALYs | −1150.42 | −1.67 | NU | g/d | −5.47 | −18.83 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Diabetes | DALYs | −550.66 | −0.80 | NU | g/d | −3.36 | −11.57 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Diabetes | DALYs | −605.54 | −0.88 | NU | g/d | −3.33 | −11.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Diabetes | DALYs | −625.75 | −0.91 | NU | g/d | −3.00 | −10.35 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2341.99 | −3.41 | PU | g/d | −1.49 | −28.40 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Diabetes | DALYs | −2617.28 | −3.81 | PU | g/d | −1.79 | −34.12 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Diabetes | DALYs | −1150.42 | −1.67 | PU | g/d | −0.90 | −17.29 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Diabetes | DALYs | −550.66 | −0.80 | PU | g/d | −0.62 | −11.91 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Diabetes | DALYs | −605.54 | −0.88 | PU | g/d | −0.61 | −11.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Diabetes | DALYs | −625.75 | −0.91 | PU | g/d | −0.55 | −10.42 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Cancer | DALYs | −7016.77 | −1.96 | GHG | kgCO2e/d | −1.89 | −83.39 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | DRCD | DALYs | −8049.00 | −1.36 | GHG | kgCO2e/d | −1.48 | −65.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −1967.18 | −1.57 | GHG | kgCO2e/d | −1.48 | −65.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | CVD | DALYs | −2382.34 | −4.03 | GHG | kgCO2e/d | −1.48 | −65.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Diabetes | DALYs | −550.66 | −0.80 | GHG | kgCO2e/d | −1.48 | −65.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Cancer | DALYs | −3148.82 | −0.88 | GHG | kgCO2e/d | −1.48 | −65.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | 438.58 | 0.12 | WU | m3 | −0.19 | −32.33 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | −5109.90 | −1.43 | WU | m3 | −0.15 | −26.10 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Cancer | DALYs | −7016.77 | −1.96 | WU | m3 | 0.01 | 2.34 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Cancer | DALYs | −3148.82 | −0.88 | WU | m3 | 0.02 | 3.51 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Cancer | DALYs | −3206.16 | −0.90 | WU | m3 | 0.02 | 2.90 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Cancer | DALYs | −1846.29 | −0.52 | WU | m3 | 0.00 | 0.02 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | 438.58 | 0.12 | LU | m2 | −1.15 | −26.15 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | −5109.90 | −1.43 | LU | m2 | −1.42 | −32.41 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Cancer | DALYs | −7016.77 | −1.96 | LU | m2 | −0.31 | −6.97 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Cancer | DALYs | −3148.82 | −0.88 | LU | m2 | −0.12 | −2.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Cancer | DALYs | −3206.16 | −0.90 | LU | m2 | −0.17 | −3.87 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Cancer | DALYs | −1846.29 | −0.52 | LU | m2 | −0.22 | −4.96 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | 438.58 | 0.12 | NU | g/d | −8.03 | −27.67 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | −5109.90 | −1.43 | NU | g/d | −9.72 | −33.48 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Cancer | DALYs | −7016.77 | −1.96 | NU | g/d | −5.47 | −18.83 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Cancer | DALYs | −3148.82 | −0.88 | NU | g/d | −3.36 | −11.57 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Cancer | DALYs | −3206.16 | −0.90 | NU | g/d | −3.33 | −11.46 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Cancer | DALYs | −1846.29 | −0.52 | NU | g/d | −3.00 | −10.35 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | 438.58 | 0.12 | PU | g/d | −1.49 | −28.40 |
4 | Modelling | Chen | 2019 | HIC/MIC | Dietary Guidelines | Cancer | DALYs | −5109.90 | −1.43 | PU | g/d | −1.79 | −34.12 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegan | Cancer | DALYs | −7016.77 | −1.96 | PU | g/d | −0.90 | −17.29 |
4 | Modelling | Chen | 2019 | HIC/MIC | Vegetarian | Cancer | DALYs | −3148.82 | −0.88 | PU | g/d | −0.62 | −11.91 |
4 | Modelling | Chen | 2019 | HIC/MIC | Pescatarian/increase fish | Cancer | DALYs | −3206.16 | −0.90 | PU | g/d | −0.61 | −11.63 |
4 | Modelling | Chen | 2019 | HIC/MIC | Flexitarian | Cancer | DALYs | −1846.29 | −0.52 | PU | g/d | −0.55 | −10.42 |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −28 000 000.00 | −11.35 | GHG | %reduction | 0.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −28 000 000.00 | −11.35 | GHG | %reduction | −10.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −28 000 000.00 | −11.35 | GHG | %reduction | −20.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −31 000 000.00 | −12.57 | GHG | %reduction | −30.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −33 000 000.00 | −13.38 | GHG | %reduction | −40.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −39 000 000.00 | −15.81 | GHG | %reduction | −50.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −39 000 000.00 | −15.81 | GHG | %reduction | −60.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −14 500 000.00 | −6.23 | GHG | %reduction | 0.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −13 200 000.00 | −5.67 | GHG | %reduction | −10.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −13 200 000.00 | −5.67 | GHG | %reduction | −20.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −13 600 000.00 | −5.84 | GHG | %reduction | −30.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −12 300 000.00 | −5.29 | GHG | %reduction | −40.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −10 500 000.00 | −4.51 | GHG | %reduction | −50.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −17 200 000.00 | −7.39 | GHG | %reduction | −60.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −9 800 000.00 | −4.24 | GHG | %reduction | 0.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −9 500 000.00 | −4.11 | GHG | %reduction | −10.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −9 000 000.00 | −3.89 | GHG | %reduction | −20.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −10 000 000.00 | −4.33 | GHG | %reduction | −30.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −10 400 000.00 | −4.50 | GHG | %reduction | −40.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −18 700 000.00 | −8.09 | GHG | %reduction | −50.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −27 100 000.00 | −11.72 | GHG | %reduction | −60.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −2 100 000.00 | −5.87 | GHG | %reduction | 0.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −2 100 000.00 | −5.87 | GHG | %reduction | −10.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −2 000 000.00 | −5.59 | GHG | %reduction | −20.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −2 200 000.00 | −6.15 | GHG | %reduction | −30.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −2 800 000.00 | −7.83 | GHG | %reduction | −40.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −2 600 000.00 | −7.27 | GHG | %reduction | −50.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −3 400 000.00 | −9.51 | GHG | %reduction | −60.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −3 800 000.00 | −16.87 | GHG | %reduction | 0.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 000 000.00 | −17.75 | GHG | %reduction | −10.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 000 000.00 | −17.75 | GHG | %reduction | −20.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 000 000.00 | −17.75 | GHG | %reduction | −30.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 400 000.00 | −19.53 | GHG | %reduction | −40.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 300 000.00 | −19.09 | GHG | %reduction | −50.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 500 000.00 | −19.97 | GHG | %reduction | −60.00 | |
5 | Modelling | Cobiac | 2019 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | DALYs | −4 700 000.00 | −20.86 | GHG | %reduction | −70.00 | |
6 | Modelling | Farchi | 2017 | HIC/MIC | Mediterranean | Cancer | % Deaths Avoided | −3.70 | −3.70 | GHG | Annual GHG Emissions Italy in Gg CO2 eqv | −8160.00 | −63.06 |
6 | Modelling | Farchi | 2017 | HIC/MIC | Mediterranean | CVD | % Deaths Avoided | −3.30 | −3.30 | GHG | Annual GHG Emissions Italy in Gg CO2 eqv | −8160.00 | −63.06 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | All-cause Mortality/Morbidity | Incidence Rate (%) | −0.10 | −6.88 | Other | EFI | −0.11 | −1.10 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | Cancer | Incidence Rate (%) | 0.12 | 35.45 | Other | EFI | −0.11 | −1.10 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | CVD | Incidence Rate (%) | 0.11 | 17.58 | Other | EFI | −0.11 | −1.10 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | Diabetes | Incidence Rate (%) | 0.07 | 10.23 | Other | EFI | −0.11 | −1.10 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | All-cause Mortality/Morbidity | Incidence Rate (%) | −0.04 | −3.15 | Other | EFI | −0.28 | −2.82 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | Cancer | Incidence Rate (%) | 0.03 | 13.70 | Other | EFI | −0.28 | −2.82 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | CVD | Incidence Rate (%) | 0.02 | 3.53 | Other | EFI | −0.28 | −2.82 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | Diabetes | Incidence Rate (%) | −0.04 | −5.73 | Other | EFI | −0.28 | −2.82 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | All-cause Mortality/Morbidity | Incidence Rate (%) | −0.15 | −10.68 | Other | EFI | −0.53 | −5.29 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | Cancer | Incidence Rate (%) | −0.10 | −28.54 | Other | EFI | −0.53 | −5.29 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | CVD | Incidence Rate (%) | −0.11 | −16.99 | Other | EFI | −0.53 | −5.29 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Mediterranean | Diabetes | Incidence Rate (%) | 0.26 | 38.72 | Other | EFI | −0.53 | −5.29 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | All-cause Mortality/Morbidity | Incidence Rate (%) | 0.37 | 32.15 | Other | EFI | −1.11 | −11.18 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | Cancer | Incidence Rate (%) | 0.19 | 78.64 | Other | EFI | −1.11 | −11.18 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | CVD | Incidence Rate (%) | −0.11 | −16.29 | Other | EFI | −1.11 | −11.18 |
7 | Empirical | Fresan | 2018 | HIC/MIC | Vegetarian | Diabetes | Incidence Rate (%) | 0.30 | 49.14 | Other | EFI | −1.11 | −11.18 |
8 | Empirical | Hobbs | 2019 | HIC/MIC | Reduce ASF no substitute | BMI | kg/m^1 | 0.50 | 1.82 | GHG | kg CO2 eqv/day | −0.05 | −1.23 |
8 | Empirical | Hobbs | 2019 | HIC/MIC | Reduce ASF no substitute | BMI | kg/m^1 | 0.50 | 1.82 | NU | gN eqv/day | −1.40 | −2.68 |
8 | Empirical | Hobbs | 2019 | HIC/MIC | Reduce ASF no substitute | BMI | kg/m^1 | 0.50 | 1.82 | Other | g SO2 eqv | 1.60 | 4.66 |
8 | Empirical | Hobbs | 2019 | HIC/MIC | Reduce ASF no substitute | BMI | kg/m^1 | 0.50 | 1.82 | GHG | kg CO2 eqv/day | −0.05 | −1.23 |
8 | Empirical | Hobbs | 2019 | HIC/MIC | Reduce ASF no substitute | BMI | kg/m^1 | 0.50 | 1.82 | NU | gN eqv/day | −1.40 | −2.68 |
8 | Empirical | Hobbs | 2019 | HIC/MIC | Reduce ASF no substitute | BMI | kg/m^1 | 0.50 | 1.82 | Other | g SO2 eqv | 1.60 | 4.66 |
9 | Modelling | Irz | 2016 | HIC/MIC | 'Sustainable Diet' | DRCD | # Deaths Avoided | 2331.00 | −3.50 | GHG | Change in CO2 emissions (kt) | −2985.00 | −5.30 |
9 | Modelling | Irz | 2016 | HIC/MIC | Reduce ASF no substitute | DRCD | # Deaths Avoided | 230.00 | −0.30 | GHG | Change in CO2 emissions (kt) | −274.00 | −0.50 |
9 | Modelling | Irz | 2016 | HIC/MIC | Reduce ASF no substitute | DRCD | # Deaths Avoided | 245.00 | −0.40 | GHG | Change in CO2 emissions (kt) | −513.00 | −0.90 |
9 | Modelling | Irz | 2016 | HIC/MIC | Substitute ASF with PSF | DRCD | # Deaths Avoided | 2507.00 | −3.80 | GHG | Change in CO2 emissions (kt) | −1574.00 | −2.80 |
9 | Modelling | Irz | 2016 | HIC/MIC | Reduce ASF no substitute | DRCD | # Deaths Avoided | 230.00 | −0.30 | Other | Change in SO2 (kt) | −7.00 | −1.00 |
10 | Modelling | Irz | 2017 | HIC/MIC | Pescatarian/increase fish | DRCD | % Deaths Avoided | 0.60 | −0.60 | GHG | % CO2 emissions Avoided | −0.40 | |
9 | Modelling | Irz | 2016 | HIC/MIC | Reduce ASF no substitute | DRCD | # Deaths Avoided | 245.00 | −0.40 | Other | Change in SO2 (kt) | −17.00 | −2.50 |
10 | Modelling | Irz | 2017 | HIC/MIC | Reduce ASF no substitute | DRCD | % Deaths Avoided | −0.30 | GHG | % CO2 emissions Avoided | −0.50 | ||
9 | Modelling | Irz | 2016 | HIC/MIC | Substitute ASF with PSF | DRCD | # Deaths Avoided | 2507.00 | −3.80 | Other | Change in SO2 (kt) | −27.00 | −3.90 |
10 | Modelling | Irz | 2017 | HIC/MIC | Reduce ASF no substitute | DRCD | % Deaths Avoided | 0.40 | −0.40 | GHG | % CO2 emissions Avoided | −0.90 | |
9 | Modelling | Irz | 2016 | HIC/MIC | 'Sustainable Diet' | DRCD | # Deaths Avoided | 2331.00 | −3.50 | Other | Change in SO2 (kt) | −67.00 | −9.50 |
10 | Modelling | Irz | 2017 | HIC/MIC | Substitute ASF with PSF | DRCD | % Deaths Avoided | −3.80 | GHG | % CO2 emissions Avoided | −2.80 | ||
10 | Modelling | Irz | 2017 | HIC/MIC | Substitute meat with other ASF | DRCD | % Deaths Avoided | −0.40 | GHG | % CO2 emissions Avoided | −0.30 | ||
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 511 348.00 | −5.91 | GHG | % Reduction GHG | −17.20 | |
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 514 093.00 | −5.94 | GHG | % Reduction GHG | −18.00 | |
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 518 627.00 | −5.99 | GHG | % Reduction GHG | −21.90 | |
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 530 696.00 | −6.13 | GHG | % Reduction GHG | −30.00 | |
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 565 073.00 | −6.53 | GHG | % Reduction GHG | −40.00 | |
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 638 866.00 | −7.38 | GHG | % Reduction GHG | −50.00 | |
11 | Modelling | Milner | 2015 | HIC/MIC | Substitute ASF with PSF | DRCD | # Reduction in YLL (at year 30) | 657 766.00 | −7.60 | GHG | % Reduction GHG | −60.00 | |
12 | Empirical | Rosi | 2017 | HIC/MIC | Vegan | BMI | kg/m^2 | −0.80 | −3.62 | GHG | g CO2 eq/day (mean) | −1361.00 | −34.38 |
12 | Empirical | Rosi | 2017 | HIC/MIC | Vegan | BMI | kg/m^2 | −0.80 | −3.62 | WU | L/d | −836.00 | −26.62 |
12 | Empirical | Rosi | 2017 | HIC/MIC | Vegetarian | BMI | kg/m^2 | −0.20 | −0.90 | GHG | g CO2 eq/day (mean) | −1623.00 | −41.00 |
12 | Empirical | Rosi | 2017 | HIC/MIC | Vegetarian | BMI | kg/m^2 | −0.20 | −0.90 | WU | L/d | −686.00 | −21.84 |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | Cancer | # Deaths Avoided/yr | 8236.00 | −9.60 | GHG | %reduction | −19.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | Cancer | # Deaths Avoided/yr | 8236.00 | 9.60 | LU | %reduction | −42.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | Cancer | # Deaths Avoided/yr | 2128.00 | −2.50 | GHG | %reduction | −3.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | Cancer | # Deaths Avoided/yr | 2128.00 | 2.50 | LU | %reduction | −4.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | CVD | # Deaths Avoided/yr | 28 674.00 | 20.30 | GHG | %reduction | −19.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | CVD | # Deaths Avoided/yr | 28 674.00 | 20.30 | LU | %reduction | −42.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | CVD | # Deaths Avoided/yr | 7169.00 | −5.10 | GHG | %reduction | −3.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute ASF with PSF | CVD | # Deaths Avoided/yr | 7169.00 | 5.10 | LU | %reduction | −4.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute meat with other ASF | Cancer | # Deaths Avoided/yr | 272.00 | −0.30 | GHG | %reduction | −9.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute meat with other ASF | Cancer | # Deaths Avoided/yr | 272.00 | 0.30 | LU | %reduction | −39.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute meat with other ASF | CVD | # Deaths Avoided/yr | 1727.00 | −1.20 | GHG | %reduction | −9.00 | |
13 | Modelling | Scarborough | 2012 | HIC/MIC | Substitute meat with other ASF | CVD | # Deaths Avoided/yr | 1727.00 | 1.20 | LU | %reduction | −39.00 | |
14 | Empirical | Soret | 2014 | HIC/MIC | Substitute ASF with PSF | All-cause Mortality/Morbidity | # deaths/1000 person-years (95% CI) | −1.13 | −16.97 | GHG | Change in CO2 eq (Gt) | −0.66 | −21.64 |
14 | Empirical | Soret | 2014 | HIC/MIC | Vegetarian | All-cause Mortality/Morbidity | # deaths/1000 person-years (95% CI) | −1.10 | −16.52 | GHG | Change in CO2 eq (Gt) | −0.89 | −29.18 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Dietary Guidelines | DRCD | # Deaths avoided (mean, thousands) in 2050 | −1649.71 | −9.49 | GHG | Food related GHG emissions (Gt CO2-eq) | −0.40 | −18.18 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Dietary Guidelines | CVD | # Deaths avoided (mean, thousands) in 2050 | −835.47 | −22.90 | GHG | Food related GHG emissions (Gt CO2-eq) | −0.40 | −18.18 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Dietary Guidelines | CVD | # Deaths avoided (mean, thousands) in 2050 | −288.95 | −14.09 | GHG | Food related GHG emissions (Gt CO2-eq) | −0.40 | −18.18 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Dietary Guidelines | Cancer | # Deaths avoided (mean, thousands) in 2050 | −395.69 | −9.93 | GHG | Food related GHG emissions (Gt CO2-eq) | −0.40 | −18.18 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Dietary Guidelines | Diabetes | # Deaths avoided (mean, thousands) in 2050 | −129.61 | −31.65 | GHG | Food related GHG emissions (Gt CO2-eq) | −0.40 | −18.18 |
15 | Modelling | Springmann | 2016 | LIC | Dietary Guidelines | DRCD | # Deaths avoided (mean, thousands) in 2050 | −4162.33 | −6.11 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.30 | −27.06 |
15 | Modelling | Springmann | 2016 | LIC | Dietary Guidelines | CVD | # Deaths avoided (mean, thousands) in 2050 | −1057.74 | −10.16 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.30 | −27.06 |
15 | Modelling | Springmann | 2016 | LIC | Dietary Guidelines | CVD | # Deaths avoided (mean, thousands) in 2050 | −1262.27 | −11.63 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.30 | −27.06 |
15 | Modelling | Springmann | 2016 | LIC | Dietary Guidelines | Cancer | # Deaths avoided (mean, thousands) in 2050 | −1617.04 | −18.25 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.30 | −27.06 |
15 | Modelling | Springmann | 2016 | LIC | Dietary Guidelines | Diabetes | # Deaths avoided (mean, thousands) in 2050 | −225.28 | −10.22 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.30 | −27.06 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegan | DRCD | # Deaths avoided (mean, thousands) in 2050 | −2366.71 | −13.61 | GHG | Food related GHG emissions (Gt CO2-eq) | −1.80 | −81.82 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegan | CVD | # Deaths avoided (mean, thousands) in 2050 | −1185.27 | −32.49 | GHG | Food related GHG emissions (Gt CO2-eq) | −1.80 | −81.82 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegan | CVD | # Deaths avoided (mean, thousands) in 2050 | −431.86 | −21.06 | GHG | Food related GHG emissions (Gt CO2-eq) | −1.80 | −81.82 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegan | Cancer | # Deaths avoided (mean, thousands) in 2050 | −594.37 | −14.92 | GHG | Food related GHG emissions (Gt CO2-eq) | −1.80 | −81.82 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegan | Diabetes | # Deaths avoided (mean, thousands) in 2050 | −155.22 | −37.91 | GHG | Food related GHG emissions (Gt CO2-eq) | −1.80 | −81.82 |
15 | Modelling | Springmann | 2016 | LIC | Vegan | DRCD | # Deaths avoided (mean, thousands) in 2050 | −6428.14 | −9.44 | GHG | Food related GHG emissions (Gt CO2-eq) | −5.80 | −68.24 |
15 | Modelling | Springmann | 2016 | LIC | Vegan | CVD | # Deaths avoided (mean, thousands) in 2050 | −2037.25 | −19.56 | GHG | Food related GHG emissions (Gt CO2-eq) | −5.80 | −68.24 |
15 | Modelling | Springmann | 2016 | LIC | Vegan | CVD | # Deaths avoided (mean, thousands) in 2050 | −1912.56 | −17.62 | GHG | Food related GHG emissions (Gt CO2-eq) | −5.80 | −68.24 |
15 | Modelling | Springmann | 2016 | LIC | Vegan | Cancer | # Deaths avoided (mean, thousands) in 2050 | −2074.30 | −23.40 | GHG | Food related GHG emissions (Gt CO2-eq) | −5.80 | −68.24 |
15 | Modelling | Springmann | 2016 | LIC | Vegan | Diabetes | # Deaths avoided (mean, thousands) in 2050 | −404.04 | −18.32 | GHG | Food related GHG emissions (Gt CO2-eq) | −5.80 | −68.24 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegetarian | DRCD | # Deaths avoided (mean, thousands) in 2050 | −2146.73 | −12.35 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.00 | −90.91 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegetarian | CVD | # Deaths avoided (mean, thousands) in 2050 | −1125.83 | −30.86 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.00 | −90.91 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegetarian | CVD | # Deaths avoided (mean, thousands) in 2050 | −384.97 | −18.77 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.00 | −90.91 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegetarian | Cancer | # Deaths avoided (mean, thousands) in 2050 | −486.77 | −12.22 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.00 | −90.91 |
15 | Modelling | Springmann | 2016 | HIC/MIC | Vegetarian | Diabetes | # Deaths avoided (mean, thousands) in 2050 | −149.16 | −36.43 | GHG | Food related GHG emissions (Gt CO2-eq) | −2.00 | −90.91 |
15 | Modelling | Springmann | 2016 | LIC | Vegetarian | DRCD | # Deaths avoided (mean, thousands) in 2050 | −5806.70 | −8.53 | GHG | Food related GHG emissions (Gt CO2-eq) | −6.00 | −70.59 |
15 | Modelling | Springmann | 2016 | LIC | Vegetarian | CVD | # Deaths avoided (mean, thousands) in 2050 | −1851.31 | −17.78 | GHG | Food related GHG emissions (Gt CO2-eq) | −6.00 | −70.59 |
15 | Modelling | Springmann | 2016 | LIC | Vegetarian | CVD | # Deaths avoided (mean, thousands) in 2050 | −1740.27 | −16.04 | GHG | Food related GHG emissions (Gt CO2-eq) | −6.00 | −70.59 |
15 | Modelling | Springmann | 2016 | LIC | Vegetarian | Cancer | # Deaths avoided (mean, thousands) in 2050 | −1858.62 | −20.97 | GHG | Food related GHG emissions (Gt CO2-eq) | −6.00 | −70.59 |
15 | Modelling | Springmann | 2016 | LIC | Vegetarian | Diabetes | # Deaths avoided (mean, thousands) in 2050 | −356.51 | −16.17 | GHG | Food related GHG emissions (Gt CO2-eq) | −6.00 | −70.59 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Flexitarian | DRCD | Average # Deaths avoided | −9803.00 | −24.40 | GHG | MtCO2 eq | −3593.00 | −56.59 |
16 | Modelling | Springmann | 2018a | LIC | Flexitarian | DRCD | Average # Deaths avoided | −1378.00 | −4.99 | GHG | MtCO2 eq | −383.00 | −38.61 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Flexitarian | DRCD | Average # Deaths avoided | −9803.00 | −24.40 | LU | M km^2 | −992.00 | −11.59 |
16 | Modelling | Springmann | 2018a | LIC | Flexitarian | DRCD | Average # Deaths avoided | −1378.00 | −4.99 | LU | M km^2 | 188.00 | 11.09 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Flexitarian | DRCD | Average # Deaths avoided | −9803.00 | −24.40 | NU | GgN | −17 003.00 | −24.10 |
16 | Modelling | Springmann | 2018a | LIC | Flexitarian | DRCD | Average # Deaths avoided | −1378.00 | −4.99 | NU | GgN | −283.00 | −4.65 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Flexitarian | DRCD | Average # Deaths avoided | −9803.00 | −24.40 | PU | GgP | −2199.00 | −19.78 |
16 | Modelling | Springmann | 2018a | LIC | Flexitarian | DRCD | Average # Deaths avoided | −1378.00 | −4.99 | PU | GgP | 81.00 | 9.34 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Flexitarian | DRCD | Average # Deaths avoided | −9803.00 | −24.40 | WU | km^3 | −203.00 | −15.01 |
16 | Modelling | Springmann | 2018a | LIC | Flexitarian | DRCD | Average # Deaths avoided | −1378.00 | −4.99 | WU | km^3 | 42.00 | 27.27 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −10 510.00 | −26.17 | GHG | MtCO2 eq | −4836.00 | −76.17 |
16 | Modelling | Springmann | 2018a | LIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −1420.00 | −5.14 | GHG | MtCO2 eq | −696.00 | −70.16 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −10 510.00 | −26.17 | LU | M km^2 | −1233.00 | −14.41 |
16 | Modelling | Springmann | 2018a | LIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −1420.00 | −5.14 | LU | M km^2 | 150.00 | 8.85 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −10 510.00 | −26.17 | NU | GgN | −17 526.00 | −24.84 |
16 | Modelling | Springmann | 2018a | LIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −1420.00 | −5.14 | NU | GgN | −363.00 | −5.97 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −10 510.00 | −26.17 | PU | GgP | −2259.00 | −20.32 |
16 | Modelling | Springmann | 2018a | LIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −1420.00 | −5.14 | PU | GgP | 74.00 | 8.54 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −10 510.00 | −26.17 | WU | km^3 | −195.00 | −14.42 |
16 | Modelling | Springmann | 2018a | LIC | Pescatarian/increase fish | DRCD | Average # Deaths avoided | −1420.00 | −5.14 | WU | km^3 | 45.00 | 29.22 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −2294.00 | −5.71 | GHG | MtCO2 eq | −1336.00 | −21.04 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −151.00 | −0.55 | GHG | MtCO2 eq | −162.00 | −16.33 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −2294.00 | −5.71 | LU | M km^2 | −97.00 | −1.13 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −151.00 | −0.55 | LU | M km^2 | 222.00 | 13.10 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −2294.00 | −5.71 | NU | GgN | −62.00 | −0.09 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −151.00 | −0.55 | NU | GgN | 212.00 | 3.49 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −2294.00 | −5.71 | PU | GgP | −37.00 | −0.33 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −151.00 | −0.55 | PU | GgP | 37.00 | 4.27 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −2294.00 | −5.71 | WU | km^3 | 52.00 | 3.85 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −151.00 | −0.55 | WU | km^3 | 13.00 | 8.44 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −3891.00 | −9.69 | GHG | MtCO2 eq | −2671.00 | −42.07 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −257.00 | −0.93 | GHG | MtCO2 eq | −389.00 | −39.21 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −3891.00 | −9.69 | LU | M km^2 | −280.00 | −3.27 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −257.00 | −0.93 | LU | M km^2 | 233.00 | 13.75 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −3891.00 | −9.69 | NU | GgN | −747.00 | −1.06 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −257.00 | −0.93 | NU | GgN | 158.00 | 2.60 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −3891.00 | −9.69 | PU | GgP | −158.00 | −1.42 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −257.00 | −0.93 | PU | GgP | 33.00 | 3.81 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −3891.00 | −9.69 | WU | km^3 | 102.00 | 7.54 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −257.00 | −0.93 | WU | km^3 | 23.00 | 14.94 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −5266.00 | −13.11 | GHG | MtCO2 eq | −4007.00 | −63.11 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −333.00 | −1.21 | GHG | MtCO2 eq | −616.00 | −62.10 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −5266.00 | −13.11 | LU | M km^2 | −463.00 | −5.41 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −333.00 | −1.21 | LU | M km^2 | 245.00 | 14.45 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −5266.00 | −13.11 | NU | GgN | −1431.00 | −2.03 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −333.00 | −1.21 | NU | GgN | 104.00 | 1.71 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −5266.00 | −13.11 | PU | GgP | −279.00 | −2.51 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −333.00 | −1.21 | PU | GgP | 29.00 | 3.34 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −5266.00 | −13.11 | WU | km^3 | 154.00 | 11.39 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −333.00 | −1.21 | WU | km^3 | 33.00 | 21.43 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −6587.00 | −16.40 | GHG | MtCO2 eq | −5342.00 | −84.14 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −405.00 | −1.47 | GHG | MtCO2 eq | −842.00 | −84.88 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −6587.00 | −16.40 | LU | M km^2 | −646.00 | −7.55 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −405.00 | −1.47 | LU | M km^2 | 256.00 | 15.10 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −6587.00 | −16.40 | NU | GgN | −2115.00 | −3.00 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −405.00 | −1.47 | NU | GgN | 51.00 | 0.84 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −6587.00 | −16.40 | PU | GgP | −399.00 | −3.59 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −405.00 | −1.47 | PU | GgP | 25.00 | 2.88 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −6587.00 | −16.40 | WU | km^3 | 204.00 | 15.09 |
16 | Modelling | Springmann | 2018a | LIC | Substitute ASF with PSF | DRCD | Average # Deaths avoided | −405.00 | −1.47 | WU | km^3 | 43.00 | 27.92 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegan | DRCD | Average # Deaths avoided | −11 353.00 | −28.26 | GHG | MtCO2 eq | −5516.00 | −86.88 |
16 | Modelling | Springmann | 2018a | LIC | Vegan | DRCD | Average # Deaths avoided | −1449.00 | −5.25 | GHG | MtCO2 eq | −857.00 | −86.39 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegan | DRCD | Average # Deaths avoided | −11 353.00 | −28.26 | LU | M km^2 | −1273.00 | −14.88 |
16 | Modelling | Springmann | 2018a | LIC | Vegan | DRCD | Average # Deaths avoided | −1449.00 | −5.25 | LU | M km^2 | 236.00 | 13.92 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegan | DRCD | Average # Deaths avoided | −11 353.00 | −28.26 | NU | GgN | −18 572.00 | −26.33 |
16 | Modelling | Springmann | 2018a | LIC | Vegan | DRCD | Average # Deaths avoided | −1449.00 | −5.25 | NU | GgN | −457.00 | −7.52 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegan | DRCD | Average # Deaths avoided | −11 353.00 | −28.26 | PU | GgP | −2556.00 | −22.99 |
16 | Modelling | Springmann | 2018a | LIC | Vegan | DRCD | Average # Deaths avoided | −1449.00 | −5.25 | PU | GgP | 58.00 | 6.69 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegan | DRCD | Average # Deaths avoided | −11 353.00 | −28.26 | WU | km^3 | −89.00 | −6.58 |
16 | Modelling | Springmann | 2018a | LIC | Vegan | DRCD | Average # Deaths avoided | −1449.00 | −5.25 | WU | km^3 | 68.00 | 44.16 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegetarian | DRCD | Average # Deaths avoided | −9917.00 | −24.69 | GHG | MtCO2 eq | −4832.00 | −76.11 |
16 | Modelling | Springmann | 2018a | LIC | Vegetarian | DRCD | Average # Deaths avoided | −1400.00 | −5.07 | GHG | MtCO2 eq | −693.00 | −69.86 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegetarian | DRCD | Average # Deaths avoided | −9917.00 | −24.69 | LU | M km^2 | −1172.00 | −13.70 |
16 | Modelling | Springmann | 2018a | LIC | Vegetarian | DRCD | Average # Deaths avoided | −1400.00 | −5.07 | LU | M km^2 | 227.00 | 13.39 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegetarian | DRCD | Average # Deaths avoided | −9917.00 | −24.69 | NU | GgN | −18 430.00 | −26.12 |
16 | Modelling | Springmann | 2018a | LIC | Vegetarian | DRCD | Average # Deaths avoided | −1400.00 | −5.07 | NU | GgN | −356.00 | −5.86 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegetarian | DRCD | Average # Deaths avoided | −9917.00 | −24.69 | PU | GgP | −2422.00 | −21.78 |
16 | Modelling | Springmann | 2018a | LIC | Vegetarian | DRCD | Average # Deaths avoided | −1400.00 | −5.07 | PU | GgP | 70.00 | 8.07 |
16 | Modelling | Springmann | 2018a | HIC/MIC | Vegetarian | DRCD | Average # Deaths avoided | −9917.00 | −24.69 | WU | km^3 | −167.00 | −12.35 |
16 | Modelling | Springmann | 2018a | LIC | Vegetarian | DRCD | Average # Deaths avoided | −1400.00 | −5.07 | WU | km^3 | 52.00 | 33.77 |
17 | Modelling | Springmann | 2018b | HIC/MIC | Increase PSF | DRCD | Average # Deaths avoided | −56.00 | −0.04 | GHG | Change in KtCO2 eq | −610.29 | −18.37 |
17 | Modelling | Springmann | 2018b | HIC/MIC | Reduce ASF no substitute | DRCD | Average # Deaths avoided | −293.00 | −0.20 | GHG | Change in KtCO2 eq | −475.11 | −11.15 |
17 | Modelling | Springmann | 2018b | HIC/MIC | Reduce ASF no substitute | DRCD | Average # Deaths avoided | −15.00 | −0.01 | GHG | Change in KtCO2 eq | 0.52 | 0.20 |
18 | Modelling | Visecchia | 2012 | HIC/MIC | Substitute ASF with PSF | BMI | % population obese | −2.64 | −27.27 | GHG | tCO2 eq emissions per year in Italy | −5 406 000.00 | −1.38 |