We have read the journal’s policy and the authors of this manuscript have the following competing interests: Patrick Brown is the founder and CEO of Impossible Foods, a company developing alternatives to animals in food-production. Michael Eisen is an advisor to Impossible Foods. Both are shareholders in the company and thus stand to benefit financially from reduction of animal agriculture. Michael Eisen and Patrick Brown are co-founders and former members of the Board of Directors of the Public Library of Science.
Animal agriculture contributes significantly to global warming through ongoing emissions of the potent greenhouse gases methane and nitrous oxide, and displacement of biomass carbon on the land used to support livestock. However, because estimates of the magnitude of the effect of ending animal agriculture often focus on only one factor, the full potential benefit of a more radical change remains underappreciated. Here we quantify the full “climate opportunity cost” of current global livestock production, by modeling the combined, long-term effects of emission reductions and biomass recovery that would be unlocked by a phaseout of animal agriculture. We show that, even in the absence of any other emission reductions, persistent drops in atmospheric methane and nitrous oxide levels, and slower carbon dioxide accumulation, following a phaseout of livestock production would, through the end of the century, have the same cumulative effect on the warming potential of the atmosphere as a 25 gigaton per year reduction in anthropogenic CO2 emissions, providing half of the net emission reductions necessary to limit warming to 2°C. The magnitude and rapidity of these potential effects should place the reduction or elimination of animal agriculture at the forefront of strategies for averting disastrous climate change.
The use of animals as a food-production technology has well-recognized negative impacts on our climate. The historical reduction in terrestrial biomass as native ecosystems were transformed to support grazing livestock and the cultivation of feed and forage crops accounts for as much as a third of all anthropogenic CO2 emissions to date [
Solving the climate crisis requires massive cuts to GHG emissions from transportation and energy production. But even in the context of large-scale reduction in emissions from other sources, major cuts in food-linked emissions are likely necessary by 2075 to limit global warming to 1.5°C [
Nutritionally balanced plant-dominated diets are common, healthy and diverse [
The Food and Agriculture Organization (FAO) of the United Nations estimates that emissions from animal agriculture represent around 7.1 Gt CO2eq per year [
However, a substantial fraction of the emissions impact of animal agriculture comes from methane (CH4) and nitrous oxide (N2O), which decay far more rapidly than CO2 (the half-lives of CH4 and N2O are around 9 and 115 years, respectively), and recent studies have highlighted the need to consider these atmospheric dynamics when assessing their impact [
Our goal here was to accurately quantify the full impact of current animal agriculture on the climate, taking into account the currently unrealized opportunities for emission reduction and biomass recovery together, and explicitly considering the impact of their kinetics on warming. Our approach differs from other recent studies [
We used publicly available, systematic data on livestock production in 2019 [
We calculated the combined impact of reduced emissions and biomass recovery by comparing the cumulative reduction, relative to current emission levels, of the global warming potential of GHGs in the atmosphere for the remainder of the 21st century under different livestock replacement scenarios to those that would be achieved by constant annual reductions in CO2 emissions.
We implemented a simple climate model that projects atmospheric GHG levels from 2020 to 2100 based on a time series of annual emissions of CO2, CH4 and N2O and a limited set of parameters. We then compared various hypothetical dietary perturbations to a “business as usual” (BAU) reference in which emissions remain fixed at 2019 levels, based on global emissions data from FAOSTAT [
The dietary scenarios include the immediate replacement of all animal agriculture with a plant-only diet (IMM-POD), a more gradual transition, over a period of 15 years, to a plant-only diet (PHASE-POD), and versions of each where only specific animal products were replaced.
We updated estimates of global emissions from animal agriculture using country-, species- and product-specific emission intensities from the Global Livestock Environmental Assessment Model [
Based on this analysis, in 2019 (the most recent year for which full data are available), global production of animal-derived foods led to direct emissions of 1.6 Gt CO2, due primarily to energy use (as our model assumes constant overall rates of consumption, we excluded emissions due to land clearing, which are associated with agricultural expansion), 120 Mt CH4 due primarily to enteric fermentation and manure management, and 7.0 Mt N2O due primarily to fertilization of feed crops and manure management (
Total CO2 equivalent emissions (A) assembled from species, product and country-specific production data from FAOSTAT for 2019 and species, product, region and greenhouse-gas specific emissions data from GLEAM [
These numbers are broadly consistent with other recent estimates [
We modeled the recovery of biomass on land currently used in livestock production using data from [
We assumed in all these hypothetical scenarios that non-agricultural emissions would remain constant; that food from livestock is replaced by a diverse plant based diet; and that, when land is removed from livestock production, the conversion of atmospheric CO2 into terrestrial biomass occurs linearly over the subsequent thirty years. (We consider alternative assumptions in the “Sensitivity Analysis” section below).
We emphasize that we are not predicting what will happen to global diets. Rather we are projecting simplified scenarios of dietary change forward through time to characterize and quantify the climate impact of current animal agriculture production. Our climate model is intentionally simple, considering only the partition of terrestrial emissions into the atmosphere, and the decay of methane and nitrous oxide, although it replicates the qualitative behavior of widely used MAGICC6 [
(A) Projected annual emissions of CO2, CH4 and N2O for Business as Usual (red) and PHASEPOD (green) assuming a 15 year transition to new diet and 30 year carbon recovery. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario.
The impact of PHASE-POD on CO2 emissions would be greatest in the period between 2030 and 2060, when biomass recovery on land previously occupied by livestock or feed crops reaches its peak, slowing the rise of atmospheric CO2 levels during this interval.
Atmospheric CH4 and N2O levels continue to increase in both BAU and PHASE-POD during the transition period, but begin to drop in PHASE-POD as the abatement of animal agriculture-linked emissions accelerates. CH4, with a half-life in the atmosphere of around 9 years, approaches a new and lower steady-state level towards the end of the century, while N2O, with a half-life of around 115 years, does so over a longer time-scale.
To capture the combined global warming impact of the changing levels of these GHGs, we calculated radiative forcing (RF), the reduction in radiative cooling by GHG absorption of infrared radiation, using the formulae described in [
Effect of eliminating emissions linked to animal agriculture and of biomass recovery on land currently used in animal agriculture on Radiative Forcing (RF), a measure of the instantaneous warming potential of the atmosphere. RF values computed from atmospheric concentrations in by formula of [
By the end of the century the RF under PHASE-POD would be 3.8 Wm-2 compared to 4.9 Wm-2 for BAU, a reduction in RF equivalent to what would be achieved by eliminating 1,680 Gt of CO2 emissions (
In 2010, the climate modeling community defined a series of four “Representative Concentration Pathways” that capture a wide range of future warming scenarios, leading to 2100 RF levels of 8.5, 6.0, 4.5 and 2.6 Wm-2 (which is approximately the RF of current atmospheric greenhouse gas levels), respectively [
Reducing 2100 RF from 4.9 Wm-2 under BAU to 2.6 Wm-2 would require a reduction of atmospheric CO2 levels by 204 ppm, equivalent to 3,230 Gt of CO2 emissions (
Using projected CH4 and N2O levels in 2100 under business as usual diet as a baseline for RF calculation, we computed the CO2 reductions necessary to reduce RF from the business as usual diet level of RF = 1.31 to the bovid-free diet level of RF = 4.09 (1300 Gt CO2), the plant-only diet level of RF = 3.83 (1680 Gt CO2), the 2.0° C global warming target of RF = 2.6 (3230 Gt CO2) and the 1.5° C global warming target of RF = 1.9 (3980 Gt CO2). For this analysis we used a corrected RF that accounts for the absence of other gases in our calculation by training a linear regression model on published MAGICC6 output to estimate from CO2, CH4 and N2O levels the residual RF impact of other gases.
Thus the 1,680 Gt of CO2 equivalent emissions reductions from the phased elimination of animal agriculture, would, without any other intervention to reduce GHG emissions, achieve 52% of the net GHG emissions reductions necessary to reach the 2100 RF target of 2.6 Wm-2 and 42% of the emissions reductions necessary to reach the 1.9 Wm-2 target [
While widely used, such single point estimates of radiative forcing tell an incomplete story, as temperature change, and other climate impacts, depend cumulatively on the temporal trajectories of changing atmospheric greenhouse gas levels.
To capture such dynamic effects, we computed, for each dietary scenario, the integral with respect to time of the RF difference between the scenario and BAU, from 2021 (the start of the intervention in this model) to a given year “y”. We designate this cumulative RF difference for year
Critical features of aCO2eq are that it operates directly on RF inferred from combined trajectories of atmospheric levels of all GHGs, and thus can directly capture the effects of arbitrarily complex interventions, and that it equates the cumulative RF impact of an intervention over a specified time window to a single number: the sustained reductions in CO2 emissions that would have the same cumulative impact.
aCO2eq is closely related to, and motivated by similar goals as, CO2-forcing-equivalent (CO2-fe) emissions [
Bars show sustained reduction in annual CO2 emissions necessary to equal cumulative reduction in radiative forcing of the given scenario in 2050 (blue) and 2100 (orange).
We next computed aCO2eq2100 for the 15 year phaseout of individual animal products and product categories (Figs
(A) Total annualized CO2 equivalents through 2100, aCO2eq2100, for all tracked animal products, and Emission Intensities based on aCO2eq2100 on a per unit production (B) or per unit protein (C) basis. For (B) and (C) we also convert the values to driving equivalents using a value of 0.254 kg CO2eq per km driven of an internal combustion engine fueled sedan in the United States from life cycle analyses described in [
Species | Commodity | Primary Production | Protein Production | Emissions CO2 | Emissions CH4 | Emissions N2O | Land Use | aCO2eq | Emissions Intensity | Emissions Intensity | Driving Equivalents |
---|---|---|---|---|---|---|---|---|---|---|---|
tonnes | tonnes protein | Mt | Mt | Mt | Mkm^2 | Gt/year | kg aCO2eq per kg | kg aCO2eq per kg protein | km driven per kg | ||
Buffalo | Meat | 4,290,212 | 619,200 | 29 | 5.00 | 0.20 | 1.0 | -1.0 | -354 | -1635 | 1394 |
Cattle | Meat | 67,893,363 | 10,435,590 | 236 | 49.30 | 2.41 | 17.1 | -13.5 | -298 | -1292 | 1172 |
Sheep | Meat | 9,648,245 | 1,354,398 | 32 | 5.02 | 0.33 | 2.5 | -1.8 | -286 | -1353 | 1126 |
Goats | Meat | 6,128,372 | 821,383 | 21 | 3.34 | 0.11 | 0.8 | -0.7 | -180 | -893 | 709 |
Pigs | Meat | 110,102,495 | 14,447,438 | 278 | 7.19 | 0.62 | 1.6 | -1.7 | -23 | -119 | 92 |
Chickens | Meat | 123,898,557 | 17,393,440 | 306 | 0.29 | 0.52 | 1.3 | -1.0 | -12 | -56 | 47 |
Ducks | Meat | 7,363,110 | 1,044,797 | 27 | 0.02 | 0.05 | 0.1 | -0.1 | -16 | -73 | 62 |
Buffalo | Milk | 133,752,296 | 4,510,017 | 119 | 10.87 | 0.45 | 1.2 | -1.7 | -13 | -376 | 50 |
Cattle | Milk | 712,883,270 | 23,889,273 | 338 | 37.63 | 1.78 | 6.3 | -6.9 | -10 | -287 | 38 |
Sheep | Milk | 10,172,020 | 624,048 | 10 | 1.72 | 0.12 | 0.1 | -0.2 | -23 | -385 | 92 |
Goats | Milk | 18,752,379 | 702,585 | 10 | 1.74 | 0.06 | 0.2 | -0.2 | -13 | -351 | 52 |
Chickens | Eggs | 88,361,696 | 10,982,733 | 159 | 0.57 | 0.35 | 0.6 | -0.5 | -6 | -49 | 24 |
Primary production data aggregated from FAOSTAT for 2019. Protein production data calculated from primary production data and protein conversion factors inferred from GLEAM. Emissions data based on protein production data and emission intensities from GLEAM. Land use data calculated from FAOSTAT protein production data and product-specific land use data from [
These product-specific aCO2eq’s can be interpreted on a per product unit (
As with the total numbers, ruminant meat has the largest emissions intensities, per unit (289 kg CO2eq per kg consumer product) and per protein (1,279 kg CO2eq per kg protein). The most efficient animal products on a per protein basis are chicken meat (56 kg CO2eq per kg protein) and eggs (49 kg CO2eq per kg protein), roughly 25 times lower than per protein emissions intensities for ruminant meat.
To connect these numbers to other sources of GHGs, we converted these emissions intensities to distances one would have to drive a typical 2021 model gas-fueled passenger car to produce the same emissions, based on a full life-cycle analysis of auto emissions [
Our default model assumes a gradual phaseout of animal agriculture over a period of 15 years, producing an aCO2eq2100 of -24.8 Gt/year. If we assume immediate elimination (
The grey line in each plot is PHASE-POD, the default scenario of 15 year phaseout, 30 year carbon recovery, livestock emissions from FAOSTAT, and a diverse plant replacement diet based on [
Our default model also assumes that biomass will recover linearly over 30 years, following [
Estimates of the biomass recovery potential of land currently used for animal agriculture have a high degree of uncertainty. Using the low estimate (
A major area of uncertainty not addressed by [
Our estimate of global emissions due to animal agriculture based on FAO data and analyses of 1.6 Gt CO2, 122 Mt CH4 and 7.0 Mt N2O differ in key ways from recent estimates of [
The models described above assume that the protein currently obtained from animal products would be replaced with a diverse plant based diet, scaled to replace animal products on a protein basis, and agriculture emissions data from FAOSTAT. We considered as an alternative emissions projected from a diverse plant based diet based on data from [
In some areas, the removal of land from use in animal agriculture may lead to an increase in wild ruminant population. Although this is difficult to model globally, this would offset some of the beneficial impacts of reductions in methane emissions from livestock [
This analysis only considered consumption of terrestrial animal products, neglecting emissions and land use (via feed production) associated with seafood capture and aquaculture. While the land and emissions impact of seafood consumption has received comparably little attention, several studies have pointed to at least 500 Mt of CO2 equivalent emissions per year from seafood [
Widely used climate models consider temporal and spatial variation in emissions; feedback between a changing climate and anthropogenic and natural emissions, carbon sequestration, atmospheric chemistry and warming potential; the impact of climate on human social, political and economic behavior. Ours does not. We ran our model on emissions data from the pathways described in [
Our analysis has provided a quantitative estimate of the potential climate impact of a hypothetical, radical global change in diet and agricultural systems. We have shown that the combined benefits of removing major global sources of CH4 and N2O, and allowing biomass to recover on the vast areas of land currently used to raise and feed livestock, would be equivalent to a sustained reduction of 25 Gt/year of CO2 emissions.
Crucially eliminating the use of animals as food technology would produce substantial negative emissions of all three major GHGs, a necessity, as even the complete replacement of fossil fuel combustion in energy production and transportation will no longer be enough to prevent warming of 1.5°C [
The transition away from animal agriculture will face many obstacles and create many challenges. Meat, dairy and eggs are a major component of global human diets [
Although animal products currently provide, according to the most recent data from FAOSTAT, 18% of the calories, 40% of the protein and 45% of the fat in the human food supply, they are not necessary to feed the global population. Existing crops could replace the calories, protein and fat from animals with a vastly reduced land, water, GHG and biodiversity impact, requiring only minor adjustments to optimize nutrition [
The economic and social impacts of a global transition to a plant based diet would be acute in many regions and locales [
Although, as discussed above, there are many uncertainties in our estimates, our assumption that “business as usual” means animal agriculture will continue at current levels was highly conservative, as rising incomes are driving ongoing growth in global animal product consumption [
While such an expansion may seem implausible, even partial destruction of Earth’s critical remaining native ecosystems would have catastrophic impacts not just on the climate, but on global biodiversity [
Given these realities, even with the many challenges that upending a trillion dollar a year business and transforming the diets of seven billion people presents, it is surprising that changes in food production and consumption are not at the forefront of proposed strategies for fighting climate change. Although all of the strategies presented as part of the recent Intergovernmental Panel on Climate Change (IPCC) report on steps needed to keep global warming below 1.5˚C [
We downloaded data for the Shared Socioeconomic Pathways (SSPs) [
Even if the negative emission technology the IPCC anticipates, BECCS (bio-energy combined with carbon capture and storage), proves to be viable at scale, it will require large amounts of land [
It is important to emphasize that, as great as the potential climate impact of ending animal agriculture may be, even if it occurred, and even if all of the benefits we anticipate were realized, it would not be enough on its own to prevent catastrophic global warming. Rather we have shown that global dietary change provides a powerful complement to the indispensable transition from fossil fuels to renewable energy systems. The challenge we face is not choosing which to pursue, but rather in determining how best to overcome the many social, economic and political challenges incumbent in implementing both as rapidly as possible.
Analyses were carried out in Python using Jupyter notebooks. All data, analyses and results presented here are available at
We obtained country, species, herd and product type specific CO2, CH4 and N2O emission data for terrestrial livestock from the public version of GLEAM 2.0 [
We obtained livestock production data for 2019 (the most recent year available) from the “Production_LivestockPrimary” datafile in FAOSTAT [
We scaled the GLEAM emission data to current production data from FAOSTAT, using GLEAM data for entire herds based on carcass weight for meat, and production weight for milk and eggs. As GLEAM does not provide data for ducks, we used values for chicken. The scaling was done using country-specific livestock production data from FAOSTAT and regional data from GLEAM.
We combined livestock production data with average species and product-specific land use data from [
The total land use for animal agriculture inferred from this analysis is 33.7 million km2, almost identical to the 33.2 million km2 estimated by [
We used the Environment_Emissions_by_Sector_E_All_Data_(Normalized) data table from FAOSTAT, projecting from the most recent year of 2017 to 2019 by assuming that the average annual growth from 2000 to 2017 continued in 2018 and 2019.
We modeled agricultural emissions under a business as usual (BAU) diet as remaining at 2019 levels. When modeling reductions in livestock consumption, we assumed protein from livestock products would be replaced with the equivalent amount of protein from current food crops, and used per unit protein emission intensities computed from FAOSTAT to infer emissions from this replacement diet. As an alternative we used emission intensities from [
In all scenarios we assume annual non-agricultural emissions remain fixed at 2019 levels through 2100. For a BAU diet we added in total agricultural emissions from the FAOSTAT “Emissions Shares” data table, effectively fixing total emissions at 2019 levels. We assumed a 15 year phaseout of animal agriculture with an accelerated rate of conversion from BAU to PHASE-POD. The specific formula we use is
We also include in the supplemental data a version of the analysis in which the hypothetical transition is instantaneous (IMM-POD).
As the transition from BAU to PHASE-POD occurs, agriculture linked emissions are set to
We assume that, when animal-derived food consumption is reduced in a year by a fraction
The total mass of gas in the atmosphere is 5.136 * 1021 g, at a mean molecular weight of 28.97 g/mole [
We therefore use conversions from mass in Gt to ppb/ppm as follows:
Both CH4 and N2O decay at appreciable rates, with half-lives of approximately 9 years for CH4 [
We ran projections on an annual basis starting in 2020 and continuing through 2100. For each gas:
We adopt the commonly used formula for radiative forcing (RF) which derives from [
Given atmospheric concentration of
We define the combined emissions and land carbon opportunity cost (ELCOC) of animal agriculture as 2
The factor of 2 accounts for the half of CO2 emissions that go to terrestrial sinks.
As the RF calculation used in MAGICC6 account for other gases and effects beyond the three gases used here, we used multivariate linear regression as implemented in the Python package scikit-learn to predict the complete RF output of MAGICC6 using data downloaded from the Shared Socioeconomic Pathways (SSPs) [
In the SSP file:
C = Diagnostics|MAGICC6|Concentration|CO2
M = Diagnostics|MAGICC6|Concentration|CH4
N = Diagnostics|MAGICC6|Concentration|N2O
MAGICC6 RF = Diagnostics|MAGICC6|Forcing
To compute aCO2eqy, the annual CO2 equivalent emission change of each emissions scenario, we first ran scenarios in which annual CO2 emissions were reduced from 50 Gt/year to 1 Gt/year in increments of 1 Gt/year, then from 1 Gt/year to 10 Mt/year in increments of 10 Mt/year, and then from 1 Mt/year to 100 kT/year in increments of 100 kT/year. For each of these calibration scenarios, and for all years
For each multi-gas emissions scenario, we similarly computed CRFDy, and determined what constant level of reduction in annual CO2 emissions alone by interpolation using the CRFDy of the calibration scenarios, and designate this annual CO
To compute per product unit and per protein emissions equivalents, we divided aCO2eq2100 for immediate elimination of the product (in kg CO2eq/year) by the annual production of the product (in kg production/year) yielding a per product unit emission equivalent measured in kg CO2eq per kg production.
For example, assuming, as our model does, that emissions and land use scale with consumption, if annual beef production were reduced by one tonne (1,000 kg) per year, it would result in corresponding annual reductions of -3,476 kg CO2, -726 kg CH4 and -36 kg N2O, and would initiate 30 year biomass recovery of 6,050,000 kg of CO2 equivalent carbon on 25.2 ha of land.
The cumulative reduction in RF, through 2100, of such annual emissions reductions and biomass recovery would be equivalent to a CO2 emission reduction of 199,000 kg/year. The ratio of these two rates, -199,000 kg CO2eq/year over 1,000 kg beef/year yields -199 kg CO2eq per kg beef as a measure of the warming impact of one kg of beef. Adjusting this for the dressing percentage of beef (the values reported by FAO, and used in these calculations, are carcass weight, of which only approximately ⅔ ends up as a consumer product) yields the values shown in
For all meat products we scaled the production amount by a typical dressing percentage of ⅔ to convert to consumer product units. For protein unit equivalents we used protein yields from GLEAM. To convert to driving equivalents we used a value of .254 kg CO2eq per km driven taken from life cycle analyses reviewed in [
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for each scenario. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiative Forcing (RF) inferred from atmospheric concentrations in (B) by formula of [
(PDF)
Effect of eliminating emissions linked to animal agriculture and of biomass recovery on land currently used in animal agriculture on Radiative Forcing (RF), a measure of the instantaneous warming potential of the atmosphere. RF values computed from atmospheric concentrations in by formula of [
(PDF)
Comparison of effects of PHASE-POD (a 15 year phaseout of animal agriculture) and a linear drawdown of all anthropogenic CO2 emissions between 2030 and 2050, and the two combined, on Radiative Forcing (RF), a measure of the instantaneous warming potential of the atmosphere. RF values computed from atmospheric concentrations in by formula of [
(PDF)
We define the Emission and Land Carbon Opportunity Cost of animal agriculture as the total CO2 reduction necessary to lower the RF in 2100 from the level estimated for a business as usual (BAU) diet to the level estimated for a plant only diet (POD). For these calculations we fix the CH4 and N2O levels in the RF calculation at those estimated for the BAU diet in 2100 and adjust CO2 levels to reach the target RF. We also calculate ELCOC for just bovid sourced foods and determine the emission reductions necessary to reach RF’s of 2.6 and 1.9, often cited as targets for limiting warming to 2.0˚C and 1.5˚C respectively. (A) Shows the results for RF directly calculated from CO2, CH4 and N2O, while (B) shows an RF adjusted for other gases using multivariate linear regression on MAGICC6 output downloaded from the SSP database.
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for shown scenarios. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Radiation Forcing. (D) Cumulative difference between scenario and BAU of Radiative Forcing.
(PDF)
(A) Projected annual emissions of CO2, CH4 and N2O for shown scenarios. (B) Projected atmospheric concentrations of CO2, CH4 and N2O under each emission scenario. (C) Cumulative difference between scenario and BAU of Radiative Forcing.
(PDF)
The equivalent CO2 emission reductions associated with different interventions in animal agriculture, aCO2eq, vary with the time window over which cumulative warming impact is evaluated. These plots show, for immediate elimination of animal agriculture (IMM-POD) and a 15-year phaseout (PHASE-POD) how aCO2eqy which is the aCO2eq from 2021 to year y, varies with y. Because all of the changes in IMM-POD are implemented immediately, its effect is biggest as it is implemented and declines over longer time horizons (the decline in the first 30 years, when biomass recovery is occurring at a constant high right, is due to the slowing of annual decreases in atmospheric CH4 and N2O levels as they asymptotically approach new equilibria). In contrast, PHASE-POD builds slowly,reaching a maximum around 2060 when biomass recovery peaks.
(PDF)
We calculated the (A) total annualized CO2 equivalents through 2100, aCO2eq2100, for all tracked animal products, and the aCO2eq2100 per unit production (B) or per unit protein (C). For (B) and (C) we also convert the values to driving equivalents, assuming cars that get 10.6 km per liter of gas.
(PDF)
The grey line in each plot is PHASE-POD, the default scenario of 15 year phaseout, 30 year carbon recovery, livestock emissions from FAOSTAT, and a diverse plant replacement diet based on [
(PDF)
PCLM-D-21-00017Ending animal agriculture would stabilize greenhouse gas levels for 30 years and offset 70 percent of CO2 emissions this century
PLOS Climate
Dear Dr. Eisen,
Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.
This paper has a clear potential to become a strong and impactful paper. The main issues that need to be addressed include a more rigorous discussion in relation to sensitivity tests for key assumptions that are fundamental to drive the results of the paper and the addition of corroboration and nuance in relation to the way that the results are presented. The latter includes discussing some of the trade-offs, barriers, and real plausibility in relation to eliminating animal agriculture.
Please submit your revised manuscript by Oct 17 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at
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Reviewer #1: The manuscript is technically sound, and the data supports the conclusions. However, as I have detailed in my reviewer report, I think the authors need to more rigorously discuss/sensitivity test several of the assumptions in their methodology, and this is why I believe the manuscript currently only partly meets publication criteria. For this reason, and several other more minor comments detailed in my report where I think data used needs to be better justified or changed (e.g their analogy to driving, aquaculture), the statistical analysis is also partly satisfied at present. I have tried to be as helpful as possible in my comments regarding what the authors need to do to meet these criteria, and I am confident that once this is complete, they will have a strong and impactful paper.
Reviewer #2: The topic of the paper, how to tackle the climate emergency and how reduced or eliminated animal agriculture can contribute, is highly relevant.
The singular focus of the paper on animal agriculture as THE climate change solution should be nuanced, as many sectors and processes contribute to GHG emissions. It is not possible to tackle climate change by focusing on one single solution (or even one single sector). In addition, some of the trade-offs and barriers/challenges in relation to eliminating animal agriculture need to be brought more clearly to the readers' attention.
Most importantly, however, the results as presented (e.g. offset of 70% possible by eliminating animal agriculture) that could justify this strong focus, need to be corroborated and presented with much more nuance. The methodology followed in the manuscript combines many different calculations based on many assumptions and with large separate uncertainties associated with it.
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PCLM-D-21-00017R1
Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO2 emissions
PLOS Climate
Dear Dr. Eisen,
Thank you again for your submission to PLOS Climate.
As relayed to you by email by Ana Maria, the reviewers identified some issues with the R1 version of your manuscript that need to be addressed before your paper can be editorially accepted. I understand that you have supplied a revised version of the manuscript incorporating responses to these points by email yesterday, but am issuing a Minor Revision to enable you to submit these new files into Editorial Manager. Once we have received the revised manuscript in the submission system, we will expedite a final editorial decision.
Thank you very much for your patience and understanding in this matter. We look forward to receiving the revised manuscript as soon as you are able.
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Jamie Males
Executive Editor
PLOS Climate
on behalf of
Ana Maria Loboguerrero
Section Editor
PLOS Climate
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PCLM-D-21-00017R2
Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO2 emissions this century
PLOS Climate
Dear Dr. Eisen,
Thank you again for submitting your manuscript to PLOS Climate and for your patience in waiting for this decision. After careful consideration, we feel that some minor adjustments are required to ensure the manuscript fully meets PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the following points:
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Jamie Males
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PLOS Climate
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PLOS Climate
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***
***
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***
I commend the authors on the efforts taken to fully address comments from me and the other reviewer. It is good to see them thoroughly engaged with, including the addition of sensitivity testing in Figure 7, and extensively in the supplementary figures, which I think makes the study much more robust and emphasizes that the conclusion of the study withstands under sensitivity testing of the assumptions. As it appears in the new results, the assumption of what plant proteins replace animal products does not have a particularly sizeable impact on the results, which I think is a useful result.
Reviewing again the results, I particularly like the contribution this study makes in Figure 8, alongside the SSPs, where there has been a lack of radical scenarios concerning meat and dairy consumption.
I believe that this study now meets the necessary conditions for publication with PLOS Climate, upon addressing a small number of comments on the new version of the manuscript below.
Not sure I agree with the removal of “this century” from the title as it details what that 68% refers to. I think you interchange between “phase out” and “phaseout” – consistency (also consistency of “plant-based” and “plant based”, e.g on PDF page 106). PDF page 89: change to “often focus on only one factor”. A thought on the Hayek et al. (2021) estimates: we should expect a rise in non-livestock ruminants under ecosystem and biodiversity recovery (e.g bison), though that would of course be hard to model and likely to have only a modest reduction in the potential GHG and warming savings of a plant-based diet transition. Had you also thought about this? It could be worth noting in the text. Good to see the strong emphasis that this debate typically overlooks ongoing emissions, not the reversible warming impact (biomass recovery). Regarding the use of Twine (2021), on my previous recommendation, I since spoke to the author of that paper and it appears he made an error, understandably given the inconsistent use of GWP weightings used by the FAO from which data was used. See Richard Twine’s blog post for more
"
Page 94 of PDF: “decay in the atmosphere on relevant timescales” – doesn’t seem worded correctly. They decay on a different timescale to CO2 is the relevant point.
Page 94 of PDF: “How… would alter 2019 net anthropogenic emissions” – I don’t think the language quite works here. The impact of phaseout isn’t altering emissions in 2019, but you are comparing the impact to emissions in 2019. Page 97 of PDF: “Coming from our not including”, suggest change to ‘our exclusion of’ Page 101 of PDF: I am trying to understand the final sentence here, and find myself confused. PHASE-POD has the same effect, through the end of the century, as a 68% cut in BAU CO2 emissions. This is comparable to eliminating all CO2 by 2050. It seems to me that CO2 elimination by 2050 is a bigger impact than a 68% cut over the century, because after 2050 that CO2 level is zero out to 2100 in the former case. I think this confusion is arising because of comparing emission totals over different time horizons? Also, eliminating all CO2 by 2050 is essentially meeting the Paris Agreement but you state that your results find the phaseout of animal products alone would not be sufficient. Is this because you are only looking at eliminating CO2, not CH4 and N20. It is likely my misunderstanding, but please consider how you can improve clarity to the reader here. Page 100 of PDF: sub-title refers to offsetting 65% but elsewhere this is written as 68%. Page 102 of PDF: use of “percent” (“62 percent”), consistency needed, with ‘%’ used elsewhere. This also appears elsewhere (e.g “38 percent”, also on PDF page 102). Page 103 of PDF: “2021 model year” – change to ‘2021 model’? ‘year’ seems unnecessary. Much better discussion of the carbon recovery of land. Page 105 of PDF: Change “from Hayek” to “from Hayek et al. (2021)”. Page 106 of PDF: “emissions projected emissions” – typo here? 0.58 to 1.47 – this is per year right? And 1.47 was the initial year of disturbance? Page 109 of PDF: is that “400 million” in the Springmann et al (2018) reference, or does it need referencing separately? It’s a good and supportive statistic, as long as it is referenced. Page 109 of PDF: Good to emphasize the conservative nature of your estimate regarding projected increase in meat consumption. If you wanted to cite it, Searchinger et al (2019) projects a 70% increase in animal-based foods between 2010 and 2050. Page 111 of PDF: “they anticipate”: change to ‘the IPCC’. Later in this paragraph you state “Thus, all currently viable solutions” after having (very reasonably) questioned viability of BECCS at scale. Perhaps rephrase to “Thus, all potential solutions” to avoid contradiction. Page 122 of PDF: “Mt” used and then “MT” later in sentence.
The topic of the paper, how to tackle the climate emergency and how reduced or eliminated animal agriculture can contribute, is highly relevant. The singular focus of the paper on animal agriculture as THE climate change solution should be nuanced, as many sectors and processes contribute to GHG emissions. It is not possible to tackle climate change by focusing on one single solution (or even one single sector). In addition, some of the trade-offs and barriers/challenges in relation to eliminating animal agriculture need to be brought more clearly to the readers' attention.
Although the focus of this paper is on animal agriculture, it was not our intention to suggest that it is the only solution. Indeed in both the text and analyses we emphasized that even if the full extent of the potential we highlight here were realized, it would still not be enough to solve the climate crisis. We have made this point clearer at multiple points in the manuscript.
*** This is highly appreciated
We also explore the challenges to eliminating animal agriculture in more detail.
*** Thanks
Most importantly, however, the results as presented (e.g. offset of 70% possible by eliminating animal agriculture) that could justify this strong focus, need to be corroborated and presented with much more nuance. The methodology followed in the manuscript combines many different calculations based on many assumptions and with large separate uncertainties associated with it.
As discussed above, we have added a section exploring the sensitivity of our results to assumptions made in our analyses so that readers can get a better sense of the degree of uncertainty in our calculations and its sources.
*** I believe the section exploring the sensitivities to the assumptions adds value to the paper
More detailed comments (about methodology as well as the other parts of the manuscript) are attached.
We have responded below to the reviewer’s comments and modified the manuscript where appropriate.
General: The introduction is a bit too "one-sided". It is important to also mention some of the positive aspects of animal production (e.g. contribution to economies, livelihoods, nutrition, soil fertility, …) -- as these would need to be taken into account as "opportunity cost" if livestock production was to stop. This is very much in line with the concept of one of the key references cited in the introduction of the paper, Hayek et al., who also explicitly refer to the "carbon opportunity cost" of using the lands for extensive food production .
We have addressed this briefly in the introduction.
*** Thanks
A wide range of sectors and processes contribute to global emissions and there is thus not one single solution (or even sector to improve) in order to tackle climate change. This point needs to be clearly stated.
In the introduction we reemphasize that eliminating animal agriculture alone would not solve the climate crisis.
*** Thanks
line 4: I do not find these figures in Hayek et al. and Strassburg et al. Can you specify page/line numbers?
Both Hayek and Strassburg estimate that historical land conversation is responsible for the release of ~800 Gt of CO2. Direct CO2 emissions from fossil fuels are estimated at 1,650 Gt of CO2 (Friedlingstein et al., 2020). Hence ~⅓ of emissions have been due to land conversion.
*** I do not seem to find the estimate of historical land conversion in Hayek et al., but maybe I am overseeing it somewhere. Line 4 in the manuscript refers specifically to “The historical reduction in terrestrial biomass as native ecosystems
p.5, last two lines: The figures mentioned in Hayek et al. and Strassburg et al. are lower.
Hayek, 3rd paragraph: Here we quantify the total carbon opportunity cost of animal agricultural production to be 152.5 (94.2–207.1) gigatons of carbon (GtC) in living plant biomass across all continents and biomes (Fig. 1 and Supplementary Table 3). We approximated the potential for CO2 removal in soil and litter as an additional 63GtC (Supplementary Table 4).
152.5 + 63 = 215.5 Gt C, which is 788 Gt CO2.
*** Thanks for pointing this out. I also appreciate the addition of “
Strassburg, see Figure 2b, which reports a maximum capacity of ~900 Gt CO2 recovery. We do not use the Strassburg data directly, rather it serves as an independent confirmation of the value from Hayek.
*** As already noted above, Strassburg et al. explicitly consider all lands converted from natural ecosystems, including those that were converted to croplands. So, if you want to keep the last part of the sentence (i.e. “currentLY devoted to livestock production”), I’d suggest you do not cite Strassburg et al. here.
p.5, line 4: Can you clarify how the figure of 1,400 Gt was arrived at?
800 Gt from carbon recovery + 80 years * 7.5 Gt / year = 1,400 Gt.
*** Thanks for the clarification.
p.5, line 6: The statement "warming is cumulative" needs a bit of nuancing, as it is only applicable to long-lived climate pollutions (i.e. not to methane).
We have rephrased for clarity. Our intention was to point out that because warming is cumulative, the timing of an increase or decrease in RF due to changes in emissions matters. This is true irrespective of the gas whose levels are being adjusted. For long-lived gases like CO2 the cumulative effect manifests with pulses of positive/negative emissions. For short-lived gases like CH4 it requires sustained changes to see the effect. But in either case the timing of when changes take place matters.
p.5, line 5-8: Some more detailed explanation underpinning the statement that "this understates impact of dietary change on global warming" would be helpful.
We have rephrased for clarity.
*** Thanks for rephrasing and for adding a few words explicitly explaining the short-lived nature of methane.
*** A few suggestions:
“…which…decay in the atmosphere on I feel that the last part of the last sentence “having a greater cumulative effect on warming” is confusing in relation to short-lived GHGs; as it is the long-lived ones that have a cumulative effect on warming. I would thus suggest you edit the sentence as follows: “Of critical importance, many of the beneficial effects on greenhouse gas levels of eliminating livestock would accrue rapidly, via biomass recovery and decay of short-lived atmospheric methane CH4. p.6, last line second last paragraph: "assuming that all other sources of emissions remain constant at 2019 levels" - this seems a fundamentally wrong assumption, as the reduced production of food/nutrition/manure/income as a result of eliminating animal agriculture would need to be compensated for.and therefore Their cooling influence would be felt for an extended period of time, having a greater cumulative effect on warming.”
We explicitly account for replacement diets in our model, and thus do not hold agricultural emissions constant. This sentence should have read “assuming that all
*** Thanks
*** Additional comments:
“The dietary scenarios include the immediate replacement of all animal agriculture with a plant-only diet (IMM-POD), a more realistic gradual transition, over a period of 15 years” – consider editing as follows: “The dietary scenarios include the immediate replacement of all animal agriculture with a plant-only diet (IMM-POD), and a more “We updated estimates of global emissions from animal agriculture by scaling country-, species- and product-specific emission intensities… with country-specific data on primary production of livestock products”: I do not think the word “scaling” is the most appropriate here; maybe just use “multiplying”?realistic gradual transition, over a period of 15 years”
General: This section also contains quite a bit of methodology. The manuscript would benefit from having all the details provided here integrated in the methodology section.
We have moved some additional methodological details to the methods where appropriate.
second paragraph: Please indicate the data source of the total human emissions.
Done.
*** Thanks
p.7 biomass recovery: This explanation is missing in the methods section. Using this figure is also flawed, as Hayek et al calculates the potential sequestration associated with converting land to native/natural state while some of the land will have to be converted to cropland for plant-based food production.
Hayek accounts for land use for a replacement diet in their numbers. This was confirmed with the author.
*** This is indeed the case; thanks for pointing out and confirming.
*** I would still like to suggest to remove the reference to Strassburg et al. as they do not present results for land currently used in livestock production separately from all agricultural land.
Comment to the new section:
“…recent modeling work by (Strassburg et al., 2020) that half of the biomass recovery potential of land currently used for animal agriculture could be realized by restoration of 25% of the relevant land”. I’d suggest to edit as follows: “…recent modeling work by (Strassburg et al., 2020) animal agriculture could be realized by restoration of 25% of the relevant land”
General: It would be helpful to start the methods section with an overview, ideally with schematic.
We drafted schematics to try to capture what we did clearly but were unable to come up with something that we felt enriched the paper, so hope that the changes to the manuscript make it clearer.
*** Accepted
There is a mismatch in the current methodology between steps that are "rough estimates" with steps that are worked out through complicated formulas that seem to imply high precision. Kindly address.
We use exclusively data from published sources that we would classify not as rough estimates, but as best current measures of the extent of global animal agriculture, emissions due to animal and non-animal agriculture, and land use. We also explicitly stayed away from any complex formula in our analyses and believe the only thing that might be characterized as such is the RF calculation, which is the standard form used in the literature.
*** I acknowledge that the term “rough estimates” might not have been appropriate. The data used does, however, come with large uncertainties. Which might lead to a large variation in terms of e.g. resulting RF values associated with the min-max range of these estimates. The new section on sensitivity deals with this in an acceptable way.
The methodology combines many different steps, all associated with large uncertainties. I believe the authors would need to address a few related points: (i) each of the separate uncertainties need to be clearly stated (methodology) and quantified (results); (ii) an estimate of the combined uncertainty needs to be included; (iii) this needs to be extensively discussed in the discussion session.
We have added a new section that explicitly addresses major areas of uncertainty and their impact on the results.
*** The inclusion of the sensitivity analysis associated with the assumptions (replacement of animal-sourced food & duration of biomass recovery) is highly appreciated.
p.18, last paragraph: Can you clarify to what the emission data was scaled?
Done.
*** OK
p.20, emissions from agriculture: Please, state explicitly which sector(s)' data you used.
Clarified.
p.19, diet-linked emissions: Please indicate which of the soybean scenarios from Behnke et al. were used (and the actual figure) for replacing the emissions associated with livestock production with emission associated with soy bean production when modeling reductions in livestock consumption? As the treatments in Behnke et al. are somehow "best practices" with e.g. low fertiliser rates and very localised, I doubt it is realistic to use this as a global GHGe estimate. Please, use a more globally representative data source and confirm that the number used is a realistic global GHGe estimate, by comparing with a number of other sources (across different agro-ecologies, systems, geographies). Can you clarify if a similar replacement (animal product replaced by soy bean) for the land use estimates is also carried out? If not, I believe this is an adjustment that should be made, thereby also keeping in mind that in large areas used for animal feed production (e.g. the arid rangelands), plant-based protein production would require larger areas of land than animal-based protein production.
In response to your comments and those of Reviewer 1 we have switched from using the soy replacement diet, which we viewed as a limiting case, with emissions data from non-animal agriculture from FAOSTAT. This has a nominal impact on the results. In the sensitivity section we also include data from Xu et al. 2021 for global plant-based diets based on a more comprehensive analysis. This reduced the projected positive impact by around 5%.
*** I believe this is an improvement.
*** New comment to the “replacement diets” section
“…scaling non-livestock agricultural emission intensities for unit protein by protein required to match that provided by the livestock being replaced” This sentence doesn’t flow
p.20, Emissions projections: BFD: write in full
This was a reference to an analysis not in the current version of the MS and has been deleted.
*** Thanks
BAU scenario: fixing emissions at 2019 level is not realistic, as "reductions are likely to be achieved through e.g. increasing agricultural efficiency, reducing food waste, limiting excess consumption, increasing yields, and reducing the emission intensity of livestock production" (as mentioned in the introduction).
We explicitly did not attempt to model any of these factors, which we agree could reduce the impact of animal agriculture in the future. We do not make any claim that emissions from either agriculture or non-agricultural sources will continue at their current rates. Rather we project current rates out to the future as a way to capture the current impact of animal agriculture cognizant of the fact that the benefits of its elimination will accrue over time. We have clarified this motivation in the introduction.
We also note that, while it is likely true that reductions in emissions from animal agriculture can be achieved, current projections are that there will be increases in global consumption of animal products, potentially offsetting increased efficiency. We address both points in the manuscript.
*** Accepted
Can you provide some more detail about the
The 30 years is based on assumptions from Hayek, via Griscom but, as estimates vary widely, we have now included 50 and 70 years recovery periods in the sensitivity section.
*** The recovery period is now clear.
*** It can be deducted from the sensitivity analysis section (in results) that the magnitude of the carbon recovery rate is extracted from Hayek et al. It would be good to mention that in the methods section too.
Estimating global non-anthropomorphic emissions: Aren't these emissions already taken into account somewhere in the FAOSTAT "Environment_Emissions_by_Sector_E_All_Data_(Normalized)"
No. The categories of emissions in this dataset are “Agriculture total”, “Agricultural land use”, “Energy”, “Industrial processes and product use”, “Waste”, “International Bunkers” and “Other n.e.c.”. The “Other n.e.c.” values for the most recent year are 14 kT CO2, 48 T CH4, 565 kT N2O, which are way too small to account for non-anthropogenic emissions.
*** OK
Projections of atmospheric gas levels (p.25): What is the data source of the starting levels?
The values in the original paper were from a database we maintain of historical GHG levels from a variety of sources. For clarity and data integrity we have updated this to single-source data from NOAA.
*** Clear, thanks
Computing emission and land carbon opportunity cost, Factor of 2: as the terrestrial sinks are already included in the calculation of atmospheric C concentration, isn't this double-counting?
It is just reversing the 2 used to go from emissions to atmospheric levels.
Because of terrestrial/oceanic sinks 1 Gt of CO2 emissions only yields an increase of 0.5 Gt atmospheric CO2, thus a decrease of 0.5 Gt of CO2 in the atmosphere is the equivalent of a reduction of 1 Gt of CO2 emissions, hence the factor of 2.
*** Thanks for the clarification
Computing Carbon Emissions Budgets for RF 2.6 and 1.9: Please explain why RF 2.6 and 1.9.
In the IPCC’s Representative Concentration Pathway framework, 2100 RF values of 2.6 and 1.9 are used, respectively, as surrogates for 2.0C and 1.5C warming.
*** This is quite clear. I believe it would be worth explicitly referring to these warming targets in the manuscript.
"RF calculations used in climate models", which climate models are being referred to?
Updated to be clear that we are specifically referring to MAGICC6.
*** Thanks
"the RF as calculated above" - which calculation exactly does this refer to (to "the complete RF output of MAGICC6" or to the calculations described in the Radiative Forcing section)?
Clarified in text.
*** Thanks
aCO2eq: first sentence: How were the CO2 emission equivalents computed? "simulations described above" - please, specify where exactly is "above", i.e. which simulations are referred to?
Clarified in text.
*** Ok
Product equivalents line 25: "per protein" missing. p.26: Please compare the calculated value of 470kg CO2 eq/kg beef with some values in the literature - e.g. the FAOSTAT data source you used for estimating the overall emissions from agriculture - and explain where the huge difference is coming from.
As noted above in response to a suggestion from Reviewer 1, we updated the calculation as described in the response to use emission reductions through 2100 (it was previously 2050 to highlight the short term impact of eliminating animal agriculture) for consistency with the rest of the manuscript and makes our estimates more directly comparable to those in the literature. This is a more conservative assumption and results in a lower value of 297kg CO2eq/kg beef.
We now compare that directly to the global mean estimate from Poore and Nemecek, and explain that the difference in magnitude comes primarily from our inclusion of carbon fixation on land taken out of agricultural use:
These product-specific aCO2eq’s can be interpreted on a per product unit (Figure 6B) or per protein unit (Figure 6C) as emissions intensities. Eliminating the consumption of a kilogram of beef, for example, is equivalent to an emissions reduction of 297 kg CO2. 38 percent (113 kg aCO2eq) comes from reduced emission, in line with the mean estimate of 99.5 kg CO2eq from a systematic meta analysis of GHG emissions from agricultural products (Poore and Nemecek, 2018), with the remaining 62 percent from biomass recovery.
*** OK; I can see that you have these new numbers in the results section
*** However, in the methods section, it still reads 470kg.
p.15: Apart from calories, protein and fat, it is also worth to say something about micro-nutrients.
All essential micronutrients are readily available at scale from non animal sources. Any reasonably balanced plant based diet can be counted on for everything but Vitamin B12 and sometimes iron. B12 can be produced very inexpensively at scale from microbial sources. Iron requires more attention, but a reasonable plant based diet can cover it.
*** This does not negate the fact that e.g. B12 deficiency is already prevalent in several developing countries where a considerable fraction of the population will not be in a position to source supplements or access a sufficiently diverse and healthy plant-based diet to compensate for the micro-nutrients they now get from low levels of consumption of animal products. I understand you do not want to discuss this in lots of detail, but would suggest that you explicitly mention nutrition security in your section on the potential economic and social impacts.
*** May I also suggest that you edit the last part of the last sentence in that section along the following lines “And, while it is expected that …, investment will also be required to prevent
*** I do appreciate very much that that section is there, by the way. Global modeling efforts, like yours, have great value in outlining the theoretical potential of drastic interventions such as a rapid global phase-out of animal production, but it is also important to note that practical on-the-ground implementation of such strategies needs contextualization and that trade-offs with social/economic/health impacts do need to be taken into account.
treatment of methane: The long-and short-term warming effects of methane and CO2 are very different and there is an ongoing debate as to how to weigh the methane emissions; it would be worth saying something about that in the discussion.
The debate about methane involves, essentially, how to credit effects over different time horizons to emission pulses. By directly modeling methane levels from emissions and decay, and relating them to RF, we avoid this issue.
*** OK
*** New comment:
Can you provide a reference for “an additional 4635 million km2 - … - would be needed to support the required growth in livestock populations”?
perspectives: Please include a short discussion on the social and political feasibility of eliminating animal production completely.
We view this paper as being about the climate potential of eliminating animal agriculture. We expressly avoided offering what would essentially be an opinion about feasibility, as that is more a statement about politics and economics than anything else.
*** Accepted
These are GWP100 values used by FAO/GLEAM
*** OK
Dear Michael and Pat, I have sent a response for your article.
Please address the comments in the attached and you will be totally ready.
Best regards,
Ana Maria
Submitted filename:
Submitted filename:
Submitted filename:
Submitted filename:
Rapid global phaseout of animal agriculture has the potential to stabilize greenhouse gas levels for 30 years and offset 68 percent of CO2 emissions this century
PCLM-D-21-00017R3
Dear Dr. Eisen,
We're pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.
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Kind regards,
Ana Maria Loboguerrero
Academic Editor
PLOS Climate