(C) PLOS One
This story was originally published by PLOS One and is unaltered.
. . . . . . . . . .



Model-based scenarios for achieving net negative emissions in the food system [1]

['Maya Almaraz', 'National Center For Ecological Analysis', 'Synthesis', 'University Of California', 'Santa Barbara', 'Ca', 'United States Of America', 'High Meadows Environmental Institute', 'Princeton University', 'Princeton']

Date: 2023-10

Most climate mitigation scenarios point to a combination of GHG emission reductions and CO 2 removal for avoiding the most dangerous climate change impacts this century. The global food system is responsible for ~1/3 of GHG emissions and thus plays an important role in reaching emission targets. Consumers, technology innovation, industry, and agricultural practices offer various degrees of opportunity to reduce emissions and remove CO 2 . However, a question remains as to whether food system transformation can achieve net negative emissions (i.e., where GHG sinks exceed sources sector wide) and what the capacity of the different levers may be. We use a global food system model to explore the influence of consumer choice, climate-smart agro-industrial technologies, and food waste reductions for achieving net negative emissions for the year 2050. We analyze an array of scenarios under the conditions of full yield gap closures and caloric demands in a world with 10 billion people. Our results reveal a high-end capacity of 33 gigatonnes of net negative emissions per annum via complete food system transformation, which assumes full global deployment of behavioral-, management- and technology-based interventions. The most promising technologies for achieving net negative emissions include hydrogen-powered fertilizer production, livestock feeds, organic and inorganic soil amendments, agroforestry, and sustainable seafood harvesting practices. On the consumer side, adopting flexitarian diets cannot achieve full decarbonization of the food system but has the potential to increase the magnitude of net negative emissions when combined with technology scale-up. GHG reductions ascribed to a mixture of technology deployment and dietary shifts emerge for many different countries, with areas of high ruminant production and non-intensive agricultural systems showing the greatest per capita benefits. This analysis highlights potential for future food systems to achieve net negative emissions using multifaceted “cradle-to-grave” and “land-to-sea” emission reduction strategies that embrace emerging climate-smart agro-industrial technologies.

Funding: The Rockefeller Foundation provided funding to B.S.H. and M.H. support this research as well as salary for M.A. and E.M.. The World Wildlife Fund organized the research team in collaboration with the National Center for Ecological Analysis and Synthesis. M.C. received funding from the Wellcome Trust, Our Planet Our Health (Livestock, Environment and People - LEAP), award number 205212/Z/16/Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: Data used to generate figures can be found in the Supplemental Materials . Additionally, diet an agricultural data can be found at: https://ora.ox.ac.uk/objects/uuid:d9676f6b-abba-48fd-8d94-cc8c0dc546a2 and at https://ora.ox.ac.uk/objects/uuid:22032714-8338-4c52-b5d8-ea1ebb254c1c . Figures were made in R, using TM world borders dataset 0.3 as the original shapefile ( https://search.r-project.org/CRAN/refmans/prevR/html/TMWorldBorders.html ). The base layer of the map used in Figs 4 and 5 is the TM-World Borders 3.0 shape file, which is available at https://thematicmapping.org/downloads/world_borders.php . The licensing on the map is CC-BY 3.0 - SA.

Copyright: © 2023 Almaraz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Biochar is listed twice as it was applied in the model as a technology that reduces nitrous oxide emissions and also increases soil carbon. Enteric fermentation is listed twice as we considered two different improved feed technologies for grass and grain fed livestock.

Emissions benefits do not include carbon dioxide removal on pasturelands or in oceans because these are treated as global goods in our model. The map was created in R version 3.6.0. The base layer of the map is the TM-World Borders 3.0 shape file, which is available at https://thematicmapping.org/downloads/world_borders.php . The licensing on the map is CC-BY 3.0 –SA. Values provided in Supplemental Material (Table E in S1 Text ).

Emissions benefits do not include carbon dioxide removal on pasturelands or in oceans because these are treated as global goods in our model. The map was created in R version 3.6.0. The base layer of the map is the TM-World Borders 3.0 shape file, which is available at https://thematicmapping.org/downloads/world_borders.php . The licensing on the map is CC-BY 3.0 –SA. Values provided in Supplemental Material (Table E in S1 Text ).

Net food sector GHG emissions from technology adoption scenarios (0%, 25%, 50%, 75% and 100% adoption) across global dietary transitions from business as usual (top) to 50% (middle) to 100% flexitarian adoption (bottom) with (right) and without (left) reductions in food loss and waste in 2050. All scenarios assume full closure of yield gaps by 2050. Technological adoption rate is based on the global additive effects of all technologies in Fig 1 . BAU caloric consumption. Values provided in Supplemental Material (Table C in S1 Text ).

2050 food system technologies targeting gross GHG emissions reductions (top) and gross carbon dioxide removal (CDR; bottom). Note the range difference on the y-axis. Rates of adoption are based on global capacity in year 2050 under a ‘business as usual’ scenario. Larger bars indicate greater reductions of greenhouse gases expressed as CO 2 eq. ‘All technologies’ include the additive effects of each technology at a given level of adoption. Yield gaps are closed, BAU caloric consumption. Values provided in Supplemental Material (Table A and Table B in S1 Text ).

Here, we use a global food system model used in the EAT-Lancet analyses (see Methods ; [ 11 ]) to examine an array of conditions and scenarios for which gross GHG reductions, gross CDR and net negative GHG emissions can be achieved in the 2050 food system. These scenarios include changes in dietary choice, land use changes, technology deployment levels, and food loss and waste reductions, thus alternating the land, fertilizer, and energy GHG emissions that are tied to the food demands of 10 billion people by 2050 (see Methods ). We combine a ‘business as usual’ (BAU) scenario with a global food system model to ascribe GHG emissions to the production of different foods [ 11 ]. We focus on agro-industrial technologies representative of food system emissions sourcing, spanning cradle-to-grave and land-to-sea, including hydro-powered fertilizer production, improved livestock feed, anaerobic digesters, soil amendments, agroforestry, seaweed farming, and reduced trawling ( Table 1 , Fig 1 ). Our analyses include both discrete categories ( Fig 2 ) and a continuous spectrum ( Fig 3 ) of dietary, technological, and food loss and waste reduction scenarios, and include both global and country-wide scenarios (Figs 4 and 5 ). We aim to explore which levers offer the most potential for achieving food system emission targets. We argue that systematic investigation of the technologies we selected to explore, in combination with dietary change and food waste scenarios, will provide immediate policy-relevant foresight and help prioritize research and practice.

Previous studies [ 10 , 13 ] have suggested that a mix of technologies, growing practices, consumer effects, and waste reduction strategies can reduce gross GHG emissions alone and in certain combinations; however, a complete assessment across the permutation and combinations of possibilities with a focus on the capacity for sector-wide net negative GHG emissions is needed to make additional science-based advancements on food system solutions to climate change. Given the recognition for deep decarbonization and GHG neutrality/negativity goals by mid-century [ 19 ], the question of how the 2050 food system can contribute to sector wide net negative emissions, and the pathways to achieve this ambition, is a relevant issue for decision makers. Addressing the capacity for the future food system to achieve sector-wide net negative emissions requires a comprehensive analysis that includes emissions reductions and CDR, separately and in combination, from global to regional scales.

Technology deployment and new land management practices offer an alternative if not synergist path for GHG emission reductions and CDR, with the potential to achieve sector wide net negative GHG emissions [ 13 , 14 ]. Some of the more promising technological interventions include those related to fertilizer production, agricultural and land management practices, and post-processing of farmland biomass and waste recycling [ 15 – 17 ]. CDR in the agricultural sector spans bioenergy carbon capture and storage (BECCS), agroforestry, land conservation, organic and rock dust soil amendments, and strategies to mitigate and upcycle food-loss and -waste through the soil [ 13 ]. These emerging technologies span the research and development spectrum as climate mitigation tools [ 18 ], however, further research is needed to overcome implementation barriers and understand known feedback effects and potential unintended consequences.

Food system transformation has the capacity to radically reduce GHG emissions and could possibly achieve sector-wide net negative emissions, which is defined as the point wherein gross GHG emissions are lower than gross GHG removal (i.e., carbon dioxide removal, carbon dioxide equivalent removal, and C sequestration, referred to as CDR hereafter). Several studies have analyzed scenarios under which GHG emissions can be reduced through consumer decisions, particularly a switch in the foods consumed and their consequent effects on agricultural GHG emissions [ 9 – 12 ]. When consumers rely more heavily on plant sourced foods grown under conventional practices [ 9 , 12 ], for example, the amount of land required to support human nutrition may be reduced [ 9 , 13 ], potentially increasing natural ecosystem CDR via land sparing and vegetation recovery. Furthermore, sheep, cattle, and goats emit methane (CH 4 ), a potent GHG. Current estimates ascribe 14% of global GHG emissions to livestock, which include emissions from feed production, enteric fermentation, manure management, processing, and transportation [ 1 ]. Plant-based diets have in principle been suggested to lower such emissions through connections back to agricultural commodities and their growing practices. While much has been written on human dietary effects on GHG emissions [ 9 – 12 ], it is less clear whether consumer effects alone can cascade to global net negative GHG emissions in the food system, which is urgently needed to reverse the role of agriculture in GHG emissions and climate change.

Balancing the planet’s resource base with the growing nutritional demands of an expanding human population in a just, equitable and inclusive way, while simultaneously reducing GHG emissions from the world’s food system, represents one of the biggest, most complex challenges of the 21st century. Current estimates suggest that the food system–a cradle-to-grave global network that grows, distributes, recycles, consumes, and disposes of resources for food production–generates 21–37% of GHG emissions each year (CO 2 equivalent (eq)) [ 1 , 2 ]. The unmitigated effects of the global food system on GHG emissions could grow by ~50–80% by 2050 [ 3 , 4 ], which, along with unmitigated fossil fuel emissions, portend unconscionable risks on the agricultural sector, including systemic crop failures, dilution of the nutritional quality of food for human and animal consumption, and especially profound impacts on small share-holder farms in developing economies [ 5 – 8 ]. Alternatively, the food system has been identified as a key sector for climate mitigation and aggressive action, particularly via the deployment of technologies that reduce GHG emissions and increase C sequestration in agricultural systems [ 9 – 13 ]. Whether the future food system adds to or reduces GHG emissions, and thereby contributes to global climate targets, hinges on a mix of consumer decisions, technology deployment, management practices, and policies.

Methods

Approach We examine the global-scale capacity for technologies, dietary transitions, and reductions in food waste to create agricultural systems with net negative GHG emissions using the global food system model used in the EAT-Lancet analysis (see section titled ‘Modelling food system emissions’ for more information). We account for the GHG emissions, CDR, and agricultural land cover change by examining historic spatial patterns of agricultural land cover and maps of C in organic biomass and soils. In addition, we examine how food system GHGs might be reduced by consumer-driven dietary transitions, reductions in food loss and waste, closing crop yield gaps (assumed in all scenarios), and the introduction of technologies that reduce emissions from food production or that increase rates of CDR on agricultural lands. Our focus is on 1) diets that vary in the proportion of plant to animal products [11], given that animal products generally produce more emissions than plant derived products, 2) emission reduction and CDR technologies representative of food system wide intervention, spanning cradle-to-grave and land-to-sea (Table 1), and 3) reductions in food discarded by retailers and consumers, which have been shown to have very high mitigation potential [1]. We selected technologies for which there was peer reviewed literature and potential to scale this century, acknowledging, however, that most climate smart technologies in the agricultural sector currently remain limited in their uptake. All estimates of climate change mitigation potential (i.e., combined C benefits from both emission reduction and CDR strategies) were reported in tons CO 2 per hectare. If available, we also reported nitrous oxide (N 2 O) and CH 4 (reported as CO 2 eq). We provide estimated climate change mitigation potential based on a select suite of technologies, however additional development of more novel technologies, for which there is great potential [18], will increase the climate change mitigation potential of this lever. Technologies were categorized according to whether they were reducing GHG emissions or acting as CDR strategies (Table 1). We carefully evaluated potential redundancies to avoid the double-counting of climate change mitigation potential. For each technology, we then modelled four scenarios based on a 25, 50, 75, and 100% global adoption rate and assessed the life cycle of agricultural GHG emissions from now to 2050 (see below for a more in-depth description of the model; Table 4). We chose these adoption rates because they give a range of the climate change mitigation potential of different strategies at various intervals of adoption, ranging from no adoption (0%, the ‘business-as-usual’ scenario) to complete adoption (100% adoption). Ultimately, the exact intervals we chose (25%, 50%, 75%, and 100%) are arbitrary, but having five rates of adoption of food system strategies provides enough resolution for readers, policy-makers, and the like to (a) understand the relative effectiveness of different strategies when implemented at the same adoption rate, but also (b) to compare the GHG benefit of different strategies when adopted at different rates (e.g., is adding biochar at 25% adoption rate more or less effective than a 50% adoption of a flexitarian diet?). Furthermore, providing a range of adoption rates is an advance beyond other food system emission modeling studies, which typically apply an adoption rate of 100%.

The food system model We used outputs from the projections resulting from the EAT-Lancet food system model, which connects food consumption across regions [11]. The EAT-Lancet model is based on the partial equilibrium multi-market food system model named International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), which is a network of economic, crop, livestock, and water models [11,20]. It projects food demand, food production, and crop land for >40 commodities in >150 regions (that are approximately equivalent to countries, it does not provide subnational estimates) from 2010 to 2050 based on associations with changes in income and population. The EAT-Lancet food system model (henceforth referred to as the “food system model”) reformulates IMPACT such that food demand is an input parameter and food production is an output parameter. Equations used in this model are detailed in the appendix of Willett et al. [11]. The food system model adjusts relationships such as trade flows, processing, feed requirements, and demand for other associated commodities (i.e., oils, sugar, etc.) based on dietary changes. These data are translated into climate impacts based on country specific analyses of CH 4 and N 2 O emissions for crops and livestock, and CO 2 emissions for seafood. We used this model for two primary reasons. First, the crop production, crop yield, crop land use, and diet demand projections are publicly available. Second, the EAT-Lancet analysis has gained widespread use and attention in the academic and policy spheres. Using the EAT-Lancet food system model, which has been extensively published and is widely known in the food system and policy spheres, provides more robust estimates than would creating our own food system model, whilst also ensuring the results of our analysis are more comparable to those published previously. The BAU scenario assumes a middle-of-the-road pathway (i.e., Shared Socioeconomic Pathway 2) for economic growth, dietary trends, and rates of population increase. Projected increase in crop yields reflect potential rates of technology change and interactions between commodities and countries, whilst international food trade continues along gradients of comparative advantage in producer and consumer surplus. In this scenario, global population is projected to grow to 9.2 billion individuals in 2050, from 6.9 billion individuals in 2010. Similar to other analyses, diets transition to include more calories in total (an ~88% estimated increase in global food production from 2010 to 2050) as well as more calories from animal-sourced foods as populations become more affluent. We also used the other food system scenarios analyzed in the EAT-Lancet report to investigate how changes to food supply and demand might interact with CDR technologies. These scenarios included different assumptions on dietary transitions (both amount of food consumed and type of food consumed), amounts of food loss and waste, and faster than BAU trends in crop yield increases. We analyze these scenarios to examine the potential climate change mitigation potential of technological implementation relative to other food system changes, as well as how the climate change mitigation potential of technology implementation might increase or decrease as other parts of the food system change. A further description of these scenarios is in the following paragraphs. The alternative diet scenario we also analyzed is where the population slowly transitions to a flexitarian diet by 2050. The flexitarian diet (as described in the EAT-Lancet report, and also known as the EAT-Lancet diet), is where dietary composition meets best recommendations for human health as described in epidemiological and nutrition literature. This diet is predominantly plant-based and contains moderate amounts of dairy, eggs, meat, and fish. Currently, it is estimated that one third of all food production is lost or wasted [21]. To examine the climate change mitigation potential of reductions in food loss and waste, and the interaction this may have with CDR technologies on agricultural landscapes, we included two food loss and waste scenarios: the first is the BAU scenario, or where current rates of food loss and waste continue into the future; and the second is where rates of food loss and waste throughout the entire food supply chain are reduced by 50% by 2050. We assumed that yield gaps, or the difference between current yields and potentially attainable yields (as estimated in Mueller et al 2012; [22]), are closed by 2050. This assumption is intended to show that climate mitigation can be achieved in concert with increases in food production, however, we acknowledge that closing yield gaps is a more complicated aim than this scenario reflects. We assume the GHG impact per unit of food produced for potentially attainable yields is identical to current yields, which is a commonly used assumption in food system models, including Tilman and Clark (2014); Balzelj et al (2014); Springmann et al (2016); Springmann et al (2018); and Willett et al (2019). We made this assumption to increase comparability to other analyses but acknowledge that yield increases could both potentially increase (e.g., through excess fertilizer application and increased irrigation) and decrease (e.g., through better fertilizer management, land sparing, and increases in soil organic matter) GHG emissions per unit of food produced [23,24]. We used these diet, loss and waste, and yield scenarios to illustrate how different food system transformations affect overall food system GHG emissions and the potential emissions reductions of implementing CDR technologies at a global scale. We recognize that some of these scenarios may be challenging to implement and will have knock affects (and feedbacks) that permeate throughout the food system. For example, diet transitions towards lower-meat diets could reduce the cost of meat in the short-term resulting from surplus supply, which could have a rebound effect of increasing meat consumption. Similarly, rapid increases in crop yields could decrease the cost of food and result in increased diet demand, while also influencing the countries in which commodities are produced by affecting the relative economic costs of agricultural production across commodities and countries. While understanding these knock-on and rebound effects of food system transformation is integral to implementing them in real-life, they are beyond the scope of the current analysis. All food system scenarios were implemented uniformly on a country-by-country basis. As such, for example, in the half flexitarian diet scenario, half the population of each country adopts the flexitarian diet whereas half the population continues to consume the BAU diet.

Estimating food system GHG emissions from production and land use change We estimated the GHG emissions from the global food system for each of the diet, crop yield, and food loss and waste scenarios. This includes GHG estimates from agricultural production, as well as potential GHG emissions resulting from agricultural expansion into natural habitats and GHG sequestration from abandoning agricultural land. To estimate emissions from agricultural production, we paired estimates of food production, consumption, and land use provided in the supplements of the EAT-Lancet analysis with results from an agro-environmental meta-analysis of life cycle assessments (LCAs) that estimated the GHG emissions per unit of food produced for different agricultural commodities [25]. The LCA meta-analysis used here has a system boundary of cradle to retail store, and provides GHG estimates from five agricultural activities: 1) fertilizer application, 2) manure management, 3) enteric fermentation, 4) methane from rice production, and 5) on-farm energy use. We then estimated emissions from food production, at both the national and global scale, by pairing estimates of the GHG emissions per unit of food produced with estimates of food production. Using this approach, we estimate global food production emissions were 10.08 Gt CO 2 eq yr-1 in 2010, which is similar to existing estimates of GHG emissions from food production [26,27]. To estimate GHG emissions from cropland expansion and cropland abandonment, we paired the estimates of changes in cropland use for each country with estimates of below and above ground biomass. To do this, we first estimated spatial changes in cropland extent at a 2.25 km2 resolution using satellite data from 2002 to 2012 (MODIS; [28]). We then overlaid this historic spatial change in cropland extent with estimates of above-ground and below-ground C stores in natural land covers based on IPCC Tier 1 methodology [29,30], further 40% of soil organic C stores are lost following conversion to agriculture, which is in line with recent estimates of the proportion of soil organic C stores lost following conversion from forest to cropland [31]. This allowed us to derive country-specific estimates of average C stores per hectare in areas that experienced cropland expansion or abandonment in the ten years between 2002 and 2012 (i.e., a different value for cropland expansion and cropland abandonment for each country). We validated this approach using current estimates of GHG emissions from cropland expansion into natural habitats. We did so by pairing historic changes in cropland extent from 2006–2010 as reported by the Food and Agriculture Organization (FAO) with the derived country-level average C stores described above. Using this approach, we estimated land use change (LUC) GHG emissions from changes in cropland extent and location to be an average of 4.0 Gt CO 2 eq yr-1 from 2006 to 2010. This is within the range of existing estimates of agriculture-related land cover change emissions [26,32]. We do not estimate LUC emissions associated with changes in pastureland because satellite images often cannot differentiate pasturelands from nature grasslands or savannahs. We used the same approach to project LUC GHG emissions in each food system scenario. Specifically, we paired the country-specific estimates of cropland LUC GHG emissions per hectare with the country-specific projections of cropland demand from the food system scenarios. In doing so, we amortized the potential GHG sequestration on abandoned croplands over a 100-year period because the full sequestration potential of abandoned agricultural lands is often only realized over a period of several decades to a century. This approach provides a conservative estimate of the GHG sequestration in abandoned croplands because we only account for the GHG sequestration potential that occurs during the time period of the analyses (e.g., before 2050).

[END]
---
[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000181

Published and (C) by PLOS One
Content appears here under this condition or license: Creative Commons - Attribution BY 4.0.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/

You are viewing proxied material from magical.fish. The copyright of proxied material belongs to its original authors. Any comments or complaints in relation to proxied material should be directed to the original authors of the content concerned. Please see the disclaimer for more details.