Data on the time and resources required for food acquisition and meal preparation across countries are not yet available. Still, we can begin to illustrate differences in the cost of meal preparation by focusing on energy use for cooking beans in East Africa, extending our previous research on food price variation in this region (Bai et al. 2020). We focus on the portion of the pulse in the basic plate, because preparing and cooking dry beans for a meal requires considerable time and fuel. Home-cooked beans have similar nutritional composition to tinned beans that are available in retail food outlets. We cannot account for all factors that go into meal preparation, but can use this example to address differences in fuel cost as one influence on the cost and affordability of each food.

To compare the cost of cooking beans or other pulses, we focus on the fuel needed to cook dry beans in East Africa, as estimated by MECS (2019), which is a simplified version of more detailed analyses such as Nerini et al. (2017). To compute fuel cost per serving, we consider the quantity of fuel needed to cook 500 g of dry beans (enough for 8.7 servings in the basic plate), which is estimated by MECS (2019) to require 0.675 kg of charcoal, 0.2 kg of gas (LPG), or 1.5 kWh of electricity when using the most common, conventional types of stove in this region. On a per-serving basis, that amounts to 77 g of charcoal, 23 g of LPG gas, or 0.17 kWh. We applied this estimate to six East African countries that are likely to use similar food preparation technologies and techniques – Burundi, Ethiopia, Kenya, Rwanda, Tanzania, and Uganda. The mix of fuels and cooking methods actually used vary widely, with the fraction of households that have access to electricity in 2017 ranging from a majority of households in Kenya (63.6) to a smaller proportion in Ethiopia (44.3), Rwanda (34.1), Tanzania (32.7), Uganda (31.8) and Burundi (9.3), as reported in World Bank (2021).

For each country, we compare the fuel costs for cooking to the cost of the food itself. In each case, the least costly pulse in the dataset is spotted beans, whose nationally-representative cost per 57 g serving in 2017 USD ranged from 8.6 cents in Rwanda to 17.2 cents in Ethiopia. We then compare that to the cost of each type of fuel. The results shown in Fig. 3 reveal no consistent ranking in fuel costs, but in two cases (Rwanda electricity and Uganda gas), the cost of fuel exceeds the cost of the beans themselves.

Our analysis so far focuses only on meal costs at national average market prices for ingredients and fuel. How each food is actually cooked varies with household circumstances, including the availability of kitchen equipment, local variation in costs at each time and place, and cultural or demographic factors that influence each household's division of labor related to meal preparation. Home cooking may also impose large health costs from indoor smoke, and how foods are prepared and served or stored can also have important consequences for foodborne illness and food waste, as well as externalities associated with deforestation and emissions. Reducing the inequities and total costs associated with inefficient cooking might sometimes be possible with innovations in food processing that would preserve and even enhance the nutritional value of foods. For the basic meal with puIses in East Africa, however, using canned beans would cost roughly 3–4 times the cost of dry beans, plus the required cooking fuel, at a price per serving that ranges from \$1.25 in Kenya to \$1.37 in Tanzania, \$1.42 in Burundi and Ethiopia, \$1.55 in Uganda and \$1.66 in Rwanda.

This case study is just one small step towards including the costs of meal preparation in future work on the cost and affordability of a healthy diet. In so doing, we build on a long and diverse literature on meal preparation as a determinant of diet quality and health outcomes, and studies of cooking fuel as a driver of household health and environmental harms. Regarding diet quality in low- and middle-income countries, the recent literature was pioneered by Kennedy and Reardon (1994) on how urbanization in Kenya and Burkina Faso led to shifts from coarse to refined grains, and includes many papers on how time use in rural

households relates to nutrition as reviewed by Johnston et al. (2018), plus later contributions on time use that include Seymour et al. (2019, 2020) and Vemireddy and Pingali (2021). Similar issues arise in studies of time use and diet quality for higher-income settings, such as Raschke (2012), Smith et al. (2013), Yang et al. (2015), and Carpio et al. (2020) in the US, or Mackay et al. (2017) in New Zealand. Regarding the health effects of indoor smoke and the environmental consequences of wood and charcoal, several studies address the prevalence of each fuel type and the drivers of change (Heltberg 2004; Bonjour et al. 2013; Shupler et al. 2019), in some cases tracing their effects to time management and fuel choice (Anderman et al. 2015).

This chapter links meal preparation to the cost and affordability of healthier diets, focusing on the relative cost of raw ingredients as opposed to pre-cooked and packaged foods, and comparing the ingredient costs to fuel costs for beans in East Africa. To address how the costs of meal preparation and the increasing availability of processed foods affect food choice, it will be especially important to identify forms of food processing that preserve and enhance nutritional values, as opposed to the growing health risks associated with existing types of ultra-processed food that have been shown in observational studies reviewed by Pagliai et al. (2021) and Lane et al. (2021), as well as one randomized control trial. The need for policies and programs to distinguish between healthier and less healthy packaged foods is important for the food safety agenda (Jaffee et al. 2020), and could help ensure that food system transformations bring healthy and nutritious diets within reach for all people at all times, in every country of the world.

#### 4 Opportunities for Action

A basic plate of healthy food is unaffordable for many of the world's most resourcepoor households, due not only to the high cost of growing or purchasing raw ingredients, but also the high cost of meal preparation within the home. Government policies and programs could build on past work such as the SOFI 2020 report, CFS (2021) and other initiatives to ensure food security for all through two specific kinds of action suggested by our findings:

The first of these potential game-changers calls on governments to make healthy diets affordable for all by using the minimal cost of a healthy diet to examine food poverty thresholds and inform eligibility for nutritional safety nets that provide cash transfers, vouchers, or in-kind support to supplement a household's own income or food production. Safety nets designed around access to a healthy diet can be the foundation for social inclusion, using twenty-first century data analysis to target and deliver assistance in all countries of the world. Recent improvements in market monitoring and analysis of diet costs allow governments to target and deliver nutrition assistance tailored to local needs, with food-based poverty lines to guide programme design parameters suited to each population, targeting by demographic group and delivering through locally adapted instruments, including cash, vouchers and in-kind assistance. As noted by Hendriks (2018), making healthy diets affordable by tackling poverty is a necessary, but not sufficient, step towards food security and nutrition, which also requires improvements in the quality and price of available foods to enable behavior change for food and nutrition security of all people at all times.

The second of these game-changers addresses hidden costs in the "last mile" of food security for households to acquire foods and prepare each meal. Food acquisition and preparation often places high burdens on caregivers' time and can impose additional financial costs for cooking fuel, equipment, and transportation. Governments should support the development of infrastructure that will reduce the burden of food acquisition and preparation, such as rural electrification, and support agrifood processing that reduces the time and fuel cost of home cooking while preserving the nutritional value of foods.

The main action step within the food system is to distinguish between helpful processing that preserves or even adds to the healthfulness of food versus harmful ultra-processing that transforms food, removing healthful aspects of foods such as whole grains and sometimes adding health risk factors such as refined carbohydrates and sugars, sodium, and trans fats. Actively making that distinction allows for support for the helpful kinds of healthy processing, often by local small and medium-sized enterprises (SMEs), while also using regulation and taxation to limit harmful forms of ultra-processing.

To guide these interventions, governments will need to continue investing in improved data collection regarding food prices, the externalities involved in true cost accounting, and the hidden costs of food acquisition and meal preparation within the home. Governments routinely collect nationally-representative market prices for a variety of foods each month to calculate their consumer price indexes, and international agencies such as the WFP, FAO and FEWS NET also collect rural market prices to target food assistance (Bai et al. 2021a). The World Bank is expanding its global office so that the ICP can focus on food prices and diet costs, in collaboration with national statistical organizations around the world (Food Prices for Nutrition 2021).

As shown in this chapter, governments can complement this work with attention to other barriers to affordability beyond market prices, such as the time and resources required for food acquisition, meal preparation and the use of cooking fuel. Accurately measuring these hidden costs is also helpful for true cost accounting of externalities associated with the environmental footprint of food production, processing, and distribution, as well as the health effects of food consumption. These costs differ among different types of agricultural ingredients, and also differ among types of processing. The hidden costs of meal preparation, as well as the environmental costs and health burden associated with diet-related disease, call for attention not only to different kinds of farm production, but also to a new kind of distinction between helpful, healthy kinds of food processing and ultra-processed foods that are an increasingly important cause of diet-related disease around the world.

In summary, the data described in this chapter lead to two game-changing actions:

(i) the establishment of poverty lines and safety nets informed by the cost of a healthy diet to ensure that all people at all times can acquire the foods needed for lifelong health, using appropriate targeting and forms of assistance, including cash transfers, vouchers, and in-kind assistance; and

(ii) actions to reduce the burden of meal preparation for healthy diets through electrification using renewable energy sources, as well as support for helpful processing that preserves the nutritional value of foods, in distinction to ultraprocessing that may remove beneficial components and add attributes that are associated with illness later in life.

Through these actions, governments can use new kinds of data and analytical methods to meet universal needs in locally appropriate ways, thereby ensuring food and nutrition security for all.

#### Annex

Table A1 Reference food items selected to convert food weight (g) to dietary energy (kcal)

Note: FCT code numbers refer to the USDA SR28 database

Table A2 Energy content of each item for isocaloric substitution within food categories

Note: The quantity of oil from the original WFP basic plate has been reduced by 10% to more closely approximate daily lipid needs. Substitutions within food categories are among versions of the food with different moisture content, but similar nutrient density per calorie, for example, switching the 270 kcal of carbohydrate-rich starchy staples from dry raw rice grains to potatoes, or the 190.6 kcal of dry beans into 190.6 kcal of canned beans, or switching the 9.9 kcal of whole tomatoes with 9.9 kcal of concentrated tomato paste. Different items in the oils and onions categories are all roughly similar in weight per kcal, so either unit of measure would yield the same result

Table A3 Macronutrient adequacy of the basic plate

Note: Data shown are grams of each macronutrient from each food and in total for the basic meal. The bottom row shows the Acceptable Macronutrient Distribution Range (AMDR), as specified by the Dietary Reference Intakes of the U.S. Institute of Medicine (2005). Composition of each food is based on the USDA National Database for Standard Reference, Release 28 (SR-28) and the reference food items listed in Table A1

Table A4 Items selected for the basic plate and its variants, by food category (n = 152)

Table A4 (continued)

Table A4 (continued)

Table A5 Candidate food items whose prices and availability are reported in ICP 2017

Table A5 (continued)

Table A5 (continued)

Table A5 (continued)

Table A5 (continued)

#### References

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# The Global Cost of Reaching a World Without Hunger: Investment Costs and Policy Action Opportunities

Bezawit Beyene Chichaibelu, Maksud Bekchanov, Joachim von Braun , and Maximo Torero

# 1 Introduction

At the heart of the 2030 Agenda1 was a promise to prioritize two objectives: to eradicate poverty and end hunger in all their forms. Worldwide, over 650 million people are estimated to have been undernourished in 2019. World hunger increased further in 2020 to 720–811 million people, exacerbated by the impact of COVID-19 (FAO, IFAD, UNICEF, WFP, & WHO 2021). Recent global projections of hunger show that the world is not on track to achieve zero hunger2 by 2030. Estimates in 2020 projected that the number of people affected by hunger will surpass 840 million by 2030, or 10% of the global population (FAO, IFAD, UNICEF, WFP, & WHO 2020). Updated estimates in 2021 projected lower, but still alarming levels of hunger

B. B. Chichaibelu (\*) · J. von Braun

Center for Development Research, University of Bonn, Bonn, Germany e-mail<EMAIL_ADDRESS>
M. Bekchanov

Bezawit Beyene Chichaibelu, Maksud Bekchanov, Joachim von Braun and Maximo Torero 2021. The global cost of reaching a world without hunger: Investment costs and policy action opportunities. In Food Policy. Volume 104, October 2021, 102151. https://doi.org/10.1016/j.foodpol.2021.102151\_34.

1 In 2015, all of the UN member states adopted seventeen goals as part of the 2030 Agenda for Sustainable Development, which set out a fifteen-year plan to achieve the goals.

2 Most studies define 'zero hunger target' as the reduction of hunger levels to less than the 5 or 3% of population. 'Absolute zero hunger target' is used throughout this manuscript to refer to a complete eradication of hunger.

Research Unit Sustainability and Global Change, Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany

M. Torero Food and Agriculture Organization (FAO), Rome, Italy

that will affect about 660 million people by 2030 (FAO, IFAD, UNICEF, WFP, & WHO 2021). The world is also not on track to achieve the 2025 and 2030 targets for child malnutrition. In 2019, 21.3% of children under five years of age were stunted globally, or 144 million (UNICEF, WHO & World Bank 2020). In 2021, the number of children suffering from stunting increased further to 149.2 million. Although there has been some progress globally since 2000, rates of stunting reduction are far below what is needed to reach the targets of 40% reduction for 2025 and 50% reduction for (FAO, IFAD, UNICEF, WFP, & WHO 2021).

COVID-19 is expected to further worsen the overall prospects for food security and nutrition. Food insecurity may appear in countries and population groups that were not previously affected. In 2020, the number of undernourished people increased by about 118 million people compared to the 2019 level as COVID-19 disrupted economies, job markets and supply chains, and inflated food prices. The pandemic is also expected to have a lasting effect beyond 2020, adding about 30 million more people to the total number of undernourished in the world in 2030 (FAO, IFAD, UNICEF, WFP, & WHO 2021). Additionally, early estimates on the impact of COVID-19 on child malnutrition suggested an additional 6.7 million children suffered from wasting in 2020 as compared to pre-COVID-19 projections. The combined effect of the increase in wasting and a 25% reduction in the coverage of nutrition and health services due to COVID-19 could cause an additional 128,605 deaths in children younger than five years in 2020 (Headey et al. 2020). While these projections are an early estimate and may not fully capture the impact of COVID-19 on food security and nutrition, they emphasize the urgent need for actions to get back on track towards achieving targets 2.1 and 2.2 of Sustainable Development Goal No. 2 (SDG2).

Investments needed to end hunger are anticipated to be extensive, costly and difficult to implement, even without considering the impacts of the COVID-19 pandemic. As policymakers still need to prioritize the allocation of resources, identifying optimal and least-cost investment options is important for practical policy. In this regard, this study developed a marginal abatement cost curve (MACC) as an original contribution to identify a mix of least-cost investment options with the highest potential for reduction in hunger. Twenty-two different interventions are considered for reducing hunger. The information about the interventions are drawn from the findings of various model- and cost-benefit and impact evaluation studies on hunger reduction measures. Some of them are more short-term interventions (such as social protection), and some are more long-term (such as agricultural R&D, or soil fertility management). The MACC of hunger reduction can be considered when asking "what are the costs of ending hunger?", depending on the number of people who are to be brought out of risk of hunger by 2030. The assessment can broadly guide global and country efforts to achieve the SDG 2 targets by 2030.

The results from the MACC indicate that ending hunger would not be prohibitively expensive, provided that a mix of least-cost measures with large hunger reduction potential are prioritized. Ending hunger by 2030 is estimated to require US\$ 39–50 billion annually until 2030. Of that, the G7 would need to contribute US\$ 11–14 billion to meet their Elmau commitment of lifting 500 million people out of hunger by 2030,3 effectively doubling current aid flows for agriculture, food and rural development. A bundle of promising investments that deliver short-term and long-term impacts would meet the goal of ending hunger by 2030. Short-term measures are needed to reach the hungry soon – including those adversely affected by COVID-19 related job losses and other socio-economic consequences – with social protection and nutrition programs. Long-term measures, such as agricultural R&D and expansion of small- and large-scale irrigation, which require high up-front investments but also have a high long-term impact, also need to be included. Optimally phasing investments is crucial: those with longer-term impact should be frontloaded to reap their benefits soon before 2030.

### 2 Review of Selected Estimates of the Cost of Ending Hunger

Here we review several estimates of the cost of achieving SDG 2, in particular, ending hunger and improving nutrition. We focus on the five most up-to-date estimates conducted by FAO, IFAD, and WFP (2015), Rosegrant et al. (2017), Torero and von Braun (2015), Laborde et al. (2016), and Shekar et al. (2017). Since some of these studies, for example, the studies by FAO, IFAD and WFP (2015) and Torero and von Braun (2015), use the same methodology as earlier works, i.e. Schmidhuber and Bruinsma (2011) and Hoddinott et al. (2013), these earlier works are not included in our review. In the case of others, for example, in the paper by Laborde et al. (2016) titled "Ending Hunger: What Would It Cost", the applied methodology is similar to another ongoing work by Ceres2030 research group; we therefore present only the studies for which the final results are readily available (see also Fan et al. 2018). Summary of the findings of the reviewed studies are presented in Table 1. We further discuss the details and interpretations of these studies in the remaining part of this section.

The "Achieving Zero Hunger" study by the Food and Agriculture Organization of the United Nations (FAO), the International Fund for Agricultural Development (IFAD), and the World Food Programme (WFP) (2015) presents the most extensive, but also most costly set of measures, including extensive social protection programmes and targeted pro-poor investments. The basic premise of the 'Achieving Zero Hunger' framework is that hunger is a result of lack of purchasing power which translates into a lack of access to sufficient and nutritious food, and therefore the target of eliminating hunger (SDG 2) can be achieved only by eliminating poverty (SDG 1). Unlike other models, it aims for absolute-zero levels of hunger globally by

3 G7 heads of states at their Summit in Elmau in 2015 committed to lifting 500 million people out of hunger and malnutrition by 2030 as part of a broader effort to be undertaken with partner countries to support the 2030 Agenda for Sustainable Development, i.e. Sustainable Development Goal 2 (SDG 2) to end hunger and malnutrition by 2030.

Table 1 Overview of selected costing studies

Source: Adapted from Mason-D'Croz et al. (2019)

Note: \* Total cost per person is calculated as total net discounted cost over the fifteen-year period (only for the 'Achieving Zero Hunger' study, the time frame is fourteen years, i.e. 2016–2030). The discount rate is assumed to be 5% (Hoddinott et al. 2013). For each modelling framework, the absolute number of people lifted out of hunger by the proposed investments is calculated as the difference between the projected number of hungry people in the 'business as usual 2030'scenario and the projected number of hungry people in the '2030 investment' scenario. These figures are retrieved from each model. The per capita total cost of hunger eradication is then calculated as the total net discounted cost divided by the number of people lifted out of hunger. We calculate only the cost per person for the investments towards hunger reduction, but not for the investments towards improvement in nutrition due to the very specific nature and outcomes of each intervention

Table 1

2030. Note that hunger is measured here by the prevalence of undernourishment (PoU), defined as chronically inadequate dietary energy intake, in line with the methodology adopted in the FAO's 'The State of Food Security and Nutrition in the World 2019' report (FAO, IFAD, UNICEF, WFP, & WHO 2019). The 'Achieving Zero Hunger' study draws upon a methodology previously used by Schmidhuber and Bruinsma (2011) and employs the partial-equilibrium model called Global Agriculture Perspectives System (GAPS). According to the modelling simulations, the twin-track approach of social protection and pro-poor development is expected to bring relatively fast but also sustainable eradication of poverty and hunger. In the short-term, public investment in social protection is expected to close the poverty gap and increase incomes, both directly and through increased productivity. In the long run, the effects of social protection will be reinforced and sustained by targeted private and public pro-poor investments, especially in rural areas, and particularly so in agriculture (see Table 1). The overall cost of achieving zero hunger would be US\$ 265 billion annually, out of which US\$ 198 billion will cover pro-poor investments and US\$ 67 billion social protection.

The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) by the International Food Policy Research Institute's (IFPRI) was applied to analyze the potential contribution of agricultural investments to achieving SDG 2, and proposes a comprehensive investment package that can reduce hunger to 5% of the global population (Rosegrant et al. 2017). These investments focus on agriculture and include agricultural research and development (R&D); resource management, especially water and irrigation; and infrastructure, mainly transportation and energy. Out of the five estimates this is the only framework explicitly modelling the impact of R&D on agricultural productivity and hunger reduction; it is also the only one to account for climate change impacts. The IMPACT model is a highly disaggregated, global partial-equilibrium multi-market model. To overcome the limitations of a partial-equilibrium model, it is linked to a global computable general equilibrium (CGE) model (GLOBE) which allows for estimating the impacts of investment in agriculture on the broader economy. Hunger is proxied by the risk of hunger based on the estimated calorie availability per day per capita. The cost of the agricultural investment package is estimated at US\$ 52 billion annually for the developing world. These investments are expected to result in a reduction of the share of the population at risk of hunger to 5%, except for Eastern and Central Africa where hunger will remain at 10% level (Rosegrant et al. 2017).

The "Toward a Zero-Hunger by 2030" study by Torero and von Braun (2015) provides global estimates for the investments necessary to reduce hunger to near zero by 2030, with the assumption that transitory undernourishment at around the 3% level, related to conflict and crises, would require different measures. The estimates are to a great extent extrapolated from Hoddinott et al. (2013) and consider hunger reduction through investing in: (i) accelerating yield enhancements, i.e. investments in agricultural R&D; (ii) market innovations, i.e. information and communication technologies (ICTs) and improving the functioning of fertilizer markets; (iii) and interventions that reduce micronutrient deficiencies (vitamin A, iodine, iron, zinc) and reduce stunting.

This framework of the study is somewhat similar to the work of Rosegrant et al. (2017) presented above, as both studies rely on IMPACT modelling assessments. Hunger level is also measured using a similar approach as in the "Achieving Zero Hunger" study (e.g., Rosegrant et al. 2017). However, the conceptual framework and the underlying assumptions vary to some extent. Agricultural R&D is expected to increase productivity, and the elasticity of yields to R&D expenditure is estimated based on a literature review. This yield growth entails both income and price effects, which will then affect the incidence of hunger. The original cost estimates for agricultural R&D in the underlying paper by Hoddinott et al. (2013) show that it would cost US\$ 733 per person to reduce the number of undernourished by 210 million by 2050 (the original time frame of the baseline paper), which translates into a prevalence of hunger reduced to 5.9%. Torero and von Braun (2015) suggest to accelerate these investments up to 2030, and couple them with the remaining investment strategies, i.e. food markets and ICTs, as well as with programmes to reduce micronutrient deficiencies and stunting, which would lift 500 million people out of hunger and attain the objective of near-zero hunger. The total cost of all measures addressing hunger and malnutrition would be US\$ 30 billion annually; out of which the cost of ending hunger would be US\$ 15 billion annually.

The "Ending Hunger: What Would It Cost" study by IFPRI and the International Institute for Sustainable Development (IISD) combines micro-, meso- and macrolevel inputs (Laborde et al. 2016). This modelling framework is based on a dynamic multi-country multi-sector CGE model (MIRAGRODEP) combined with household surveys. The framework that combines modelling with household surveys allows for more efficient targeting of interventions in the model assessment due to more detailed classification of households at risk of hunger. Based on the surveys, households are differentiated in accordance with their location (urban or rural), income sources and levels (farm or non-farm), and farm ownership. Consequently, strategies of social protection are matched with the households with lower income, measures of supporting farm production are considered for households which owns farm, and rural support were proposed to the rural households. This household-level targeting in the model is expected to result in estimations of improved spending efficiency of the hunger reduction investments in comparison to the other models which are based on national averages (Laborde et al. 2016). As noted by Fan et al. (2018), the MIRAGRODEP model's targeting approach, together with the narrow focus on reducing hunger in isolation of other SDGs, produces one of the lowest cost estimates, US\$ 11 billion annually. Hunger is measured by the PoU, as defined above in the discussion of the "Achieving Zero Hunger" study. Rather than targeting absolute zero hunger the MIRAGRODEP's objective is set to reducing the PoU to 5% or less. Two other sub-goals of SDG 2, i.e. raising agricultural productivity and doubling smallholders' income (target 2.3) and ensuring sustainable agricultural systems (target 2.4) are also accounted for in the design of interventions. Three types of interventions are included in the MIRAGRODEP model: social safety nets, directly targeting consumers through food subsidies; farm support to increase farmers' productivity and incomes; and rural development, mainly through infrastructure investments (see Table 1). These interventions are expected to affect calorie consumption by increasing poor households' incomes, as in 'Achieving Zero Hunger' study, or by decreasing food prices. The importance of interventions addressing nutrition are also acknowledged, however because of household data limitations, they are not accounted for in the modelling framework (Laborde et al. 2016).

Finally, the "Investment Framework for Nutrition" was proposed by the World Bank (WB) (Shekar et al. 2017). This framework has a narrow scope in comparison to the other models and frameworks presented here, because its adopted methodological framework is very simple and transparent. Rather than aiming at reducing hunger, as in the other models, the WB framework estimates the financial needs for improved nutrition targets. More specifically it aims to (i) reduce the number of stunted children under five by 40%, (ii) reduce the number of women at reproductive age affected by anaemia by 50%, (iii) increase the rate of exclusive breastfeeding in the first six months up to at least 50%, and (iv) reduce and maintain childhood wasting to less than 5%. These targets correspond to the World Health Assembly's Targets for Nutrition, but also contribute to SDG 2 (Shekar et al. 2017). The case for investing in nutrition is very strong: ending malnutrition is critical for long-term human capital, labor productivity and broad economic development (Fink et al. 2016; Horton and Steckel 2013; Hoddinott et al. 2008). At the same time, nutrition interventions are considered to be among the most cost-effective (Horton and Hoddinott 2014). The interventions included in the framework are identified based on two criteria: (1) strong evidence of their impact; (2) relevance for low- and middle-income countries. The selected interventions range from staple-food fortification and micronutrient supplementation to public provision of supplementary food and behavior promotion campaigns. To estimate the total cost of scaling up the selected nutrition interventions, financial needs are first analyzed for the highestburden countries based on the unit-cost data obtained from a literature review; these results are then extrapolated to all low- and middle-income countries. The estimates suggest that to reach the nutrition targets it will cost around US\$ 7 billion annually between 2015 and 2025; more than half of this amount targeted at reducing stunting (Shekar et al. 2017).

The five estimation approaches presented above provide a very wide range of estimates for the total investment necessary to achieve SDG 2, i.e. ending hunger and improving nutrition. These differences are largely attributable to the different objectives and policy questions asked, interventions and investment strategies considered, as well as definitions, methods and assumptions used (Mason-D'Croz et al. 2019; Fan et al. 2018). The differences in the approaches adopted by the costing frameworks make it difficult to directly compare the resulting estimates. We calculated the estimated cost per person of hunger eradication for all the modelling frameworks except the WB's Investment Framework for Nutrition that only provides estimates of nutrition-specific interventions (Table 1). These estimated costs per person vary widely, from more than US\$ 4,000 in the 'Achieving Zero Hunger' study to just above US\$ 300 in Torero and von Braun (2015). The number of people lifted out of hunger also differs substantially, from 650 million in the 'Achieving Zero Hunger' study, 580 million in the IMPACT modelling study, 500 million in Torero and von Braun (2015), to only 290 million in the MIRAGRODEP modelling study. These differences are accounted for by differences in modelling assumptions, and the scope of each framework in terms of suggested investments and interventions. Rather than providing clear-cut answers, the studies suggest that a variety of diverse investment strategies can contribute to ending hunger.

Although all five estimation approaches address the issue of financial needs for the achievement of SDG 2, the scope of each framework is narrower than the scope of SDG 2 itself. SDG 2 has five targets, the first two concerned with ending hunger and ending all forms of malnutrition by 2030. The remaining three targets concern doubling agricultural productivity and the income of small-scale food producers by 2030, ensuring sustainable food production systems by 2030, and maintaining the genetic diversity of seeds, plants and animals, including wild species by 2020. Three of the models focus on either eradicating or substantially reducing hunger. However, the definitions of hunger vary between the studies, and are based either on food access, as in the 'Achieving Zero Hunger' and the MIRAGRODEP modelling studies, or food availability, as in the IMPACT modelling study; none consider all four dimensions of food security, i.e. food availability, access, utilization, and stability. Only two frameworks, the WB's Investment Framework for Nutrition and the estimates by Torero and von Braun (2015), explicitly model the nutrition outcomes; with the latter being the only one to address both objectives of hunger eradication and improved nutrition in one framework. The other four studies only assume that investment to reduce hunger will also help to reduce malnutrition. Finally, only one of the studies, MIRAGRODEP based 'Ending Hunger: What Would It Cost', factors in the question of sustainability in agriculture.

There are important trade-offs between the scope of a modelling framework and the complexity of the methodology used. Looking at the five frameworks reviewed here, it seems that the narrower the scope of the study, the more detailed and accurate the estimates, as in the case of the MIRAGRODEP model. The combination of macro-level and household-level data in the MIRAGRODEP model is an interesting methodological development in comparison to studies based on national averages of dietary intake, as it allows not only for assessing the cost-effectiveness of interventions but could also better capture the distributional effects of investments across heterogeneous households based on their specific socio-economic characteristics, which are largely omitted in most analyses. Also, only a few models explicitly include the investments necessary to create enabling environments for achieving SDG 2; admittedly, these are relatively difficult to present in monetary terms.

Last but not least, the financing strategy with respect to the pacing of investments, allocation of financial resources between competing objectives, distribution of the burden of investment between various financing sources, and the sustainability of results beyond 2030, especially in the context of large economic, climatic or political shocks, is rarely considered in detail in the reviewed frameworks. In particular, the issue of how to spread investments over time is not discussed in much detail in any of the models; instead, the costing estimates are presented in terms of annual averages. However, this has serious implications not only for the resource mobilization strategy and therefore the feasibility of timely investments, but can also affect the economy-wide outcomes of the intervention.

Another question is how to allocate limited financial resources between the various SDGs and the development targets specific to SDG 2. Of course, the case for investing in zero hunger target is evident, as the right to food is considered to be among the most basic of human rights. However, in the context of scarce financial resources, the potential synergies between different objectives, as in the case of eradicating hunger (SDG 2) and poverty (SDG 1), need to be found. Also, potential conflicts, for example between doubling agricultural productivity (SDG 2.3) while preserving the natural environment (Sachs et al. 2019), e.g. ensuring sustainable food production systems (SDG 2.4), need to be addressed to make the proposed investment strategies efficient. Additionally, the long-term sustainability of the proposed investment frameworks are rarely explicitly addressed. The time horizon of the models ends in 2030, aside from the 'Investment Framework for Nutrition' which ends in 2025 (Shekar et al. 2017). The latter is the only one to include a 5-year maintenance period (2021–2025); in general, however, the question of how to sustain the results beyond 2030 is not discussed. In the broader frameworks, like the 'Achieving Zero Hunger' or MIRAGRODEP frameworks, the implicit assumption is that pro-poor investments in agriculture and their expected long-term economy-wide growth effects will be sufficient to maintain zero or 5% hunger levels worldwide. While this might hold if the proposed frameworks' scenarios hold, the reduction in hunger might be reversed in the case of major economic, climate or political shocks, as the last decade has proven (FAO 2018). Only the IMPACT model-based study includes the effects of climate change in its modelling framework (Rosegrant et al. 2017); and none of the models discuss the challenges of achieving zero hunger in fragile states, i.e. conflict and post-conflict states.

#### 3 Overview of Approaches to Reducing Hunger

Despite continued global agricultural output growth and considerable reduction in hunger since the 1960s, food insecurity still persists, albeit with huge differences between countries, within countries and even households (FAO et al. 2015). The nature of food insecurity has been also changing as increasing demand for processed food and consequently higher consumption of unhealthy fats, sugars and salts are exacerbating obesity and micronutrient deficiency (Barrett 2021). Thus, interventions focused on agricultural productivity improvement alone will not be enough to achieve the goal of sustainable food security. Achieving global food security would require not only improvements along food supply chains but also additional efforts in health, education, information and research systems. A sustainable development of the food system should go along with ensuring food and nutrition security and without compromising the social, economic and environmental futures for the generations to come (HLPE 2014). This study looks within and beyond agricultural system to identify the intervention options and investments needed to alleviate hunger or undernourishment.4 Performing food systems analysis considering multiple interventions entails an assessment of the relevant processes that influence the four dimensions of food security food availability, access, utilization and stability.5 Food availability emphasizes the need to address the supply side of food security to ensure sufficient quantities of food is available to individuals either through food production or imports. Food access on the other hand points to the importance of ensuring individuals have the resources necessary to obtain sufficient quantities of food. Going beyond availability and access to food, food utilization focuses on dietary quality and highlights the importance of ensuring individual's ability to utilize the energy and nutrients in the food they consume. Food stability reflects the stability of the three dimensions and reminds us of the importance of taking into consideration seasonal or temporary food prices and shocks in hunger prevention policies.

Food security can be enhanced through multiple investment options that intensify agricultural production and improve agricultural productivity. For instance, agricultural R&D and extension efforts to enhance crop and livestock production can boost food supply. Innovations in improved crop varieties, methods to improve soil fertility, and efficient irrigation technologies can also increase agricultural productivity and address food availability. The resulting increase in agricultural productivity further contributes to increased agricultural income, improved purchasing power and reduced food prices, which when combined with innovations in post-harvest technologies can improve access to food, increase calorie consumption, increase dietary diversity, and thus enhance food accessibility and utilization.

Market platform and infrastructure improvements help in reducing post-harvest losses and enhance access to food. Improved storage systems, better roads, availability of food processing, and equitable food distribution systems can greatly improve access to food by consumers. Trade rules at the international and intranational level also greatly impact on food access. Some infrastructural improvements such as electricity access and information and communication technologies (ICTs) can improve food supply, distribution and access systems. For example, using mobile phones, farmers can access information about the weather and market conditions, allowing them to better manage water resources and fetch higher prices for their produce. ICT and storage systems are also important to plan and predict food supplies and hence, stabilize food market prices. At the same time,

4 This study uses the prevalence of undernourishment (PoU) as the main indicator for hunger. The PoU identifies the proportion of the population whose habitual, daily, per capita dietrary energy consumption (DEC) level is lower than their dietary energy requirement (Cafiero and Gennari 2011). It is computed from aggregated country-level data on food availability that is annually compiled in FAO's Food Balance Sheets and data on food consumption from surveys which is available for certain countries.

5 Future work on estimating the cost of ending hunger could further consider interventions that can address the two additional dimension "agency" and "sustainability" that have become crucial for transforming food systems towards the direction needed to meet the SDGs (HLPE 2020)

interventions that can enhance the incomes and purchasing capacity of the population can improve food affordability, which is an important aspect of food access. For some marginalized groups with inadequate income and informal jobs, social security programmes such as food vouchers and financial assistance can be considered.

Severe cases of child malnutrition, caused by nutrient insufficiency and certain diseases, require nutrition-specific interventions. According to Bhutta et al. (2013), at least 20.3% of the current child stunting rate could be averted if ten evidencebased nutrition-specific interventions were scaled up to cover 90% of the population in countries with high stunting burden. These interventions include periconceptional folic acid supplementation or fortification, maternal balanced energy protein supplementation, maternal calcium supplementation, multiple micronutrient supplementation in pregnancy, promotion of breastfeeding, appropriate complementary feeding, vitamin A and preventive zinc supplementation in children aged 6–- 59 months, management of severe acute malnutrition, and management of moderate acute malnutrition.

The quality of maternal- and child-care practices is also one of the non-nutritional factors that affect the nutritional outcomes of children (Smith and Haddad 2015). Women play a key role in children's nutritional outcome as they give birth to them, breastfeed them and are their primary caretakers. Hence, maternal education has numerous positive impacts on the quality of maternal care that mothers receive during and after pregnancy and consequently on the quality of care that their children receive, ranging from the amount of breastfeeding to seeking health care in case of illnesses (Ruel et al. 2013). The strong link between female education and nutritional outcomes of children, particularly for stunting, has been well established (Headey 2013; Smith and Haddad 2015). The specific intervention options considered in this study are described in the next section.

### 4 Marginal Abatement Cost Curve Approach and Investment Scenario Assumptions

#### 4.1 The Marginal Cost Curve and Key Steps of the Process

Policymakers need to prioritize the allocation of resources to competing hunger reduction measures by identifying the sets of least-cost investment options that have the potential to yield the greatest reduction in hunger in a defined time horizon. It is therefore essential that policymakers and practitioners can compare the different hunger reduction measures and make economically efficient investment decisions. In this regard, MACCs can be helpful as a policy tool in ranking investments options. Applications of MACCs are common in the economic assessment of climate change mitigation options (Schneider et al. 2007; Kesicki and Ekins 2012; Bockel et al. 2012; IPCC 2014; Eory et al. 2018) and have been also extended into the assessment of effective water policies (Addams et al. 2009). This study implements MACC approach in hunger reduction research. By developing a realistic and policy-relevant global MACC of different hunger reduction measures, the study allows to assess their cost-effectiveness and contributes to the evaluation of actions that should be prioritized and implemented to achieve target 2.1 of SDG 2 by 2030.

MACCs are developed based on either modelling outcomes or multiple expert opinions (Kesicki 2013). MACCs derived from top-down modelling provides internally consistent estimations, follows to smooth and continuous dynamics but do not account for the effects of specific interventions (Klepper and Peterson 2006). Expert based MACCs, despite being criticized for double counting and interaction possibility, are richer in terms of reflecting technology details as they are constructed by summarizing and synthesizing the average costs and abatement effects of multiple interventions. As our assessments are based on the cost and hunger reduction effects of multiple interventions from multiple studies, the framework of expert-based MACC is relevant here.

The global hunger reduction MACC represents the relationship between the costeffectiveness of different hunger reduction interventions and the hunger reduction potential of each intervention. It reflects the additional costs of lifting people out of hunger by each intervention. Elaboration of the MACC were conducted step-bystep, by first identifying the variety of intervention options that can effectively reduce hunger, and then by determining the cost and hunger reduction potential of the interventions. The related assessment is conducted through a literature review and an integrated evaluation of model-based, econometric or cost-benefit analysis studies. While the cost and hunger reduction potential (number of people lifted out of hunger) were readily found in the literature for some of the interventions, additional calculations or assumptions based on expert assessments were considered for others. Particularly, hunger reduction potential was derived through additional calculations considering conversion factors and elasticity coefficients when food security enhancement effects such as additional food supply, income, or prevented levels of undernourishment due to the interventions are available. The cost of implementing some interventions were also estimated considering the prices of food, costs of social protection, or transaction costs of trade (details of these calculations and assumptions behind are provided in the Supplementary Material of the original publication). Finally, the interventions are ranked from the cheapest to most expensive, based on their marginal costs (average cost of lifting an individual out of hunger) to represent the cost of achieving incremental levels of hunger reduction.

### 4.2 Reference Scenarios of Hunger Trend and Investment Options

In a MACC based economic assessment of investment options, reference scenarios are built either based on a "business as usual" scenario, using historical trends to identify future developments, or based on alternative scenarios that consider climate

Fig. 1 Hunger levels expected under various socio-economic and climate change scenarios. (Source: Authors' own elaboration based on Hasegawa et al. (2018) and FAO et al. (2020))

change impacts and socio-economic developments of the future. Model-based foresight exercises highlight how food and agricultural systems could evolve in an inherently uncertain future. These foresight exercises provide alternative scenarios on food security in which challenges are addressed to varying degrees, building on historical trends of factors that determine the performance of socio-economic and environmental systems. According to the bio-economic model-based assessments of AIM/CGE, GLOBIOM and IMPACT, under various climatic and socio-economic development scenarios the world will be home to between 251 to 842 million undernourished people in 2030 (Fig. 1; Hasegawa et al. 2018). If population growth were to be largely controlled, high economic growth rates (SSP1) were maintained and climate change effects were neglected (dark green, blue and brown lines in the first bunch of the lines), the number of undernourished people would be reduced to between 251 to 437 million. Yet, when climate change (RCP6.0) is considered in the modelling assessments the number of undernourished people is expected to be between 288 and 443 million (light green, blue and orange lines in the first bunch of the lines). Under the worst scenario, with high population growth, economic stagnation, high-income inequality (SSP3), and a climate change impact (RCP6.0), the number of undernourished people is expected to be between 617 and 842 million (light green, blue and orange lines in the third bunch of the lines). All three modelling assessments indicate similar trends of hunger reduction under various socio-economic and environmental changes. Yet, the magnitude of the reduction differs across the modelling assessments.

The worst scenarios of the projected number of undernourished people in 2030 lie close to the projection presented in the recent report of the state of food security and nutrition in the world without considering the impact of the COVID-19 pandemic, i.e. about 840 million (Fig. 1; FAO et al. 2020). The pandemic is expected to further accelerate the projected increase in the number of people facing hunger, at least in the immediate future. As the global economy contracts due to containment measures of COVID-19, it is anticipated that hunger will also increase globally, hampering the progress of global efforts geared towards achieving the SDG 2 targets. A 4.9 to 10% decline in global GDP growth is estimated to lead to an additional 100 to 194 million people into hunger globally in 2020 and 2021. In the worst-case scenario, the pandemic could potentially increase the number of undernourished people to 909 million by 2030 (FAO et al. 2020).

In the MACC analysis, the reference scenarios of hunger trends presented above serve in determining the number of the undernourished people that would need to be lifted out of hunger to achieve target 2.1 of SDG 2 by 2030 and hence the investments required to reach the target. In this study, the projection that consider the impact of COVID-19 is used as the reference scenario for the population at risk of hunger in 2030, since it is the recent authoritative foresight study on hunger that considers the impact of the pandemic on hunger.

The cost and hunger reduction potential of the various investment options considered in this study are also analyzed relative to a "business as usual" or reference scenario of investments, wherein the costs of investments are assumed to remain frozen or grow following historical trends. The costs in the reference scenario include all investments required to achieve the projected level of implementation of the intervention options by 2030, including the capital, operational, and programme costs where applicable. For instance, the IMPACT model-based projection, from the study by Rosegrant et al. (2017), is used as a reference scenario for the interventions such as agricultural R&D, water resource management, and infrastructure. Rosegrant et al. (2017) used IFPRI's IMPACT model together with a global computable general equilibrium model (GLOBE) and several linked post-solution models to evaluate investment requirements, land-use changes, greenhouse gas (GHG) emissions, biodiversity, water quality, and micronutrient availability and dietary diversity under the business as usual scenario. In addition to the climate change assumptions, Rosegrant et al. (2017) consider investment in agricultural R&D, water resource management, and infrastructure under the business as usual scenario. The projections of these investments under the business as usual scenarios are based on historical trends and expert opinions of long-term developments in the agricultural sector. Investments in water resource management are modelled endogenously combining the IMPACT model with a suite of water models. Similarly, all investment options considered in this study are compared to a reference scenario to identify the incremental cost of implementing the investments.

### 4.3 Opportunities of Investments in Policies and Programs for Hunger Reduction

For estimating hunger reduction potential, 22 interventions were selected based on the framework described in Sect. 3. Details of these interventions and overview of data and approaches used in calculating their hunger reduction potential and implementation costs are presented briefly in Table 2 to make all of the assumptions transparent. This would address the common critique, inherent in MACC, of a lack of transparency. Twelve of the twenty-two interventions are related to interventions for enhancing crop yields at farm levels through improved technologies, extension services, crop protection measures, soil fertility management and irrigation development. Five of the interventions are related to improved ICT, infrastructure and trade that improve food distribution efficiency. Three of the interventions consider supporting marginal groups of society increasing access on food. The last two interventions serve to reducing child malnutrition through enhanced child and maternal care. The details of the calculations to estimate the hunger reduction potential and costs of the interventions are presented further in the Supplementary Material of the original publication.

### 4.4 Investments to Reduce Hunger: Marginal Cost Curve Results

After ranking the considered interventions in accordance with their average cost per undernourished, a MACC of hunger reduction potential was elaborated (Fig. 2). Specific parameters of MACC such as the width (number of people lifted out of hunger) and length (annual cost per individual lifted out of hunger) of each bar (intervention) and additional indicators such as cumulative costs and cumulative hunger reduction potentials are provided in Table 3. According to the estimation, overall, the measures included in the MACC have the potential to lift over a billion people out of hunger over ten years between 2020 and 2030. To meet the G7 commitment of lifting 500 million people out of hunger by 2030, an average annual investment ranging between about US\$ 11 to 14 billion will be required.6 This would be achieved through a mix of least-cost intervention options –agricultural R&D efficiency enhancement, agricultural extension services, agricultural R&D, ICT – agricultural information services, small-scale irrigation expansion in Africa, female literacy improvements, and scaling up existing social protection. Following the 2030 hunger projection by FAO et al. (2020) and taking the preliminary estimates on the impact of COVID-19 on hunger (based on the less pessimistic

6 As can be seen from Fig. 2, the per capita cost estimate of lifting 500 million people out of hunger is within range of the prior estimates by Torero and von Braun (2015) and Laborde et al. (2016) that vary between US\$ 30 to 38 per person lifted out of hunger.

Table 2 Investment options for hunger reduction and investment scenarios assumptions

Table 2 (continued)

Table2(continued)

Fig. 2 Marginal cost curve of the suggested interventions to eradicate hunger

Note: The MACC for hunger shows the cost of each hunger reduction measure such that each bar represents a single intervention where the width shows the number of individuals lifted out of hunger, the height its associated annual per capita cost, and the area its associated total annual cost. The total width of the MACC reflects the total hunger reduction possible from all interventions, while the sum of the areas of all of the bars represents the total annual cost of reducing hunger through the implementation of all interventions considered. The positions of the bars along the MACC reflect the order of each intervention by their cost-effectiveness based on the annual per capita costs. When moving along the MACC from left to right, the cost-effectiveness of the interventions worsens as each next intervention becomes more expensive than the preceding. It is important to note that this figure is subject to considerable uncertainty given various assumptions made in the calculation, missed synergies and potential overlap between interventions and impact of extreme events not considered when estimating the costs

scenario) into consideration, the global goal of ending hunger by 2030 may require an investment of about US\$ 39 to 50 billion to lift about 840 to 909 million people out of hunger.

As illustrated in Fig. 2, investing in agricultural R&D efficiency enhancement, agricultural extension services, 'ICT – Agricultural information systems', are low cost options that have a relatively large hunger reduction potential. Scaling up existing social protection programmes and establishing new programmes to serve food insecure households can reduce the number of people at risk of hunger by about 206.2 million at an annual per capita cost of about US\$ 35.7 and US\$ 88.9 per undernourished. To address the potential increase in the number of people at risk of hunger estimated in 2020 and 2021 of about 137.9 million, an additional US\$ 12.3 billion will need to be spent in social protection. While investing in women's education also provides a least-cost option to reduce hunger, investment in

 lifted out of hunger is then calculated as the annual cost divided by the number of people lifted out of hunger. The cumulative figures for the numberpeople lifted out of hunger and annual costs across the interventions reflect the total hunger reduction possible from all interventions and the total annualinvestments required. Total cost per person lifted out of hunger is calculated as total net discounted cost over the ten-year period (with the exception of COVID-19 social protection, ICT and scaling new and existing social protection programmes where the time frame is 2–5 years respectively, i.e. 2020–2021 and2020–2024). The discount rate is assumed to be 5%, following Hoddinott, et al. (2013). The total cost per person lifted out of hunger is then calculated as thetotal net discounted cost divided by the number of people lifted out of hunger

nutrition-specific investments can significantly reduce hunger by about 30 million at a total incremental average cost of about US\$ 5 billion per year.

Investments in 'African Continental Free Trade Area (AfCFTA) agreement', 'Food loss and waste reduction', 'Irrigation efficiency enhancement', improvements in international trade (completion of the DDA), 'Infrastructure', 'Soil-water management', and 'Large-scale irrigation expansion' can considerably decrease undernourishment by about 232.2 million. These hunger reduction measures are relatively expensive investment options that require a longer time for implementation and hence would need to be frontloaded earlier in the decade to have a large effect soon before 2030.

It is also important to note that the marginal cost curve elements include many investments that contribute to long-term development and sustainability, beyond 2030 and not restricted to hunger reduction. For instance, investments in agricultural R&D and research efficiency, irrigation expansion and water use efficiency, soil water management and infrastructure all have a long-term impact going further to 2050 and also have much broader development impacts beyond the reduction of hunger, like poverty, child malnutrition, and the environment (Rosegrant et al. 2017). The composition of the investments facilitates an increase in resilience for populations affected by hunger today or at risk of hunger in this decade. Since it is beyond the scope of this study, such lagged benefits of investments and their impact on other development outcomes beyond hunger has not been considered in this study and hence the estimates presented might understate the full benefits of these investments.

#### 5 Uncertainties and Caveats of the Assessment

#### 5.1 Uncertainties Associated with Data and Assumptions

e The developed MACC of hunger reduction can considerably contribute to debates over the prioritization of efforts and allocation of investments to achieve the global goal of ending hunger. However, it is important to note that the cost assessments and rankings of the interventions are subject to various levels of uncertainties due to various assumptions made in the calculations. Thus, these estimates should be perceived as only the best possible estimations based on available data. Due to the limited availability of data for conducting a proper quantitative analysis of uncertainty, we here present a qualitative analysis of uncertainty (Table 4). This assessment is related to the description of calculation steps presented in Table 2. W further use narrative analysis of potential impacts of various uncertainties on the levels of costs and hunger reduction potential and shape of the MACC.

According to the assessments, despite the very low cost of 'Agricultural R&D efficiency enhancement', this cost level is subject to 'high' uncertainty and hunger reduction potential is subject to 'moderate' uncertainty (Table 4). The cost of 'Agricultural extension services' is characterized with 'low' uncertainty but its hunger reduction potential is characterized with 'high' uncertainty. Most of the

Table 4 Uncertainties of potentials and implementation costs of the hunger reduction interventions

Note: Levels of uncertainty are defined with "+" for 'low', "++" for 'moderate' and "+++" for 'high'. Evidence-based data estimated through econometric assessment or obtained from reliable statistical sources were considered with 'low' uncertainty. We assume 'moderate' uncertainty if the data was found out from simulation modelling studies or derived through additional calculations considering data with low uncertainty. 'High' uncertainty emerges in case value was obtained through additional calculations based on data with 'moderate uncertainty' or based on pure assumptions

remaining interventions except 'Small-scale irrigation in Africa' and 'Female illiteracy improvement' are subject to 'moderate' or 'high' levels of uncertainty.

### 5.2 Missed Synergies, Overlaps and Other Investment Options

As each intervention in the MACC is considered independently with its marginal costs and hunger reduction effects, beneficial synergies among interventions are not captured. For instance, interventions such as constructing irrigation systems and implementation of ICT in water distribution systems may have additional synergetic benefits. Yet, the presented MACC indicates conservative estimates of mixes of interventions. Consequently, it is possible that the costs are overestimated and hunger reduction impacts are underestimated for such cases.

Some overlap or double counting may also exist between the considered interventions as there are likely a group of people who have already been lifted out of hunger through one policy yet may benefit from the second policy. It may occur for example in places where many people are quite near the threshold of undernourishment. This implies that a certain group of people could be counted multiple times and hunger reduction potential of the interventions might be overestimated. Also, in places where the gap between adequate and actual nourishment is high, a bundle of complementary interventions may be required to lift people out of hunger rather than a single policy (Banerjee et al. 2015; Barrett et al. 2020). Hence, there may be some undercounting in the event of bundled programs which could instead lead to an underestimation of the hunger reduction potential of interventions and overestimation of the costs. However, since the MACC is built based on an aggregated assessment of interventions at the global level, our study does not highlight such details. Further studies should differentiate hunger levels, their exact causes and precise solutions to address this issue. Bottom-up integrated assessment models may capture such synergies and reduce double counting consequently allowing for developing consistent MACCs.

Analyzing the hunger reduction potential and costs of the selected twenty-two options available from the recent studies, it is likely that we omitted other hunger reduction interventions where costs and hunger reduction potentials were either not available or not widely discussed in the literature. As an example, food production and harvesting in marine environments including the production and harvesting of seafood and seagrasses were excluded as the option was not widely assessed at the global level for hunger prevention. Likewise, alternative foods produced from insects and non-traditional food crops were not considered as their health safety and upscaling potential have not been properly examined. With more advances in the sciences, new interventions can come into the scene and they may change the shape of the MACC for hunger reduction.

### 5.3 The Impact of Extreme Events on the Cost of Hunger Reduction

Investment options reviewed in this study did not explicitly include climate change or the effects of extreme events like conflicts, pandemics and extreme weather events on hunger reduction. Even though it is understood that important drivers of acute food insecurity in 2020 were conflict, economic fallout of COVID-19, and extreme weather events. In 2020 an estimated 99 million people were in acute food insecurity because of conflict situations (Food Security Information Network & Global Network Against Food Crises 2021). This statistic may only be roughly compared with the statistic of undernourishment (768 million) as the two statistics are based on very different concepts.

In 2019, six out of ten people eligible for global humanitarian food assistance were residing in countries with ongoing conflicts (Development Initiatives 2020). Also, the share of children suffering from stunting residing in conflict zones has considerably increased within the last two decades (FAO et al. 2017). Conflicts directly and indirectly impact on food insecurity. For instance directly through resource loss when fields are rendered unusable due to mines, and indirectly through disruption of markets and trade. Barrett (2021) makes the argument that the hunger crises today and of the future are fundamentally humanitarian and conflict resolution issues rather than shortcomings with agri-food systems, and addressing hunger requires targeted humanitarian and conflict resolution efforts instead of agri-food innovations. While it is widely regarded that conflict resolution can have a profound impact on hunger reduction, estimating the cost, such as diplomatic and peace keeping engagements, and impacts on other interventions would be multifaceted and quite complex (Kemmerling et al. 2021), and hence was not considered in this study.

Studies reviewed in our analysis were conducted before the COVID-19 pandemic and did not consider the impacts of such an event or similar pandemics on the goal of hunger reduction and the cost to other interventions. Nonetheless, it is noteworthy that as a pandemic event like COVID-19 impacts all aspects of society and the economy it surely would have a considerable impact on the effort to achieving zero hunger before 2030, given that all the resources needed to achieve this goal would be directed towards the fight against the pandemic. This is especially true for financing, which is mainly from wealthy nations but immediately repurposed and prioritized for healthcare investments and economic stimulus for their citizenry, thus leaving the fight against hunger in peril, howbeit temporarily. The extent and impact of this temporary neglect due to the pandemic are not assessed in this study.

Climate change impacts not only hunger levels but also the costs and potential of the targeted hunger prevention interventions. Rising temperatures and consequent drought increases the value of water and reduce the efficiency of the projects related to irrigation improvement (IPCC 2019). Also, intensified flooding events induced by temperature rise destroys agricultural production systems as well as affect hydraulic infrastructure reducing the resilience capacity to cope with temperature anomalies and systems, exposing the farmers and rural community to hunger. Meanwhile, climate change mitigation efforts are increasingly and deservedly prioritized, but sometimes at the expense of the efforts to reduce hunger. For instance, available land likely to be used for solar power generating projects instead of agriculture, hydroelectric power dams are prioritized regardless of the impact on surrounding farms, and available public funds are allocated to climate change mitigation efforts limiting finance to hunger reduction programmes. In our study, these linkages and the extent of the impact on the interventions were not considered.

#### 5.4 Effects of Scaling on Marginal Costs

As the marginal cost of each intervention is assumed to be fixed per undernourished person lifted out of hunger, the aggregated marginal cost curve appears like a staircase (step) function. Growing marginal cost in the aggregated marginal cost curve is due to the ranking of the individual interventions. In reality, increasing (not fixed) marginal costs of hunger reduction are expected for each intervention. Due to the scaling effects, it is not surprising that the additional cost of reducing the number of undernourished people increases with the number of undernourished people lifted out of hunger. Lifting the first group of people out of hunger requires less investment than the last group of hungry people since the cost of reaching the most vulnerable and hard to reach populations is a lot more than to reach those that are less in need of food assistance and support. Based on our data collected from the literature review of contributions and costs of various interventions, continuous aggregated MACC can be derived by replacing the staircase graph with the polygon graph (See Supplementary Material available in the original publication). The polygon-type graph can be fitted to obtain smoothly growing cost functions that are similar to the ones obtained through modelling. Combining interventions considering their varying marginal costs is an alternative option to assess aggregated MACC yet it may require top-down modelling application (Klepper and Peterson 2006). Such assessment would most likely change the shape of the aggregated MACC, having more people lifted out of hunger at a low price at the beginning but increasing the costs of interventions even further for the remaining groups of people who require food support the most.

### 5.5 Other Limitations and Some Strengths of the Marginal Cost Curve Approach

MACC can be used to identify promising policies and programmes for investment. This facilitates priority setting by governments and investment stakeholders from the private sector and civil society. An advantage of MACC analysis is also its transparency. However, the concept has several limitations which have been already highlighted in previous studies (Kesicki and Ekins 2012; Bockel et al. 2012; Eory et al. 2018). One of the limitations relates to the fact that the MACC presents the incremental cost of reducing hunger for a single point in time. Hence, it cannot capture intertemporal dynamics and technological inertia. Education and R&D investments for example yield gains after sometime. In a static MACC, lagged effects for such investments are not effectively captured in the MACC.7 Another aspect is that the MACC concentrates on hunger reduction and thus attributes the entire cost of the interventions only to hunger reduction. This is an overestimation in terms of economic cost-benefit considerations, as most of the interventions considered in this analysis generate various ancillary benefits, including reducing poverty and enhancing health, environmental sustainability, and education. Nevertheless, the MACC can be considered useful for an assessment of various potential interventions to reduce hunger based on a synthesis of studies from different fields based on multiple methodologies.

Additional analysis – for example at regional or perhaps national levels – is also warranted for prioritizing the measures for implementation and setting policies to promote them. Additional studies could focus on extending the analysis by identifying additional cost-effective measures in specific country contexts which can further contribute to hunger reduction. Technical and behavioral challenges to implementing the identified least-cost measures need to be considered in the prioritization process, despite their economic attractiveness.

While most of the parameters used in building the global hunger MACC are compiled from system- and economy-wide model-based studies, the cost and hunger reduction potential of several interventions were assessed based on a specific and large-scale cost-effectiveness studies. A next step, in this respect would be to evaluate the various measures using bottom-up integrated assessment modelling that could capture synergies and trade-offs between the different measures, as well as risks and uncertainties. Theoretically, that would be an advantage, but it remains difficult to embed the level of granularity and programmatic detail in such modelling, as pursued with the 22 interventions considered in the MACC approach here. Yet, additional quantitative sensitivity analysis and interpretation of the different results would be helpful to policymakers to support their decision-making.

7 However, Rosegrant et al. (2017) capture the lag effects of investments in agricultural R&D in the investment-yield estimation model using a perpetual inventory method, where investments in agricultural R&D contribute to the stock of knowledge over time. The lag structure in the perpetual inventory method used in the study followed a gamma distribution where the impact of R&D investments peaked after ten years from initial investment and then sunk to zero after ten years from its peak.

#### 6 Policy Implications of MACC Analyses

This study has synthesized the findings of various model- and cost-benefit analysisbased studies on food and nutrition security interventions to assess the expected levels of the hunger reduction and the costs of achieving zero hunger by 2030. The most recent 'State of Food Security and Nutrition in the World' report estimated levels of undernourishment by 2030 to be about 630 million without considering the impact of COVID-19, or 660 million when considering the impact of COVID-19 on hunger.

MACC analyses are a basis for policy strategies and policy mobilization. The MACC for hunger reduction developed by synthesizing the outcomes of multiple studies indicates the overall potential of the interventions identify what it takes to end hunger by 2030. Considerable investment is required, but it is a question of political commitment to get the finance mobilized at national and global levels and the actual investments implemented in sound ways. Compared to the hundreds of billions of US\$ for economic rescue packages to mitigate COVID-19 in many OECD countries, the investments to end hunger presented in this analysis are rather modest. The results from the MACC indicate that:

Yet, given the finding that investments to end hunger are rather modest, the troublesome question arises, what political economy forces prevent the required actions? Obviously, the spending priorities of those who could mobilize the resources seem not sufficiently oriented toward overcoming hunger, and the voice and influence of the undernourished seem too weak to enforce the investment action. Attempting to comprehensively answer this question goes beyond the scope of this paper. However, research into assessing the political economy for each of the considered interventions in the MACC might help to identify a set of politically acceptable second-best MACC elements that might differ from the marginal costs.

Acknowledgment This chapter was published in Food Policy, 104, October 2021. The supplementary material is also provided in the online version of this paper.

#### References

FAO (2020) FAOSTAT statistical database. FAO, Rome. http://www.fao.org/faostat/en/

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# Financing SDG2 and Ending Hunger

Eugenio Díaz-Bonilla

### 1 Introduction

The adequate functioning of food systems is crucial to achieving multiple Sustainable Development Goals (SDGs) by 2030 (von Braun et al. 2020b). This chapter focuses on financing the transformation of food systems to help achieve crucial components of SDG2,1 including ending hunger by 2030. Given those objectives, the analysis centers mainly on agricultural production and rural development, as well as on poor and food-insecure consumers, both rural and urban, as part of the more general collection of issues around production and diets in food systems. Even with that focus, the interventions2 considered have important implications for a variety of other nutritional and environmental objectives.

The chapter compares the additional costs of achieving SDG2, including zero hunger (as estimated by von Braun et al. 2020a and studies referenced therein), with potential financial sources. The estimates of potential funding use the framework in Díaz-Bonilla et al. (2021), which identifies two flows of funds "internal" to food systems (consumer food expenditures, which are the sales/revenues that the agents in the agri-food system use to finance their operations), and four that are "external" to food systems (international development flows, public budgets, banking systems, and capital markets) (Fig. 1).

E. Díaz-Bonilla (\*)

1 The focus is on ending hunger (2.1); doubling agricultural productivity and the incomes of small farmers (2.3); and fostering sustainability and resilience within food production (2.4). In the text, references to SDG2 must be understood in this vein.

2 "Interventions" refer to public sector actions, including policies, programs, investments, expenditures, taxes and subsidies, laws and regulations, and institutional aspects, that seek to address a specific problem.

International Food Policy Research Institute (IFPRI), Washington, DC, USA e-mail<EMAIL_ADDRESS>
J. von Braun et al. (eds.), Science and Innovations for Food Systems Transformation, https://doi.org/10.1007/978-3-031-15703-5\_35

Fig. 1 Flow of funds for food systems. (Source: Díaz-Bonilla et al. (2021))

The main questions analyzed here are: given the estimated costs involved in such a transformation, what are the options for financing the interventions needed, what is their quantitative availability, and how can those potential sources of finance be reallocated and mobilized to achieve SDG2, including ending hunger?

Adequate macroeconomic policies, a supportive business environment, and peace are basic requisites for the operation of food systems. Also, different policy interventions can influence the size and allocation of consumer expenditures3 and the production outlays of the operators of food value chains (the internal flows) in ways that help achieve different SDGs (see a discussion in Díaz-Bonilla et al. 2021). However, the internal flows are not the focus of this chapter, but rather the availability and mobilization of external flows to food systems, which can augment the internal flows to finance the additional costs of reaching SDG2 and ending hunger.

This chapter is structured as follows. Section 2 focuses on the costs of achieving SDG2, based on the work referenced in von Braun et al. (2020a). Section 3 presents estimates of the current values of the external funds that can complement the internal flows and help finance the additional expenditures and investments needed to achieve the desired objectives. Section 4 compares the costs in Sect. 2 with the availability of funds estimated in Sect. 3 and evaluates different financial alternatives for effectively mobilizing the additional resources needed. The analysis considers the total amount of financial resources available; whether some of them can be reallocated towards the desired objectives; and, if that is not enough, where the additional money may come from, considering overall budget constraints. A conclusion is that, in the aggregate, there are enough financial resources to achieve

3 Consumer food expenditures are estimated within a range of 8–10 trillion dollars annually (updated from Díaz-Bonilla 2021).

SDG2. However, Sect. 5 argues that it is not only a matter of overall availability of financial resources (which must be further assessed at the level of individual countries as well), but of adequately designing and implementing national programs. Therefore, the section presents the idea of a Zero Hunger Alliance & Fund (based on suggestions advanced by different global leaders4 and by Action Track One of the United Nations Food Systems Summit, UNFSS) to help developing countries design, finance and implement zero hunger programs. Section 6 summarizes all proposals and Sect. 7 concludes.

#### 2 Costs of Interventions to Achieve SDG2 and End Hunger

The estimates of the costs related to SDG2 and ending hunger are based on the work reported in von Braun et al. (2020a, b), with the background of two other studies, ZEF and FAO (2020) and IFPRI et al. (2020). Those studies consider a variety of interventions to end hunger, increase agricultural incomes, and achieve certain environmental outcomes, mainly related to mitigation and adaptation to climate change. The number of people estimated to avoid hunger depends on the costs and range of the interventions considered (Table 1).

The costs of eliminating hunger are not linear, with each further reduction in the number of people affected becoming more expensive (ZEF and FAO 2020). The largest estimate, of about 163 billion dollars annually, would save about 1050 million people from hunger by 2030.5 ZEF and FAO (2020) reckons that, without the interventions considered and under intermediate climate scenarios, the number of hungry people in 2030 would be about 900 million. But, as the study clarifies, this projection does not consider the possibility of additional humanitarian, health, or

Table 1 Estimates of ending hunger and other SDG2 goals

Source: Based on the cited studies

4 Pope Francis, for instance, advocated a "Global Fund" to end hunger http://www.vatican.va/ content/francesco/en/messages/food/documents/papa-francesco\_20201016\_messaggio-giornataalimentazione.html

5 FOLU (2019) estimates the costs of 10 "transitions" needed for the transformation of food systems at 300–350 billion dollars per year until 2030. Those transitions involve several SDGs; but considering only those more directly related to SDG2, the costs would be about 170–190 billion dollars (close to the high estimates in ZEF and FAO 2020).

environmental crises. The matrix of financing in Sect. 4 considers the intermediate estimate of lifting 870 million people from hunger, as well as and in addition to the target of about 1 billion people avoiding hunger (both as a cushion against future crises and because the additional interventions support climate change adaptation and mitigation as well).

# 3 Possibles Sources of Funding

Each of the following subsections discusses quantitative estimates of the annual current values of the external sources.6 They will be compared later with the additional costs shown in Table 1.

1. International development flows

International development flows include concessional development assistance and non-concessional lending by bilateral agencies, multilateral development banks (MDBs), and some large private philanthropic funds. Using disbursements7 in current values (from FAOSTAT), the annual average for the period 2014–2018 has been some 256 billion dollars for all uses/sectors, and 11.1 billion for agriculture,8 forestry and fishing (AFF), or some 4.3% of all development flows. If development flows to other sectors related to SDG2 (such as water and sanitation) are included, then disbursements in 2018 were estimated to be about 15 billion dollars (ZEF and FAO 2020).

2. Public budgets

Many public policies and expenditures influence the operation of food systems. Considering the interventions related only to SDG2, the analysis centers on two main types of public expenditure: on AFF and on social protection. There is also a brief discussion of additional fiscal expenditures related to the COVID-19 pandemic.

6 Remittances are an important flow of funds. However, they are basically intra-family flows, which may not be possible or desirable to reallocate through public policies.

7 The values of disbursements are different from the net flows in the case of loans (concessional or not), because repayments of the principal of the previous loans must be deducted.

8 Agriculture includes Agro-industry, General Environment Protection, Food and Nutrition Assistance, and Rural Development. Some countries report expenditure for the General Government, others only for the Central Government, and some of them report both. Table 2 has been calculated with the larger of the two values reported.

#### (a) Agriculture, forestry, and fishing

Table 2 shows total government outlays (current US dollar average 2014–2019) and outlays on AFF,9 using FAOSTAT data.10

Developing countries,11 not including China, show total outlays of some 5 trillion dollars, and 125 billion for AFF, which represents about 2.5% of total expenditures. While developing countries spend a larger percentage of their budgets on AFF than developed ones, it is also necessary to consider those expenditures against the size of the agricultural sector, using the Agricultural Orientation Index (AOI; last column of Table 2 with the median values by regions). It is calculated as the percentage of agricultural expenditures over total expenditures divided by the share of agricultural GDP in total GDP. A number smaller (greater) than 1 indicates that the share of government spending on agriculture is less (more) than the share of agriculture in GDP, suggesting that there would be under- (over-) spending in the sector relative to its economic relevance. Clearly, developed countries spend more as a proportion of their agricultural sectors than developing countries (excluding China).

Although the levels of public spending alone do not determine the performance of the agricultural sector, different studies show that the types of expenditure matter, particularly their orientation toward the provision of public goods (see, for instance, Fan, ed. 2008).

Also, as noted, these numbers do not include other public expenditures relevant for agriculture, such as rural infrastructure, or for the food system as a whole. These considerations suggest the need to utilize a broader food-system focus to analyze the level and composition of public expenditures at the country level that are relevant for achieving the desired SDGs.

#### (b) Social protection

Another important type of expenditure related to SDG2 and ending hunger is for programs of social assistance (i.e., those more directly linked to poverty and vulnerability that are financed by general revenues from the government and not

9 Using the distinction made by FAO (2012), they cover public outlays in agriculture (aimed specifically at enhancing primary production), but not for agriculture (which are government expenditures in other sectors that can also have a positive impact on the agricultural sector). Further, the classification does not include all of the expenditures that can support the whole food system (Díaz-Bonilla 2015).

10The OECD compiles producer support estimates in agricultural products only (not including fisheries and forestry) and for a more limited number of countries than FAOSTAT, but with a very useful disaggregation of interventions that include budgetary and non-budgetary transfers involving consumers (see https://www.oecd.org/switzerland/producerandconsumersupportestimatesdatabase. htm). Suggestions about reallocating or "repurposing" 600–700 billion USD in agricultural "subsidies" are based on those estimates. However, the OECD database shows that not all the transfers are agricultural "subsidies" that can be repurposed.

11What is considered a "developed" or "developing" country varies across datasets. Therefore, the numbers presented must be considered approximations for those groups.

Table 2 Government outlays (current dollars, average 2014–2019)

Source: Author, with data from FAOSTAT

by contributions from beneficiaries—known as "non-contributory programs"). Here, we focus on the social assistance programs using data from the World Bank's ASPIRE database. It is based on household surveys, and therefore it may not capture all governmental programs. Also, the database focuses on developing and emerging countries only. On the other hand, it provides a useful disaggregation of social protection programs and of the distribution of benefits across the population. 12

Table 3 shows an estimate of the money allocated to those programs in current dollars for the period 2014–2018, using the categories in ASPIRE, except Cash Transfers and Social Pensions (CT+SP), which aggregates the three separate categories of conditional cash transfers, unconditional cash transfers, and social pensions.13

For the countries in the ASPIRE database, the median of social assistance expenditures is less than 1.2% of their GDP. Another key characteristic to consider

12These are part of the broader category of social protection, which includes programs financed by the beneficiaries ("contributory"). Developing countries spend about 1.1 trillion dollars (916 billion without China) in social protection (annual average 2010–2017; based on IFPRI's SPEED database using data from the IMF), or about 3.5–4.0% of the GDP.

13The ASPIRE database covers 125 countries, 43 from Africa, mostly from sub-Saharan Africa (AFR), 15 from East Asia and the Pacific (EAP) (including China), 29 from Europe and Central Asia (ECA) (including Russia, Hungary, Ukraine), 22 from Latin America and the Caribbean (LAC), 10 from the Middle East and North Africa (MENA), and 6 from South Asia (SAR) (including India).

Table 3 Estimated expenditures for social assistance programs

Source: ASPIRE and WDI/WB

is the distribution across the population.14 Social assistance is intended for the poorest segments of a population, and if properly targeted, the largest percentages should go to the poorest quintile, with no benefits accruing to the richest ones. However, in the case of Africa, the poorest quintile receives 11.3% of the benefits (the average for the countries; the median is 8%), while the richest quintile receives 41.5% (average) and 38.9% (median). The East Asia and Pacific region also shows a distribution that is biased toward the rich, with the poorest quintile receiving about 17% (average and median), far less than the richest quintile (average of 33.4% and median of 22%). Other world regions show a better distribution, with the poorest quintile receiving somewhat more than 30% (average and median), but the richest quintile still getting 10–16% of the benefits. These numbers suggest significant problems with the targeting of these programs that are intended to help the poor and hungry.15 In particular, countries in Africa seem to suffer the dual problem of both lower levels of expenditure overall (a median of about 0.9% of GDP) and ineffective targeting of the poorest groups.

14The estimates are from the author using all the annual household surveys for all the countries in the database (several countries have more than one household survey, and the years for each country vary; the average year of the surveys in the database is 2011). Benefit incidence is calculated as the percentage of benefits going to each quintile relative to the total benefits going to the population (Sum of all transfers received by all individuals in the quintile)/Sum of all transfers received by all individuals in the population).

15These are data from household surveys, which do not capture the wealthier segments of the population well; therefore, what appears as the richest quintile in the survey may not be so in real life.

#### (c) Brief consideration of expenditures related to COVID-19

The current pandemic is posing further challenges for fiscal accounts. Governments have implemented a variety of policies and investments in health, social protection, and support for employment and production, all of which require the use of a variety of unconventional monetary and fiscal instruments. As reported by the IMF policy tracker for governmental COVID-19 actions, developing and emerging countries made a strong additional fiscal outlay, surpassing 1.2 trillion dollars in 2020 (counting only additional public expenditures as of this writing), with 1.1 trillion dollars spent on non-health measures of social protection and maintenance of employment (excluding China, the respective values are 700 billion dollars and 680 billion dollars). It will be difficult for those levels of expenditure to be sustained in the future, considering the debt already accumulated. These considerations will determine whether developing countries have the flexibility to increase public expenditures for SDGs in the aftermath of the pandemic.

#### 3. Banking system

While, in the previous sections, the focus was on public flows, the transformation of food systems will also require significant private investments from all operators in the food value chains. The internal cash flows from food operations (based on consumers' food purchases) can be expanded by loans from the banking system (which is discussed here) or by operations in capital markets (analyzed in the next subsection).

Table 4, also based on FAOSTAT data, shows the total amount of loans outstanding at a point in time,16 which was provided by the banking sector to producers in agriculture, forestry, and fisheries (including household producers, co-operatives, and agri-businesses)17 and for all sectors (the average for 2014–2019, in current dollars).

There are no data on net disbursements (loans minus repayments of principal), but the change in stocks may be an indicator of net flows. For total credit, the yearly average change in stocks for 2015–2019 is about 1.6 trillion dollars globally; but the average for developing countries (excluding China) is only 87 billion dollars. The average annual change in loans for AFF during 2015–2019 is 24 billion dollars worldwide. The estimated flows for AFF in developing countries would be around 14.2 billion dollars, or some 9.5 billion dollars if China is excluded.18

Table 4 also shows the percentage of AFF loans as a share of total loans. In the case of developing countries without China, the coefficient is about 4% of total loans. But, as with public expenditures, a more revealing indicator of the importance of lending to the AFF sector is the Agricultural Orientation Index (AOI) (calculated

16It should be noted that this is a stock, while the data in the previous sections were flows.

17There is no information about loans to other operators in food systems.

18The actual annual flow of loans for AFF may be larger, considering that some short-term credit may be extended and liquidated within the year, and thus does not affect stocks from one year to the next.

Table 4 Value of loans outstanding, total and for AFF (Current Dollars; Average 2014–2019)

Source: Author, based on FAOSTAT

as the percentage of AFF credit in total credit, divided by the percentage of agricultural GDP in total GDP). The last column in Table 4 provides the median AOI for the countries in each region. As in the case of public expenditures, developing countries show far smaller AOIs than developed countries,19 and values for Africa are lower than for other developing regions.

#### 4. Capital markets

Capital markets at the global and national levels offer another source of external funds. Here, the focus is on socially- and environmentally-oriented investments,20 a potentially relevant source of funds for the transformation of food systems, considering the global trend toward investments that consider broader objectives along with financial returns.

However, definitions of these new investments are evolving, and therefore data on the actual volume of operations vary. Just as an indicator, Díaz-Bonilla (2021) cites that the issuing of Green Bonds in 2019 was 260 billion USD and, of Social Bonds, some 131 billion USD in 2020.21

The largest shares of investments in those categories take place in developed countries, and the amounts oriented towards agriculture and the transformation of

19In Oceania, the values are dominated by Australia and New Zealand.

20Díaz-Bonilla (2021) also included an estimate of foreign direct investments (FDI) for AFF and Food, Beverage and Tobacco. FDI for agriculture and agro-industries, in the aggregate, is part of the internal flows within food systems, but for individual countries, they can be considered additional financing.

21Aimed, respectively, at specific environmental and social objectives. When both objectives are combined, they are called sustainable bonds. There are also other themed bonds, such as "blue bonds" for sustainable fisheries.

food systems are small. For instance, the survey of impact investments in GIIN (2020) shows that only 8.1% of the funds (average 2018–2019) were allocated to food and agriculture.22

The challenge is to mobilize these resources for investments in support of the transformation of food systems to achieve SDG2, including ending hunger.

### 4 Matrix of Financing and Interventions to Shape the Flows of Funds

Table 5 is an indicative matrix of financing that compares the current levels of the different sources discussed in Sect. 3 with the costs identified in Sect. 2. It assumes certain percentage of financing for each group of interventions from the individual flow of funds: for instance, some of them may only be financed by public expenditures, while others could receive credit from the banking system or investments from capital markets.

Table 5 shows the current values of flows of funds in those categories for developing countries calculated in the previous section (excluding China).23

Overall, there seem to be enough aggregate resources (except, perhaps, in the case of banking systems for the 1 billion target).24 The next subsections discuss policy options for each source to ensure that those resources can be mobilized to achieve SDG2 and end hunger. As mentioned, the analysis considers several questions: what the amount of available financial resources is; whether some of them can be reallocated towards the desired objectives; and, if that is not enough, then where the additional money may come from, considering the overall availability of financial resources ("budget constraints").

A general constraint is defined by global aggregate savings: they amount to about 21.6 trillion dollars (average of 2015–2019), but are distributed very unevenly across regions (see details in Díaz-Bonilla ). Further, global savings are the counterpart to world investments. Therefore, any proposal to increase investments in certain activities would require adjustments in other investments and/or consumption, with 2021

22There are also several bonds issued by MDBs with agri-food components. But that money is then lent to developing countries as part of the international development flows discussed above and should not be (double) counted here.

23The estimate for capital markets is a rough approximation, partially combining (to avoid double counting) the value of social bonds issued by developing countries, as surveyed in the Climate Bond Initiative and HSBC (2021) (although they were not necessarily financing aspects of SDG2) and of the results for impact investment flows into agriculture, according to the survey in GIIN (2020).

24The financing matrix in Table 5 is just an example; different percentages of financing by sources can be considered that also depend on the instruments to be utilized. For instance, if governments decide to scale up cash transfers that include grants for productive activities and environmental sustainability, then, the additional costs of improved technologies would be financed by the public sector, instead of loans from the banking system.

Table 5 Matrix of incremental costs and financing, and reference flows (USD billions)

a"Other interventions" include: Efficiency enhancement in irrigation; Global large-scale expansion of irrigation; Nitrogen-use efficiency; Food loss reduction; Soil-water management; Optimal crop planting and varieties (Adaptation); Soil Carbon Sequestration (Mitigation) economy-wide repercussions that must be considered. Also, there may be "budget constraints" at the level of each individual flow of funds that need to be analyzed.

#### (a) International development flows

International development flows for food systems should be increased by about 15 billion dollars above current levels (within the range suggested in IFPRI et al. 2020). Here, it is further suggested that 2 billion dollars of that increase be allocated to support the Zero Hunger Alliance & Fund (outlined below). If total international development flows cannot be increased (because bilateral development aid is limited by budgetary and political factors in donor countries, and net flows of non-concessional loans from MDBs are also constrained by their capital base and restrictive financial policies), this implies a reallocation of funds from other activities. For example, some of the development funds are supporting investments with high green-house gas (GHG) emissions, such as coal-based energy (UNFCCC 2021). At COP26,25 25 countries and public finance institutions committed to ending financing abroad of projects with unabated (i.e., without carbon-capture) fossil fuel energy by the end of 2022, and those funds can then be reallocated to ending hunger. Similarly, other funds can be reallocated from activities with lower priority.

Another option currently being discussed is to reallocate a percentage of the new issue of Special Drawing Rights (SDRs) in the IMF to support developing countries (the new allocation has been about 650 billion dollars, of which about 60% went to developed countries).26 At the time of this writing, there is a discussion at the IMF about options for developed countries to donate or lend part of the SDRs that they do not need to support low-income countries. Here, an additional alternative is suggested to use the SDRs in a way that further multiplies their impact for broader objectives: the allocation of, for example, 2% of SDRs to a fund to guarantee "zero hunger bonds" issued by developing countries (explained later).27

In general, international development funds should be used more strategically to leverage and mobilize private funds. In addition, multilateral and bilateral organizations should better coordinate their own operations to avoid the fragmentation of relatively isolated initiatives and competition across international agencies at the national level.

#### (b) Public expenditures

Table 5 estimates that public expenditures for agriculture and rural development and for social assistance would have to be increased by about 6% and 11%, respectively, to eliminate the risk of hunger for about 870 million people. If the objective is lifting

25https://unfccc.int/news/end-of-coal-in-sight-at-cop26

26A smaller reallocation already happened to the IMF's Poverty Reduction and Growth Trust (PRGT), which provides concessional loans to low-income countries (https://www.imf.org/en/ About/FAQ/special-drawing-right).

27A larger allocation of 10% of the SDRs has been suggested in Díaz-Bonilla (2021) to guarantee "pandemic recovery bonds."

1 billion people from hunger, along with other climate mitigation and adaptation measures, then public expenditures in agriculture and rural development will need to expand by almost 20% and those in social assistance by about 12%.

These are aggregate numbers. Focusing on individual countries, indicators such as the AOI for agricultural expenditures or the percentage of social assistance expenditures in total GDP show that developing countries in general, and particularly in Africa and Asia (not counting China), devote comparatively fewer resources than other regions to those crucial interventions. Here, it is suggested that individual developing countries should try to increase their AOIs to about 0.5 and social assistance expenditures to at least 2% of the GDP.

Specific public expenditure reviews can help determine the adequacy of both the level and composition of public expenditures dedicated to SDG2, as well as their efficiency, efficacy and equity. Certainly, targeting could be improved in social and agricultural programs, and better instruments can be utilized, such as the evolving type of enhanced social safety net.28

Part of the additional resources can come from reallocation of agricultural subsidies with negative impacts on poverty, nutrition, and the environment (see, for instance, Laborde et al. 2020). Using data from OECD, the total amount of expenditures that can be subject to that repurposing in developing countries (excluding China) was estimated at about 52 billion dollars (Díaz-Bonilla 2021).29

Other expenditures with negative effects that should be phased down, following the Glasgow Climate Pact document agreed to at COP26 (paragraph 36), are subsidies to fossil fuels, globally estimated at some 800 billion dollars (Parry et al. 2021); the money saved can be reallocated to activities linked to SDG2 and ending hunger.

However, reallocating/repurposing, along with better targeting, even with improved instruments, may not be enough to reach the levels needed to achieve SDG2 and end hunger, and therefore expenditures and revenues may have to be increased.

One way to achieve this is by improving tax administration so as to reduce tax evasion. Also, developing countries should reassess the multiple exemptions to value-added and sales taxes: in several countries, they represent an important loss of revenue, help rich, as well as poor, consumers, and do not address challenges of nutrition or environmental sustainability. Implementing taxes on unhealthy and/or environmentally damaging food products can shift incentives while collecting

28Cash transfers in the rural sector have been expanding to include poverty, nutrition, environmental, and productive payments (De La O Campos et al. 2018). Recent work by the World Bank has extended the framework for social inclusion to multidimensional programs that include social safety nets, livelihoods and jobs, and financial inclusion (see Andrews et al. 2021).

29The total amount of agricultural subsidies that can be repurposed (average 2014–2018 in current dollars) is less than 240 billion dollars: about 132.5 billion dollars in OECD countries, of which the EU represents 82.5 billion, and 105.8 billion dollars for non-OECD countries, of which China represents 62.1 billion dollars. For developing countries (excluding China, but including OECD members that are developing countries), the value is about 52 billion dollars (Díaz-Bonilla 2021).

additional revenue. Taxes on international trade, including with impacts on fiscal accounts and on production and consumption incentives, should be analyzed considering SDG2 and hunger. Further, more progressive taxation of incomes and wealth will strengthen revenues. Finally, pricing the externalities of fossil fuels should be implemented, not only to shift incentives away from high GHG emissions, but as a source of revenue (Parry et al. 2021).

The Zero Hunger Alliance & Fund, discussed below, can help developing countries conduct the specific fiscal analyses involved in the reallocation, refocusing and scaling up of public expenditures needed to support programs to end hunger, considering the constraints posed by the fiscal response to the pandemic.

Additionally, all countries, but particularly the developed ones that have greater influence on the operation of global financial markets, must be more active at the international and national levels to implement stronger controls on money laundering and tax havens that facilitate illegal financial outflows and tax evasion from developing countries. Also, proposals for a more unified system of taxation of international corporations, with an established formula to allocate the taxable base and a common minimum corporate tax, must be implemented.30 These initiatives would help many developing countries to increase fiscal revenues that are currently being lost through corruption and tax evasion.

#### (c) Banking systems

Expansion of irrigation and the adoption of improved agricultural practices (needed to reach SDG2 and end hunger) will require financing from the banking system. Estimates in Table 5 suggest that, in the aggregate, credit to the agricultural sector in developing countries (excluding China) will have to increase by some 40% in flows (for the central estimates of 870 million people avoiding hunger). While this is an aggregate estimate, for individual countries, it is suggested that they target an AOI for credit of at least 0.5.

For the banking sector to play this role, the systemic barriers that limit the supply of financial services for agriculture, small farmers, and the poor and vulnerable (women, disadvantaged ethnic groups, and youths) must be addressed. This requires a country-level analysis of the banking system considering the following aspects.31 First, the adequacy of the overall macroeconomic and regulatory framework. Lending to the agricultural sector is affected by macroeconomic volatility, and by regulations that are designed for the urban sector and for activities with more regular cash flows than agriculture. Second, the origin and use of the funds that are to be intermediated (such as deposits; budget allocations by the government; rediscounts by the monetary authorities; regulatory mandates to lend to the agricultural sector; loans from international organizations; and others). In particular, it is suggested here

30Some advances were made in October 2021 at the G20 presidential meeting in Rome (https:// www.reuters.com/business/g20-leaders-endorse-global-minimum-corporate-tax-deal-2023-start-2021-10-30/).

31What follows is based on Díaz-Bonilla (2015); Díaz-Bonilla et al. (2019).

that an updated version be used of the monetary policies that sustained agri-food development in the 1960s and 1970s, implemented then by what have been called "developmental central banks," and which, with the 2008 global recession and the current pandemic, have been revived mainly in developed countries under the name of quantitative easing (Díaz-Bonilla 2015).32 Such an approach must be implemented within a consistent monetary program that maintains control of inflation.

Additional funds may also come from reallocation of credit that now supports activities with negative externalities such as deforestation or fossil fuels. In that regard, it would be important to properly implement the "Glasgow Leaders' Declaration on Forests and Land Use" at COP26, and implement the disclosure recommendations concerning climate-related financial risks, as suggested by the Task Force on Climate-Related Financial Disclosures (TCFD) created in 2015 by the Financial Stability Board (FSB).33

s The third component of the analysis is the types of banking and financial institutions that can intermediate those funds. There is a wide variety of formal and informal banking and financial operators, with their own advantages and disadvantages. Here, it is suggested that the role of public development banks (PDBs) be strengthened. They were dismantled in many developing countrie during the 1990s because of concerns about corruption, inefficiency, and fiscal costs. However, several of them, including those with an agricultural orientation, have been reformed to operate with incentives, performance metrics, and controls to avoid the problems of the past, while pursuing developmental objectives. In fact, during the UNFSS, and following the November 2020 "Finance in Common Summit" of PDBs, a coalition of those banks to help finance the transformation of food systems was announced.34 That coalition can help PDBs address the pervasive market failures in agricultural and climate credit markets that affect small farmers and firms, particularly those operated by women, youths and vulnerable ethnic groups.

The fourth aspect to consider relates to financial instruments. A central one is credit, particularly longer-term operations, which face agricultural-sector-specific problems, such as the dispersion and small scale of customers, and weather and other risks. Innovative insurance schemes, technical assistance, and better weather and market information can mitigate some of those risks. Supply-chain and value-chain lending offer a flexible form of financing that can include small farmers; input and equipment suppliers should also be considered as potential vehicles for lending to small and family farmers. In any case, credit for long-term investment may require funding from public fiscal or monetary sources (as suggested above).

32The U.S. Federal Reserve operated as a developmental central bank to help the U.S. economy in the 1930s. More recently, with the recent crises, central banks, mainly in developed countries, have revived the use of those dedicated lines of credits to buy both public and private credit instruments.

33Established by the G-20 in 2009, to ensure that the financial system is resilient to all forms of risk. 34https://foodsystems.community/public-development-banks-coalition/

Beyond the obstacles to credit, there is a dearth of other financial products and services for small farmers, rural populations, and SMEs. This is true both on the financing side (such as leasing, warrants, and the discount of invoices, all of which require the adaptation of regulations and operational mechanisms) and on the payments and savings side (for instance, simplified checking and savings deposits, which are an important risk mitigation tool for rural households). In general, digital technology can reduce transaction costs and generate more information about potential customers, lowering risk for financial institutions.

#### (d) Capital markets

Table 5 suggests that capital market operations will have to increase significantly above current estimated levels to achieve SDG2 and zero hunger, plus other environmental and health objectives. The net-zero deforestation commitments and emissions disclosures already mentioned in the banking section can also help shift incentives and financial flows in capital markets. Additionally, we present two other ideas here.

First, it is necessary to develop a robust pipeline of investable opportunities (including individual projects, impact investment funds, and/or thematic bonds) with the adequate profile of risk/reward to attract investors, and clear, measurable, and monitorable impact objectives, aligned with achieving SDG2 and ending hunger.

An international project preparation/incubation/acceleration facility could be set up to link investable opportunities for small farmers and rural populations in social and environmentally relevant activities with private capital, based on CGIAR technologies and leveraging the presence of its centers in more than 100 developing countries, where they work with a variety of national agricultural research institutes (NARIs) (see the detailed proposal in Díaz-Bonilla et al. 2018).35

The facility would identify projects involving small and family farms; aggregate and structure them (as different types of investable vehicles), with adequate rates of return and risk profiles, and with value sizes that compensate investors for the transaction costs and due diligence requirements; provide technical assistance to both small farmers and investors, particularly in relation to sustainable technologies (based on the work of the CGIAR centers and participant NARIs); and define and monitor metrics for the impacts desired. This facility can also support enhanced environmental lending by public and private banks. International development funds, as well as some national public expenditures, can be used more strategically in this facility as blended finance with private sector funds, including for the purpose of de-risking investments.

35More recently, there have been other similar ideas regarding the need for an institutional device to link investable opportunities and investors (see, for instance, Millan et al. (2019) and Finance for Biodiversity Initiative (2021)).

A second proposal is to guarantee "zero hunger" bonds to finance related public programs. In particular, as mentioned, it is suggested here that 2% of the new allocation of SDRs of 650 billion dollars be assigned to a fund, which could be set up within the IMF, to guarantee the interest rate payments of zero hunger bonds issued by developing countries as part of the Zero Hunger Alliance described later.

Although the specific design of those bonds will have to be discussed with potential private and institutional investors, some features to consider are the following: they will be a "consol" or perpetual bond;36 possibly issued in dollars; paying an adjustable rate with a cap (say, 5%, which is close to the average nominal yield since the 1950s); and may be callable, with call protection (for example, until 2050). Other official development aid and private philanthropic funds could be utilized as well to guarantee the interest payments, and thus eliminate country risk for the countries that join the international alliance to eliminate hunger (discussed below).

This alternative will greatly increase the impact of the SDRs: for instance, 13 billion dollars can guarantee an issuance of between 52 and up to 130 billion dollars in zero hunger bonds (under the assumptions in the footnote37). The funds raised, in addition to the zero hunger objective, would also help finance sustainable agricultural technologies and other environmental interventions addressing climate change mitigation and adaptation.

Certainly, the financial scheme suggested here can also be utilized for special bonds with other purposes, such as financing pandemic-related expenditures (a "pandemic recovery bond" is discussed in Díaz-Bonilla 2021 and von Braun and Díaz-Bonilla 2021).

### 5 The Need for Country-Based Institutional Arrangements: A Zero Hunger Alliance & Fund38

The quantitative estimates and the financing matrix discussed above suggest that, in the aggregate, there are enough additional financial resources to save anywhere from 870 to 1 billion people from hunger by 2030, effectively achieving zero hunger.

However, the potential sources of financing and whether they are sufficient cannot be judged solely at the aggregate level; they also need to be assessed in each individual country. And even if the domestic resources exist and can be

36Alternatively, 100-year bonds can be considered, with payment periods during the last 10 years.

37The guarantee fund holds between 2 and 5 years in interest payments at the rate of 5%; the default rates of the interest payments that have to be covered by the guarantee fund are similar to those of the IMF or the World Bank; and the erosion that those payments inflict on the guarantee fund is covered by additional international public money. The multiplier effects changes with different assumptions.

38A more detailed explanation is in Díaz-Bonilla (2021).

mobilized, they can only be transformed into solid programs to end hunger and achieve SDG2 if individual countries are willing and capable to do so.

However, institutionally weak governments may not be able to design the programs and coordinate the work of their own Ministries and agencies, as well as the international organizations operating in their countries. They could benefit from the establishment of institutional mechanisms to help developing countries design, finance, and implement their programs. The fiscal constraints imposed by the public responses to the current pandemic reinforce the need for these country-based arrangements.

Therefore, it is suggested that a "Zero Hunger Alliance and Fund" (ZHAF, or "the alliance") be established, with the objective of operating as an international mechanism to assist the developing countries that formally join the alliance in the design, financing, and implementation of their zero hunger plans.

The proposal outlined here builds on the idea of a Zero Hunger Fund, which was suggested by Action Track One of the UNFSS and has considered the experiences of other initiatives.39 It also expands the scope of the announcement by the UN Secretary-General in his "Chair Summary and Statement of Action on the UN Food Systems Summit" about appointing Resident Coordinators and UN Country Teams to develop and implement national programs, while enjoining the Romebased agencies (FAO, IFAD, and World Food Program) with the coordination of a UN-based hub to support the follow-up activities to the Food Systems Summit.