metadata: authors: 0: author: Havlík,Petr
1: author: Valin,Hugo Jean Pierre
2: author: Gusti,Mykola
3: author: Schmid,Erwin
4: author: Forsell,Nicklas
5: author: Herrero,Mario
6: author: Khabarov,Nikolay
7: author: Mosnier,Aline
8: author: Cantele,Matthew
9: author: Obersteiner,Michael
count: World
docna: 0: docna: Climate change impacts and mitigation in the
            developing world : an integrated assessment of the
            agriculture and forestry sectors
docty: Policy Research Working Paper
subtopic: Climate Change Impacts,Climate Change Mitigation and Green House Gases,Science of Climate Change,Climate Change and Health,Social Aspects of Climate Change,Climate Change and Environment,Food Security,Energy Demand,Energy and Environment,Energy and Mining
teratopic: Science and Technology Development,Poverty Reduction,Energy,Environment,Health, Nutrition and Population
abstracts: cdata!: This paper conducts an integrated
            assessment of climate change impacts and climate mitigation
            on agricultural commodity markets and food availability in
            low- and middle-income countries. The analysis uses the
            partial equilibrium model GLOBIOM to generate scenarios to
            2080. The findings show that climate change effects on the
            agricultural sector will increase progressively over the
            century. By 2030, the impact of climate change on food
            consumption is moderate but already twice as large in a
            world with high inequalities than in a more equal world. In
            the long run, impacts could be much stronger, with global
            average calorie losses of 6 percent by 2050 and 14 percent
            by 2080. A mitigation policy to stabilize climate below 2°C
            uniformly applied to all regions as a carbon tax would also
            result in a 6 percent reduction in food availability by 2050
            and 12 percent reduction by 2080 compared to the reference
            scenario. To avoid more severe impacts of climate change
            mitigation on development than climate change itself,
            revenue from carbon pricing policies will need to be
            redistributed appropriately. Overall, the projected effects
            of climate change and mitigation on agricultural markets
            raise important issues for food security in the long run,
            but remain more limited in the medium term horizon of 2030.
            Thus, there are opportunities for low- and middle-income
            countries to pursue immediate development needs and thus
            prepare for later periods when adaptation needs and
            mitigation efforts will become the greatest.
repnme: repnme: Climate change impacts and mitigation in
            the developing world : an integrated assessment of the
            agriculture and forestry sectors
display_title: Climate change impacts and
            mitigation in the developing world : an integrated
            assessment of the agriculture and forestry sectors
listing_relative_url: /research/2015/11/25250682/climate-change-impacts-mitigation-developing-world-integrated-assessment-agriculture-forestry-sectors
topicv3: Climate Change Impacts m1326159 1773,Agriculture and Food Security m1326205 435,Energy and Extractives m1326595 507,Climate Change m1326829 567,Climate Change Mitigation m1327079 579
publishtoextweb_dt: 2015-11-09T00:00:00Z
historic_topic: Science and Technology Development,Poverty Reduction,Energy,Environment,Health, Nutrition and Population
no_of_pages: 56
author_keys: 0: author_key: 1359603
model_extractions: WPS7477 Policy Research Working Paper 7477 Shock Waves: Managing the Impacts of Climate Change on Poverty _Background Paper_ Climate Change Impacts and Mitigation in the Developing World An Integrated Assessment of the Agriculture and Forestry Sectors _Petr Havlík Hugo Valin Mykola Gusti Erwin Schmid David Leclère Nicklas Forsell Mario Herrero Nikolay Khabarov Aline Mosnier Matthew Cantele Michael Obersteiner_ Development Economics Climate Change Cross-Cutting Solutions Area November 2015
Policy Research Working Paper 7477

# **Abstract**

This paper conducts an integrated assessment of climate change impacts and climate mitigation on agricultural commodity markets and food availability in low- and middle-income countries. The analysis uses the partial equilibrium model GLOBIOM to generate scenarios to 2080. The findings show that climate change effects on the agricultural sector will increase progressively over the century. By 2030, the impact of climate change on food consumption is moderate but already twice as large in a world with high inequalities than in a more equal world. In the long run, impacts could be much stronger, with global average calorie losses of 6 percent by 2050 and 14 percent by 2080. A mitigation policy to stabilize climate below 2°C uniformly applied to all regions as a carbon tax would also result in a 6 percent reduction in food availability by 2050 and 12 percent reduction by 2080 compared to the reference scenario. To avoid more severe impacts of climate change mitigation on development than climate change itself, revenue from carbon pricing policies will need to be redistributed appropriately. Overall, the projected effects of climate change and mitigation on agricultural markets raise important issues for food security in the long run, but remain more limited in the medium term horizon of 2030. Thus, there are opportunities for low- and middle-income countries to pursue immediate development needs and thus prepare for later periods when adaptation needs and mitigation efforts will become the greatest.

This paper was commissioned by the World Bank Group’s Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank Group’s flagship report: “Shock Waves: Managing the Impacts of Climate Change on Poverty.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at havlikpt@iiasa.ac.at and valin@iiasa.ac.at.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Climate Change Impacts and Mitigation in the Developing World: An Integrated Assessment of the Agriculture and Forestry Sectors*

Petr Havlík<sup>a</sup> , Hugo Valin<sup>a</sup> , Mykola Gusti<sup>a</sup> , Erwin Schmid<sup>b</sup> , David Leclère<sup>a</sup> , Nicklas Forsell<sup>a</sup> , Mario Herrero<sup>c</sup> , Nikolay Khabarov<sup>a</sup> , Aline Mosnier<sup>a</sup> , Matthew Cantele<sup>a</sup> , Michael Obersteiner<sup>a</sup> Keywords: food security, climate change, land use change, bioenergy, poverty JEL: Q10, Q23, Q54, Q56

* Corresponding authors: Petr Havlík (havlikpt@iiasa.ac.at) and Hugo Valin (valin@iiasa.ac.at). Analysis

contributing to this study was partly conducted in partnership with the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).

The results and views expressed in this document are the sole personal responsibility of the authors and do not reflect those of their institutions of affiliation. Any errors or omissions remain the responsibility of the authors.

a International Institute for Applied Systems Analysis (IIASA), A‐2361, Laxenburg, Austria.

b Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, A‐1200 Vienna, Austria c Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, 4067, Australia
# **Summary of main findings**

We generate scenarios through 2080 with the partial equilibrium model GLOBIOM and look at the impacts of climate change and climate mitigation policies at different time horizons, with a focus on the medium term 2030 as these impacts could hamper immediate development goals. The main results are:

# **On the climate change impact and adaptation side**

- In the most optimistic scenario, average crop yield across climate models is found to decrease by 2% globally by 2030, even when the positive effects of elevated CO2 concentration are accounted for. Without CO2 effects, these impacts could be as severe as ‐10% in 2030 already.

- Climate change will hit regions unevenly but least advanced countries will be particularly affected. The most severely exposed regions by changes in crop yield are South Asia, Eastern Asia and Pacific, and Latin America, where agriculture will need to adapt drastically. Impacts in Sub‐Saharan Africa and North Africa Middle‐East should be partly attenuated by CO2 effects, but will also become severe if this effect does not materialize.

- The societal impacts of these changes will depend on the adaptation capacity of the different regions. Various adaptation channels can be used to mitigate the impact of climate change on food supply: i) change in crop management at field level (input level, irrigation), ii) change in the choice of crops cultivated, iii) reallocation of production within regions, iv) increase in cultivated areas, v) international trade adjustments.

- The possibilities to use these mechanisms will be influenced by the level of development of the agricultural sector as well as the investment capacity and the institutional environment, locally and globally. We look at two different contexts of these dimensions across the two Shared Socio‐Economic Pathways SSP4 (low adaptation capacity and dysfunctional markets and institutions for least advanced countries) and SSP5 (high adaptation capacity, integrated markets and an enabling institutional environment in least advanced countries).

- At the global level, and for the two background socioeconomic scenarios, no significant price variation is observed in the long run if climate change is contained at 2 degrees. On some specific markets, effects could however be visible as soon as 2030: +15/27% for sorghum (with/without CO2 effects), +7/25% for millet, +4/15% for soya, +2/11% for corn, +0/7% for cassava.

- The impact on food supply remains very limited by 2030. On average across climate scenarios and SSPs, global consumption decreases by 0.6% with CO2 effects and 1.9% without CO2 effects compared to a situation without climate change. These impacts become more severe and more largely influenced by the baseline assumption over time. Impacts reach ‐2.0/‐4.3% (with/without CO2 effects) by 2080 under a macroeconomic scenario favorable to adaptation (SSP5) and up to ‐4.4/‐8.7% under poor adaptation capacity (SSP4).

- South Asia and Sub‐Saharan Africa are the regions the most impacted in 2030 but with CO2 effects, impacts remain limited under SSP5 at ‐30 kcal/cap/day and ‐16 kcal/cap/day, respectively, and moderate at ‐50 kcal/cap/day and ‐14 kcal/cap/day under SSP4. The impacts are more pronounced by 2080. South Asia consumption decreases by ‐‐182 kcal/cap/day and ‐125 kcal/cap/day in SSP4 and SSP5, respectively. Sub‐Saharan Africa loses ‐180 kcal/cap/day under SSP4 and ‐129 kcal/cap/day under SSP5. Without CO2 effects, these impacts are more than doubled by 2080 and are the most acute for sub‐Saharan Africa: under SSP4, the region could lose up to ‐382 kcal/cap/day in 2080, whereas the impacts would be contained at around ‐220 kcal/cap/day under SSP5.

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# **On the climate change mitigation side**

- In order to limit the global warming to 2 ̊C by 2100 compared to preindustrial times, agriculture, forestry and other land use (AFOLU) emissions would be required to decrease by 64% in 2030, compared to their 2000 level. In such a scenario, land use change would have to become a net carbon sink, and any increase in direct emissions from agriculture would be limited to+7%. Large abatement would be provided by Latin America (LAM) and Sub‐Saharan Africa (SSA), which would contribute by 55% and 21% to the global AFOLU abatement, respectively.

- Total amount of bioenergy is already projected to increase by 12% by 2030 without climate change policies, and a major shift from traditional biomass use to industrial bioenergy production is expected. Under a full mitigation policy, an additional 80% of biomass for energy would be required by 2030. LAM would contribute 28% to this increase and SSA 24%.

- Mitigation policies implemented through a uniform global carbon price would have negative effects on agricultural production. Crop production globally would be by 4% lower by 2030 compared to the reference scenario. Livestock production would be slightly more affected, with production down by 5% globally for meat (9% for milk) compared to the reference scenario, and higher impacts in Latin America (‐9% for meat/milk) and South Asia (‐15%  for meat/milk), and in Sub‐Saharan Africa (‐17%/‐21% for meat/milk). Forestry would be little affected by 2030 with a 1% increase globally.

- Agricultural commodity prices would increase as a result of a carbon price. Crop prices would increase just moderately by 4% and livestock prices by 7% on average. The livestock price increase would be particularly high in regions with GHG emission intensive production systems, and even higher under SSP4 with slow productivity improvements ‐ +13% in South Asia (+8% for SSP5), +17% in Latin America, and +22% in Sub‐Saharan Africa. These changes in prices will have negative effects on food availability, although they could benefit some specific farming sectors.

- Food availability would decrease globally by 3% under the climate stabilization scenario compared to reference levels by 2030. Developing regions would be more affected than developed ones as poorer population groups are more sensitive to price fluctuations, and the strongest decrease would occur for livestock product consumption in sub‐Saharan Africa (‐ 12%).

- On the producer side, climate mitigation policies will impact the total level of revenue in agriculture and forestry through changes in the cost of production (pricing of emissions), in the levels and type of production (less conventional products and more demand for bioenergy feedstocks), and through new income opportunities such as payments for carbon sequestration through afforestation. Net social benefits would however depend on how these revenues are shared between land owners and workers. On average, global impacts are limited by 2030. However, SSA is again seriously affected. This is because the region loses on revenues from conventional agricultural production, is unable to create within the medium term sufficient new income from carbon sequestration, and the new source of income in terms of commercial biomass production for energy is not sufficient to cover the losses.

- In order to avoid that mitigation policies have more severe impacts on development than climate change itself, the carbon price revenue needs to be redistributed appropriately. The adverse effects of mitigation policies could be further reduced by targeting just particular land use sectors: Expanding biomass production appears a better compromise than halting deforestation, whereas direct restrictions on crop and livestock emissions pose the largest problems to food security.

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# **1. Introduction**

There is now compelling evidence that the climate is changing globally at a pace unprecedented in human history and that this change is directly related to anthropogenic greenhouse gas (GHG) emissions (IPCC, 2013). Impacts of these changes will be felt in many domains, but few appear as exposed and critical to human basic needs as agriculture. Agricultural development has indeed formed the basis for the emergence of civilizations and sustainable provision of food and revenues for poor farmers is still today a concern in many low‐ and middle‐income countries. As the world is still comprised of close to one billion undernourished people, climate disturbances in regions already facing significant weather variability represent a critical challenge. At the same time, agriculture, forestry and other land use (AFOLU) are responsible for a quarter of all anthropogenic GHG emissions (Smith _et al._ , 2014). By 2030, land related mitigation strategies including reduction of direct emissions from agriculture and deforestation, increased carbon sequestration through afforestation, and most importantly, substitution of fossil energy sources by biomass feedstocks, could account for 20‐60% of the mitigation effort necessary to slow climate change (Rose _et al._ , 2012). A considerable share of biomass for energy use is projected to originate in developing countries, and the potential for emissions reductions and sink enhancement from other sources is largest there. As both climate change impacts and climate change mitigation are projected to have the strongest effect on land use sectors in the same regions ‐ the low‐ and middle‐income countries ‐ it is useful to examine the two sides of this coin in a single coherent framework.

A large body of literature has investigated the social impacts from climate change, and an extensive overview is provided through the synthesis work of the Intergovernmental Panel on Climate Change (IPCC), in particular Working Group II (IPCC, 2014a). Understanding the extent and location of these impacts is a complicated task, in particular for the agricultural sector, due to the complexity of the chain of effects and their uncertainties. Increases in GHG atmospheric concentrations and additional anthropogenic perturbations of the climate system affect the spatial and temporal distribution of weather regimes which contribute to shaping the diversity of agricultural activities throughout the world. But beyond the shared diagnostic of a future increase in the mean surface temperature, the different general circulation models (GCMs) do not agree on the magnitude, location and direction of change for variables that are crucial to agricultural activities, such as precipitation.

Crop yield and harvest quality as well as operational aspects of agricultural production are directly affected by changing patterns of precipitation, temperature, wind, air moisture and incoming shortwave solar energy, but also indirectly through water availability for irrigation. Livestock is also affected through pasture yield and quality, heat stress and water availability (Thornton _et al._ , 2009). Other forms of stress, particularly dependent on climate (pests, disease, sea level rise) can also affect agricultural activities. Crop and vegetation models permit estimation of the impacts of such drivers on crop and pasture yields. In conjunction with scenarios which account for various climate projection uncertainties, these models translate climatic impact into a biophysical impact. In addition to climate projection related uncertainties, crop models face their own intrinsic sources of uncertainties (e.g., the response of crop growth to elevated atmospheric CO2 concentration remains a large source of divergence in model results). Assumptions on the nitrogen stress is also raised as a critical parameter underpinning model result discrepancies (Rosenzweig _et al._ , 2014).

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The effects of climate change on farm systems and population depend on not only the biophysical impacts but also the manner in which agents react and how these dynamics propagate through economic markets. Producers can adapt by changing their production methods or switching to other land‐based products; cultivated areas can also be varied, and markets and trade used to accommodate supply and demand imbalances as illustrated for the crop sector in Nelson _et al._ (2014) and for the livestock sector in Havlík _et al._ (2015). The ultimate socioeconomic impacts can vary according to geographical location and occupation. Three‐quarters of the world’s poor live in rural areas of Africa, Asia and Latin America and they are particularly vulnerable as their low income relies more heavily on these activities. But the poor are also the most exposed on the consumption side, as they purchase products with low degree of transformation and hence are more sensitive to market price fluctuations, and the food purchases represent large part of their expenditure. Estimation of impact therefore requires economic assessment tools and market equilibrium models are the most common approaches to estimate this final level of impact in the chain of effects. Large comparison exercises have been performed to compare the impacts on food production and consumption of climate change, such as the Agricultural Model Intercomparison and Improvement Project (AgMIP, see von Lampe _et al._ , 2014). All models project a reduction of food availability for households but the final impact on consumption is usually lower than initial biophysical impacts (Leclère _et al._ , 2014; Mosnier _et al._ , 2014; Valin _et al._ , 2014).

Agriculture is also a key sector for mitigation of GHG emissions. Agricultural non‐CO2 emissions represent about half of the total AFOLU emissions. Livestock is directly responsible for more than 70% of these emissions, with enteric fermentation and manure left on pasture being by far the most important sources. The remainder of the emissions come mainly from synthetic fertilizers applied to soils and rice cultivation. Extant literature generally differentiates between three types of mitigation wedges: technological options and structural adjustments on the producer side, and reduced consumption on the consumer sider. A rather comprehensive overview of technological options which may include use of anaerobic digesters, N‐inhibitors and others, can be found in Beach _et al._ (2008), Smith _et al._ (2008), or more recently Hristov _et al._ (2013). Within the livestock sector, Herrero _et al._ (2013) illustrated the huge variances in terms of GHG efficiency (unit of product per unit of GHG emission) which exist for each product/species across current production systems and regions. Their results suggest that even optimization of production within the current systems provides substantial opportunities for emissions reduction. Havlík _et al._ (2014) also found that optimal allocation of livestock production across systems is to a large extent a cost‐effective mitigation measure, along with other structural adjustments like relocation of production within and across regions. However, the most straightforward measure to reduce direct emissions from agriculture still seems to be reducing consumption of livestock and other GHG intensive products (Stehfest _et al._ , 2009; Popp _et al._ , 2010), particularly in regions where overconsumption causes serious health problems. However, the economic and social consequences of this option, as well as associated policy levers, have not yet been fully elucidated.

Emissions from forestry and other land use, including emissions from deforestation, contribute about the same proportion to total anthropogenic emissions as direct emissions from agriculture. Although these emissions represent a third of all anthropogenic emissions generated over the period 1750‐2011, they are currently the only source of GHG which has slowly decreased in absolute numbers since the last decade or so (Smith _et al._ , 2014). Agricultural land expansion is the major cause of land use change (Geist and Lambin, 2002; Gibbs _et al._ , 2010). At the same time, crop yield growth over the past 50 years 5
prevented the conversion of more than a billion of hectares which otherwise would have be converted to arable land, and hence helped to avoid some 590 GtCO2e of emissions (Burney _et al._ , 2010). Land productivity growth in the agricultural sector could play an important role in avoiding GHG emissions also in the future (Havlík _et al._ , 2013). However, the net effect will depend on technology and on the strength of a potential rebound effect (Valin _et al._ , 2013). Overall, climate change mitigation through reducing emissions and increasing the sink in the forestry and other land use sectors appears to be a very cost‐effective measure (Kindermann _et al._ , 2008a), which causes relatively little competition with food production (Havlík _et al._ , 2014), and which is projected to play a crucial role in attaining ambitious mitigation targets (Rose _et al._ , 2012; Kriegler _et al._ , 2014).<sup>1</sup> Energy produced from biomass can under certain circumstances be considered close to carbon neutral because during combustion only carbon previously stored in the biomass is emitted. If such a system were linked to carbon capture and sequestration (CCS), which consists of permanently storing the carbon emitted during combustion, the system would actually provide negative emissions/sink (Obersteiner _et al._ , 2001). Recent studies based on integrated assessment models agree on the need for large scale bioenergy deployment to reach ambitious stabilization targets. Although the feasibility of large scale CCS deployment is still uncertain (Fuss et al. 2014), its role in the stabilization scenarios is crucial.; Kriegler et al. (2014) found that limiting atmospheric GHG concentration to 450 ppm CO2 equivalent by 2100 would be 2.5 times more expensive without CCS technology availability. Lignocellulosic biomass from dedicated plantations would be the major source of biomass, next to residues, and more than 500 million ha of land would be needed to produce the required amounts (Popp _et al._ , 2010). In one respect this represents a challenge to increase agricultural land productivity further, not only to allow for carbon sequestration but also to provide land to satisfy these new demands along with sufficient food production. On the other hand, the demand for energy feedstock will present a new income opportunity in agricultural and forest sectors.

As illustrated in the structure of the IPCC Assessment Reports, analyses of climate change impacts and mitigation have been conducted independently and so far have yet to allow for direct comparison of the relative effects of impacts and mitigation. The present paper tries to fill this gap within a consistent scenario set drawing on the new matrix framework structured along Shared Socio‐economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) (van Vuuren _et al._ , 2014). SSPs provide socioecomic drivers such as population, economic growth, and technological change, for alternative future scenarios attempting to span the space of plausible futures. Here we focus on SSP4 and SSP5, representing unequal fragmented world and fast growing integrated world, respectively. RCPs define the future radiative forcing trajectory and are labelled by the end value by 2100 in W.m<sup>‐2</sup> . They can be used both for impact scenarios assessment where they define the level of climate change, and for mitigation scenarios assessment where they define the mitigation target. We do not attempt to present an exhaustive ensemble of scenarios here but rather focus on the most extreme variants, delineating the span of possible future outcomes. For this, we selected RCP2.6 and RCP8.5, which corresponds to temperature increase below 2°C for the former, compared to pre‐industrial times, and beyond 4°C for the latter. This reflects two contrasted situations of limited and extreme climate change impacts on the one side, and high mitigation efforts and no mitigation efforts on the other side.

> 1 This however presupposes that expansion of wood plantation could take place in the large areas of land not already used as cropland or grassland, overcoming some potential hidden barriers such as labor constraints, infrastructure development needs, and non‐degradation of the available land.

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Another advantage of this approach is that it allows consideration of the mitigation efforts and the climate impacts separately in the analysis; RCP2.6 has a negligible climate change impact but a high level of mitigation, and RCP8.5 has a strong climate change impact but no mitigation.

The assessment is carried out with the Global Biosphere Management Model, GLOBIOM (Havlík _et al._ , 2011; Havlík _et al._ , 2014), a partial equilibrium bottom‐up model that has recently participated in the Impact, Adaptation and Vulnerability (IAV) community activities, like AgMIP (von Lampe _et al._ , 2014) and ISI‐MIP (Nelson _et al._ , 2014), and has been applied to autonomous impact assessments (Leclère _et al._ , 2014; Mosnier _et al._ , 2014; Havlík _et al._ , 2015), but at the same time has a long track record in climate change mitigation assessments (Mosnier _et al._ , 2012; Reisinger _et al._ , 2012; Böttcher _et al._ , 2013; Havlík _et al._ , 2013; Valin _et al._ , 2013; Cohn _et al._ , 2014; Havlík _et al._ , 2014), including bioenergy (Havlík _et al._ , 2011; Frank _et al._ , 2013; Lauri _et al._ , 2014). The model is used here to compare impacts on production, agricultural markets and food consumption with a focus on low and middle income countries. Although the climate change impacts and mitigation efforts are expected to substantially in magnitude towards the end of this century, here we take a development perspective and try to identify whether there are major challenges to be expected in the medium term.

The rest of the paper is structured as follows: in a Section 2, we briefly present the modeling chain used for the assessments of climate change impact and mitigation effects; in Section 3, we detail the set of scenarios chosen for this analysis; in Section 4, we expose the most important results, and in Section 5 we put these results into perspective while underlying uncertainties. A brief summary of the most important results, caveats to be considered and future pathways conclude this paper.

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# **2. Modeling framework**

The analysis provided throughout this paper relies on an integrated assessment framework designed for analyzing the chain of climate change impact, and climate change mitigation. The global partial equilibrium agriculture and forest sector model GLOBIOM (Havlík _et al._ , 2011; Havlík _et al._ , 2014) constitutes the central component of this framework. GLOBIOM is combined with the EPIC crop model (Williams, 1995) to calculate the impact of climate change on the agricultural sector, according to a framework described in Leclère _et al._ (2014) and Havlík _et al._ (2015). For a comprehensive accounting of greenhouse gas emissions from afforestation, deforestation and forest management and the effect of mitigation, GLOBIOM is coupled with the Global Forest Model (G4M, Kindermann _et al._ , 2008a).<sup>2</sup> GLOBIOM is a partial equilibrium model covering the agricultural, forestry and bioenergy sectors. It represents the world partitioned into 30 economic regions, in which a representative consumer tries to optimize his consumption, depending on his income, preferences and product prices. On the production side, producers maximize their margins and the model solves the market equilibrium corresponding to the overall welfare maximization based on the spatial equilibrium modeling approach (Takayama and Judge, 1971; McCarl and Spreen, 1980). More extensive information on the model structure and parameterization can be found in (Havlík _et al._ , 2011; Havlík _et al._ , 2014).

The supply side of the model relies on a detailed spatial resolution based on the concept of Simulation Units, which are aggregates of 5 pixels belonging to the same altitude, slope, and soil class, within the same 30 arcmin pixel, and in the same country. For crops, livestock, and forest activities, different production systems are parameterized using sectoral biophysical models, such as EPIC for crops, RUMINANT for livestock (Herrero _et al._ , 2013), and G4M for forestry. For this study, the supply side spatial resolution is aggregated to 120 arcmin (about 200 x 200 km at the Equator). The model is calibrated to the year 2000 FAOSTAT activities levels and prices, and is then recursively solved in 10 year time‐steps until 2100.

Production in the model reacts to economic incentives with activities and land use allocation as important aspects of the model responses. Six land cover types are distinguished: cropland, grassland, short rotation tree plantations, managed forest, unmanaged forest and other natural vegetation. Depending on the relative profitability of the individual activities and on the recursivity constraints, the model can switch from one land cover type to another. Comprehensive greenhouse gas accounting for agriculture forestry and other land use is implemented in the model. Detailed description of these accounts and additional background information are provided in Valin _et al._ (2013) and Havlík _et al._ (2014).

EPIC is an important component of the framework for the estimation of climate change impacts. This crop model is used in GLOBIOM to simulate yield associated to each location, management practice and climatic conditions. The model estimates the biophysical and environmental parameters of 18 crops for three different types of management systems (low input rain‐fed, high input rain‐fed and irrigated systems). To predict impacts of climate change, daily climate input data of solar radiation, min and max temperature, precipitation, relative humidity and wind speed are taken as inputs from

> 2 G4M is a spatially explicit global forest dynamics model used to project patterns of deforestation and afforestation based on relative profitability of forestry compared to other land based activities. It can be used as a GLOBIOM module in order to refine estimates of carbon stock losses and carbon sequestration under different land use and carbon pricing policies.

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the climate models. Future increase in atmospheric CO2 is also taken into account and increases both light‐use and water‐use efficiency. Such a representation of CO2 effects generates estimates that lie in the upper range of the wide span of results found in the literature. However, since the strength of the final CO2 fertilization effect is still debated (Tubiello _et al._ , 2007), we consider two different levels of CO2 fertilization responses – full and none – which frame the space of the uncertainty in this respect. EPIC already includes in its modeling some adjustments in crop response to climate (field scale autonomous adjustments). This includes in particular changes in fertilizer and irrigation water use, as well as shifts in annual planting and harvesting dates. Other larger scale adjustments mechanisms are handled directly in GLOBIOM.

Competition between agricultural and forestry uses of land is at the core of the climate mitigation challenge, due to the large needs for biomass resource to substitute fossil energy. Biomass supply for energy production can be sourced in GLOBIOM from dedicated forest plantations or from managed forests. Dedicated plantations potential are determined on the basis of suitability maps and net primary productivity (NPP) estimates as documented in Havlík _et al._ (2011). The available woody biomass resources from managed forests are inputs to GLOBIOM from the G4M model for each forest area unit determined by mean annual increments, which are based on NPP maps from (Cramer _et al._ , 1999) and from different downscaling techniques as described in (Kindermann _et al._ , 2008b). The main forest management options considered by G4M are variation of thinning levels and choice of rotation length. The rotation length can be individually selected or optimally estimated to maximize increment, stocking biomass or harvestable biomass.

The model set‐up described above has been applied successfully to various scientific and policy applications (see section 1) but has not yet been used to look at climate impact and climate mitigation simultaneously. For this purpose, a consistent scenario framework is necessary, as outlined in the following section.

# **3. Baseline and scenario description**

We apply in this paper the new IPCC scenario framework which adopts a matrix approach to look at climate change impacts and mitigation strategies. This framework is articulated around two orthogonal dimensions of scenario drivers: socioeconomic drivers with the Shared Socioeconomic Pathways (SSPs) and level of radiative forcing with the Representative Concentration Pathways (RCPs) for the climate change component (van Vuuren _et al._ , 2011).

# **3.1. Socioeconomic scenarios and drivers**

Five different SSPs have been developed in the framework of climate change research (O’Neill _et al._ , 2014). These correspond to five different visions of the future structured around two axes: level of challenge to adaptation and level of challenge to mitigation. In this paper we focus on two particularly contrasted scenarios for the developing world: SSP4 with pronounced inequality between developed and developing countries, and SSP5, where developing countries are catching up with the developed ones.

SSP4, commonly labelled “Inequality”, is characterized by a world where emissions can be kept low through development of new technologies but least advanced countries experience very limited growth and a significant rise of inequalities increases the vulnerability to climate change of the poorest 9
regions. SSP4 is also characterized by decreased collaborations between regions of the world with a decline of international trade flows.<sup>3</sup> SSP5, commonly labelled “Conventional development”, is characterized by much more investment in human capital and a decrease of inequalities and vulnerability to climate change. Under this scenario however, high economic growth is based on conventional technologies with high carbon and resource intensities, which increases the challenge for mitigation; international cooperation is high and characterized by strong trade relations. However, a lack of environmental consideration leads to patterns of consumption putting higher pressure on natural resources, particularly in terms of diets.<sup>4</sup> The SSP narratives aim to describe alternative futures in a comprehensive way, covering all sectors of the economy. Here, we have considered only a subset of the scenario elements, based on their relevance and compatibility with the model structure. . Table 1 summarizes the covered elements. The SSPs provide quantitative information for a handful of drivers, namely economic growth (GDP) and demographic change in terms of total population distributed by sex, age and education. The magnitude and direction of many other drivers is provided only qualitatively or semi‐quantitatively (O’Neill _et al._ , 2014). Semi‐quantitative drivers relevant for agriculture and forestry were quantified for this study mostly following the approach presented in Herrero _et al._ (2014).

Crop yield projections are estimated based on historical development over the period 1980‐2010 using an econometric relation linking the yields to GDP per capita development. The projections hence vary across SSPs and are performed for four classes of countries grouped by level of income (Herrero _et al._ , 2014). GDP per capita change has been selected as the explanatory variable because it can be considered as proxy both for resources available for investment in the necessary R&D and for the demand developments, and because it is provided quantitatively as part of the SSPs.

_Table 1. Differentiated drivers and their quantification the two SSPs used in this paper_

|**Driver**|**Method / Source**|**Scenario**|**pattern**|
|---|---|---|---|
|||**SSP4**|**SSP5**|
|**Population**|IIASA population<br>projection (Lutz_et al._,<br>2014)|High population growth<br>globally, due to high growth<br>rates in Africa and Asia|Low population growth<br>globally, due to low growth<br>rate in Africa and Asia|
|**GDP per capita**|OECD GDP projection<br>(IIASA, 2015)|Strong GDP per capita<br>growth in advanced<br>regions; moderate growth<br>in middle income countries;<br>low growth for least<br>advanced countries|Strong GDP per capita<br>growth in all regions of the<br>world|
|**Crop yield projection**|Growth rates<br>correlation with<br>patterns of GDP per|Strong yield growth for<br>advanced regions,<br>moderate yield growth in<br>middle income countries,|Strong yield growth for all<br>regions, with catching up of<br>middle income regions on<br>advanced regions.|

3 The SSP4 scenario is used as a backbone for the analysis of a “Poverty” case in the World Bank report on Climate Change and Poverty.

4 The SSP5 scenario is used as a backbone for the analysis of a “Prosperity” case in the World Bank report on Climate Change and Poverty due to the catching up patterns observed for income per capita in developing countries. In our analysis, SSPs and level of global warming are not considered correlated, making such an interpretation possible. It should be noted however that in practice, much higher cost would have to be supported by the energy sector to limit drastic increase in GHG emission levels. This question goes however beyond the scope of this paper 10
||capita (Herrero_et al._,<br>2014)|low yield growth in least<br>advanced countries||
|---|---|---|---|
|**Food demand**<br>**projections**|FAO diet projections<br>for SSP4 (Alexandratos<br>and Bruinsma, 2012)<br>and authors’ own<br>assumptions for SSP5<br>based on scenario<br>narratives|Diets follow similar<br>patterns as projected by<br>FAO, in particular<br>consumption of meat<br>remains stable in<br>developed regions, and diet<br>remain mostly vegetarians<br>in India|Food consumption around<br>the world converges to<br>Western diet types, with<br>higher intake of meat and<br>milk than in SSP4.|
|**Trade policies**|Authors’ own<br>assumption based on<br>scenario narratives|Trade barriers increase<br>considerably compared to<br>today’s situation between<br>all large regions of the<br>world|Trade costs remain<br>unchanged compared to<br>today’s situation|
|**Food waste**|Authors’ own<br>assumption based on<br>scenario narratives|Constant waste level in<br>supply chain and food<br>consumption|Constant waste level in<br>supply chain and increased<br>waste in food consumption|

Income elasticities for food demand per capita are calibrated for SSP4 on food consumption projections from FAO (Alexandratos and Bruinsma, 2012) using GDP assumption from the same source. Therefore, food demand calculated for SSP4 takes into account the specificities of the population and GDP per capita development of this scenario. For SSP5, income elasticity patterns are changed to represent a nutrition transition in developing countries with a catching up in the long run of developing regions towards level of consumption of animal products in developed regions, which leads for instance to higher poultry meat demand for India. Additionally, the level of waste on the food consumption side is adjusted to the narrative of each scenario, and we assume fixed domestic waste level in SSP4 at the historical levels and increased domestic waste for SSP5.

Trade assumptions are also varied depending on the SSP. For SSP5, the narrative specifies that global cooperation favors trade and we keep the standard trade specification of GLOBIOM, reproducing current levels of tariffs and trade cost progressively decreasing over time. For SSP4, however, cooperation between countries is low and economic integration fails with many regions becoming isolated. This leads to higher trade costs in our scenario which significantly restricts expansion of trade in the baseline as well as the capacity to adapt to climate or policy shocks.

# **3.2. Climate change impact scenarios**

The most recent generation of climate change scenarios available at the time of this study corresponds to the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (Taylor _et al._ , 2011). In CMIP5, more than 50 climate models were used to simulate four radiative forcing scenarios (Representative Concentration Pathways, or RCPs). The four RCPs cover a range of radiative forcing, going from 2.6 to 8.5 W/m<sup>2</sup> in the year 2100 (van Vuuren _et al._ , 2011). Depending on the climate model, these levels of radiative forcing would spread the global temperature increase above the pre‐industrial levels, from below 1 °C for RCP2.6 to about 7 °C for RCP8.5, the median across the models for the latter RCP8.5 being just below 5 °C (Rogelj _et al._ , 2012).

The ISI‐MIP provided impact modelers with spatially interpolated and bias‐corrected climate datasets for all four RCPs and for five GCMs (GFDL‐ESM2M, HadGEM2‐ES, IPSL‐CM5A‐LR, MIROC‐ESM‐CHEM, 11
NorESM1‐M) selected to span the CMIP5 range of global mean temperature changes and relative precipitation changes (Warszawski _et al._ , 2013). Of the five GCMs, ISI‐MIP retained HadGEM2‐ES as the reference model, and we do the same in this study. Under RCP8.5, HadGEM2‐ES projects a global temperature increase for 2050 of about 2.5 °C and an average increase in precipitation of about 3 percent. This ranks HadGEM2‐ES as the hottest and driest of the five GCMs, with potentially the most negative effects on agricultural production.

For this paper, we focus on the two most extreme RCPs: RCP2.6 and RCP 8.5. This choice appears the most practical as it allows to infer the effect of the intermediate emission pathways impacts by approximate interpolation. Effects of different RCPs are then compared to a situation without additional forcing, here represented by the historical climate observed between 1980 and 2010.

In the first scenario, RCP 2.6, a radiative forcing of 2.6 W/m<sup>2</sup> is assumed by the end of the century, after peaking at 3 W/m<sup>2</sup> before 2050. In terms of GHG concentration, this scenario is equivalent to about 450 ppm of CO2 equivalent in the atmosphere by 2100. It corresponds to a limited level of climate change, at about +1 ±0.3 °C by 2046‐2065 and +1 ±0.4 °C by 2081‐2100 on the earth surface, compared to 1980‐2010 levels. On emerged land, the temperature increase is on average +1.2 ±0.6 °C by 2081‐ 2100 (Collins _et al._ , 2013).

The second scenario, RCP 8.5, corresponds to a high increase in radiative forcing, reaching 8.5 W/m<sup>2</sup> by the end of the century. Under this scenario, GHG concentration in the atmosphere increases up to 1370 ppm of CO2 equivalent by 2100. This leads to significant magnitude in climate change at about +2 ±0.4 °C by 2046‐2065 already and +3.7 ±0.7 °C by 2081‐2100, compared to 1980‐2010 level. On emerged land, anticipated temperature increase is even higher, at +4.8 ±0.9 °C by 2081‐2100 (Collins _et al._ , 2013). This scenario can be seen as an extreme climate change situation; however the recent emission developments exceed even the RCP8.5 emission levels for the corresponding years (Peters _et al._ , 2013).

The level of uncertainty in temperature and precipitation change comes from the uncertainty related to the impact of GHGs atmospheric concentration on climate, as predicted by general circulation models (GCMs). For the same level of radiative forcing, GCMs tend to broadly agree on magnitude of temperature changes, but notably disagree on the regional patterns, and on evolution of precipitation patterns. Considering different sets of possible impacts is therefore crucial to reflect the model projection diversity. Therefore, for each RCP, we look at the impacts of GHG concentration on climate according to five different GCMs: HadGEM2‐ES, IPSL‐CM5A‐LR, GFDL‐ESM2M, MIROC‐ESM‐CHEM and NorESM1‐M. These provide spatially explicit results for temperature and precipitation changes that are then used in the EPIC crop model on a global grid (see section 2). As a standard assumption, we keep the default modeling of the CO2 effects as represented in the EPIC model, which includes crop response in terms of photosynthesis (fertilization effect) as well as the impact on water‐use efficiency resulting from decreased evapotranspiration. Because the resulting yield impact of these CO2 effects is an important source of uncertainty, we also consider one additional scenario as a sensitivity analysis on RCP8.5 where CO2 effects are disabled. This scenario, labelled RCP8.5* in the paper, is run with the HadGEM2‐ES climate change projections. Under RCP8.5, HadGEM2‐ES projects a global temperature increase for 2050 of about 2.5 °C and an average increase in precipitation of about 3 percent. This ranks HadGEM2‐ES as the hottest and driest of the five ISI‐MIP models, with potentially the most 12
negative effects on agricultural production (Warszawski et al., 2013). The list of climate scenarios is summarized in Table 2.

_Table 2 Climate change scenarios setting_

|**Scenario name**|**GHG concentration**|**GCM**|**CO2 concentration**|
|---|---|---|---|
|**Reference**|1980‐2010 average|None|Constant at 2005 level<br>(380ppm)|
|||HadGEM2‐ES||
|**RCP26**||IPSL‐CM5A‐LR||
|**.**<br>**(5 scenarios)**|450 ppm CO2‐eq|GFDL‐ESM2M|Increase at 450 ppm)|
|||MIROC‐ESM‐CHEM||
|||NorESM1‐M||
|||HadGEM2‐ES||
|**RCP 85**||IPSL‐CM5A‐LR||
|**.**<br>**(5 scenarios)**|1370 ppm CO2‐eq|GFDL‐ESM2M|Increase at 1370 ppm)|
|||MIROC‐ESM‐CHEM||
|||NorESM1‐M||
|**RCP 8.5***|1370 ppm CO2‐eq|HadGEM2‐ES|Constant at 2005 level<br>(380ppm)|

# **3.3. Mitigation policies**

The Integrated Assessment Modeling (IAM) community has put substantial efforts over the past years into quantifying the alternative SSP reference scenarios, as well as the stabilization scenarios corresponding to the RCPs’ radiative forcing levels. Unfortunately, this work has not yet been finalized and therefore we have decided to anchor the mitigation part of this report rather in the set of scenarios representing the backbone of the IPCC WGIII 5<sup>th</sup> Assessment Report on mitigation (IPCC, 2014b). These scenarios are the outcome of the Stanford Energy Modeling Forum Study 27 (EMF27, (Kriegler _et al._ , 2014). The scenarios are constructed along two dimensions: _i)_ climate policy assumption, and _ii)_ technology assumption. Out of the five alternative policy assumptions, we have decided to consider the only one which was compatible with the climate change impact scenario RCP2.6, and that was an idealized policy scenario where all regions and all sectors participate starting in the year 2010 and implement a uniform carbon price ensuring a cost‐effective achievement of atmospheric concentration of 450 ppm CO2e by 2100.

Similar to the uncertainty with respect to the climate change effects on agricultural markets, the level of the mitigation efforts required from the AFOLU sector and bioenergy substitution for attaining a specific level of climate stabilization is also subject to substantial uncertainties. The cost competitive mitigation efforts demanded from the land use sectors will depend on the technical and economic potential of AFOLU and bioenergy assumed in each integrated assessment model, on the assumptions about developments in other economic sectors, on societal preferences and also on new knowledge and technology which may become available over the long time horizon over which climate change mitigation is planned.

EMF27 explored in total nine technology assumptions and their effects on carbon price and bioenergy demand. The estimated AFOLU contribution to the mitigation efforts compatible with cost effective achievement of the 450 ppm target goes from ‐2% to 69% in the period 2010‐2030. A comparable range of uncertainty exists also for energy from biomass, which depending on the model is required to 13
<!-- Start of picture text --> 1000<br>— Reference<br>— FullTech<br>800 —— LimBio<br>—— LowEl<br>@<br><!-- End of picture text --> <!-- Start of picture text --> 8 600<br>(a)<br>Q<br>w<br>S 400<br>N<br>A<br><!-- End of picture text --> <!-- Start of picture text --> 200<br>0<br>2010 2020 2030 2040 2050 2060 2070 2080<br><!-- End of picture text -->
<!-- Start of picture text --> 200<br>— Reference<br>— FullTech<br>—— LimBio<br>> 150 — LowEl<br>2<br><!-- End of picture text --> <!-- Start of picture text --> co)<br>i<br>oO<br>S 100<br>e<br>a<br>=<br>a<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>2010 2020 2030 2040 2050 2060 2070 2080<br><!-- End of picture text -->
_Table 3. Overall scenario design in the paper_

|||**Climate change **<br>||
|---|---|---|---|
|**Socioeconomic**<br>**pathway**<br>**(2000‐2100)**|**Impacts**<br>**–**<br>**Mitigation**<br>**Policy**<br>**Present climate**|**Low climate change**<br>**RCP2.6**|**High climate change**<br>**RCP8.5**|
|**SSP4**|**Impacts**<br>1 reference SSP4|5 climate impact<br>scenarios for 450 ppm<br>with CO2effects|5 climate impact<br>scenarios for 1370<br>ppm with CO2effects<br>+ 1 scenario without<br>CO2effects|
||**Mitigation**<br>**policy**|3 stabilization<br>scenarios at 450 ppm<br>stabilization with<br>alternative<br>technological<br>assumptions||
|**SSP5**|**Impacts**<br>1 reference SSP5|5 climate impact<br>scenarios for 450 ppm<br>with CO2effects|5 climate impact<br>scenarios for 1370<br>ppm with CO2effects<br>+ 1 scenario without<br>CO2effects|
||**Mitigation**<br>**policy**|3 stabilization<br>scenarios at 450 ppm<br>stabilization with<br>alternative<br>technological<br>assumptions||

# **4. Results**

The GLOBIOM model is used to assess the impact of climate change and climate change mitigation scenarios between 2000 and 2080. In the sections below, we focus our results on primarily short term impacts (2030) and long term impacts (2080) for six large regions dominated by low and middle income countries (LMI): Latin America, Eastern Europe & Central Asia, North Africa & Middle‐East, Sub‐Saharan Africa, South Asia and Eastern Asia & Pacific. More details on the regional grouping are provided in the appendix.

# **4.1. Climate change impacts**

The scope of our analysis goes from climate change impacts on yield, through adjustments in producer strategies up to market rebalancing, which translates into price adjustments and ultimately food availability at the consumer level. This logic is also followed in the presentation of our results below. Climate shocks are modeled by shocking the 17 crop yields, as well as grass productivity in each grid cell of the model. This leads to change in level of production, market prices and consumption in the different economic regions.

## **4.1.1. Impact of climate change on yield**

The magnitude of impacts on average crop yields globally is presented in Figure 3 for the different scenarios. The climate change impacts are represented as relative change between the projected yield in 2030 or 2080 with climate change effects, compared to projected yield without these effects.

16
World 2030 <!-- Start of picture text --> 0 0 |<br>— -10 _ -10<br>&=<br><!-- End of picture text -->

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<!-- Start of picture text --> MIROC-ESM-CHEM MIROC-ESM-CHEM<br>“30 = NorESM1-M “30 = NorESM1-M<br>1 ~ 1 mTM<br>; ° °<br><!-- End of picture text --> <!-- Start of picture text --> fe}c fe}Cc<br>RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5<br><!-- End of picture text -->
<!-- Start of picture text --> Latin America & Carib. 2080 Mid. East & North Afr. 2080 East Asia & Pac. 2080<br>30 '' 30 ': 30 ''<br>20 20 i 20<br><!-- End of picture text --> <!-- Start of picture text --> Zz 10 z 10 : Zz 10 '<br>>g=5-10°'' g=>5 -10°'''' =>5 -10 ''<br><!-- End of picture text --> <!-- Start of picture text --> 30 H 30 t co] '<br>' g : g '<br>RCP 26 RCPB5 2 RCP 26 RCP85S 2 RCP 26 RCPBS 2 Climate models<br>= HadGEM2-ES<br><!-- End of picture text --> <!-- Start of picture text --> Sub-Saharan Africa, 2080 South Asia‘ 2080 Europe & Cent. Asia; 2080 IPSL-CM5A-LR<br>30 :' 0 : 20 :' = GFDL-ESM2MMIROC-ESM-CHEM<br>1 H ' lm NorESM1-M<br>20 \ 20 :H 20 '<br><!-- End of picture text --> <!-- Start of picture text --> = 10 z 10 : z 10<br>=&‘2<br>g° g ° : a °<br>2 -10 3 -10 i 3 0<br>ca - 1 a '<br><!-- End of picture text --> <!-- Start of picture text --> 2 2 20<br>20 20 20<br>' g H H g<br>RCP 26 RCP8S 9 RCP 26 RCP8S 2 RCP 26 RCP8S 9<br><!-- End of picture text -->
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<!-- Start of picture text --> 2030 2080<br>30 30<br>RCP2.6 RCP2.6<br><!-- End of picture text --> <!-- Start of picture text --> 20 =m RCP8.5 20 m RCP8.5<br>mTM RCP8.5* mTM RCP8.5*<br>& 10 & 10<br>Cc Cc<br>ao oO<br><!-- End of picture text --> <!-- Start of picture text --> =- O = = oO<br>5 . 5 | ll<br>22<br>@-10 @ -10<br><!-- End of picture text -->

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<!-- Start of picture text --> Latin America & Carib. Mid. East & North Afr. East Asia & Pac.<br>RCPS.5 2080 RCP€.5 2080 RCPS.5 2080<br>m SSP4<br>20 mTM SSP5 20 20<br>FE: FE: 5<br><!-- End of picture text --> <!-- Start of picture text --> =e = iad<br>DYeov2o<br>a<br>-20 a -20 a 20<br>-40 -40 -40<br><!-- End of picture text --> <!-- Start of picture text --> toa 2 & irs Bg > toa 2 & re se. taa 2 & res<br>3 Fo 3 FoO 8 io<br>Sub-Saharan Africa South Asia Europe & Cent. Asia<br>RCPS.5 2080 RCPS.5 2080 RCPS.5 2080<br><!-- End of picture text --> <!-- Start of picture text --> 20 20 20<br>2a= i=ova o=)=<br>£££<br>o0 3 it] 5 iv]<br>= < =e<br>CT2ov2o2<br>a<br>-20 a -20 a -20<br><!-- End of picture text --> <!-- Start of picture text -->
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<!-- Start of picture text --> Crops World SSP4 Ruminant meat World SSP4 Milk World SSP4 Pig and poultry World SSP4<br>55<br><!-- End of picture text --> <!-- Start of picture text --> °<br>° ° i Hl a °<br><!-- End of picture text --> <!-- Start of picture text --> Fd & Fd &<br>& $s & $s<br>gs * s§ * gs * s§ *<br>iiii<br>=280 = 2000 = eo = 2080<br>RCP 2.6 RCP8.5 RCP 8.5* RCP 2.6 RCP8.5 RCP 8.5* RCP 2.6 RCP8.5 RCP 8.5* RCP 2.6 RCP8.5 RCP 8.5*<br><!-- End of picture text -->
<!-- Start of picture text --> Latin America & Carib. 2030 Mid. East & North Afr. 2030 East Asia & Pac. 2030<br>z 10 _ = 10 _ z 10<br><!-- End of picture text --> <!-- Start of picture text --> & & &<br>£5 gos gos<br>&&&<br>=<br><!-- End of picture text --> <!-- Start of picture text --> <= <<br>SSP4 SSP5 SSP4 SSP5 SSP4 SSP5 Scenario<br>= RCP26<br>TM RCP8&5<br><!-- End of picture text --> <!-- Start of picture text --> Sub-Saharan Africa 2030 South Asia 2030 Europe & Cent. Asia 2030<br>z_ 10 z_ 10 2_ 10<br><!-- End of picture text --> <!-- Start of picture text --> && &<br>§5§5g<br>&& &<br>s ° 2 ° s °<br><!-- End of picture text --> <!-- Start of picture text --> <= <= <=<br>SSP4 SSP5 SSP4 SSP5 SSP4 SSP5<br><!-- End of picture text -->
World SSP4 World SSP5 <!-- Start of picture text --> S 2 S 2<br>(0)a oD<br>Cc Cc<br>2 £<br>(S) -4 (e) -4<br>Cc Cc<br>2 AS)<br>a rom<br><!-- End of picture text --> <!-- Start of picture text --> no wn<br>Cc Cc<br>8 2030 8<br>3 8 mTM 2050 3 8<br>o m 2080 oO -<br>uw uw<br><!-- End of picture text --> <!-- Start of picture text -->
-10 -10<br>RCP 2.6 RCP 8.5 RCP 8.5* RCP 2.6 RCP 8.5 RCP 8.5*<br><!-- End of picture text -->
<!-- Start of picture text --> Latin America & Carib. 2030 Mid. East & North Afr. 2030 East Asia & Pac. 2030<br>€=1=£ 1=#<br>%0&0<br><!-- End of picture text --> <!-- Start of picture text --> aa a<br>55g<br>#oe 3 aa3 B 3<br>3434g<br><!-- End of picture text --> <!-- Start of picture text --> S8 S<br>“4 “5 8<br>SSP4 SSP5 SSP4 SSP5 SSP4 SSP5 Scenario<br>RCP2.6<br>= RCPS5<br><!-- End of picture text --> <!-- Start of picture text --> Sub-Saharan Africa 2030 South Asia 2030 Europe & Cent. Asia 2030<br>€=1=£ 1=#<br>%0&0<br><!-- End of picture text --> <!-- Start of picture text --> Fd5oe 3 cy5a “3 ga a<br>3434g<br><!-- End of picture text --> <!-- Start of picture text --> 38 8<br>a) “ 45 a)<br>SSP4 SSP5 SSP4 SSP5 SSP4 SSP5<br><!-- End of picture text -->
<!-- Start of picture text --> Latin America & Carib. Mid. East & North Afr. South Asia<br>2030 2030 2030<br>444<br>33 3<br><!-- End of picture text --> <!-- Start of picture text --> 2030 3g 2 go 2 og 2<br>oO oO oO<br>10 8 1 efee, seen & 1 8 1 afege= of= nee<br>0 o+= aes 0<br><!-- End of picture text --> <!-- Start of picture text --> gz ReegeSSn2 REE:Segue aeateahn2e 8282gasgne REESEa§n2 REEEESagas ow agicns<br>Py 6 | z & e- @ é Fa == AgriN20Forest& LUC CO2<br><!-- End of picture text -->

<!-- Start of picture text --> & Cent. Asia Sub-Saharan Africa East Asia & Pac.<br>2 4 2030 4 2030 4 2030<br>033<br>g<br><!-- End of picture text --> <!-- Start of picture text --> Shige— 825G3— 82 g 2 82 a :<br>Ese27 Ese2-2 Fa} 1 Fa 1 I i Fa} 1 H | in HTT<br>etane Silane a<br>ssP4 SSP5 9 9 9<br><!-- End of picture text -->

<!-- Start of picture text --> Sosme Sasue Sassme Saesne Sasnue Sosne<br>a7uw 4 a7rs 4 wus7a irs7-a 5754ra sc75rs<br>SSP4 SSP5 SSP4 SSP5 SSP4 SSP5<br><!-- End of picture text -->
SSP4 scenario. This result is consistent with the EMF27 results themselves, where the models estimate that under this stabilization scenario, AFOLU emissions would need to be reduced by ‐4% to 99% (Smith _et al._ , 2014). According to our results, Latin America, Sub‐Saharan Africa and Eastern Asia & Pacific would contribute 45%, 26% and 13% of the global AFOLU abatement, respectively, providing together the large majority of the global mitigation effort in this sector. Under this stabilization scenario, direct emissions from agriculture remain roughly at their 2000 level, and net emissions from land use change approach zero, mostly because of reduced emissions from deforestation accompanied by afforestation. As one would expect, under the Low Energy Intensity scenario (LowEI), less mitigation effort is required from the AFOLU sector; total AFOLU emissions would then be about 20% higher than under FullTech. With limited role of bioenergy in the mitigation portfolio (LimBio scenario), agricultural emissions would be required to go even lower than under FullTech, and land use change would need to become a net sink of emissions already in 2030. This shows the need for substitution of negative emissions from bioenergy with carbon capture and storage by negative emissions from afforestation in order to achieve the climate stabilization.

# **4.2.2. Biomass supply for energy**

Total amount of bioenergy is in the reference scenario without mitigation policy projected to slowly decrease until 2050 because of the decreasing importance of traditional fuelwood use, and to start to increase afterwards because modern bioenergy becomes competitive even without mitigation policies. Figure 13 provides an overview of how biomass supply for bioenergy would develop under stabilization scenarios. In the FullTech stabilization scenario, demand for bioenergy by 2030 would be multiplied by two compared to the reference scenario. Sub‐Saharan Africa, Latin America and Eastern Asia & Pacific would again be important players supplying each about 20% of the total biomass demand. This result is in line with the findings by Rose _et al._ (2012) that up to 80% of the biomass for energy production would come from non‐OECD countries. With respect to the source of the biomass, our projections suggest that while in Latin America the quasi‐totality of the biomass would come from dedicated plantations, in Sub‐Saharan Africa and Eastern Asia & Pacific, 30%, respectively 20%, of the total biomass would come from traditional forests. This differentiation is due to high plantation yields and large areas of suitable land in the case of Latin America, and vast areas of tropical forests where currently only a small fraction of the biomass, high quality woods, is extracted from the managed zones. The required biomass supply would be 43% lower in the LowEI scenario, and, it would be 15% lower also under the LimBio scenario. The demand for biomass is projected to continue to grow substantially beyond 2030. Under the FullTech scenario, biomass demand would more than triple between 2030 and 2080. This new demand represents future income opportunities in the forestry and agricultural sectors.

27
<!-- Start of picture text --> Latin America & Carib. Mid. East & North Afr. South Asia<br>2030 2030 2030<br>14 14 14<br>4 12 ; = 4 12 e 12<br>10 10<br><!-- End of picture text --> <!-- Start of picture text --> I I a5A 684 _ = - _ = 7a= 684 a&5 68o49qe"a H| il5. a Z]<br>6 i | 245 . 2 2<br><!-- End of picture text -->

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<!-- Start of picture text --> os5nu2 ssue osm0u2 ssgue s§n2e ssne<br>a 2a =a ao =a sa a 4 =a<br>ind ir ir ira ind ind<br>ssP4 SSP5 SSP4 SSP5 SSP4 SSP5<br><!-- End of picture text -->
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<!-- Start of picture text --> World SSP4 2030 World SSP5 2030<br>: 1 Hy t<br>:. 11 :. i)i)<br>-100 ‘ -100 :<br><!-- End of picture text --> <!-- Start of picture text --> 8 Scenarios climate change ' 8 : H<br>2 _300 = HadGEM2-ES : 1 S .300 : 1<br>© IPSL-CMSA-LR! ' ® : !<br>22 = GFDL-ESM2M: 1 2 H '<br>© 490 = MIROC-ESM-CHEMNorESM1-M 1‘1I2° 400 i:.!'!<br>ii H '<br>Scenarios mitigation :<br><!-- End of picture text --> <!-- Start of picture text -->
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One important source of uncertainty is the response of the climate to change in GHG atmospheric concentration, as well as the resulting patterns of change in terms of precipitation and temperature changes. We used in this paper results from five GCMs but a larger set of results is produced by the CMIP5 community (see full list and results overview in (Collins _et al._ , 2013)). Uncertainties for projections are large in these models, related to various processes difficult to model and whose net effect remain largely uncertain: e.g., the dynamics of cloudiness (in particular in the tropics), the role of surface albedo, or climate induced changes in carbon fluxes (Andrews _et al._ , 2012; Vial _et al._ , 2013; Friedlingstein _et al._ , 2014). As an illustration, although the projected changes in global mean temperature are much less uncertain compared to regional patterns or precipitation changes, CMIP5 models estimate such a global temperature increase to range between 2.1 and 4.7 °C (Andrews _et al._ , 2012) for a given degree of radiative forcing (quadrupling). As detailed in section 3.2, our climate change scenarios were specifically selected to span a large set of climate responses, and this source of uncertainty should be kept in mind when looking at the average results on a RCP.

The second source of uncertainty stems from the modeling of the crop responses themselves. For this paper, we relied on only one biophysical model, EPIC. However, as illustrated in Rosenzweig _et al._ (2014), the most commonly used biophysical models predict very different responses to change in temperature and precipitation under high global warming. A pattern commonly observed in crop impact research is a small increase in crop yields in high and middle latitudes under low to moderate global warming and negative impact in tropical areas. When large ensemble of models are compared, impacts by 2050 on major crops remain within the range ‐25%‐+10% for four fifths of the projections (Porter _et al._ , 2014). However, when looking at details per crop and management type (low or high input, irrigation), results can vary considerably depending on model parameterization (Rosenzweig _et al._ , 2014). One important parameter for instance appears to be the modeling of nitrogen stress and the consideration of historical fertilization patterns. To this extent, our modeling framework explicitly takes into account the diversity of management systems and their specific response to climate change (Leclère _et al._ , 2014).

Another important source of uncertainty at the crop level is the influence of elevated atmospheric CO2 concentration. According to the IPCC, when CO2 effects are taken into account, crop prices are “as likely as not”, with range of expected responses from ‐30% to + 45% (Porter _et al._ , 2014). Recent studies illustrate that CO2 effects are not only likely to buffer a part of the impact for crops, but also to boost grassland productivity, with important implications for the livestock sector. Havlík _et al._ (2015) report a global average effect of climate change of +12.2% on grass yield globally using the EPIC model, and +45% with the LPJmL model for a RCP8.5 scenario with CO2 effects by 2050. Without CO2 effects, these impacts would be +2% and +7%, respectively. However, as emphasized by Rosenzweig et al., dispersion across model results are much higher with CO2 effects than without, and substantial additional efforts should be devoted to CO2 effects modeling. Additionally, there is some indication that crop quality, in terms of nutrient content, may decrease under CO2 effects, which would moderate the expected benefits of yield increases (Myers _et al._ , 2014). For these two reasons, the RCP8.5* scenario that does not include the effects of elevated CO2 concentration is an important counterfactual to consider in this paper, based here on the HadGEM2‐ES model.

There are however some other sources of yield impacts that are absent or imperfectly represented in our modeling framework and could lead to either higher or lower climate change biophysical impacts. Some management responses are partially reflected in a crop model such as EPIC (adjustment of 38
planting dates, inputs of fertilizer for high input systems, periods of harvest), but more detailed studies suggest that this gain could be higher, for example if including switch among existing cultivars (Leclère _et al._ , 2013; Osborne _et al._ , 2013). While this would place our results on the pessimistic range, including the impact of changes in water resource availability and droughts would go in the opposite direction: while we don’t capture them, literature based on observation has reported frequent underestimation of gridded simulation methods to predict crop response to extreme events (Lobell _et al._ , 2011; Lobell _et al._ , 2014). Similarly, additional negative impacts not accounted for include: rising tropospheric ozone concentration, and potentially higher occurrence of pests and diseases, or increased competition with weeds (Porter _et al._ , 2014). Furthermore, impacts of climate change on livestock are considered here through the feed availability channel only, whereas many other channels could also influence the future of the sector, in particular the role of heat and water stress on the productivity and mortality rate of animals, and the risk of increased spread of vector‐borne diseases (Havlík _et al._ , 2015).

For all the reasons above, the biophysical impacts of climate change are subject to a relatively high uncertainty, although some large patterns remain observed. As an illustration, Leclère _et al._ (2014) try to disentangle in their modeling analysis the signal‐to‐noise ratio associated with the biophysical shocks coming from the climate change uncertainty. They find negative impacts on crop yield for North America (excl. Canada), Brazil, Sub‐Saharan Africa and most of Eastern and South Asia. However, only for Brazil, the signal appears higher than the noise and the negative impact considered as robust across scenarios. The robustness of economic response in models is for this reason limited, and is additionally affected by their own intrinsic parameterization uncertainty, as evidenced by the diversity of economic model specification and parameterization.

Several comparison exercises looked at how different economic models respond to similar sets of climate scenarios to understand where the largest sources of uncertainty lie in economic impact assessments. In the framework of the ISI‐MIP project, Nelson _et al._ (2014) compared the economic responses of 9 global economic models applied to agricultural issues, including GLOBIOM. They showed that models tend to increase the spread of biophysical input shocks when coming to variables such as final yield, area and production responses, but that consumption responses were much less dispersed than the input. Models attributed differently the adjustments to the shock across the different adaptation channels in the model, management, expansion, trade and demand. Most computable general equilibrium models (CGEs) showed less reaction to the climate shock (average below +25% after equilibrium at horizon 2050 for a RCP8.5 without CO2 effects). Partial equilibrium models (with the exception of GCAM) and the CGE MAGNET showed average responsiveness in prices above +25% for the same shock, with the strongest price sensitivity for MAGPIE (Nelson _et al._ ), Fig S5). In response to price changes, adjustments differed across models, AIM, FARM, GCAM, and MAGNET adjusting mainly through area change, MAGPIE mainly through endogenous yield change, and ENVISAGE, GLOBIOM, GTEM relying almost equally on the two effects. Contribution from trade to absorption of the shock was also very different across models (Nelson _et al._ ), Fig S13 and S14): five models predicted limited trade adjustments (all CGEs except MAGNET, as well as MAGPIE), two models moderate trade adjustments (MAGNET, IMPACT) and two models large trade adjustments (GLOBIOM, GCAM). In the case of GLOBIOM, it is noteworthy that the trade setting for the comparison exercise is the same as considered for this paper for the integrated trade representation of the SSP5 scenario.

The final impact on food consumption in Nelson _et al._ (2014) was estimated to be ‐3% for RCP8.5 by 2050, under SSP2. The climate scenarios used were considered without CO2 effects but included a large 39
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is included because of its direct link to basic human needs in terms of food but also because of being the main source of livelihood for most of the poor. Land use and land use change are also directly linked with biodiversity and the poor but present considerable challenges in terms of monitoring and implementation in general. Havlík _et al._ (2014) showed how different the carbon price effects on food availability can be depending on whether a carbon price targets non‐CO2 emissions from agriculture or CO2 emissions from land use change. Finally, biomass and its potential competition with food and biodiversity for land and water have been extensively discussed in the literature.

In order to get further insight in this question, we have considered three alternative scenarios to the idealized policy case under FullTech technology assumption: FullTechAgr – a variant of the FullTech scenario with bioenergy demand maintained at the level of the reference scenario without mitigation, and carbon price applied only to non‐CO2 emissions from the agricultural sector; FullTechLuc – similar as FullTechAgr but carbon price applied only to CO2 emissions from land use change and forest management; and FullTechBio – bioenergy demand corresponds to the stabilization scenario FullTech, but carbon price is not applied on AFOLU emissions. The effects of these scenarios on food consumption are illustrated in Figure 24.

In terms of AFOLU emissions, implementing the carbon price solely on emissions from agriculture has almost no effect, as only 13% of the abatement achieved under the idealized FullTech scenario by 2030 is reached. On the other hand, implementing carbon tax on emissions from land use change alone provides more than 90% of the abatement achieved under FullTech. This comes as no surprise, since a large majority of the emission reductions came under FullTech by 2030 from avoided land use change. Implementing the bioenergy mandate alone would lead to increased emissions from land use change by some 5% compared to the reference scenario without mitigation policy. This risk is well recognized in the literature on ILUC (Induced Land Use Change), particularly for biofuels, but has been discussed also by Wise _et al._ (2009) in the Integrated Assessment Modeling context. Since in our framework bioenergy demand is driven by the bioenergy mandates only, the amount of biomass supplied in FullTechAgr and FullTechLuc scenarios is the same as in the reference scenario without climate change, while it corresponds to the FullTech level in the FullTechBio scenario.

Agricultural commodity prices would be affected very differently depending on the targeted sector. If emissions from agriculture only were targeted, the impact on prices would be about half compared to the idealized policy implementation by 2030. Targeting emissions from land use change would have almost no effect on crop prices, and also the effect on livestock prices would be just about a third compared to the full implementation. By 2030, the effect of the bioenergy mandate would stay below 1% for both crop and livestock prices. We have shown above that the FullTech stabilization scenario under SSP4 would remove almost 70% of the food availability growth which would otherwise happen between 2000 and 2030. If only agricultural emissions were targeted, FullTechAgr, 37% of the reference increase would still be lost. From this perspective, the FullTechLuc scenario appears very attractive because 80% of the reference food availability growth is achieved, while more than 90% of the AFOLU emissions abatement is also achieved. Hence, the cost of abatement in the land use change sector in terms of lost food availability is relatively low. This result corresponds well to the findings in Havlík _et al._ (2014) as presented in the Total Abatement Calorie Cost (TACC) framework. Finally, the bioenergy mandate only, FullTechBio scenario, has by 2030 also a limited impact on food availability, less than 15% loss of the reference scenario growth. This result is in line with Lotze‐Campen _et al._ (2014) who find that even substantial demand for biomass for energy use, has on its own negligible 41
<!-- Start of picture text --> 0 LatinAmerica& Carib. 2030 0 Mid. East& North Afr. 2030 0 East Asia & Pac. 2030<br>z4Z4z<br>&& &<br><!-- End of picture text --> <!-- Start of picture text --> F 2 é 2 F 2<br>f° f° fe<br><!-- End of picture text --> <!-- Start of picture text --> ssP4 SSP5 ssP4 SSP5 ssP4 SSP5 Scenario<br>= FullTech<br><!-- End of picture text --> <!-- Start of picture text --> ° Africa 2030 ° South Asia 2030 0 Europe & Cent. Asia 2030 == FullTechLucFullTechBio<br>z4Z4z<br>&& &<br><!-- End of picture text --> <!-- Start of picture text --> ; ° ; ‘ i ,<br>f° f° fe<br><!-- End of picture text --> <!-- Start of picture text --> SSP4 SSP5 SSP4 SSP5 SSP4 SSP5<br><!-- End of picture text -->
income countries are relatively more affected than some other regions due to their geographical location, and to the higher vulnerability of their food consumer to change in agricultural prices.

The agriculture and forestry sectors will likely be increasingly asked to contribute to mitigation efforts as well. This participation to achieve GHG reductions could present opportunities for land use sectors, with new demand for biomass, potential payments for carbon sequestration and higher margins for producers. However, mitigation policies could also bring significant challenges, because production costs will also increase with expansion of the production, and the deployment of new technologies will require some significant restructuring of the sectors. More worrying, the consumers will be more directly affected by the increased market prices, with potential challenges for food security.

Comparing the climate change impacts on food availability with the effects of mitigation, if mitigation is not implemented carefully, it could potentially bring worse effects for food availability than climate change itself, at least in the medium term. Hence, a careful design of mitigation policies needs to be adopted by targeting priority sectors and sources where emission abatement can be achieved at lowest costs, without hampering food provision. The contribution of the poorest regions to such scheme should be envisaged prudently, in order to avoid a double burden for people already facing the most severe climate change impact challenges. On the other side, the carbon tax revenue represents a potentially substantial source of new fiscal income, which if managed properly, could be used for funding of direct development programs and hence converted from a thread to an opportunity for the poor.

This study presents a first attempt to provide consistent quantitative assessment of climate change impacts and mitigation in agriculture and forestry on markets and food availability in low and medium income countries. Future improvements could go in several directions. For instance, we have ignored the combined effects of climate change and mitigation. We are convinced that this remains a second order effect of the extreme climate scenarios considered by the present paper, however if intermediate scenarios such as RCP4.5 and RCP6.0 with non‐negligible aspects of both climate change impacts and mitigation were to be considered, the two phenomenon might represent important feedbacks; e.g. climate change impacts reducing biomass productivity and hence making the provision of the bioenergy sector more expensive _versus_ afforestation occupying vast areas of land making it not available for crop area expansion to moderate the negative climate impacts.

To conclude, the projected effects of climate change and mitigation on agricultural markets raise important issues for food security in the long run, but remain more limited in the medium term horizon of 2030, providing opportunities for low‐ and middle‐income countries to pursue immediate development needs and thus prepare for later periods when adaptation needs and mitigation efforts will become the greatest.

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# **Appendices**

# **A. List of crops used in the paper**

_Table‐A 1. List of crop codes used in GLOBIOM_

|**CODE**|**NAME**|
|---|---|
|WHEA|Wheat|
|RICE|Rice|
|CORN|Corn|
|SOYA|Soybean|
|RAPE|Rapeseed|
|BARL|Barley|
|CASS|Cassava|
|SUNF|Sunflower|
|MILL|Millet|
|SRGH|Sorghum|
|SUGC|Sugar cane|
|BEAD|Dry beans|
|COTT|Cottonseed|
|CHKP|Chick peas|
|SWPO|Sweet|
||potatoes|
|POTA|Potatoes|
|GNUT|Groundnuts|
|OPAL|Oil palm fruit|

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# **B. Regional grouping**

_Table‐A 2. List of regions used in the paper, mapping with GLOBIOM regions and corresponding countries_

|**Macro region**|**GLOBIOM**|**Countries**|
|---|---|---|
||**regions**||
|**West Europe**|**EU Baltic**|_Estonia, Latvia, Lithuania_|
||**EU Central East**|_Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia, Slovenia_|
||**EU Mid West**|_Austria, Belgium, Germany, France, Luxembourg, Netherlands_|
||**EU North**|_Denmark, Finland, Ireland, Sweden, United Kingdom_|
||**EU South**|_Cyprus, Greece, Italy, Malta, Portugal, Spain_|
||**ROWE**|_Gibraltar, Iceland, Norway, Switzerland_|
|**Eastern Europe**<br>**and Central Asia**|**Former USSR**|_Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova,_<br>_Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan_|
||**Turkey**|_Turkey_|
||**RCEU**|_Albania, Bosnia and Herzegovina, Croatia, Macedonia, Serbia‐Montenegro_|
|**North America**|**Canada**|_Canada_|
||**United States**|_United States_|
|**Latin America**|**Brazil**|_Brazil_|
|**and the**<br>**Caribbean**|**Mexico**|_Mexico_|
||**RCAM**|_Bahamas, Barbados, Belize, Bermuda, Costa Rica, Cuba, Dominica, Dominican_<br>_Republic, El Salvador, Grenada, Guatemala, Haiti, Honduras, Jamaica, Nicaragua,_<br>_Netherland Antilles, Panama, St Lucia, St Vincent, Trinidad and Tobago _|
||**RSAM**|_Argentina, Bolivia, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname,_<br>_Uruguay, Venezuela, RB_|
|**Eastern Asia and**<br>|**China**|_China_|
|**Pacific**|**RSEA OPA**|_Brunei Daressalaam, Indonesia, Singapore, Malaysia, Myanmar, Philippines,_<br>_Thailand_|
||**RSEA PAC**|_Cambodia; Korea, Dem. People’s Rep.; Lao PDR; Mongolia; Vietnam_|
||**Pacific Islands**|_Fiji Islands, Kiribati, Papua New Guinea, Samoa, Solomon Islands, Tonga, Vanuatu_|
|**Pacific**<br>|**ANZ**|_Australia, New Zealand_|
|**Developed**|**Japan**|_Japan_|
||**Korea, Rep.**|_Korea, Rep._|
|**South Asia**|**India**|_India_|
||**RSAS**|_Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan, Sri Lanka_|
|**Middle‐East and**<br>**North Africa**|**Middle East and**<br>**North Africa**|_Algeria, Bahrain, Egypt, Arab Rep., Iran, Islamic Rep., Iraq, Israel, Jordan, Kuwait,_<br>_Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syrian Arab Republic,_<br>_Tunisia, United Arab Emirates, Yemen, Rep._|
|**Sub‐Saharan**<br>**Africa**|**Congo Basin**|_Cameroon, Central African Republic, Congo, Rep., Congo, Dem. Rep., Equatorial_<br>_Guinea, Gabon_|
||**Eastern Africa**|_Burundi, Ethiopia, Kenya, Rwanda, Tanzania, Uganda_|
||**South Africa**||
||**Southern Africa**<br>**(Rest of)**|_Angola, Botswana, Comoros, Lesotho, Madagascar, Malawi, Mauritius,_<br>_Mozambique, Namibia, Swaziland, Zambia, Zimbabwe_|
||**West and**<br>**Central Africa**|_Benin, Burkina Faso, Cape Verde, Chad, Côte d'Ivoire, Djibouti, Eritrea, Gambia,_<br>_Ghana, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal,_<br>_Sierra Leone, Somalia, Sudan, Togo_|

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# **C. List of scenarios**

The list of scenarios below has been used for this paper. These have been run under two macroeconomic context: SSP4 and SSP5. Therefore, 36 scenarios were in total analysed.

_Table‐A 3. Full list of scenarios used in the paper_

|**ID**|**GCM**|**CLIMATE**<br>**CHANGE**|**CLIMATE**<br>**MITIGATION**|**COMMENT**|
|---|---|---|---|---|
|**B**|None|No CC|None|Baseline|
|**C1**|HadGEM2‐ES|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C2**|IPSL‐CM5A‐LR|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C3**|GFDL‐ESM2M|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C4**|MIROC‐ESM‐<br>CHEM|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C5**|NorESM1‐M|RCP2.6|None|Climate scenario RCP2.6 with CO2effects|
|**C6**|HadGEM2‐ES|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C7**|IPSL‐CM5A‐LR|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C8**|GFDL‐ESM2M|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C9**|MIROC‐ESM‐<br>CHEM|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C10**|NorESM1‐M|RCP8.5|None|Climate scenario RCP8.5 with CO2effects|
|**C11**|HadGEM2‐ES|RCP8.5*|None|Climate scenario RCP8.5withoutCO2effects|
|**CX1**|Avg|RCP2.6|None|Average over GCMs RCP2.6 with CO2effects|
|**CX2**|Avg|RCP8.5|None|Average over GCMs RCP8.5 with CO2effects|
|**CX3**|Avg|RCP8.5*|None|Average over GCMs RCP8.5withoutCO2effects<br>(equal to C11)|
|**M1**|None|No CC|FullTech|Mitigation policy 2 degrees ‐ high energy intensity<br>world ‐ high biomass use|
|**M2**|None|No CC|LowEI|Mitigation policy 2 degrees ‐ low energy intensity<br>world ‐ high biomass use|
|**M3**|None|No CC|LimBio|Mitigation policy 2 degrees ‐ limited biomass use|
|**M1A**|None|No CC|FullTechAgr|Mitigation policy 2 degrees ‐ M1 ‐ carbon tax on<br>agriculture only|
|**M1B**|None|No CC|FullTechLUC|Mitigation policy 2 degrees ‐ M1 ‐ carbon tax on<br>land use emissions only|
|**M1C**|None|No CC|FullTechBio|Mitigation policy 2 degrees ‐ M1 ‐ bioenergy<br>expansion only|

52
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