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Global food markets, trade and the cost of climate change adaptation

Aline Mosnier, Michael Obersteiner, Petr Havlík, Erwin Schmid, Nikolay Khabarov, Michael Westphal, Hugo Valin, Stefan Frank, et al. Food Security The Science, Sociology and Economics of Food Production and Access to Food ISSN 1876-4517 Food Sec. DOI 10.1007/s12571-013-0319-z

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Author's personal copy Food Sec. DOI 10.1007/s12571-013-0319-z

ORIGINAL PAPER

Global food markets, trade and the cost of climate change adaptation Aline Mosnier & Michael Obersteiner & Petr Havlík & Erwin Schmid & Nikolay Khabarov & Michael Westphal & Hugo Valin & Stefan Frank & Franziska Albrecht

Received: 15 July 2013 / Accepted: 2 December 2013 # Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2014

Abstract Achieving food security in the face of climate change is a major challenge for humanity in the 21st century but comprehensive analyses of climate change impacts, including global market feedbacks are still lacking. In the context of uneven impacts of climate change across regions interconnected through trade, climate change impact and adaptation policies in one region need to be assessed in a global framework. Focusing on four Eastern Asian countries and using a global integrated modeling framework we show that i) once imports are considered, the overall climate change impact on the amount of food available could be of opposite sign to the direct domestic impacts and ii) production and trade adjustments following price signals could reduce the spread of climate change impacts on food availability. We then investigated how pressure on the food system in Eastern Asia could be mitigated by a consumer support policy. We found that the costs of adaptation policies to 2050 varied greatly across climate projections. The costs of consumer A. Mosnier (*) : M. Obersteiner : P. Havlík : N. Khabarov : H. Valin : S. Frank : F. Albrecht International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, 2361 Laxenburg, Austria e-mail: [email protected] A. Mosnier : E. Schmid University of Natural Resources and Life Sciences, Feistmantelstraße 4, 1180 Vienna, Austria M. Westphal Asian Development Bank, 6 ADB Avenue, Mandaluyong City 1550, Philippines M. Westphal Abt Associates, 4550 Montgomery Avenue, Suite 800 North, Bethesda, MD 20814-3343, USA F. Albrecht University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria

support policies would also be lower if only implemented in one region but market price leakage could exacerbate pressure on food systems in other regions. We conclude that climate adaptation should no longer be viewed only as a geographically isolated local problem. Keywords Climate change . Integrated modeling . International trade . Adaptation . Food security . Eastern Asia

Introduction Temperature and precipitations explain a significant share of year-to-year variation of agricultural production (Lobell and Field 2007). Consequently, agriculture is one of the most exposed sectors to future climate change. It is estimated that past global warming since the 1980s has already significantly offset crop yield gains from technological progress as has been experienced in Russia for wheat and in China for maize (Lobell et al. 2011). Shifting precipitation patterns have also been observed, leading to more frequent droughts in some areas or more frequent floods in others (IPCC 2012). In the future, the global warming trend is expected to continue especially under increasing Greenhouse Gas (GHG) concentrations in the atmosphere (Meehl et al. 2007). Changes in temperature, precipitation and CO2 concentrations will affect plant growth, the spread of pests and diseases and also the nutritive value of crops (Long 2012; Schmidhuber and Tubiello 2007). The overall impact of climate change remains uncertain, but the large range of risks calls not only for its early assessment but also for the assessment of the capacity to adapt to it (Parry et al. 2005; Smith 1997). Past studies have highlighted contrasting impacts of climate change across and within countries. Some temperate and cold regions could benefit from warming while tropical and subtropical areas could be harmed by higher temperatures

Author's personal copy A. Mosnier et al.

during the growing season (Parry et al. 1999). In Europe, warming in the middle and higher latitudes could extend the growing season, allowing greater productivity of cultivars and leading to positive impacts on agriculture (Olesen and Bindi 2002). In the U.S., increase in the frequency of heavy downpours and droughts and extension of invasive weeds northwards are seen as major risks of climate change for agriculture (Hatfield et al. 2008). The most severe losses due to future climate change are expected to be in Sub-Saharan Africa and South Asia due to larger temperature increases relative to precipitation changes, limited crop management strategies and/or the dominance of C4 staple crops which are less sensitive to the CO2 fertilization effect (Iglesias et al. 2011; Knox et al. 2012; Roudier et al. 2011). In most countries, food consumption mainly relies on domestic production but regional agricultural markets are increasingly connected through international trade. For instance, China’s external dependency for oilseeds has increased from 8 % in the mid-1990s to almost 50 % currently (Nepstad et al. 2006). Rice has also become a major component of urban diets in Western African countries where rice imports represent more than 30 % of the domestic consumption (Dalton and Guei 2003; Nwanze et al. 2006). A crucial issue is to assess to what extent negative impacts of climate change in one region could be offset by positive impacts in other regions and the overall impact on food security (Gregory et al. 2005). The supply of food at reasonable prices under climate change will need a combination of different adaptation strategies (Garnett et al. 2013). Some adjustments in individuals’ behavior following market signals (autonomous adaptation) will occur to reduce the negative outcome of climate change or exploit opportunities (Tol et al. 1998). For instance, farmers have a long experience in dealing with weather variability (Thomas et al. 2007). However, public interventions could facilitate adaptation and reduce losses following climate change, especially for the most vulnerable population. Most of the climate change adaptation literature has focused on producer-side responses and policies (Howden et al. 2007; Smit and Skinner 2002; Waha et al. 2013). In addition to farmlevel adaptation as well as whether or not positive and negative climate shocks would tend to cancel out at the global level, international trade could also reduce impacts of weather shocks on regional food markets (Adams et al. 1998; Godfray et al. 2010; Huang et al. 2011). Furthermore, some policies could be implemented to protect consumers in the context of higher food prices (Nelson et al. 2010). This has currently received little attention in the climate change adaptation literature. We used an integrated modeling framework to investigate both biophysical and economic impacts of climate change by 2050 and the effects of a consumer subsidy on the average calorie availability per capita. We integrated climate change impacts on the yields of 17 crops computed with a global

gridded crop model (EPIC) into GLOBIOM, a global economic land use model. Bilateral trade flows were endogenously computed between the 31 regions of the model. We were thus able to take into account i) the impacts in the rest of the world when assessing climate change impacts on food availability in one region and ii) the evolution of comparative advantage with climate change when assessing adaptation needs in one region. Autonomous adaptation is represented in GLOBIOM through adjustments in input use, optimal crop mix, sourcing and composition of food intake. In addition, we simulated the impacts and costs of a consumer subsidy that would restore calorie intake to no-climate change levels. We focused in particular on four East Asian countries, Mongolia, China, Japan and South Korea. Although they are neighbors, they are heterogeneous in population size, food preferences and agricultural sectors, allowing us to identify influences of climate change in relation to food consumption and other vulnerability factors that are potentially very different. Moreover, although China has a huge land area, only 7 % of its surface is considered suitable for agriculture and China’s climate has already experienced obvious warming since 1960 (Piao et al. 2010; Zhang and Huang 2012).

The integrated modeling framework To model the impacts of climate change, we employed the whole chain of GCMs-biophysical model-economic model as described in Fig. 1. Climate projections were first incorporated into the crop biophysical model EPIC to assess the impact of future climates on crop yields and the changes in crop yields were then incorporated into in the economic model GLOBIOM to simulate the impacts of climate change on agricultural markets. Climate projections Here we focus on the A2 scenario from the Special Report on Emissions Scenarios (SRES). It describes “a very heterogeneous world with continuously increasing global population and regionally oriented economic growth that is more fragmented and slower than in other storylines” (Nakicenovic and Swart 2000). It corresponds to one of the strongest temperature rises: +3.4 Celsius degrees by 2100. For the historical period (1961–1990), the average CO2 concentration increased from 316 to 352 ppm, and future concentrations are assumed to increase to 444 ppm and 522 ppm for 2030 and 2050 respectively (Meehl et al. 2007). We used the 60-year global dataset of meteorological forcing from Princeton University1 (Sheffield et al. 2006) and investigated possible impacts of climate change based 1

http://rda.ucar.edu/datasets/ds314.0/

Author's personal copy Global food markets, trade and the cost of climate change adaptation Fig. 1 Overview of the integrated modeling cluster GCMs-EPIC-GLOBIOM to assess climate impacts and climate adaptation policy where “mri” stands for MRICGCM2.3.2 climate model, “cnrm” for CNRM-CM3 climate model and “ukm” for UKMOHadGEM1 climate model

on projections provided by different Global Climate Models (GCMs). We used historical daily data from the Princeton dataset to calculate future daily values based on changes in mean monthly temperature and mean monthly precipitation coming from GCMs and relative to the historical baseline 1961–1990 (Hempel et al. 2013). Three GCMS have been selected among 17 GCMs (Randall et al. 2007) to reflect contrasting climate projections based on changes in the Climate Moisture Index (CMI) between 2046 and 2055 and the baseline period 1961–1990 (Fig. 1). CMI is an indicator of aridity which is a function of both annual precipitation and average annual potential evapotranspiration (Strzepek 2012). When looking at the global average, MRI-CGCM2.3.2 (“mri”) projections represents a future global wet climate, CNRM-CM3 (“cnrm”) a midrange climate, and UKMO-HadGEM1 (“ukm”) a global dry scenario by 2050. However, we notice that regional climate change could look quite different with for instance CNRMCM3 being the driest of the three GCMs for Eastern Asia and all 3 GCMs simulating a drier climate for China, Japan and Mongolia (Fig. 4). EPIC biophysical model The Environmental Policy Integrated Climate model (EPIC, version 0810) was applied at global scale and simulates major biophysical processes in agricultural ecosystems (Williams 1995). The major components in EPIC are weather/climate simulation, hydrology, erosion-sedimentation, nutrient and

carbon cycling, pesticide fate, plant growth and competition, soil temperature and moisture, tillage, cost accounting, and plant environment control. Potential biomass was adjusted to actual biomass through daily stress caused by extreme temperatures, water and nutrient deficiency or inadequate aeration. EPIC uses temperature, precipitation and CO2 concentrations from the different climate models to simulate inter alia potential evapotranspiration, crop yields, and input requirements under different climates (Fig. 1). As plants convert CO2 to carbohydrates in photosynthesis, higher CO2 concentrations should have a positive effect on many crops, enhancing biomass accumulation and final yield (i.e. carbon fertilization). In addition, higher CO2 concentrations reduce plant stomatal openings—the pores through which plants transpire, or release water—and thus reduce water loss. The so-called C3 crops, such as rice, wheat, soybeans, legumes as well as trees, should benefit more than the C4 crops, such as maize, millet, and sorghum. Most global crops models incorporate the carbon fertilization effect, and ceteris paribus, elevated CO2 concentrations result in higher yields. However, projections of the impact of future climate on crop yields could differ in the sign of the change depending on whether the carbon fertilization effect is included or not. The magnitude of the fertilization effect on crop yields globally is still debated and thus a source of uncertainty. Crop yield potential and input requirements were assessed for each Simulation Unit of current crop areas (Global Land Cover-GLC 2000 database 2003), 17 crops and three input


systems (low input rainfed, high input rainfed and high input irrigated). Simulation Units correspond to the intersection of Homogeneous Response Units (HRUs) i.e. aggregates of 5 arcmin pixels of same slope, altitude and soil characteristics, with a 30 arcmin grid, and country boundaries (Skalský et al. 2008). This results in a partition of the world’s area into 103,000 spatial simulation units. GLOBIOM economic model Crop yield potential, phosphorous, nitrogen and water requirements under historical climate and under climate change were then introduced in GLOBIOM (Global Biosphere Management Model; Fig. 1). GLOBIOM is a global recursive dynamic partial equilibrium model, which includes crops, livestock and forestry (Havlík et al. 2013, 2011). Market equilibrium was determined by maximizing the sum of producer and consumer surplus subject to resource, technological, and policy constraints. It provides simulation coverage for 18 of the globally most important crops, a range of livestock production activities, major forestry commodities, and multiple bio-energy transformation pathways (Mosnier et al. 2013). The world is divided into 31 economic regions (Appendix 1). Prices, demand for food, feed and energy, processed quantities and bilateral trade flows are endogenously determined at the level of these regions while the crops, timber and livestock production are computed at a 200×200 km grid level within country boundaries which accounts for heterogeneous spatial land productivity. The GLOBIOM model was run over the period 2000–2050 and was solved in 10 year-steps. Demand is driven by change in population, income per capita and prices. China has more than 1 billion people while Mongolia only 2 million, Japan 127 million and South Korea 46 million. The population is supposed to start declining already after 2010 in Japan and after 2030 in China and South Korea, but the population doubles between 2000 and 2050 in Mongolia (Asian Development Bank). The GDP per capita continues to increase in Japan and South Korea but at a slow pace leading to a reduction in the economic development gap with China and Mongolia (Asian Development Bank). Income and price elasticities were sourced from USDA (Seale et al. 2003). Elasticity trajectories over time reflect Engel’s curves in line with evolution of diet such as that projected by FAO (Alexandratos et al. 2006). The shifters for calorie consumption from one period to another were defined at the level of nine aggregated commodity categories – cereals, roots, sugar, pulses, oilseeds, ruminant meat, non-ruminant meat milk and eggs – and applied uniformly to single GLOBIOM products in the same commodity category (Table 1 in Appendix). The calorie intake per capita and the diet composition were assumed to remain stable in the next decades in

Japan and South Korea with a large share of cereals and a small share of animal calories. On the contrary, we assumed that animal calorie consumption per capita will significantly rises in China (pork) and Mongolia (ruminant meat and milk) following growth in income (Fig. 5). The final demand was endogenously determined according to the evolution of production costs and the equilibrium price. This analysis included both tariffs and transportation costs differentiated among products and trading partners (Bouët et al. 2004). Trade calibration was applied to reconcile observed bilateral trade flows, regional net trade, prices, and trading costs for the base year (Jansson and Heckelei 2009). Within one period, trade cost increased with shipped quantities, mimicking transportation capacity constraints. Scenarios Biophysical impacts We first investigated what would be the impact of climate change corresponding to 2050 with the current crop area and systems of production distribution (Global Land Cover-GLC 2000 database 2003; You and Wood 2006) using EPIC simulated changes in 17 crop yields. For the impact of climate change on domestic production, we compared the total equivalent crop calorie production with the historical climate and with climate change projections using FAO crop to calorie conversion coefficients. To compute the biophysical impact on calorie imports per region, we applied the average change in calorie production in the exporting region to the 2000 bilateral trade flow – computed as an average over 1998– 2002 – for each crop (See Appendix 2 for the detailed methodology). After having investigated the biophysical impacts of climate change, we used GLOBIOM to assess how autonomous adaptation can buffer the climate change impacts on consumers. Projected impacts of climate change with market feedbacks In GLOBIOM, autonomous adaptation to climate change occurs through i) location of the production, by changing the optimal crop shares at the local level, ii) management, by increasing or decreasing fertilizers and irrigation use in agriculture, iii) trade, by changing trading quantities and relative importance of different trading partners and iv) consumption, by changing the amount and the structure of food consumption. Projected impacts of climate change with adaptation policy Finally, we mimicked an adaptation policy acting on the consumer side and quantified its cost with GLOBIOM. We defined the adaptation policy as a consumer subsidy, which

Author's personal copy Global food markets, trade and the cost of climate change adaptation

allowed consumers in every region to reach at least the same calorie consumption level as without climate change. The purpose of this policy was to allow consumers to buy more expensive food to reach a certain consumption level. By increasing the demand, this policy also indirectly supported producers through higher market prices. The consumer subsidy was introduced at the region level and split into two components: the required subsidy to achieve the same calorie consumption from animal origin and the required subsidy to achieve the same calorie consumption from vegetable origin. The total cost of the climate adaptation policy was the sum of these two components times the number of additionally consumed calories per region. We compared i) a regional implementation of the policy in the four Eastern Asian countries, and ii) a global implementation of the policy in which consumer subsidy was set-up in all regions.

Results Combined local and external biophysical impacts of climate change on crop calorie availability The overall biophysical impact of climate change on global crop calorie production varied between +2 and −3 % depending on the GCM. Despite heterogeneous climate projections globally, we observed some common regional patterns in the simulations of biophysical impacts of climate change across the three GCMs (Fig. 2). In China, our results always showed a decrease in crop calorie production in the North Plain, which is very densely populated and where most of the grains of the country are currently produced, and an increase in crop calorie production in the Sichuan Basin and above the Xi Jiang River under 2050 climate projections. Total crop calorie production was always expected to increase with climate change in South Korea and in most of Japan which is consistent with a recent study on climate change impact on rice in Japan (Iizumi et al. 2011). Among the main trading partners of Eastern Asian countries, we observed a negative impact of climate change on crop production in North America and a positive impact on Australia in 2050 while there was more uncertainty about the climate change impact in Latin America. In line with the existing literature, we found a negative impact of climate change on crop calorie production in Sub-Saharan Africa and South Asia and a positive impact on European production (Olesen and Bindi 2002; Roudier et al. 2011). We show that trade adjusted biophysical impacts of climate change on domestic crop calorie availability could be of opposite sign of the direct biophysical impact on the domestic production (Fig. 3, “Biophysical only”). For instance, in two of the three GCMs, South Korea and Japan, who are highly dependent on staple crops imports from North America,

experienced a total crop calorie availability reduction for food consumption while the impacts on domestic production were consistently positive. From our biophysical results and taking into account the current structure of agricultural trade, Central America, Western Africa, and the U.S. were negatively affected by climate change through both direct impacts on domestic production and indirect impacts on imports for at least two GCMs projections (Table 2 in the Appendix). Resilience of the food system to climate change and other factors of vulnerability In GLOBIOM we took into account the fact that population and economic growth will lead to higher calorie demand by 2050, especially in the Southern latitudes. We project an average increase in crop calorie consumption by 40 % globally by 2050 compared to 2000. Overall, the range of the climate change impact on global crop calorie production by 2050 was reduced from [−3 %; +2 %] with the only biophysical impacts to [−2 %; +0.03 %] with the market feedbacks. Economic feedbacks generally lead to higher calorie production in Eastern Asia compared to the only biophysical impacts of climate change. The crop calorie production for food was still reduced in China, but the magnitude of the reduction was limited to 4 % as compared to the biophysical impacts of 10 % (Fig. 3, “Autonomous adaptation”). This can be partly explained by domestic shifts in area of production and in management. The results showed increases in wheat production in the South of China and in rice production in the Sichuan basin. Mongolia is a landlocked country where transportation costs are high and imports are concentrated on few trading partners. Negative impacts of climate change in partner countries lead to higher import prices that were mainly compensated by higher domestic production. On the contrary, China has more flexibility to adjust trade patterns. Our results show for instance some shift from negatively impacted North America to positively impacted Australia and Europe in Chinese import sourcing. Improvement in rice productivity under climate change also stimulated exports from Japan and South Korea leading to higher domestic production. On the consumption side, the global wet scenario would lead to the lowest impact on calorie intake in Eastern Asia (Fig. 3, “Food”). Across the three climate change projections, average crop calorie consumption per capita remained almost constant in Japan and South Korea because higher domestic productivity was able to compensate higher import prices. In Japan, crop price index decreased with climate change with all three GCMs projections [−5 %; −3 %] and in South Korea for two of the three [−1 %; +4 %]. China is expected to experience increases in crop prices regardless of the climate change scenario [+1 %; +6 %] (Table 3 in the Appendix), leading to a reduction in the average crop calorie intake by 1 % to 3 %. Moreover, Mongolia faces the highest crop price rise among


the four Eastern Asian countries (+38 %) and the strongest reduction of the daily crop calorie average intake (−12 %). Higher prices in Mongolia are explained by less flexibility in trade as well as in the demand which is concentrated on few products. From our results, the average impact of climate change on world crop price index varied between 0 and 5 % across GCMs in 2050 compared to historical climates. Rather moderate impact of climate change on world crop prices have already been highlighted by previous global economic studies. Parry et al. (1999) have estimated that global cereal prices could increase between 0 and 20 % in 2050 with most of the simulations being in the range of 0–6 %. Recent estimates computed with the GTAP model (Hertel et al. 2010) also indicate an average world price rise of 3.6 % for cereals due to climate change in 2030. The livestock sector was impacted through changes in feed prices and in land prices due to cropland area adjustments. The price index for livestock products increased up to 2 % more globally with climate change by 2050 (Appendix, Table 2), leading to a reduction in average global animal calorie consumption by 2 %. The most important impacts were observed in Sub-Saharan Africa where the average animal calorie consumption decreased by 4.5 % with climate change. In Eastern Asia, the strongest price rises were for pig meat in China and beef in South Korea in 2050. This is explained by the negative impact of climate change on corn in China and on cereals in North America from which Japan and South Korea’s livestock feed is mainly imported. The main trading partner regions for beef - U.S. and Australia - also experienced increases in meat prices with climate change. The overall impact of climate change on food consumption in Mongolia was lowered by the grass-based ruminant systems. However, this requires further investigation as steppe grasslands could be very sensitive to climate change in Mongolia (Xiao et al. 1995), an impact not represented here. Income transfer to consumers to offset climate change impacts The consumer adaptation policy was first introduced in Eastern Asian countries and was modeled as a subsidy to consumers, allowing them to reach the same level of calorie intake, both from crops and from animal origin, as if under no climate change conditions. Between 50 and 70 % of the additionally consumed calories were projected to be produced domestically in China, while in Mongolia more than 80 % of the additionally consumed calories were imported (Fig. 3, “Regional Adaptation Policy”). We computed the cost of such a regional policy to vary widely across GCMs from 80 million in the global wet scenario to more than 8 billion USD per year in 2050 in the global mid-range scenario, which corresponds to the driest scenario

Fig. 2 Biophysical impact of climate change in 2050 on current cultivated area in percent change of crop calories production compared to the situation with historical climate in China, Japan, South Korea and Mongolia (box) and in the rest of the world. The results are aggregated at the following regional level: China (CHN), Japan (JPN), Mongolia (MOG), South Korea (ROK), India (IND), Latin America (LAM), North America (NAM), Former Soviet Union (FUS), Europe (EUR), Central America (CAM), Sub-Saharan Africa (SSA), Middle East and North Africa (MED), South Asia (SAS) and Oceania (OCE). Calories production is computed for18 crops which are listed in Table A1

in Eastern Asia. However, even if Chinese calorie imports only increased by 9 % maximum with the consumer support policy, it still represents large quantities on international markets. Higher exports to the Eastern Asian region lead to higher prices in Southeast Asia and Brazil where the average calorie intake per capita was further reduced in addition to the climate change impact. Thus, market leakage effects of domestic adaptation policies could raise equity and food security issues on the international level. When we implemented a global policy that would restore calorie consumption to no climate change levels in each region of the world, higher global demand would further increase world prices stimulating additional production in all regions. Comparing the regional and global policy scenarios, we show that food imports tend to be lower and domestic production higher in Eastern Asia under the global policy scenario (Fig. 3, “World Adaptation Policy”). The regional cost of the global adaptation policy strongly increased in Eastern Asian countries, reaching a minimum of 2 billion USD and a maximum of 30 billion USD per year in 2050. We computed the global cost of the global adaptation policy to be in the range of 12 to 119 billion USD per year in 2050 in the global wet scenario and in the global mid-range scenario, respectively.

Discussion and conclusion This study illustrates that looking only at crop yield projections in one region is inadequate to derive conclusions on climate change impacts on food security. Heterogeneous impacts of climate change across the world will change the relative competitive advantages in agricultural production leading to new arbitrages between imports and domestic production. If trade is flexible enough, in the regions where climate change will increase crop productivity, more exports will reduce the gains for domestic consumers while in negatively impacted regions, more imports could ameliorate food price increases. According to our results, diversity in food diet, agricultural production and trading partners could help to reduce the negative consequences of climate change on food consumers. We have simulated the effects of a consumer subsidy to stimulate food consumption up to the no-climate change level


Author's personal copy A. Mosnier et al. Fig. 3 Relative impacts of climate change on availability of crop calories production and imports compared to the historical climate in China, South Korea, Japan and Mongolia in 2050. Relative difference with results obtained with historical climate data. The methodology used is described in Appendix 2

in a context of rising food prices. This leads to an increase in domestic food production in the regions where it is implemented and also favors major exporters. However, non-coordinated national implementation of adaptation policies could be harmful for other countries when the increase in domestic food prices is transmitted globally. We also find that the cost of such a policy implemented globally varies from a factor of 1 to 10 – between 12 and 119 billion USD – across the three GCMs projections by 2050, highlighting the large impact of climate uncertainties on our estimates (Ramirez-Villegas et al. 2013). But this range of adaptation costs corresponds to less than a tenth of a percent of the projected global GDP in 2050. The success and the overall cost of a consumer-oriented adaptation policy depend on several factors that are not taken into account in this study. First, technological change in crop or livestock breeding (Huang et al. 2004; Liu et al. 2010) could reduce price increases due to climate change and consequently the required financial support to consumers. Second, reliable transportation infrastructures inside the country are an important prerequisite for allowing food transfer from food-surplus regions to food-deficit regions (Bourguignon et al. 2008). Third, the advantage of a consumer strategy is the possibility of relying on trade to adjust to climate shocks but constraints in foreign currency availability and export restrictions from large exporting countries are still obstacles to trade-based adjustments (Gilbert and Tabova 2011). Finally, instead of a universal scheme, a climate adaptation policy might favor a funding mechanism that is more targeted to the food insecure

populations. Such a scheme might reduce the costs of the adaptation policy even if transaction costs of a more targeted implementation mechanism could be significant. One of the shortcomings of this study is related to income representation. Assumptions on GDP projections are constant across scenarios i.e. there is no impact of climate change on the overall GDP level, and since we use a partial equilibrium model, feedbacks from the agricultural sector to the rest of the economy are not represented. This could increase the costs of adaptation in the countries where agricultural goods represent a significant share of the GDP and/or of the total exports. Moreover, we didn’t take into account income heterogeneity among households. This could lead to an underestimation of the impacts of climate change on food consumption in the case of farmers’ losses due to yield reduction, which are not compensated by higher prices. We also followed a deterministic approach with climate change being represented as a progressive change in average growing conditions. Consequently, it does not take into account the possibility of higher risk exposure of farmers due to higher inter-annual variability of climate and higher frequency of extreme events. In the context of high uncertainty of the impacts of future climate change, investments in agronomic research and in rural infrastructures should be complemented by policies, which aim at protecting consumers. Building efficient mechanisms that can channel international funding to populations vulnerable to climate change is a long process that cannot be set up just in times of crisis (Grosh et al. 2008). Existing safety


ANZ: Australia, New Zealand; Brazil; Canada; China; Congo Basin: Cameroon, Central African Republic, Congo Republic, Democratic Republic of Congo, Equatorial Guinea, Gabon; Eastern Africa: Burundi, Ethiopia, Kenya, Rwanda, Tanzania, Uganda; EU Baltic: Estonia, Latvia, Lithuania; EU Central East: Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia, Slovenia; EU Middle West: Austria, Belgium, Germany, France, Luxembourg, Netherlands; EU North: Denmark, Finland, Ireland, Sweden, United Kingdom; EU South: Cyprus, Greece, Italy, Malta, Portugal, Spain; Former USSR: Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan; India; Japan; Mexico; Middle East and North Africa (MENA): Algeria, Bahrain, Egypt, Iran, Iraq, Israel,

Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, Yemen; Mongolia; Pacific Islands: Fiji Islands, Kiribati, Papua New Guinea, Samoa, Solomon Islands, Tonga, Vanuatu; RCAM: Bahamas, Barbados, Belize, Bermuda, Costa Rica, Cuba, Dominica, Dominican Republic, El Salvador, Grenada, Guatemala, Haiti, Honduras, Jamaica, Nicaragua, Netherland Antilles, Panama, St Lucia, St Vincent, Trinidad and Tobago; RCEU: Albania, Bosnia and Herzegovina, Croatia, Macedonia, Serbia-Montenegro; ROWE: Gibraltar, Iceland, Norway, Switzerland; RSAM: Argentina, Bolivia, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela; RSAS: Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan, Sri Lanka; RSEA OPA: Brunei Daressalaam, Indonesia, Singapore, Malaysia, Myanmar, Philippines, Thailand; RSEA PAC: Cambodia, Korea DPR, Laos, Viet Nam; South Africa; South Korea; Southern Africa: Angola, Botswana, Comoros, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Reunion, Swaziland, Zambia, Zimbabwe; Turkey; United States of America (USA); Western Africa: Benin, Burkina Faso, Cape Verde, Chad, Cote d’Ivoire, Djibouti, Eritrea, Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Somalia, Sudan, Togo.

Fig. 4 Changes in the Climate Moisture Index (CMI) with respect to historical climate (2046–2055 vs. 1961–1990, A2 emissions scenario) show positive values for wetter climates and negative values of drier

climates. The spatial resolution of the climate data from the GCMs was 1°. The ranking of the models for CMI was done by total area-weighting averages for the global and Northeast Asian countries

net programmes should be used but also improved and extended to other places. Finally, coordination of adaptation policies internationally should avoid negative spillover effects of local adaptation strategies to other regions.

Appendix 1: Methodology GLOBIOM’s 31 regions


Fig. 5 Food demand projections in GLOBIOM: average calorie consumption per capita per day (on the vertical axis) in Northeast Asian countries and the evolution of diet composition. RMMEAT refers to

ruminant meat, MGMEAT refers to monogastric meat, and OSDVOL refers to oilseeds. Only GLOBIOM-modeled products are included (Table 1)

Table 1 Correspondence between food commodity categories and single products in GLOBIOM

Appendix 2: Methodology to assess the biophysical impact of climate change on crop calorie food availability in a region

Crop calories Cereals

Oilseeds

Pulses Roots

Sugar

Animal calories Barley Corn Millet Rice Sorghum Wheat Cotton Groundnut Oil palm Rapeseed Soybeans Sunflower Dry beans Chickpeas Cassava Potatoes Sweet potatoes Sugarcane

Monogastric meat Ruminant meat Milk Eggs

Pork Poultry meat Beef Sheep and goats Milk Eggs

Indexes: r: region r1: importing region r2: exporting region c: crop m: management system a: simulation unit s: climate scenario in 2050 (s0 if historical climate) Parameters: Prod: impact of climate change on the regional production in tons AreaInit: cultivated area in 2000 in hectares Yield: crop yield in ton per hectare Trade: bilateral trade flow from region r1 to region r2 in tons Imports: total imports of a region in tons


Conso: food consumption in tons α: share of the domestic production in regional food consumption β: share of the import from region r2 in total imports of region r1 Cal: calorie content of a crop FoodAv: total food availability from crops in a region in calories Δ is used to indicate the relative change of a parameter.

(4)

Importsr1;c;s ¼

X

β ⋅Trader2;r1;c ⋅ΔProd r2;c;s r2 r1;r2;c

(5)

ΔImportsr;c;s ¼

We first compute the average impact of climate change on the regional production:

Importsr1;c;s Importsr1;c;s0

(1)

Prod r;c;s ¼

X a;m

AreaInitr;a;c;m Yield r;a;c;m;s

The final impact of climate change on total crop calorie availability for food consumption is the combination of the impact on domestic production and on imports: (6)

(2)

αr1;c;s ¼ ΔProd r;c;s ¼

Prod r;c;s Prod r;c;s0

Prod r1;c;s −

X r2

Trader1;r2;c;s

Consor1;c;s

(7) FoodAvr;s ¼

X c

Consor;c;s ⋅Calc ⋅

αr;c;s ⋅ΔProd r;c;s þ 1−αr1;c;s ⋅ΔImportsr:c:s

Then we compute the average impact of climate change on the imports: (3) (8) Trader2;r1;c βr1;r2;c ¼ X Trader2;r1;c r2

ΔFoodAvr;s ¼

FoodAvr;s FoodAvr;s0


Appendix 3: Results Table 2 Biophysical impact of climate change on crop calorie production, imports and food availability by 2050 mri

ukm

Food cons. Eastern Asia China 2% Japan 2% Mongolia −3 % South Korea 4% Other Asia Former USSR 3% India 0% RSAS 2% RSEA OPA 2% RSEA PAC 3% Central and South America Brazil 1% Mexico 0% RCAM RSAM Africa Congo Basin South Africa Eastern Africa Southern Africa Western Africa Pacific ANZ Pacific Islands North America Canada USA Mediterranean MENA Turkey Europe EU Baltic EU Central East EU Mid−West EU North EU South RCEU ROWE

0% 5% −1 4 0 5 −2

% % % % %

7% 6%

Production

cnrm

Imports

Food cons.

Production

Imports

Food cons.

Production

Imports

2 5 4 7

% % % %

4 0 −9 2

% % % %

−1 −7 −16 0

% % % %

−1 3 1 7

% % % %

−5 −13 −30 −7

% % % %

−8 0 −5 0

% % % %

−8 3 −2 2

% % % %

1 −2 −7 −2

% % % %

4 0 2 2 3

% % % % %

−2 2 3 2 4

% % % % %

3 −3 2 −3 0

% % % % %

5 −3 2 −2 0

% % % % %

−12 1 −5 −8 −3

% % % % %

−3 −8 −6 −4 −3

% % % % %

−2 −8 −7 −5 −3

% % % % %

−8 −2 2 1 1

% % % % %

0% 0%

7% −1 %

−3 % −6 %

−4 % −4 %

1% −12 %

−2 % −4 %

−3 % −4 %

5% −2 %

0% 5%

1% 5%

−13 % −2 %

−16 % −2 %

−9 % −1 %

−4 % 0%

−7 % 0%

−1 % 4%

−1 −1 −2 −2 −9

−5 7 −2 0 −7

−6 8 −3 0 −8

2 1 1 1 −2

−2 3 0 5 −2

% % % % %

7% 1%

4 6 4 5 1

% % % % %

0% 9%

−2 2 −1 0 −5

% % % % %

2% 1%

−3 2 0 0 −4

% % % % %

% % % % %

% % % % %

% % % % %

% % % % %

3% −3 %

−2 % 4%

2% 0%

2% −5 %

−3 % 3%

4%

5%

0%

−15 %

−18 %

−4 %

−5 %

−1 %

−19 %

−19 %

−8 % −13 %

5% −5 %

9% −5 %

−6 % −8 %

3% 10 %

4% 11 %

2% 3%

−1 % 9%

2% 11 %

−6 % −3 %

1% 9%

1% 11 %

1% −1 %

0 −7 −3 −4 1 −5 2

6 4 6 3 3 4 4

5 7 7 7 6 5 7

% % % % % % %

6 7 8 9 8 5 8

% % % % % % %

5 1 2 4 4 0 5

% % % % % % %

5 3 2 3 1 0 3

% % % % % % %

6 4 4 6 1 0 3

% % % % % % %

% % % % % % %

% % % % % % %

7 5 7 6 4 4 6

% % % % % % %

−1 −4 −2 −1 2 −5 2

% % % % % % %

Author's personal copy Global food markets, trade and the cost of climate change adaptation Table 3 Evolution of food price index in the world and in the four Eastern Asian countries in 2050 (Base 2000−1) and percent change in price index with climate change compared to the historical climate

Crop products World

Hist.

mri

ukm

cnrm

1.21

1.21 0% 1.24 1% 0.96 −5 % 1.16 5%

1.26 4% 1.27 3% 0.98 −3 % 1.52 38 %

1.28 5% 1.31 6% 0.96 −4 % 1.33 21 %

China

1.23

Japan

1.00

Mongolia

1.10

South Korea

1.05

1.04 −1 %

1.04 −1 %

1.09 4%

Animal products World

1.27

1.27 0% 1.18 0% 0.94 0% 2.21 0% 1.13 3%

1.30 2% 1.20 2% 0.94 0% 2.23 1% 1.14 5%

1.30 2% 1.22 3% 0.94 0% 2.21 0% 1.13 3%

China

1.18

Japan

0.94

Mongolia

2.20

South Korea

1.09

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Aline Mosnier is a Research Scholar with IIASA’s Ecosystems Services and Management (ESM) Program. Since 2008, she has contributed to the development of the GLOBIOM land use model, especially on international trade aspects and biofuels. Since 2010, she has been responsible for the adaptation of the GLOBIOM model to regional contexts, especially in the tropics to provide estimates of future deforestation and to support national REDD strategies (www.redd-pac.org). Ms Mosnier holds a master’s degree in development economics from CERDI-Universite d’Auvergne and is a PhD candidate in agricultural economics at the Life Sciences University of Vienna and at AgroParisTech.

Author's personal copy Global food markets, trade and the cost of climate change adaptation Michael Obersteiner is leader of the Ecosystems Services and Management (ESM) Program at the International Institute for Applied Systems Analysis (IIASA) and has been leading the Group on Global Land-Use Modeling and Environmental Economics since 2001. Dr. Obersteiner’s research experience stretches from plant physiology and biophysical modeling in the areas of ecosystems, forestry and agriculture to environmental economics, bioenergy engineering and climate change sciences as documented in his publications record. During the past decade, Dr. Obersteiner has been the principle investigator at IIASA of more than 30 international projects covering diverse fields of different scales and numerous funding organizations.

Petr Havlík is a Research Scholar at the Ecosystems Services and Management (ESM) Program. He joined IIASA in July 2007 after studies in the Czech Republic and in France in economics and management and agricultural economics. Dr. Havlik received his PhD in 2006 on the jointness in production of environmental and agricultural goods. At IIASA, D r. H a v l i k h a s b u i l t t h e GLOBIOM model which is used to analyze the competition for land use among agriculture, forestry, and bioenergy. He is the now leading the team of researchers working on applications and further development of the GLOBIOM model and is responsible for many international projects.

Erwin Schmid is professor for Sustainable Land Use and Global Change at the University of Natural Resources and Life Sciences, Vienna. He is head of the Department of Economics and Social Sciences and teaches and researches in the fields of agricultural, environmental and resource economics. He has been responsible for global crop potential simulations under historical and future climates with the EPIC model that have been used by the GLOBIOM model developed at IIASA.

Niko lay K habarov join ed IIASA’s Ecosystems Services and Management (ESM) Program as a Research Scholar in January 2007. His main research interests are mathematical modeling and optimization with various applications (disasters, agriculture, and energy). Dr. Khabarov is also working on methods for estimating the value of information, application of new risk management approaches, and assessment of adaptation options and potentials in the context of climate change. Dr. Khabarov received his PhD in mathematics in 2004 from Moscow State University for developing new methods for solving optimal control problems.

Dr. Michael I. Westphal is a Senior Associate/Scientist at the international consulting firm, Abt Associates, where he works on a variety of climate change and international development projects, including agriculture. Prior to this he worked for and consulted for the World Bank, Asian Development Bank, and the United Nations Development Programme. He co-led the Asian Development Bank study, “Economics of Climate Change in East Asia” and was a co-author of the World Development Report 2010: Development and Climate Change. He received his PhD in Environmental Science, Policy and Management from the University of California at Berkeley, focusing on ecological modeling.

Hugo Valin is a Research Scholar at the Ecosystems Services and Management Program at IIASA (International Institute for Applied Systems Analysis). His research topics are land use change at the global scale, with a particular emphasis on the impact of bioenergy, greenhouse gas emissions and impact from climate change. He is heavily involved in the development of applied economic models to investigate these issues and contributes to the development of the partial equilibrium model GLOBIOM and the computable general equilibrium model MIRAGE. He is also involved in several research networks around agricultural issues, such as AgMIP (www.agmip.org), and FoodSecure (www.foodsecure.eu).

Author's personal copy A. Mosnier et al. Stefan Frank joined the Ecosystem Services and Management (ESM) Program as a Research Assistant in January 2011. Mr. Frank graduated from the University of Natural Resources and Life Sciences Vienna and the Vienna University of Economics and Business in 2010. Mr. Frank is currently working on the development of the GLOBIOM model where his main responsibility lies in the further improvement of the European agricultural sector. Most recently, he has been working on indirect land use change, biofuels and European biofuel sustainability criteria as well as soil organic carbon emissions from cropland.

Franziska Albrecht is a University Assistant at the Department of Geography and Regional research at the University of Vienna. Mrs. Albrecht received her MSc degree in Forestry Science in 2011 from the University of Natural Resources and Life Sciences, Vienna. From August 2011 to May 2013 she was working for the Ecosystem Services and Management Program at the International Institute of Applied System Analysis (IIASA) Laxenburg, Austria. Currently, she is a PhD candidate at the University of Vienna. Her research includes the extraction of climate signals from tree rings using remotely sensed datasets.