Mitigation Strategies for Greenhouse Gas Emissions from Agriculture

Article pubs.acs.org/est

Mitigation Strategies for Greenhouse Gas Emissions from Agriculture and Land-Use Change: Consequences for Food Prices Miodrag Stevanović,*,†,‡ Alexander Popp,† Benjamin Leon Bodirsky,§,∥ Florian Humpenöder,† Christoph Müller,§ Isabelle Weindl,§,⊥ Jan Philipp Dietrich,† Hermann Lotze-Campen,§,# Ulrich Kreidenweis,†,‡ Susanne Rolinski,§ Anne Biewald,§ and Xiaoxi Wang§,# †

Sustainable Solutions, Potsdam Institute for Climate Impact Research (PIK), Potsdam, D-14412, Germany Economics of Climate Change, Technische Universität Berlin (TU Berlin), Berlin, D-10623, Germany § Climate Impacts and Vulnerabilities, Potsdam Institute for Climate Impact Research (PIK), Potsdam, D-14412, Germany ∥ Commonwealth Scientific and Industrial Research Organisation (CSIRO), St Lucia, 4067 Qld, Australia ⊥ Technology Assessment and Substance Cycles, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, D-14469, Germany # Department of Agricultural Economics, Humboldt Universität zu Berlin (HU Berlin), Berlin, D-10099, Germany ‡

S Supporting Information *

ABSTRACT: The land use sector of agriculture, forestry, and other land use (AFOLU) plays a central role in ambitious climate change mitigation efforts. Yet, mitigation policies in agriculture may be in conflict with food security related targets. Using a global agro−economic model, we analyze the impacts on food prices under mitigation policies targeting either incentives for producers (e.g., through taxes) or consumer preferences (e.g., through education programs). Despite having a similar reduction potential of 43−44% in 2100, the two types of policy instruments result in opposite outcomes for food prices. Incentive-based mitigation, such as protecting carbon-rich forests or adopting low-emission production techniques, increase land scarcity and production costs and thereby food prices. Preference-based mitigation, such as reduced household waste or lower consumption of animal-based products, decreases land scarcity, prevents emissions leakage, and concentrates production on the most productive sites and consequently lowers food prices. Whereas agricultural emissions are further abated in the combination of these mitigation measures, the synergy of strategies fails to substantially lower food prices. Additionally, we demonstrate that the efficiency of agricultural emission abatement is stable across a range of greenhouse-gas (GHG) tax levels, while resulting food prices exhibit a disproportionally larger spread.

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on food markets.7 Agriculture and the food sector are therefore in a central position for reaching these SDG targets. Another Sustainable Development Goal for which agriculture is crucial is combating climate change. The international community has recognized that global warming should not exceed a 2 °C increase above the preindustrial global mean temperature level8 in order to reduce the risk of negative large-scale climate change impacts.9 For this purpose, a fast and comprehensive action in reducing GHG emissions is needed.10,11 The agriculture, forestry, and other land use sector (AFOLU) will play an important role in ambitious mitigation efforts, being itself responsible

INTRODUCTION

The recently published UN Sustainable Development Goals (SDG) have set the objectives for “no poverty”, “zero hunger”, and “good health and well-being” as the top-three items on the new global political agenda. All three are related to food consumption and food prices. In the most impoverished world regions, food is still the largest expenditure item in the household budget, and agriculture is a major source of income.1 With regard to health, nutrition-related chronic diseases are one of the main factors causing premature death globally, and the casualties of unbalanced diets and overconsumption even outnumber the consequences of dietary deficiencies.2 In the future, income growth is projected to reduce hunger and poverty, but also to increase overconsumption and food wastage in emerging and developed economies.3−5 In combination with a growing population,6 this trend will put even higher pressure © XXXX American Chemical Society

Received: August 24, 2016 Revised: November 18, 2016 Accepted: November 22, 2016

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DOI: 10.1021/acs.est.6b04291 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Environmental Science & Technology

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for 21−24% of total annual GHG emissions12,13 and offering several levers for emission abatement. Approximately half of the AFOLU emissions stem from land-use change and deforestation (15% of all emitted anthropogenic carbon-dioxide, CO2), while the other half comprises nitrous-oxide (N2O) and methane (CH4) from various agricultural activities (i.e., more than half of total non-CO2 GHG emissions).12 Current scientific and especially political debate is mainly centered on reducing emission on the AFOLU production side.14 The focus is put on halting deforestation of tropical forests and other carbon rich biomes in order to avert CO2 emissions from landuse change,15,16 or on increasing agricultural management efficiency (e.g., use of fertilizers for crop production, water-use in paddy rice cultivation, manure management, and nutritional feed composition for enteric fermentation in livestock production systems) for reduction of non-CO2 emissions in agriculture.17−19 Beyond the production side, options that consider shifts in the patterns for demanded agricultural products, such as reduction of overall food consumption and food waste, with a diet transition toward less GHG-intensive products, can also result in lower AFOLU-related GHG emissions.18−20 However, the trade-offs rising from AFOLU emissions reduction policies on one hand and food security including balanced diets on the other, have not yet been sufficiently investigated. Previous studies mainly considered the abatement potential of mitigation policies and related adjustments on the production side.21,22 Studies that assess AFOLU-mitigation policy impacts on food prices mainly focus on effects form large-scale carbon-dioxide removal measures (second generation bioenergy and afforestation).23−26 In this article we analyze those mitigation strategies that are related in particular with emissions reduction from agricultural production of food, which we additionally evaluated in terms of their secondary effects on food prices in order to provide insights into eventual trade-offs or cobenefits with food security dimensions. Moreover, the analysis of policy instruments to mitigate climate change has mainly targeted incentive-based policies such as taxes, subsidies, or other regulatory mechanisms.14,27 Although they are highly efficient to reduce emissions, they also increase marginal production costs and thereby affect food price stability. Policies targeting consumer preferences, such as education programs, public consumption in schools, market transparency initiatives, a change in consumption choice structure, the ban of advertising, or moral suasion campaigns are rarely captured by policy debates,28 and receive even less attention by quantitative modeling assessments. This may stem from the difficulty of empirical estimates of the impacts of such instruments, and less from the fact that they are not relevant or effective options.28 We use here the spatially explicit, global agro−economic model MAgPIE (Model of Agricultural Production and its Impacts on the Environment) that simulates regional agricultural supply and demand,16,18,29−32 to assess the effects of mitigation policies on GHG emissions in the AFOLU sector and related impacts on food prices. To simulate incentive-based instruments, a range of diagnostic mitigation scenarios are defined by a GHG emission tax which initiates GHG emission abatement actions. Policies targeting consumer preferences are simulated by a non-price-induced reduction of household waste and livestock-products consumption.

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METHODS

MAgPIE Model. The main tool of the analysis is the MAgPIE model,16,18,29−32 which is a global partial equilibrium agro−economic model that operates on a spatially explicit scale, where local biophysical conditions (crop yield, water availability, and terrestrial carbon content) influence decision making for optimal agricultural production patterns. The objective function in MAgPIE is the costs of global agricultural supply, which are minimized such that the demand for agricultural products is fulfilled. Agricultural demand is aggregated at the level of ten MAgPIE defined geo-economic regions (Supporting Information Figure S1). Food demand is exogenously calculated, based on an econometric regression model that projects per capita caloric intake on a national level, considering historical patterns and socio−economic assumption of future growth in population and income (Figure S2).5,7,31 Material demand is assumed to be proportional to total food demand (Figure S2). Agricultural demand in addition comprises demand for animal feed (feed crops, fodder, grazed biomass) calculated based on feed baskets content,33 and biomass demand for biofuel production (Figure S3).34 The demand implementation accounts for the long-term income effect on agricultural consumption, but the model is limited with respect to representing short-term demand adjustments to changes in prices. Regional agricultural supply is endogenously determined based on costs of production and spatially explicit agricultural productivity levels. The costs account for input factors of production, transport, and investment costs for conversion of other land types into arable land, irrigation infrastructure, and yield-increasing technological progress.35 Input of local biophysical conditions (land, water, terrestrial carbon) and crop yields is provided on the gridded resolution (0.5° × 0.5° geographic longitude−latitude) from the global crop model LPJmL (Lund−Potsdam−Jena model with managed Land) that dynamically simulates growth of different crop varieties, vegetation types, hydrological conditions, and carbon stocks stored in soils, vegetation, and litter, taking into account all relevant biogeochemical processes and physical conditions.36,37 We assume that the biophysical input in MAgPIE is invariant to change in atmospheric emissions concentration. The information on crop yields, water availability, and carbon content is aggregated from the gridded resolution into 600 regional clusters for the facilitation of the nonlinear optimization.38 Interregional reallocation of agricultural production is determined by an exogenous rate of international trade liberalization, which implies that a certain share of agricultural goods are endogenously traded based on regional comparative advantage apart from historical trade patterns.30 The optimization of agro−economic decisions on the regional level results in optimal land- and water-use patterns of agricultural production for sixteen simulated crop types, regional production of five livestock commodities, and optimal investments in technology, cropland, and irrigation expansion. MAgPIE estimates flows of CO2, CH4, and nitrogen (N) related emissions. CO2 emissions are computed from land-use change dynamics, i.e. from conversion of different biomes into agricultural land and consequent loss of terrestrial carbon stocks. Land conversion into cropland can occur from pasture, forest (pristine and unmanaged), and other natural vegetation (e.g., savannahs, shrublands) land pools. In addition, two other pools are assumed to be constant in area over time: forestry B


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Environmental Science & Technology (managed forest) and urbanized areas (around 1% of total land39). The land also serves as a sink for atmospheric carbon when cropland is set aside from agricultural production and following regrowth of natural vegetation generates negative emissions from land-use change. Modeled CH4 emissions stem from agricultural practices associated with livestock production (enteric fermentation from ruminant animal husbandry and animal waste management) and paddy rice cultivation, employing activity-specific emission factors.18 N-related emissions are calculated by the modeled nitrogen cycle31 and are sourced from agricultural management, mainly organic and inorganic fertilization. Non-CO2 emissions are calculated according to 2006 IPCC guidelines.40 The reduction of GHGs is incentivized by an imposed price (tax) per ton of emitted gas. In the case of CO2 emissions, the price serves as an incentive to restrain land-use conversion and consequent carbon release. Reduction of CH4 and N emissions is possible by applying technical mitigation at additional cost, also triggered by an emission price. Examples of technical mitigation include anaerobic digesters for CH4 capture from animal waste, changing animal diets, and use of fertilizer spreaders, etc. The cost of technical mitigation options is estimated according to the regional marginal abatement costs curves that are assessed for a wide spectrum of mitigation technologies and practices in Lucas et al. 2007.18,31,41 As the model is a partial-equilibrium model, tax revenues are not recycled. Scenarios. The baseline scenario in this analysis integrates assumptions from shared socio−economic pathway 2 (SSP2) storyline,42 which can be considered as a business-as-usual case. SSP2 represents a continuation of current trends into the future, resulting in a demographic peak at ∼9.5 million people in 2070 and a moderate growth in income except for a relatively quicker growth in the emerging economies.43 To assess different AFOLU abatement potentials, we construct mitigation scenarios along two dimensions of agricultural market actions. The first mitigation dimension accounts for GHG emission reduction by incentivizing a change in the management of AFOLU sector (incentive-based mitigation), while the second dimension looks at the sustainable modification of agricultural demand (preference-based mitigation). The incentive-based mitigation measures considered here include avoided deforestation and improved agricultural management with increased and less-polluting efficiency. They are incentivized by a penalty on released GHG emission, which is paid according to an emission pricing policy, starting at the level of 30 $US/t CO2eq in the year 2020 with an annual tax growth rate of 5% (Table 1, Figure S4). This emission price path is consistent with the stringent climate policy of arriving at ∼2 °C warming target by the end of the 21st century at 50% probability.44 The emissions subject to the pricing policy include CO2, N2O, and CH4 gases. On the other side, preference-based mitigation is defined according to policies leading to behavioral changes in consumption of agricultural products by aiming at diet change and better waste management. This scenario reflects regionspecific gradual change in food consumption, including 50% reduction of food waste, converging to 2750 kcal/cap/day with maximum of 15% of livestock products constituting the daily food intake by 2050.16,45 This livestock product consumption target represents 50% of present day livestock product shares in developed countries. Thus, we refer to this demand management scenario as to a “demitarian” scenario (Figure S2).46 Lastly, we define a combined mitigation scenario that couples both incentive- and preference-based mitigation strategies in order to

Table 1. Scenarios Design NoTAX baseline demand

baseline

demitarian demand

preferencebased mitigation

TAX5

TAX10

TAX20

complementary GHG taxing mitigation scenarios

TAX30 incentivebased mitigation combined mitigation

a

Scenarios are defined across two dimensions: food demand and GHG emission pricing. Food demand variants include projected continuation of current patterns (“baseline”) and managed demand with 50% less waste and halved consumption of livestock products by 2050 with respect to current intake in developed countries (“demitarian”). GHGs emission taxation variants are defined over different tax levels ($US) for emitted AFOLU GHGs in tCO2eq starting in 2020 with further annual tax growth rate of 5%. Tax scenarios range from zero (“NoTAX”) to 30 $US/tCO2eq (“TAX30”). The ends of the tax pathways range (“NoTAX”,”TAX30”) are used for the main scenarios in the analysis, while additional tax scenarios (“TAX5”, “TAX10”, “TAX20”) in combination with food demand variants serve as a complementary GHG tax cases for GHG abatement potential analysis

test for synergetic effects of studied emission reduction options (Table 1). Additionally, three more GHG pricing scenarios are constructed at lower tax levels (5, 10, and 20 $US/t CO2eq in 2020) in combination with the food demand dimension in order to complement the analysis of the effectiveness of different GHG taxes on residual emissions and to better understand effects on food prices (Table 1). All scenarios are run in MAgPIE until the end of the century in 10-year time periods.

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RESULTS GHG Emissions. Global annual GHG emissions in the AFOLU sector in the baseline scenario are estimated at 11.4 Gt CO2eq/yr (per year) in 2005 and remain at approximately the same annual level until the middle of the century, after which they gradually decline to 7.7 Gt CO2eq/yr in the year 2100 (Figure 1a). The contribution of the three considered pollutants changes significantly over the simulated period. While N2O emissions increase from 1.7 to 2.4 Gt CO2eq/yr, emission of CH4 almost doubles to its annual maximum of 7.1 Gt CO2eq/yr in 2060, and then weakens to 6.1 Gt CO2eq/yr in 2100. This is due to the stabilization and decline in projected food demand and world population (Figure S2), but also because of assumed improvements in livestock productivity and feeding practice in developing regions, as a result of nutritional management that improves the digestibility (quality) of feed for ruminants (animals).47 Contrary to the increasing trends in non-CO2 emissions in agricultural production, the carbon emissions from deforestation decline over time, from about 5.8 Gt CO2eq/yr at the beginning of the period (due to large land-use change in Africa and Latin America, Figures S5−S8) to almost zero emission from the midcentury onward, owing to the reduction in demand and transition from extensive to intensive agricultural production (Figure S9). Total global GHG emissions for the period 2020−2100 cumulate to 935 Gt CO2eq, but the net balance is reduced by 25 Gt of negative CO2 emissions from regrowth of natural vegetation on abandoned cropland (Figure 1b). The largest share of total cumulative emissions (62%) comes from CH4 (585 Gt CO2eq), followed by 24% (221 Gt CO2eq) of N2O, and 14% (129 Gt) of CO2. C


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Figure 1. AFOLU GHG emissions: (a) annual global GHG emissions (in Gt CO2eq/yr), and (b) cumulative global GHG emissions (in Gt CO2eq). Cumulative emissions are calculated from year 2020 to 2100 for all scenarios with the purpose of comparison of the effects of a GHG emissions reduction policy which is designed to start in 2020. Emission types are color coded (see legend). Net CO2 emissions equal the difference between emitted positive CO2 and negative sequestered CO2 emissions through vegetation regrowth.

In the combined scenario, the non-CO2 emissions are further abated, leading to N2O and CH4 emissions reduction to 53% and 44%, respectively, relative to the baseline. The combination of technical mitigation of agricultural non-CO2 gases and change in consumers’ food preferences manifests this strong GHG abatement potential without much delay in time. However, a maximum of abatement capacity is reached around 2050 (Figure 1). Repercussions of Emission Reduction Measures on Food Prices. As an indicator of changes in food commodity prices, we use the Paasche price index that weights current prices based on current food baskets in a respective year. Model results show that the global consumer price index is generally constant over the projection period in the baseline scenario, but differs across mitigation scenarios. Whereas food and waste management policy in the preference-based scenario even lowers the prices over time, food prices driven by emission pricing in the incentive-based mitigation scenario are almost 2.5 times higher in the year 2100 compared to 1995 and to the level in the baseline scenario (Figure 2a). This increase in food price index is mostly driven by the pricing of CH4 emission stemming from the livestock sector (Figure 2). In the combined scenario, i.e. when both the tax on GHG emissions and the shift from animal products in demand occur, the food prices are lower by approximately 35 percentage points in comparison to the incentive-based mitigation scenario. Still, the prices in the two scenarios nearly catch up at the end of the century which is dictated by a very high GHG tax level in the year 2100, albeit residual emissions are lower in the combined scenario.

Compared to the baseline case, cumulative GHG emissions in 2100 are effectively reduced in the incentive-based mitigation scenario by 43%. The abatement of the individual GHGs ranges from almost entirely eliminating positive CO2 emissions to 30% N2O and 36% CH4 reduction by the end of the century. The maximal annual potential for N2O emission reduction is reached around 2060, when it equals a reduction of ∼0.9 Gt CO2eq/yr, the rate that stays constant in spite of further high increases in emission tax. Methane emissions display the same dynamics, but at a higher maximum reduction volume of ∼3 Gt CO2eq/yr (Figure 1a). In contrast to non-CO2 emissions, the implementation of the GHG tax (in 2020) has an immediate effect of preventing further CO2 emissions, since the conversion of carbon rich biomes into cropland is instantly halted. As a response, agricultural supply is maintained by increases in intensification through higher investment in yield-increasing technologies in all the regions (Figure S9). Preference-based mitigation options are similarly effective in terms of reducing the emissions. Changes in lifestyle lead to GHG abatement of 44% in positive CO2, 27% in N2O, and 29% in CH4 cumulative emissions. In addition, there is a greater potential for negative CO2 emissions through carbon uptake which happens when regrowing natural vegetation occupies land previously used for agriculture. As less livestock products are consumed, there are considerable implications on land-use dynamics since large areas used for feed production and animal grazing are no longer required (Figures S5−S7). Cumulative negative CO2 emissions amount to 90 Gt in the preference-based scenario, almost seven times as much as in the incentive-based mitigation case. D


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Figure 2. Global and regional consumer food price index: projections for the baseline, incentive-based, preference-based, and combined mitigation scenarios. (A) Left panel depicts the price index for all food commodities. Right panel shows a decomposition of the food price index into commodities from vegetal (“Food Crops”) and animal origin (“Animal Products”). Index values are normalized to 100 in year 1995. The index weights current commodity prices on current food baskets of the respective year. Food baskets are defined on exogenous regional demand. (B) Levels of regional price indices years 2050 and 2100. The scenarios are color coded and points represent levels of baskets comprising food crops (×) and animal products (○) separately. Regional acronyms (Figure S1): AFR (Sub-Saharan Africa), CPA (Centrally Planned Asia), FSU (Former Soviet Union), EUR (Europe incl. Turkey), LAM (Latin America), MEA (Middle East−North Africa), NAM (North America), PAO (Pacific OECD), PAS (Pacific Asia), SAS (South Asia).

Complementary Analysis of GHG Tax Effects on Attained GHG Abatement and on Food Prices. The previous results revealed that a policy of pricing GHG emissions in the AFOLU sector, although successful in cutting emission levels, can lead to significant increases in global and regional food prices, even when modifications in consumption patterns reduce overall demand for agricultural products (combined scenario). The annual emission reduction rate, especially for agricultural GHG gases N2O and CH4, reaches its maximal potential around the second half of the century and stays constant afterward, despite increasing levels of GHG tax (Figure S4). We therefore analyze the response of residual GHG emissions and the global food price index by defining three additional mitigation scenarios characterized by the initial GHG price levels of 5, 10, and 20 $US/tCO2 in the year 2020 in contrast to TAX30 and NoTAX scenarios used for the underlying GHG emission pricing mitigation analysis (Table 1). A decrease in GHG tax leads to a marginally lower realized potential of emission reductions (Figure 3a). The CO2 emissions are slightly more affected by a change in GHG price. The difference across TAX30−TAX5 scenarios in emitted cumulative positive CO2 emissions is 23 and 21% compared to the

The global food price index dynamics is in the same manner reflected on the regional level across the scenarios (Figure 2b, Figure S10). Regional prices are driven by region-specific production systems and associated costs, but also by the segmented agricultural market since distortive interregional trade policies persist to a certain degree also at the end of the century. Even when increasingly more affluent developing regions shift to a meat richer diet, the consumption and waste reductions in the preference-based mitigation scenario decrease food prices for all simulated regions with respect to the baseline scenario. The implementation of an emission pricing policy, however, mostly affects regions with more land extensive agricultural production, or regions with generally faster growing agricultural demand. For example, in Africa and Latin America food prices rapidly increase when further cropland expansion comes at a cost of released carbon during land conversion (Figures S5 and S11). On the other hand, in the Indian subcontinent where demand for agricultural products considerably increases with demographic dynamics (Figure S2), food prices rise through taxation of N2O and CH4 emissions (Figures S12 and S13). Here, regional supply can be maintained by investing in intensified agricultural management (Figure S9) or through changes in interregional trade flows. E


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Figure 3. GHG tax effects on global attained GHG abatement and on global food prices: (a) cumulative residual emissions and (b) global food prices across different GHG tax levels and demand projections variants.

for the GHG price level of 50 and 100 $US/t CO2eq, respectively.48 For the preference-based mitigation scenario, several studies exist that emphasize the positive effects of changing diets in reducing AFOLU related emissions. We contrast the mitigation potential of our modified demand patterns to Bajželj et al. 2014,49 since it has a comparable scenario of 50% food waste reduction, a similar healthy diet prescription, and an assumption on intensification and increase in land productivity. Our analysis estimates some 5.6 Gt CO2eq/yr of residual emissions in 2050, including the negative CO2 emissions from regrowth of vegetation, which is closely comparable with 5.9 Gt CO2eq/yr in Bajželj et al. 201449 estimated in their data-based assessment with agricultural biomass flow and land-use distribution analysis. The literature shows that these midcentury estimates of annual GHG emissions can get even lower if more drastic, albeit hypothetical, patterns in global food consumption are to occur in the future. For example, non-CO2 agricultural emissions can be reduced to 3.1 Gt CO2eq/yr in a more vegetarian diet where 75% of animal products is substituted by pulses and cereals50 (compared to 6.4 Gt CO2eq/yr of non-CO2 emissions in our demitarian scenario), or to 1.1 Gt CO2eq/yr in case of vegan diet.20 We also verify this effect in a sensitivity test with different levels of reduction of animal products share in final consumption (Figures S2 and S14). In addition, the sensitivity analysis shows that while the effect on the mitigation potential in a preferencebased strategy is proportional to the reduction in fraction of consumed animal products, the decline in food prices is progressively stronger toward more vegetarian diets (Figure S14). Whereas the estimates for mitigation potential of strategies with change in consumer preferences are fairly robust as long as the correct change in demand for livestock products is assessed, the abatement levels from a tax imposed on non-CO2 agricultural emission relies much on the likelihood that the future technologies for reducing N2O and CH4 agricultural emissions will be developed. The results of incentive-based mitigation strategies are, thus, sensitive to the assumption on marginal abatement cost curves. As assessed in Lucas et al.,41 the

case when no emission price is implemented in the baseline and demitarian demand projections, respectively. Further, effectiveness of different GHG prices on the non-CO2 emissions is also not substantial. As shown in our results (Figure 3a), reducing the price of emitted GHGs by factor 6 from TAX30 scenario to TAX5 scenario would contribute to only approximately 6% of unachieved N2O and CH4 emissions reduction. While the GHG reduction potential remains almost unchanged, the impact on global food prices is unequivocally negative in terms of increasing food prices with every higher level of emission prices (Figure 3b). This is a consequence of the direct pricing of residual non-CO2 emissions in agriculture, since in many aspects of agricultural production those emissions cannot be entirely avoided. Given the small difference in attained abatement across different GHG tax levels, the spread of associated price indices is disproportionally broader (Figure 3b).

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DISCUSSION Within this study we analyze the potential of GHG emission abatement in the AFOLU sector and related repercussions on food commodity prices. The implementation of such a policy is assumed along three scenarios: incentive-based mitigation through an imposed GHG emissions price, preference-based mitigation through food diet shifts from resource intensive products together with better waste management, and a combination of both strategies. In addition, we provide a complementary analysis by testing abatement and price responses regarding different GHG tax levels. Comparison with Other Studies and Uncertainty. The reduction potential of GHG emissions as a result of an imposed emission tax is well established in the literature. Avoided deforestation is viewed as a highly cost-effective mitigation strategy, since already at the GHG cost of 20 $US/t CO2 potentially 1.6−4.3 Gt CO2/yr can be abated,15,16 which is also attested by our results. Similarly, the modeled reductions in non-CO2 emissions in this study are in the range of reported potentials for agriculture, reaching the midrange of 1.5 and 3.5 Gt CO2eq/yr F


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food commodity prices, especially in developing regions (Figure 2b), passing the GHG tax burden rather on the consumer side. Those changes in prices could affect the poor in spite of expected growth in regional average income (Figure S15), either by impacts on food security or by market share losses for less competitive farmers.55 Accordingly, mitigation policy impacts on poor households need to be well assessed in order to prevent aggravation of poverty56 and widening income inequality. Some agricultural producers could also experience an increase in profits owing to higher comparative advantage against other competitive farmers with greater marginal cost of production. In this study, the change in global consumption value is estimated at additional 441 billion $US in 2020 (0.6% of projected GDP) in the incentive-based mitigation compared to the baseline scenario, with the further annual increase of around 4% of additional expenditure. If a mitigation policy goal concurrently aims at agricultural price stability, some social safety programs can be contrived, e.g. by exploring consumer price subsidies or transfers to poor households. Recycling of governmental revenue from the tax on agricultural GHG emissions can be one way to support consumer budgets, but at the same time those revenues can be used to invest in low-GHG footprint technologies for agricultural production. A more meticulous analysis of such policies of tax-revenue transfers, with recycling of rents and feedback from carbon market, goes beyond the definition of the partial equilibrium model used in this study. Considering the incentive-based mitigation policy implementation, the current absence of a first-best solution in form of a global GHG emission tax could make other second-best supply side solutions challenging to entirely succeed. For instance, emissions from deforestation represent the main source of AFOLU-emitted CO2, but second-best efforts, such as the REDD+ project, could prove inefficient in the long run because of a selective approach of project locations and potential for carbon leakage from conversion of nonforest biomes, such as savannahs and grazing lands,16 but also because of existence of implementation risks, such as the state of institutions, transaction costs, and other investment risks.57 This means that the abatement of non-CO2 agricultural emissions would heavily rely on financial governmental support for cleaner agricultural production in terms of subsidies in combination with expanded insurance schemes.58 The high increase in food prices is almost exclusively driven by the taxation of the livestock sector. Under the assumption of limited technical mitigation,41 related livestock system GHG emissions cannot be entirely eliminated, and a high GHG tax on residual CH4 and N2O emissions will only add to the cost of production without reaching higher abatement rates. In our results across different GHG tax levels and demand projection variants in the complementary analysis, we show that even lower pricing of GHG emissions can still achieve large abatement of agricultural emissions with significantly lower impact on food prices (especially in the developing countries, Figure S16). However, the uniform GHG tax would be determined from a multisectoral interplay of mitigation efforts and available technologies. If the aim of a mitigation policy should account for stable food prices (also for animal products), then the nonCO2 agricultural emissions in the food sector should be taxed independently from other sectors. In this manner, a small tax could incentivize deployment of mitigation technologies and practice in agriculture, while simultaneously avoiding the risk of greater market shifts and related consequences on food prices.

abatement costs are strongly dependent on the baseline choice and associated emissions growth from different socio− economic developments: higher GHG emissions in the baseline scenario would result with higher non-CO2 residual agricultural emissions, at a much higher cost and thus higher food prices. With regard to caveats of the study design, the absence of price-induced adjustments of demanded quantities in the market in our simulations could contribute to the upper bias in the land-based mitigation effects on prices and ensuing impacts on consumption. The GHG emission pricing scenarios in particular show an isolated AFOLU mitigation effect on agricultural prices and are viewed as scenarios in which a reduced agricultural consumption due to climate change mitigation is unintended by a policy maker. On the other hand, the scenarios that combine both the GHG emission pricing and agricultural demand dimensions provide an insight in the effect of hypothetical demand reaction to GHG tax induced change in food prices. But in spite of a strong consumption shifts from animal products, those prices still remain at high levels globally (Figure 2). A similar dynamics is attested by Wirsenius et al.,27 who show that a consumption tax levied on emission footprint (N2O and CH4) of livestock products in Europe still leads to 16% of increase in prices of ruminant meat despite a decrease in respective demand by 15%. As a further caveat, some land-based mitigation options in the AFOLU sector are omitted in the study design: for example, options aiming at carbon sequestration from the atmosphere and storage in soil and vegetation, such as afforestation or largescale biomass deployment for energy production to replace fossil fuel. Those measures could add to a greater pressure on land use systems51 and create a higher opportunity cost of cropland, leading to a competition with food production. Several studies provide evidence to this claim, where they find doubling23 or tripling25 of crop prices, or a 4-fold increase in livestock prices24 if a carbon tax creates an incentive to reforest land and to supply bioenergy in order to reduce industrial emissions. Another aggravating factor for agricultural prices stability is a carbon tax on fuel used in agriculture, which is not covered here, but would likely contribute to additional net-losses for agricultural consumers.52 Climate and the AFOLU sector mutually affect each other. In this analysis, we omitted the climate feedback since climate change and CO2 fertilization effects on crop yields and terrestrial carbon storage have only a marginal impact on the mitigation potential in the AFOLU sector under a moderate climate change53 (given that in the rest of the economy adequate mitigation measures are implemented and the climate target is reached at the end of the century). However, some damaging effects could surface on a regional level, especially in the tropics where radiative forcing of moderate climate leads to higher increase in temperature. For example, the land allocation in Sub-Saharan Africa undergoes a considerable transition under the incentive-based mitigation scenario with an emission tax. The cropland is reduced to half from its baseline case, which is the consequence of an extensive prevention of deforestation (Figures S5−S7). Additional warming effects from climate change could only exacerbate already high prices in this scenario. Policy Implications. Food prices are only one indicator for insufficient food access, safety, and stability,54 but could effectively point at policy shortcomings in terms of food security. Our results indicate that pricing of global GHG emissions would unavoidably lead to detrimental increases in G



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On the other hand, tools for achieving mitigation policy goals on the demand side need to be comprehensively conceived. The limitation of this study is that we assume no costs for implementation of preference-based strategies, due to the lack of empirical studies on this issue. However, the measures governments could employ do not necessarily demand high costs. Those strategies should target preferences in order to evoke a behavioral change (education on food consumption and healthy diet, market transparency on food origin, moral suasion, and marketing restriction of animal products, etc.). The annual marketing investments of the food industry are in the United States alone about 30 billion $US,59 indicating a high relevance and potential for preference-based instruments. Social barriers and established habits could be hard to overcome, but when correctly informed on carbon footprint in food products (e.g., carbon labels), some consumers would be willing to switch to low-carbon substitutes.60 Also, the consequences of food scandals indicate that food preferences can be altered substantially and enduringly.61 In addition to the goals of a climate policy, it can be argued that measures for consumption modification have intersections with goals of a health policy or other environmental policies.49,62 Considering overnutrition with diets rich in fats and sugar can lead to serious health conditions and obesity, which in turn could generate enormous expenses and high opportunity costs,63 trans-policy shared goals can be devised.64 Our analysis provides evidence that shifts in preferences for agricultural products can stabilize and lower food prices while having a similar effect in reducing GHG emissions as a GHG tax policy does, but without the emission-leakage risk. Ensuing contraction in agricultural area and transformation of abandoned cropland to a carbon sink through regrowth of vegetation also represents an opportunity for ecosystems recovery, biodiversity habitat restoration, and other services for local communities. Further cobenefits would as well breach into the health sector. A low tax on AFOLU emissions could in addition trigger cleaner agricultural production. Complementing a climate change mitigation policy instruments mix with mechanism for change in food preferences should be promoted in order to avoid adverse impacts of climate change mitigation on stable utilization of food.

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REFERENCES

(1) World Bank. Global Consumption Database. http://datatopics. worldbank.org/consumption/home. (2) World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks; Geneva, 2009. (3) Kearney, J. Food consumption trends and drivers. Philos. Trans. R. Soc., B 2010, 365 (1554), 2793−2807. (4) Gustavsson, J.Global food losses and food waste: extent, causes and prevention: study conducted for the International Congress “Save Food!” at Interpack 2011 Düsseldorf, Germany. Presented at Food and Agriculture Organization of the United Nations ASME/Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, N., and Electronic Systems; Food and Agriculture Organization of the United Nations: Rome, 2011. (5) Bodirsky, B. L.; Rolinski, S.; Biewald, A.; Weindl, I.; Popp, A.; Lotze-Campen, H. Global Food Demand Scenarios for the 21st Century. PLoS One 2015, 10 (11), e0139201. (6) KC, S.; Lutz, W. The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 2014, DOI: 10.1016/ j.gloenvcha.2014.06.004. (7) Valin, H.; Sands, R. D.; van der Mensbrugghe, D.; Nelson, G. C.; Ahammad, H.; Blanc, E.; Bodirsky, B.; Fujimori, S.; Hasegawa, T.; Havlik, P.; et al. The future of food demand: understanding differences in global economic models. Agric. Econ. 2014, 45 (1), 51−67. (8) UNFCCC. Copenhagen Accord; 2009. (9) Smith, J. B.; Schneider, S. H.; Oppenheimer, M.; Yohe, G. W.; Hare, W.; Mastrandrea, M. D.; Patwardhan, A.; Burton, I.; CorfeeMorlot, J.; Magadza, C. H. D.; et al. Assessing dangerous climate change through an update of the Intergovernmental Panel on Climate Change (IPCC) “reasons for concern. Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (11), 4133−4137. (10) Meinshausen, M.; Meinshausen, N.; Hare, W.; Raper, S. C. B.; Frieler, K.; Knutti, R.; Frame, D. J.; Allen, M. R. Greenhouse-gas emission targets for limiting global warming to 2°C. Nature 2009, 458 (7242), 1158−1162. (11) Luderer, G.; Pietzcker, R. C.; Bertram, C.; Kriegler, E.; Meinshausen, M.; Edenhofer, O. Economic mitigation challenges: how further delay closes the door for achieving climate targets. Environ. Res. Lett. 2013, 8 (3), 34033. (12) IPCC. Climate Change 2014: Mitigation of Climate Change; Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., et al., Eds.; Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA, 2014. (13) Tubiello, F. N.; Salvatore, M.; Ferrara, A. F.; House, J.; Federici, S.; Rossi, S.; Biancalani, R.; Condor Golec, R. D.; Jacobs, H.; Flammini, A.; et al. The Contribution of Agriculture, Forestry and other Land Use activities to Global Warming, 1990−2012. Glob. Change Biol. 2015, 21 (7), 2655−2660. (14) Bustamante, M.; Robledo-Abad, C.; Harper, R.; Mbow, C.; Ravindranat, N. H.; Sperling, F.; Haberl, H.; de Siqueira Pinto, A.; Smith, P. Co-benefits, trade-offs, barriers and policies for greenhouse gas mitigation in the agriculture, forestry and other land use (AFOLU) sector. Glob. Change Biol. 2014, 20 (10), 3270−3290. (15) Kindermann, G.; Obersteiner, M.; Sohngen, B.; Sathaye, J.; Andrasko, K.; Rametsteiner, E.; Schlamadinger, B.; Wunder, S.; Beach, R. Global cost estimates of reducing carbon emissions through avoided deforestation. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (30), 10302− 10307. (16) Popp, A.; Humpenöder, F.; Weindl, I.; Bodirsky, B. L.; Bonsch, M.; Lotze-Campen, H.; Müller, C.; Biewald, A.; Rolinski, S.; Stevanovic, M.; et al. Land-use protection for climate change mitigation. Nat. Clim. Change 2014, 4, 1095. (17) Martin, C.; Morgavi, D. P.; Doreau, M. Methane mitigation in ruminants: from microbe to the farm scale. Animal 2010, 4 (3), 351− 365.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b04291. Additional model description and detailed results (PDF)

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Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; phone: +49 331 288 20762; fax: +49 331 288 2039. ORCID

Miodrag Stevanović: 0000-0003-1799-186X Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research leading to these results has received funding from the European Union’s Seventh Framework Program under grant agreement 603542 (LUC4C). H


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(36) Bondeau, A.; Smith, P. C.; Zaehle, S.; Schaphoff, S.; Lucht, W.; Cramer, W.; Gerten, D.; Lotze-Campen, H.; Müller, C.; Reichstein, M.; et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 2007, 13 (3), 679−706. (37) Müller, C.; Robertson, R. D. Projecting future crop productivity for global economic modeling. Agric. Econ. 2014, 45 (1), 37−50. (38) Dietrich, J. P.; Popp, A.; Lotze-Campen, H. Reducing the loss of information and gaining accuracy with clustering methods in a global land-use model. Ecol. Modell. 2013, 263, 233−243. (39) Popp, A.; Calvin, K.; Fujimori, S.; Havlik, P.; Humpenöder, F.; Stehfest, E.; Bodirsky, B. L.; Dietrich, J. P.; Doelmann, J. C.; Gusti, M.; et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 201610.1016/j.gloenvcha.2016.10.002 (40) IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories; National Greenhouse Gas Inventories Programme, 2006. (41) Lucas, P. L.; van Vuuren, D. P.; Olivier, J. G. J.; den Elzen, M. G. J. Long-term reduction potential of non-CO2 greenhouse gases. Environ. Sci. Policy 2007, 10 (2), 85−103. (42) O’Neill, B. C.; Kriegler, E.; Ebi, K. L.; Kemp-Benedict, E.; Riahi, K.; Rothman, D. S.; van Ruijven, B. J.; van Vuuren, D. P.; Birkmann, J.; Kok, K.; et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 2015, DOI: 10.1016/j.gloenvcha.2015.01.004. (43) SSP Database. https://secure.iiasa.ac.at/web-apps/ene/SspDb/ dsd?Action=htmlpage&page=welcome. (44) Rogelj, J.; McCollum, D. L.; Reisinger, A.; Meinshausen, M.; Riahi, K. Probabilistic cost estimates for climate change mitigation. Nature 2013, 493 (7430), 79−83. (45) Bodirsky, B. L.; Popp, A.; Lotze-Campen, H.; Dietrich, J. P.; Rolinski, S.; Weindl, I.; Schmitz, C.; Müller, C.; Bonsch, M.; Humpenöder, F.; et al. Reactive nitrogen requirements to feed the world in 2050 and potential to mitigate nitrogen pollution. Nat. Commun. 2014, 5, 10.1038/ncomms4858 (46) Westhoek, H.; Lesschen, J. P.; Rood, T.; Wagner, S.; De Marco, A.; Murphy-Bokern, D.; Leip, A.; van Grinsven, H.; Sutton, M. A.; Oenema, O. Food choices, health and environment: Effects of cutting Europe’s meat and dairy intake. Glob. Environ. Change 2014, 26, 196− 205. (47) Herrero, M.; Havlík, P.; Valin, H.; Notenbaert, A.; Rufino, M. C.; Thornton, P. K.; Blümmel, M.; Weiss, F.; Grace, D.; Obersteiner, M. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl. Acad. Sci. U. S. A. 2013, 110 (52), 20888−20893. (48) Smith, P.; Bustamante, M.; Ahammad, H.; Clark, H.; Dong, H.; Elsiddig, E. A.; Haberl, H.; House, J.; Jafari, M.; Masera, O.; et al. Agriculture, Forestry and Other Land Use (AFOLU). In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., et al., Eds.; Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014. (49) Bajželj, B.; Richards, K. S.; Allwood, J. M.; Smith, P.; Dennis, J. S.; Curmi, E.; Gilligan, C. A. Importance of food-demand management for climate mitigation. Nat. Clim. Change 2014, 4 (10), 924−929. (50) Hedenus, F.; Wirsenius, S.; Johansson, D. J. A. The importance of reduced meat and dairy consumption for meeting stringent climate change targets. Clim. Change 2014, 124 (1−2), 79−91. (51) Humpenöder, F.; Popp, A.; Dietrich, J. P.; Klein, D.; LotzeCampen, H.; Bonsch, M.; Bodirsky, B. L.; Weindl, I.; Stevanovic, M.; Müller, C. Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett. 2014, 9 (6), 64029. (52) Schneider, U. A.; McCarl, B. A. others. Implications of a carbonbased energy tax for US agriculture. Agric. Resour. Econ. Rev. 2005, 34 (2), 265. (53) Humpenöder, F.; Popp, A.; Stevanovic, M.; Müller, C.; Bodirsky, B. L.; Bonsch, M.; Dietrich, J. P.; Lotze-Campen, H.; Weindl, I.; Biewald, A.; et al. Land-Use and Carbon Cycle Responses

(18) Popp, A.; Lotze-Campen, H.; Bodirsky, B. Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Glob. Environ. Change 2010, 20 (3), 451−462. (19) Smith, P.; Haberl, H.; Popp, A.; Erb, K.; Lauk, C.; Harper, R.; Tubiello, F. N.; de Siqueira Pinto, A.; Jafari, M.; Sohi, S.; et al. How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob. Change Biol. 2013, 19 (8), 2285−2302. (20) Stehfest, E.; Bouwman, L.; van Vuuren, D. P.; den Elzen, M. G. J.; Eickhout, B.; Kabat, P. Climate benefits of changing diet. Clim. Change 2009, 95 (1−2), 83−102. (21) Blandford, D.; Gaasland, I.; Vårdal, E. The trade-off between food production and greenhouse gas mitigation in Norwegian agriculture. Agric., Ecosyst. Environ. 2014, 184, 59−66. (22) Golub, A.; Hertel, T.; Lee, H.-L.; Rose, S.; Sohngen, B. The opportunity cost of land use and the global potential for greenhouse gas mitigation in agriculture and forestry. Resour. Energy Econ. 2009, 31 (4), 299−319. (23) Wise, M.; Calvin, K.; Thomson, A.; Clarke, L.; Bond-Lamberty, B.; Sands, R.; Smith, S. J.; Janetos, A.; Edmonds, J. Implications of Limiting CO2 Concentrations for Land Use and Energy. Science 2009, 324 (5931), 1183−1186. (24) Reilly, J.; Melillo, J.; Cai, Y.; Kicklighter, D.; Gurgel, A.; Paltsev, S.; Cronin, T.; Sokolov, A.; Schlosser, A. Using Land To Mitigate Climate Change: Hitting the Target, Recognizing the Trade-offs. Environ. Sci. Technol. 2012, 46 (11), 5672−5679. (25) Calvin, K.; Wise, M.; Kyle, P.; Patel, P.; Clarke, L.; Edmonds, J. Trade-offs of different land and bioenergy policies on the path to achieving climate targets. Clim. Change 2014, 123 (3−4), 691−704. (26) Kreidenweis, U.; Humpenöder, F.; Stevanović, M.; Bodirsky, B. L.; Kriegler, E.; Lotze-Campen, H.; Popp, A. Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett. 2016, 11 (8), 85001. (27) Wirsenius, S.; Hedenus, F.; Mohlin, K. Greenhouse gas taxes on animal food products: rationale, tax scheme and climate mitigation effects. Clim. Change 2011, 108 (1−2), 159−184. (28) Garnett, T.; Mathewson, S.; Angelides, P.; Borthwick, F. Policies and Actions to Shift Eating Patterns: What Works?; The Royal Institute of International Affairs: Chatham House, 2015. (29) Lotze-Campen, H.; Müller, C.; Bondeau, A.; Rost, S.; Popp, A.; Lucht, W. Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agric. Econ. 2008, 39 (3), 325−338. (30) Schmitz, C.; Biewald, A.; Lotze-Campen, H.; Popp, A.; Dietrich, J. P.; Bodirsky, B.; Krause, M.; Weindl, I. Trading more food: Implications for land use, greenhouse gas emissions, and the food system. Glob. Environ. Change 2012, 22 (1), 189−209. (31) Bodirsky, B. L.; Popp, A.; Weindl, I.; Dietrich, J. P.; Rolinski, S.; Scheiffele, L.; Schmitz, C.; Lotze-Campen, H. N2O emissions from the global agricultural nitrogen cycle − current state and future scenarios. Biogeosciences 2012, 9 (10), 4169−4197. (32) Stevanović, M.; Popp, A.; Lotze-Campen, H.; Dietrich, J. P.; Müller, C.; Bonsch, M.; Schmitz, C.; Bodirsky, B. L.; Humpenöder, F.; Weindl, I. The impact of high-end climate change on agricultural welfare. Sci. Adv. 2016, 2 (8), e1501452. (33) Weindl, I.; Lotze-Campen, H.; Popp, A.; Bodirsky, B.; Rolinski, S. Impact of Livestock Feeding Technololgies on Greenhouse Gas Emissions. In Climate Change in World Agriculture: Mitigation, Adaptation, Trade and Food Security; International Agricultural Trade Research Consortium, 2010. (34) Lotze-Campen, H.; von Lampe, M.; Kyle, P.; Fujimori, S.; Havlik, P.; van Meijl, H.; Hasegawa, T.; Popp, A.; Schmitz, C.; Tabeau, A.; et al. Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agric. Econ. 2014, 45 (1), 103−116. (35) Dietrich, J. P.; Schmitz, C.; Lotze-Campen, H.; Popp, A.; Müller, C. Forecasting technological change in agricultureAn endogenous implementation in a global land use model. Technol. Forecast. Soc. Change 2014, 81, 236−249. I


Article

Environmental Science & Technology to Moderate Climate Change: Implications for Land-Based Mitigation? Environ. Sci. Technol. 2015, 49 (11), 6731−6739. (54) Schmidhuber, J.; Tubiello, F. N. Global food security under climate change. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (50), 19703− 19708. (55) Swinnen, J.; Squicciarini, P. Mixed Messages on Prices and Food Security. Science 2012, 335 (6067), 405−406. (56) Hussein, Z.; Hertel, T.; Golub, A. Climate change mitigation policies and poverty in developing countries. Environ. Res. Lett. 2013, 8 (3), 35009. (57) Lubowski, R. N.; Rose, S. K. The Potential for REDD+: Key Economic Modeling Insights and Issues. Rev. Environ. Econ. Policy 2013, 7 (1), 67−90. (58) Luo, L.; Wang, Y.; Qin, L. Incentives for promoting agricultural clean production technologies in China. J. Cleaner Prod. 2014, 74, 54− 61. (59) Nestle, M.; Jacobson, M. F. Halting the obesity epidemic: a public health policy approach. Public Health Rep. 2000, 115 (1), 12− 24. (60) Shewmake, S.; Okrent, A.; Thabrew, L.; Vandenbergh, M. Predicting consumer demand responses to carbon labels. Ecol. Econ. 2015, 119, 168−180. (61) Allais, O.; Nichèle, V. Capturing structural changes in French meat and fish demand over the period 1991−2002. Eur. Rev. Agric. Econ. 2007, 34 (4), 517−538. (62) WHO. Diet, Nutrition and the Prevention of Chronic Diseases: Report of the Joint WHO/FAO Expert Consultation; World Health Organization: Geneva, 2003. (63) Wolf, A. M.; Colditz, G. A. Current Estimates of the Economic Cost of Obesity in the United States. Obes. Res. 1998, 6 (2), 97−106. (64) Briggs, A. D. M.; Kehlbacher, A.; Tiffin, R.; Garnett, T.; Rayner, M.; Scarborough, P. Assessing the impact on chronic disease of incorporating the societal cost of greenhouse gases into the price of food: an econometric and comparative risk assessment modelling study. BMJ. Open 2013, 3 (10), e003543.

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Mitigation Strategies for Greenhouse Gas Emissions from Agriculture

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