Agricultural Policy and Greenhouse Gas Emission in Brazil

Transcription

1 Agricultural Policy and Greenhouse Gas Emission in Brazil Yuri Clements Daglia Calil, Texas A&M University, ; Carmine Paolo de Salvo, Inter- American Development Bank, Selected Paper prepared for presentation at the 2018 Agricultural & Applied Economics Association Annual Meeting, Washington, D.C., August 5-August 7 Copyright 2018 by [authors]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

2 Agricultural Policy and Greenhouse Gas Emissions in Brazil Abstract 1. Background Agricultural policy support can be defined as the gross transfer to agriculture from consumers and taxpayers (OCDE, 2016). When governments decide to apply policy distortions to a given crop, they create incentives (or disincentives) that drive economic decisions and lead to increase (or decrease) crop/grassland areas. Different crops generate distinct emissions (sequestrations) of greenhouse gases. Thus, governments also influence greenhouse gas emissions and climate change through their agricultural policies. The Food an Agricultural Organization (FAO) projects the growth of the world demand for food to be between 15% and 40% in the coming decades. Brazil is one of the largest food suppliers in the world and has the potential to expand its production capacity, increasing its agricultural production by 30% by At the same time, Brazil signed the Paris Climate Change Agreement in December 2015 (COP 21) proposing a reduction of its national GHG emissions by The Brazilian government intends therefore to strengthen its low-carbon agriculture, restoring 15 million hectares of degraded pastures, and implementing an integrated production system in another 5 million hectares by Objective and relevance of the research The general objective of this research is to contribute to bridging the gap and expanding the boundary of knowledge about the relationship between agricultural policy support and greenhouse gas emissions (GHG) in Brazil. The relationship and mutual influences between the two variables is particularly interesting in the context of the application of the Paris Agreement, as their analysis allows to check the consistency of agricultural policy measures with the climate change mitigation commitment of the Brazilian government. Given the relative importance of Brazilian agriculture in the economy of the country and the weight Brazil carries in the global climate change negotiations, this research assumes particular relevance. 3. Methodology and data The paper investigates the relationships between support for agricultural products and GHG emission in Brazil, applying the Josling et al. (2016) methodology, that has already been applied by the Inter-American Development Bank (IDB) in Jamaica. The policy support data, and specifically the Producer Single Commodity Transfer (PSCT) indicator, the production value at the farm gate, and the value of production (VoP) were collected from the Agrimonitor database. Agrimonitor is an IDB initiative meant to carry out agricultural policy analyses in Latin America and the Caribbean, using the Producer Support Estimate (PSE) methodology, developed by the Organization for Economic Cooperation and Development (OECD). The System for Greenhouse Gas Emissions and Removals Estimates (SEEG) provided instead the GHG statistics. SEEG uses several sources to estimate GHG emissions for Brazil, according to the Intergovernmental Panel on Climate Change (IPCC) guidelines (SEEG, 2017). Some examples of sources are the Brazilian Inventories of Anthropogenic Greenhouse Gas Emissions and Removals (prepared by the Ministry of Science, Technology, and Innovation

3 (MCTI)), government reports, institutes, research centers, sector entities and non-governmental organizations. The Center for Sustainability Studies at the Getulio Vargas Foundation (GVces) provided simulated carbon price. After mapping the Agrimonitor database to the SEEG database for the period , we construct several indices for the commodities included in Agrimonitor for Brazil: beef, maize, milk, pigmeat, poultry, sugar, rice, and soybeans. The first index, the Agricultural Carbon Equivalent (ACE) allows to express carbon emissions linked to the agricultural sector in monetary terms. The second, the Net Output Value (NOV) index allows the analysis of the net contributions of major Brazilian agricultural commodities to the economy. The third index, the ACE%, shows the percentage of value generated by the specific commodities that is offset by ACE. Finally, the Net Social Value (NSV) index captures how much each unit of environmental cost is related to net revenues. 4. Preliminary results and potential for generating discussion Preliminary results show that beef and milk have the highest environmental costs and generate the lowest value per unit of environmental cost. The adoption of technology in recent years is, however, reducing their environmental impact. Soybeans and maize are associated with the lowest environmental costs and produce higher values per unit of environmental costs. As a result, policy efforts aiming to the mere reduction of GHG emissions should focus on the support to and promotion of soybeans and maize instead of beef and milk. These results are likely to generate a stimulating discussion during the session as their application to actual policy making would entail a complicated priority definition. GHG emission and their reduction in the agricultural sector are strictly linked to several other economic and social variables and objectives, which might not be coherent among them. An interesting and challenging debate is therefore expected to take place on these issues and their relative importance in policy making.

4 List of Abbreviations ACE: Agricultural Carbon Equivalent GVces: Center for Sustainability Studies at the Getulio Vargas Foundation CO2 e: Carbon Dioxide Equivalent GHG: Greenhouse Gas GWP: Global warming potential IDB: Inter-American Development Bank IPCC: Intergovernmental Panel on Climate Change MCTI: Ministry of Science, Technology, and Innovation NCI: National Inventory NOV: Net Output Value PCST: Producer Commodity Specific Transfers PSE: Producer Support Estimate UNFCCC: United Nations Framework Convention on Climate Change VoP: Value of Production (at Farm Gate)

5 1 Introduction Agricultural policy support can be defined as the gross transfer to agriculture from consumers and taxpayers (OCDE, 2016). When governments decide to apply policy distortions to a given crop, they create incentives (or disincentives) that drive economic decisions and lead to increase (or decrease) crop/grassland areas. Different crops generate distinct emissions (sequestrations) of greenhouse gases. Thus, governments also influence greenhouse gas emissions and climate change through their agricultural policies. The Food an Agricultural Organization (FAO) projects the growth of the world demand for food to be between 15% and 40% in the coming decades. Brazil is one of the largest food suppliers in the world and has the potential to expand its production capacity, increasing its agricultural production by 30% by At same time, Brazil signed the Paris Climate Change Agreement in December 2015 (COP 21) proposing a reduction of its national GHG emissions by The Brazilian government intends therefore to strengthen its low-carbon agriculture, restoring 15 million hectares of degraded pastures, and implementing an integrated production system in another 5 million hectares by The general objective of this research is to contribute to bridging the gap and expanding the boundary of knowledge about the relationship between agricultural policy support and greenhouse gas emissions in Brazil. The relationship and mutual influences between the two variables is particularly interesting in the context of the application of the Paris Agreement, as their analysis allows to check the consistency of agricultural policy measures with the climate change mitigation commitment of the Brazilian government. Given the relative importance of Brazilian agriculture in the economy of the country and the weight Brazil carries in the global climate change negotiations, this research assumes particular relevance. Therefore, we re looking to answer the following research questions: - What are the relationships between policy support for agricultural products and GHG emission in Brazil? Is there consistency between Brazilian agrarian support policies and Brazilian GHG emissions objectives?

6 Hypothesis: There is no consistency between Brazilian agricultural support policies and Brazilian GHG emissions objectives since agricultural policies select products to be supported only on the basis of economic criteria. Specifically, the proposed research project aims to: Describe the levels of agricultural policy support and GHG emission in Brazil; Analyze GHG emissions estimates alongside the incentives provided by agricultural policy in Brazil; Suggest some policy adjustments in light of the results.

7 2 Data and Methods This section describes the methodology and data used to achieve the proposed objectives. To investigate the relationships between support for agricultural products and GHG emission in Brazil, we apply the Josling et al. (2016) methodology, that has already been applied by the Inter-American Development Bank (IDB) in Jamaica. The policy support data, and specifically the Producer Single Commodity Transfer (PSCT) indicator, the production value at the farm gate, and the Value of Production (VoP), were collected from the Agrimonitor database. Agrimonitor is an Inter-American Development Bank (IDB) initiative to carry out agricultural policy analyses using the Producer Support Estimate (PSE) methodology, developed by the Organization for Economic Co-operation and Development (OECD), in Latin American and Caribbean countries. The PSCT variable is defined as follows: The annual monetary value of gross transfers from consumers and taxpayers to agricultural producers, measured at the farm gate level, arising from policies linked to the production of a single commodity such that the producer must produce the designated commodity in order to receive the transfer (IDB, 2017). The System for Greenhouse Gas Emissions and Removals Estimates (SEEG) provides the Greenhouse Gas Emissions statistics. SEEG uses several sources to estimate GHG emissions from Brazil, according to the Intergovernmental Panel on Climate Change (IPCC) guidelines (SEEG, 2017). Some examples of sources are the Brazilian Inventories of Anthropogenic Greenhouse Gas Emissions and Removals (prepared by the Ministry of Science, Technology, and Innovation (MCTI)), government reports, institutes, research centers, sector entities and non-governmental organizations. The GHG variable is defined as follows: CO 2 e (t)gwp AR5, which represents all GHG contained in the Brazilian national inventory (e.g. CO2, CH4, N2O, and HFCs) expressed in tons (t) of carbon equivalent (CO2 e) constructed according to global warming potential (GWP) and conversion factor of the 5 th IPCC assessment report (AR5), the most recent and updated metric (SEEG, 2017). Agrimonitor and SEEG have some different commodities in their databases as can be seen in Table 1 of the Annex. Agrimonitor includes coffee, cotton, and wheat, whereas SEEG does not. At the same time, SEEG includes asses, buffalo, goat, horse, bean, cassava, mule, sheep, and

8 vinasse while Agrimonitor does not. Table 1 shows how the two databases were combined. Following Josling et al. (2017), sugar cane corresponds to refined sugar. Table 1 Correspondence Between SEEG and Agrimonitor Sectors SEEG Commodities Beef Cattle Maize Dairy Cattle Pigmeat Poultry Meat Sugar Cane Rice Soybeans Agrimonitor Commodities Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Table 1 links the GHG from SEEG database to the Agrimonitor policy support database. However, to allow the comparison of the databases, all main indicators should be expressed in the same unit. In this case, monetary value. This way, it is possible to fulfill the objective to analyze GHG emissions estimates alongside the incentives provided by agricultural policy in Brazil. The Center for Sustainability Studies at the Getulio Vargas Foundation (GVces) provides simulated carbon price. The GVces developed the Emissions Trading System Simulation to offer a market tool for carbon pricing, which until now represents the only initiative with market experience in South America. During 2016, GVces ran the third operational cycle of the project with the participation of more than 30 companies from different industries. The trade simulations took place at the Instituto BVRio Environmental Stock Exchange, using a fictitious currency, EPcents (Ec$), and real GHG emission data. Ec$ 1,00 was set equal to R$ 1,00. Allowances were allocated through auctions and free allocation in the simulation and the reference carbon price we consider is the 2016 average trading price. P CO2 e(t) = R$ 36,50. The Brazilian Ministry of Finance is conducting the Partnership for Market Readiness (PMR) Brazil Project whose objective is to asses the convenience and the inclusion of carbon

9 pricing in the package of instruments of National Policy on Climate Change 1 (PNMC) after Specifically, the PMR Brazil Project evaluates instruments such as tax emissions, carbon market and a combination of both to be implemented in the country. Once the reference price is available, following Josling et al. (2016) the Agricultural Carbon Equivalent (ACE) is estimated. ACE monetizes the carbon emissions and is defined as follows: ACE = (CO 2 e (t)gwp AR5 ) P CO2 e(t) As a result, the evaluation of the relationship between CO 2 e (t)gwp AR5 and PSCT is possible once both are in monetary unit.. In addition, to estimate the contributions of majors Brazilian agricultural commodities to the economy the Net Output Value (NOV) was calculated. The NOV is defined as follows: NOV = VoP PSCT ACE Two additional indices measure the relationship between agricultural policy and GHG emission named ACE index and NET index.the first one calculates the percentage of VoP that is offset by the agricultural carbon equivalent. I ACE is defined as follows: I ACE = ACE VoP The second index captures how much each unit of environmental cost is related to revenues net of government support. I NET = VoP PSCT ACE The Brazilian data in the Agrimonitor database covers twenty-two years, from 1995 to Thus, the same period was considered in the analysis using the SEEG database. The period was divided into two intervals, from 1995 to 2005 and from 2006 to 2016 to capture any significant change in the behavior of the series. 1 More info:

10 Results 3.1 GHG Emissions The Brazilian agricultural GHG emissions have been growing in the last twenty-two years from 371,614,448 CO 2 e (t)gwp AR5 in 1995 to 499,374,537 CO 2 e (t)gwp AR5 in 2016, that is, a growth of 34% during this period. Figure 1 shows the linear behavior of the GHG time series that has an average growth of 1.4% per year, which represents an average increase of 6 million of CO 2 e (t)gwp AR5 every year. Figure 1 Brazilian Agricultural Emissions of CO 2 e (t)gwp AR , mm y = x R² = Source: SEEG, elaborate by the author The groups of commodities used to map SEEG database to Agrimonitor database (beef, corn, dairy, pigs, poultry, sugar-cane, rice, and soybeans) account, on average, for 91% of total Brazilian agricultural emissions. Thus, the analysis conducted with this sample is considered sufficiently representative of the agricultural sector. The majority of GHG emissions came from direct discharge (91% on average), while the remaining 9% came from indirect emissions. The activity that contributed most to GHG emissions was animal with an average 89% during the period, followed by vegetal, 6% a.a., and others, 5% a.a (SEEG, 2017). IPCC enumerates six specific processes that generate GHG emissions: rice cultivation, enteric fermentation, manure management, field burning of agricultural residues, agricultural soil

11 management, and emission and removal of carbon dioxide of agricultural soil (not counted in Brazilian inventories). Figure 2 e shows the evolution of these processes in Brazil. Figure 2 Brazilian Agricultural Emissions of CO 2 e (t)gwp AR5 by Emission Process , mm Rice Cultivation Manure Management Agricultural Soil Management Enteric Fermentation Field Burning of Agricultural Residues Source: SEEG, elaborate by the author The process associated with the highest level of emissions is enteric fermentation, which accounts for an average 67% of the total, without significant variations (a standard deviation of 1.3%). Enteric fermentation occurs during the digestion process of herbivorous ruminants such as cattle, buffalo, sheep or goats. It is the most significant source of CH 4 in the country, and its intensity depends on several factors, among which the type of food (MCTI, 2015). The second emission-generating process is agricultural soil management, with 25% of overall farm emission, on average. The management of soil can drastically change the emissions of CO 2. On the one hand, agricultural soils farmed under conventional planting system (i.e. use plowing and harvesting) and degraded pastures tend to emit CO 2 to the atmosphere. On the other hand, agricultural soils farmed using modern techniques such as no-tillage systems, well-managed pastures, integrated crop-livestock-forest systems, and planted forests tend to remove CO 2 from the atmosphere. The application of nitrogen fertilizers (both synthetic and animal origined), as well as the deposition of animal manure directly on pasture (not subject to management) foster the emissions of N 2 O. (Li et al., 2000). Figure 3 details the specific emissions process that adds up to 25%, on average, of total agricultural emissions; the other 75% the SEEG database classify as others. The two most

12 important sub-categories are deposition of manure on pasture and leaching which represent, respectively, 34% and 30% of specific emission excluding the category others. Figure 3 Brazilian Agricultural Emissions of CO 2 e (t)gwp AR5 by Specific Emission Process* , mm Application of Organic Residues Atmospheric deposition Deposition of manure on pasture Synthetic Fertilizers Leaching Agricultural residues(waste) Organic soils Source: SEEG; * excluded the category others that accounts for 75% of emissions, elaborate by the author The results are shown in Figure 2 and Figure 3 lead to the conclusion that Cattle is the subsector that contributes the most to total Brazilian agricultural emissions. In fact, beef and milk cattle determine about 90% of agricultural emissions reported in Table 2 and Table 3 2. Beef cattle alone explains 76% of total GHG emission (Table 4). The relative importance of livestock does not change throughout the period (Table 2 and Table 3). Although there is stability in the participation share of each commodity, recently the shares of poultry and soybeans have been increasing. Beef and Vail emissions had a substantial increase in the first period, from 1995 to 2005, 31%, however, in the next eleven years, from 2006 to 2016, the growth shrank to 6%. Although milk emissions rose by 4% in the first period, they showed a reduction of 10% in the second period, totaling a decrease of 5%. The livestock emissions behavior, given its weight, determined an overall reduction of GHG growth from 24% in the first period to 6% in the second period. 2 Table 2 and Table 3 concerns about Agrimonitor Commodities that accounts for about 90% of total agricultural emissions.

13 Table 2 Brazilian Agricultural Emissions of CO 2 e (t)gwp AR5 by Agrimonitor Commodities, , mm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: SEEG, elaborate by the author Table 3 Brazilian Agricultural Emissions of CO 2 e (t)gwp AR5 by Agrimonitor Commodities, , mm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: SEEG, elaborate by the author During the overall period, , soybeans emissions showed the highest growth, 275%, while poultry emissions exhibit the second highest growth, 100%. In the last eleven years, , soybean emissions increased 84%, followed by those cause by maize, 50%, and poultry, 49%.

14 3.2 Comparison of Agrimonitor Commodities Table 4 shows the shares of the Agrimonitor commodities for the various variables discussed in the data and method section. Overall, the average output value share for the periods and does not change. The two most important commodities were cattle and soybeans, generating amounts that account for about 45% of the Brazilian agricultural products analyzed. The beef share of average VoP decreases 2.6% from period 1 to period 2, while at the same time the soybean share of average VoP increases 4%. Corn, milk, poultry, and sugar totaled 43% of the average VoP average during and 45% of VoP average shares during Table 4 Share of Agrimonitor Commodities in Support and Emissions, Single Greenhouse Agricultural Value of Net Output Commodity Gas Carbon Commodity Output Value Transfer Emissions Equivalent VoP PSCT GHG ACE VoP-ACE-SCT Part 1: Beef and Vail 24.2% -10.4% 76.6% 76.6% 12.2% Maize 10.4% -94.1% 0.4% 0.4% 11.0% Milk 10.7% -40.7% 13.3% 13.3% 9.3% Pigmeat 6.5% 3.1% 2.5% 2.5% 7.3% Poultry Meat 11.5% 1.9% 1.7% 1.7% 13.5% Refined Sugar 10.8% 370.7% 1.3% 1.3% 18.5% Rice 5.3% -74.3% 3.6% 3.6% 4.4% Soybeans 20.7% -56.3% 0.7% 0.7% 23.9% Total 100% 100% 100% 100% 100% Part 2: Beef and Vail 21.6% 19.8% 75.8% 75.8% 17.9% Maize 10.7% 13.7% 0.6% 0.6% 11.3% Milk 9.3% 23.6% 12.9% 12.9% 8.6% Pigmeat 5.7% 2.4% 2.6% 2.6% 6.0% Poultry Meat 11.4% 0.6% 2.1% 2.1% 12.4% Refined Sugar 13.5% 4.1% 1.7% 1.7% 14.6% Rice 3.1% 22.1% 3.1% 3.1% 2.6% Soybeans 24.7% 13.7% 1.1% 1.1% 26.6% Total 100% 100% 100% 100% 100% Source: Agrimonitor, SEEG; elaborate by the author

15 The shares of single commodity transfer, column two of Table 4, reflect the policy priorities and the size of each sector. There were significant changes from period 1 to period 2. The negative sugar transfers of R$ million overshadowed the results for the period, (Annex A, Table 4). Indeed, corn, rice, and soybeans received the highest total incentives during the period with, respectively, R$ 9729 million, R$ 7684 million, and R$ 5818 million. These three commodities accounted for 83% of total PSCT. The weight of incentives changed in the following period, , for example, milk moved from fourth place to first place with 23% of the average total PSCT in the second period, which represents an increase of transfer from R$ 4203 million to R$ million. Rice followed it with 22% and beef with 19.8%. The total incentives for the main commodities of Brazilian agriculture increased R$ million from one period to another. In light of section 3.1, the weight of supports has shifted from products that emit less GHG ( maize and soybeans) to those that emit more GHG (beef and milk). Government policy does not necessarily follow production revenue once PSCT values do not accompany VoP values as shown in Figure 4. PSCT had structural changes in 1999 and 2008 while VoP has grown sharply since Figure 4 PSCT and VoP: Total of Brazilian Agriculture , mm Total PSCT Total VoP Source: Agrimonitor, PSCT on the left axis and VoP on the right axis; elaborate by the author

16 As discussed in section 3.1, almost all GHG emissions of Brazilian agriculture come from beef cattle or milk cattle. However, incentives (PSCT) is not aligned with GHG emissions, suggesting, therefore, that environment concerns is not a driven policy priority for Brazilian policymakers. In fact, among the goods shown in Figure 5, the one with the highest correlation between incentives and GHG emissions is sugar with a coefficient of 0.6, followed by soybeans, 0.5, beef, 0.4, milk, 0.3, poultry, 0.2 and maize, 0.1. Figure 5 PSCT and GHG: selected Brazilian commodities , mm Source: Agrimonitor and SEEG, PSCT on the left axis and GHG on the right axis; elaborate by the author Agricultural Carbon Equivalent (ACE) in Table 4does not account for carbon sequestration. Two reasons led to this decision. First, the lack of an official database. SEEG presents a database for removals of carbon in the soil due to agricultural practices. However, SEEG warns that their information is based only on one exercise and the National Inventory of Brazil

17 does not include sequestration statistics. Second, the lack of a methodology that appropriately maps the SEEG database to the analyzed commodities. The inclusion of carbon sequestration could change the conclusions since the number of withdrawals could add up to 45% of total emission in The modernization of Brazilian agriculture in the last twenty years causes it to sequester more GHG. For example, well management of grassland and crops cultivated under tillage system has increased the amount of emission removed since 1999, as demonstrated by Figure 5. Figure 6 Agricultural GHG Removals in Brazil , mm Source: SEEG, GHG on the left axis and % of total emissions on the right axis; elaborate by the author If fact, the cost of emissions, ACE, may change the perception of Policymaker who evaluates the relevance of each commodity to the country by their value of production, VoP. For instance, if guided only by VoP, one can conclude that Beef was the most important agricultural product during the period adding up to 24% of total value of production (Figure 7, Graph A). In contrast, when considering the ACE, Beef would be ranked as fifth most relevant accounting for 12% of total value of production net of emission cost (Figure 8, Graph A). Overall, when the costs of emissions are considered, the relative contribution to the agricultural sector of soybean, beef, sugar, poultry, maize, milk, pigmeat, and rice were 26%, 18%,

18 14%, 12%, 12%, 9%, 6%, and 3%, respectively in the period The ACE is relatively small when confronted with the value of production, with an average of 7% in the second period. In the first period, ACE was higher with an average of 22% of the value of production. Figure 7 Value of Production Shares, average and Source: Agrimonitor; elaborated by the author Figure 8 Value of Production Net of ACE Shares, average and Source: Agrimonitor, SEEG; elaborated by the author Now, we take into consideration the transfers from consumers and taxpayers to agricultural producers (PSCT) for each good. The net output value (NOV), VoP ACE PSCT, the contribution of main agricultural commodities to Brazilian economy, increased from an average of R$ million per year during to an average of R$ million per year during In this latter period, the crops with relatively little support (PSCT) and low cost of emissions (ACE) delivered the most significant economic benefits, showing incongruity in the policy priorities in which concerns incentives and GHG emiision. Although pig, poultry, sugar, and soybeans had 21% of the incentives, they emitted only 7.5% of the total emissions and generated 60% of total net benefit (NOV)..

19 Beef emissions represent over 75% of total GHG, and the net contribution to the agricultural sector was 12.2% of NOV during the first period, Graph A of Figure 8, and 17.9% in the second period, Graph B of Figure 8. The second largest GHG emitter, milk, received the highest incentives in the period , however, was only the sixth in NOV. Overall, the most substantial net contribution to the agricultural sector has been given by soybeans, which represent only about 1% of the emissions. Figure 8 NOV: shares of value of production net of ACE and PSCT, average and Source: Agrimonitor, SEEG; elaborate by the author To measure the relationship between agricultural policy and GHG emission Table 5 shows two different indexes. The first one calculates the percentage of VoP that is offset by the agricultural carbon equivalent. Beef and Milk have the highest environmental costs. Beef emission costs averaged 69% of their value of production in the period The adoption of technologies and the increase of productivity made the cattle index fall to an average of 24% in the period Chart A of Figure 9 shows the growth of meat production with relative stability in the number of heads while milk in Chart B has had smooth and steady growth.

20 Table 5 Indexes to link Policy and GHG by Commodities, ACE as Percent of VOP (%) (VOP-SCT)/ACE ratio Beef and Vail 69.1% 23.7% 2 4 Maize 0.9% 0.4% Milk 24.5% 9.5% 4 11 Pigmeat 9.3% 3.0% Poultry Meat 3.6% 1.2% Refined Sugar 2.5% 0.8% Rice 16.0% 6.5% 7 13 Soybeans 0.8% 0.3% Source: Agrimonitor, SEEG; elaborate by the author Soybeans and Maize have the lowest environmental costs. Soybeans emission costs averaged 0.8% of their value of production in the period , which decreased to 0.3% in the following period, The same occurs with maize, a decrease of the index from one period to another (from 0.9% to 0.4%). Chart C of Figure 9 shows that the soybean area and yields increase concomitantly in Brazil. Chart D shows that corn production in the last years has grown faster than its area. As a result, from the environment point of view, the first index suggests that policy to reduce GHG emissions should focus on Soybeans and Maize instead of Beef and Milk. In the period , meat and milk received on average 31% of the policy transfers, while soy and corn received 35%.

21 Figure 9 Evolution of Selected Commodities Source: Agrimonitor, SEEG; elaborate by the author The second index of Table 5 captures the relative value of goods net of emission costs. Soybeans and maize have most favorable ratios, signaling a higher value per unit of environmental cost. Beef, milk, and rice have the worst indices denoting a lower value per unit of environmental cost. It is important to note that the average of the index increased considerably from one period to the other, with the most prominent growth affecting beef (167%), followed by milk (148%).

22 4 Conclusions Taken together, the results of this study suggest that Beef and Milk have the highest environmental costs and generate the lowest value per unit of environmental cost, while Soybeans and Maize have the smallest environmental costs and produce more value per unit of environmental costs.. However, the government incentives moved from corn, rice and soybeans, which in decade accounted for 83% of total PSCT, to Milk, Rice and Beef, which in decade accounted for 65% of total PSCT. Brazilian policymakers have been giving considerable weight to livestock in their incentives, an average over 40% of support. Beef and milk cattle are responsible for more than 80% of agricultural GHG emissions. On the other hand, corn and soybeans have received more or less the same incentive of these products and are responsible for less than 2% of the emissions. The findings of this study suggest some inconsistency between Brazilian agricultural support policies and Brazilian objective of reducing GHG. Thus, a policy with less weight in livestok would bring more aligniment with the country GHG emissions goals. Furthermore, Brazil has been implementing coordinated actions for sustainable development since the gorvernment volountarily commited to reduce GHG at COP-15 (2009). Specifically, the Ministry of Agriculture coordenate the Plano de Agricultura de Baixo Carbono (Low Carbon Agriculture Plan), ABC Plan, that uses adaptation and mitigation strategies to reach, by 2020, goals such as recover 15 million hectares of degraded pastures, increase the area under zero tillage from 25 million hectares to 33 million hectares, reduce greenhouse gas emissions by 160 million tonnes of CO2 equivalent annually. The ABC Plan also offers subsidized rural credit for farmers who adopt sustainable practices and incorporates a series of initiatives - promotion of sustainable agriculturae practices, training of rural technicians and producers, environmental regulation, land regularization, technical assistance and rural extension, research, development & innovation, among other actions. ABC Plan is a possible explanation for the improvement in the results from to decade. One of the limitations with the conclusion that policy efforts aiming to the mere reduction of GHG emissions should focus on the support to and promotion of soybeans and maize instead

23 of beef and milk is that it does not consider other important policy factors as employment and number of benficiaries. For instance, milk farms, in general, are small proterties, where 1.07 million of farmers, 80% of total farms, produce lesst than 50 liters/day (IBGE, 2018). In contrast, the country has 0.22 million soybean farmers (IBGE, 2018). GHG emission and their reduction in the agricultural sector are strictly linked to several other economic and social variables and objectives, which might not be coherent among them. As a result, policymaking would entail a complicated priority definition. Therefore, futures studies may address the challenge of optimal choice under these circumstances.

24 5 References GVces, Emissions Trading System Simulation Cycle Final Report. Retrieved from: Josling, T., Alleng, G. P., Carmine, P de S., Boyce, R., Mills, A. and Valero, S Agricultural Policy and Greenhouse Gas emissions in Jamaica. Retrieved from: IBGE, Censo Agropecuario Retrieved from: cao/default.shtm LI, C. Modeling trace gas emissions from agricultural ecosystems. Nutrient Cycling in Agroecosystems, v.58, p , MCTI, Emissões de Metano por Fermentação Entérica e Manejo de Dejetos de Animais, Relatórios de Referência: Agricultura, 3º Inventário Brasileiro de Emissões e Remoções Antrópicas de Gases de Efeito Estufa, Brasília, DF. Retrieved from: IDB, Glossary. Retrieved from OECD Overview of the OECD Indicators of Agricultural Support. In OECD S Producer Support Estimate and Related Indicators of Agricultural Support (Chapter 2). Retrieved from

25 6 Annex Table 1 SEEG and Agrimonitor Commodities Categories SEEG Category CO2e(t) GWT- AR Agrimonitor Category PSCT , BRLmn Beef Cattle 301,064,409 Beef and Vail Corn 2,144,081 Maize Dairy Cattle 51,760,218 Milk Pigs 9,980,761 Pigmeat 69.9 Poultry 7,588,217 Poultry Meat 13.4 Sugar Cane 5,937,054 Refined Sugar Rice 13,241,405 Rice Soybeans 3,527,918 Soybeans Asses 875,951 none Bubalino 2,471,380 none Goats 2,368,419 none Horses 5,897,754 none Bean 275,233 none Synthetic Fertilizers 17,204,707 none Cassava 671,009 none Mules 1,053,117 none Other Crops 726,337 none Sheep 4,092,767 none Organic soils 4,041,306 none Vinasse 982,552 none none Coffee none Wheat Cotton - Cotton Planted Forest - none Cultivated Crops Under Conventional System - none Crops cultivated under tillage system - none Well Managed Grassland - none Degraded Grassland - none Integrated Systems-Cropland-Livestock-Forest - none Total 435,904,

26 Table 2 Value of Production , BRLmn Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: Agrimonitor Table 3 Value of Production , BRLmn Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: Agrimonitor

27 Table 4 Single Commodity Transfer by Commodity, , BRLmn Beef and Veal Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: Agrimonitor Table 5 Single Commodity Transfer by Commodity, , BRLmn Beef and Veal Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: Agrimonitor

28 Table 6 GHG Emissions by Agrimonitor Sector , CO2e(t) GWP - AR5 / mm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: SEEG Table 7 GHG Emissions by Agrimonitor Sector , CO2e(t) GWP - AR5 / mm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Source: SEEG

29 Table 8 Monetary Value of Agricultural Carbon Emissions (ACE) , BRLmm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Table 7 Monetary Value of Agricultural Carbon Emissions (ACE) , BRLmm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total

30 Table 9 Value of Production Value of Externalities (ACE) , BRLmm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Table 10 Value of Production Value of Externalities (ACE) , BRLmm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total

31 Table 11 Value of Production Net of ACE and PSCT , BRLmm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total Table 12 Value of Production Net of ACE and PSCT , BRLmm Beef and Vail Maize Milk Pigmeat Poultry Meat Refined Sugar Rice Soybeans Total