The vulnerability of renewable energy to climate change in Brazil

ARTICLE IN PRESS Energy Policy 37 (2009) 879–889

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

The vulnerability of renewable energy to climate change in Brazil Andre´ Frossard Pereira de Lucena a,, Alexandre Salem Szklo a, Roberto Schaeffer a, Raquel Rodrigues de Souza a, Bruno Soares Moreira Cesar Borba a, Isabella Vaz Leal da Costa a, Amaro Olimpio Pereira Ju´nior b, Sergio Henrique Ferreira da Cunha b a

´ria, Ilha do Funda ˜o, Energy Planning Program, Graduate School of Engineering, Federal University of Rio de Janeiro, Centro de Tecnologia, Bloco C, Sala 211, Cidade Universita CEP: 21941-972 Rio de Janeiro, RJ, Brazil b Empresa de Pesquisa Energe´tica—EPE, Head Office: Avenida Rio Branco, 1–111 andar, Centro, CEP: 20.090-003 Rio de Janeiro, RJ, Brazil

a r t i c l e in fo

abstract

Article history: Received 13 August 2008 Accepted 15 October 2008 Available online 6 December 2008

Energy supply in Brazil relies heavily on renewable energy source. The production of energy from renewable sources, however, greatly depends on climatic conditions, which may be impacted in the future due to global climate change (GCC). This paper analyzes the vulnerabilities of renewable energy production in Brazil for the cases of hydropower generation and liquid biofuels production, given a set of long-term climate projections for the A2 and B2 IPCC emission scenarios. The most important result found in this study is the increasing energy vulnerability of the poorest regions of Brazil to GCC. Both biofuels production (particularly biodiesel) and electricity generation (particularly hydropower) may negatively suffer from changes in the climate of those regions. Other renewable energy sources—such as wind power generation—may also be vulnerable, raising the need for further research. However, the results found are fundamentally dependent on the climate projections which, in turn, are still highly uncertain with respect to the future evolution of greenhouse gas emissions, greenhouse gas concentrations in the atmosphere and GCC. Therefore, in such long-term scenario analyses, the trends and directions derived are the ones to be emphasized rather than the precise results one arrives. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Global climate change Renewable energy Vulnerability

1. Introduction The Brazilian energy sector relies heavily on renewable energy sources. Some 47% of all energy produced in the country came from renewable sources in 2007. In the power sector, this reliance is even greater. Hydroelectric power plants accounted for 83% of Brazil’s power generation in 2006 (MME, 2007). Bioenergy has become increasingly important in the Brazilian energy sector not only for liquid biofuels production, but also for electricity generation. Sugarcane products are now the second most important primary energy source—after petroleum—reaching 16.6% of the total primary energy production in the country in 2006 (MME, 2007). Also, a National Biodiesel Program was created in December 2004, and in January 2005 biodiesel was inserted in the national energy matrix with the enactment of Law 11,097. The law provides for the addition of 2% biodiesel to diesel oil by 2008, and 5% by 2013. The availability and reliability of these renewable sources, however, are a function of climate conditions, which can vary in

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E-mail address: [email protected] (A.F.P. de Lucena). 0301-4215/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2008.10.029

light of global climate changes (GCC) related to the emission of greenhouse gases. Long-term energy planning in Brazil has not examined the possible impacts of GCC on the vulnerability of renewable energy sources. Therefore, the focus of this study is to analyze the vulnerabilities of renewable energy production in Brazil to GCC. This is done by assessing the impacts that new climate conditions, such as those projected for the 2071–2100 period, could have on the production of hydropower and liquid biofuels in the country. Two GCC scenarios resembling the two emission scenarios A2 (high emission) and B2 (low emission) proposed by the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (IPCC, 2000) were translated into variations in energy supply for hydropower and the production of liquid biofuels. Although GCC may affect the supply1 of other renewable sources of energy—such as windpower2 and non-renewable sources—such as gas-fired thermoelectric generation as shown

1 GCC may also impact the consumption of energy, especially in the case of greater use of air conditioning in the residential and services sectors. This was investigated in Shaeffer et al. (2008). 2 For further details on the vulnerabilities of wind power to GCC in Brazil, see Schaeffer et al. (2008), which estimated the impact of GCC on the Brazilian wind power potential.

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in Shaeffer et al. (2008)—as well, these other energy sources were not examined in this work. This paper focused only on hydropower and liquid biofuels, for they are the two most important renewable energy sources in Brazil, accounting for 30.8% of the total primary energy production in the country in 2006 (MME, 2007). The next section presents the GCC scenarios and the database used in this study. Section 3 shows the methodological approach and the results for hydropower. Section 4 analyzes liquid biofuels production. Finally, Section 5 presents the concluding remarks as well as the proposed recommendations for future improvements in this kind of analysis.

2. GCC scenarios and database The two IPCC emission scenarios on which the climate projections used in this study were based—A2 and B23—are two of four qualitative storylines (A1, A2, B1 and B2) characterized by different economic and energy development paths. They describe divergent futures in an attempt to cover a significant portion of the underlying uncertainties in the key driving forces for greenhouse gases emissions (IPCC, 2000). The A2 scenario (pessimistic, high emission) describes a heterogeneous world, where regional oriented economic development is emphasized. In this scenario, there is less emphasis on economic, social, and cultural interactions between regions, which become more self-reliant and tend to preserve the local identities. Also, per capita economic growth and technological change are uneven and slow, which do not help to narrow down the gap between now-industrialized and developing parts of the world. Final energy intensities in the A2 scenario decline with a pace of 0.5–0.7% per year (IPCC, 2000). In the B2 scenario (optimistic, low emission), there is an increased concern for environmental and social sustainability at the national and local levels. This scenario presents a world with continuously increasing global population at a rate lower than that of A2, intermediate levels of economic development and also more regionally heterogeneous technological innovations. The final energy intensity of the B2 scenario declines at about 1% per year, in line with the average historical experience since 1800 (IPCC, 2000). The A2 and B2 IPCC emission scenarios were translated into climate projections for Brazil by a team of Brazilian climate specialists from CPTEC/INPE using the PRECIS (Providing REgional Climates for Impacts Studies) model. This is a regional climate model system developed by the Hadley Centre which downscales the results of the HadCM3 global climate model.4 It uses the present and future concentrations of greenhouse gases and sulphur projected by the A2 and B2 IPCC emission scenarios to make regional climate projections which are consistent with the global model5 (Marengo, 2007a). For the purpose of this study, the PRECIS model provided projections for precipitation and temperature at a 50 km  50 km square resolution for the 2071–2100 period (Ambrizzi et al., 2007; Marengo et al., 2007). Climate models are approximate representations of very complex systems. The level of uncertainty about the impacts of 3 For a more complete description of the A2 and B2 emission scenarios assumptions, see IPCC (2000). 4 The lateral boundary conditions for the PRECIS model is given by the global atmosphere general circulation model HadAM3P, which constitutes the atmospheric component of the ocean-atmosphere global climate model HadCM3, forced with sea surface temperature anomalies (Marengo, 2007a). 5 For more detailed information on the methodological aspects of the PRECIS model see Marengo (2007a) and Jones et al. (2004).

the concentration of greenhouse gases on the global climate (global climate model), and the Brazilian in particular (regional climate model), becomes evident when comparing the results of different climate models (Marengo, 2007b). In this sense, given the already large uncertainties associated with the future evolution of greenhouse gases emissions (the A2 and B2 storylines), greenhouse gas concentrations in the atmosphere, GCC, and the uncertainties added by the modeling tools used to translate the projected climate conditions into impacts on the Brazilian energy sector, the results of this study should be interpreted with caution. Also, in addition to the uncertainties of the energy models, the estimated impacts of GCC on the Brazilian energy sector presented in this study are intrinsically dependent on the climate projections adopted. Therefore, in such a long-term scenario analysis, the trends and directions are the ones to be emphasized rather than the precise results provided, given the cumulative uncertainties related to the study.

3. Hydropower generation Hydropower dominates electricity generation in Brazil, and large hydro dams dominate the sector. With 653 hydropower plants in operation in 2006, the 24 largest plants, with installed capacity higher than 1000 MW, accounted for more than 50% of the country’s total installed capacity (ANEEL, 2007). There is still a considerable unused hydropower potential (estimated at about 170 GW, EPE, 2006) scattered unevenly throughout Brazil, but largely located in the north region and away from the main consuming centers of the southeast region, thus entailing higher electricity transmission costs as well as environmental constraints. Because of the integrated operation of the national power grid (SIN) and the seasonal complementarities among the country’s different regions, power generation at each hydro plant depends, to a large extent, on the incoming water flow and its variability at different times of the year. Thus, the relevant climatic variable6 for the analysis proposed here is the long-range outlook for the rainfall regime in the face of a possible new climate reality (Ambrizzi et al., 2007; Marengo et al., 2007). 3.1. Methodology To assess the impact of a new rainfall regime on the electricity generation from hydroelectric power plants, it was necessary first to project how it would affect the incoming flow at each hydroelectric facility in the SIN. Secondly, with the projected flow series in hand, an operation simulation model called SUISHI-O was used to calculate the impacts on energy generation. The first step is far from trivial. The water cycle is a global phenomenon of closed circulation of water between the surface and the atmosphere, driven by solar energy associated with gravity and the Earth’s rotation. Thus, the water from precipitation reaching the ground can be subject to infiltration, percolation and evaporation upon being exposed to solar energy (Tucci, 2004). The portion not infiltrating the soil, evaporating or being taken up by the vegetation becomes runoff (flow), which can be used for various purposes, including electricity generation. The analysis proposed here faced the challenge of, given the poor availability of historical data on precipitation, evaluating the 6 Other climatic variables, such as temperature, are also relevant. However, this study focused on the impacts of different rainfall regimes only, since it is the most relevant climatic variable to affect river flow.

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effect that a different rainfall regime would have on the natural flow to each hydropower plant in the SIN. This lack of data is a major limitation, especially given the magnitude of the analysis (148 hydro plants in the SIN alone). Given this limitation on the availability of rainfall figures, the impacts of the GCC scenarios on the flow regime in the relevant Brazilian basins were assessed in two stages: the first consist in estimating the future flows at each power plant feeding the national grid using univariate time-series models. This was possible because there is good availability of historical flow data at hydroelectric facilities (ONS, 2007). Firstly, then, individual flow series were generated for the period 2071–2100 using seasonal twelve period ARMA models applied to the historical flow series. In the second stage, the impact of the alterations in the rainfall regime is incorporated into the projected flow series. Due to lack of data on precipitation for all flow gauges, it was necessary to define reference7 plants from which the results were extrapolated to other hydropower plants in the same basin. Finally, with the flow estimates in hand, an operation simulation model was used in an attempt to quantify the variation on the Brazilian interconnected system’s hydro power generation. In the first stage of the methodology, it is assumed that the flow time series for gauge i(fit) have log-normal distribution,8 then lit ¼ ln(fit) has normal distribution N(mi, s2i ). Now, making yit ¼ (litmi)/si, where yitN(0,1), we can estimate a seasonal ARMA (or SARMA) model that captures the underlying structure of the stochastic process yit (Box and Jenkins, 1976; Hipel and McLeod, 1994). Therefore, maintaining the historical mean and variance (mi, s2i ), the projected series for flow in gauge i in period t+k would be equal to (reference scenario) R

f itþk ¼ exp½ðyitþk si Þ þ mi 

(1)

where yit+k ¼ E[yit+k|Yi], the conditional expected value of yit+k given the observed values of the time series Yi, estimated by a SARMA model. It is assumed, here, that the underlying structure of the stochastic process (namely flow) does not alter as a result of the variations in the rainfall regime due to GCC. Instead, these variations would only impact the mean and variance of this process. Thus, the projected series of flow would no longer be calculated using the historical mean and variance (mi, s2i ) as in Eq. (1) but, instead, new values which would incorporate the impact of changes in the rainfall regime in the A2 and B2 scenarios. Therefore, three flow series were projected: one of reference9 (Eq. (1)), which maintains the historical mean and variance (mi, s2i ), and two other that incorporate the impact of a new rainfall regime projected by the PRECIS model (Ambrizzi et al., 2007; Marengo et al., 2007), based on the A2 and B2 IPCC scenarios (IPCC, 2000). The new rainfall regime for these scenarios would, thus, alter the mean and variance of the projected flow series, which would, then, be equal to: A2 Scenario: A2

A2 f itþk ¼ exp½ðyitþk sA2 im Þ þ mim 

(2)

7 The criterion for selecting the reference plants is that of the greatest installed capacity, with a reservoir and those with the greatest data availability. 8 Monthly riverflow series often require a natural logarithmic transformation to cause the residuals of the fitted model to be approximately normally distributed and homoscedastic (Hipel and Mcleod, 1994). In fact, a visual inspection of the histogram of the riverflow series used in this study suggests a log-normal distribution pattern. 9 In an attempt to control for model biases, the two carbon emission scenarios are compared to a reference scenario, in order to have a common basis of comparison which uses the same methodology.

881

B2 Scenario: B2

B2 f itþk ¼ exp½ðyitþk sB2 im Þ þ mim  A2 im ,

A2 im )

B2 im ,

(3) B2 im )

s and (m s indicate the new mean and where (m standard deviation for gauge i at month m for the A2 and B2 scenarios, respectively. These were calculated in the second stage of the proposed methodology, described below. The mean and standard deviation for each month of the year was calculated for each reference plant. By doing this, it was possible to cope with the discontinuities and the lack of temporal intersection of the precipitation data. Moreover, the correlation between precipitation and flow figures increased considerably by doing this procedure, by averaging outliers. Based on the mean and standard deviation for each month, a cross-sectional time series linear regression model was then estimated for each basin. The cross-section dimension being the reference plants for a specific basin and the temporal dimensions the twelve months in a year. The seasonal pattern was accounted for by inserting a dummy variable (DT) for the regression line’s slope in the dry period (from August to January, when it goes from the driest to the wettest month10), which considerably improved the quality of the regression fit. The estimation equation for the monthly mean flow is, for both scenarios, then av

av

lnðmim Þ ¼ aav þ b1 lnðavprecim Þ þ b2 DT im lnðavprecim Þ þ av im

(4)

where, ln(mim) is the natural logarithm of average flow for month m at reference plant i; ln(avprecim) is the natural logarithm of average precipitation for month m at reference plant i; DTim is a dummy variable for the DT period; aav, b1av and bav 2 are the av estimated regression coefficients, where bav and (bav+ 1 1 b2 ) in particular, represents the elasticity of monthly average flow with respect to precipitation in non-DT months and DT months, respectively; and av im is the error term. The estimation equation for the monthly standard deviation of flow is, for both scenarios, then sd

sd

lnðsim Þ ¼ asd þ b1 lnðsdprecim Þ þ b2 DT im lnðsdprecim Þ þ sd im

(5)

where ln(sim) is the natural logarithm of average flow for month m at reference plant i; ln(sdprecim) is the natural logarithm of average precipitation for month m at reference plant i; DTim is a dummy variable for the DT period; asd, b1sd and bsd 2 are the sd sd estimated regression coefficients, where bsd 1 and (b1 +b2 ) in particular, represents the elasticity of monthly average flow with respect to precipitation in non-DT months and DT months, respectively; and sd im is the error term. The relevant regression coefficients for the analysis are bav 1 and sd sd bav 2 , for mean flow and b1 and b2 , for standard deviation of flow. These represent, respectively, the elasticity of average flow with respect to average precipitation and elasticity of the standard deviation of flow with respect to the standard deviation of precipitation. When a month is in the DT period, its elasticity for av av mean (Eav) is equal to (bav 1 +b2 ), otherwise, it is equal to b1 . The sd same goes for the elasticity for standard deviation (E ). By working with linear rainfall–flow model for elasticities, the seasonal patterns could not be fully captured statistically, raising the need for further development of the rain–flow model. On the other hand, elasticities have the benefit of being a mean to extrapolate the impact of precipitation to hydro plants for which 10 In this period, the soil is still saturating, therefore an increase in rain has a smaller effect on the runoff than in the February–June period, when the soil is already saturated and more water from rain runs straight to river flow.

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historical rainfall data are not available, assuming the impacts of the change in the rainfall regime is the same throughout the basin. Thus, with the relative difference between the historical and projected precipitation average and standard deviation in hand, it is possible to estimate the correspondent effect on flow by means of the estimated elasticity. Formally, for five year periods, (mA2 im , B2 B2 sA2 im ) and (mim , sim ) can be calculated as follows. From 2071 to 2075 for the A2 and B2 scenario: ( " !#) avprecA2ð7175Þ A2ð7175Þ im mim ¼ mi 1 þ Eav 1 (6) avprecim ( A2ð7175Þ im

s

" sd

¼ si

1þ E (

"

mB2ð7175Þ ¼ mi 1 þ Eav im ( B2ð7175Þ im

s

¼ si

" sd

1þ E

!#) sdprecA2ð7175Þ im 1 sdprecim

(7)

!#) avprecB2ð7175Þ im 1 avprecim

(8)

!#) sdprecB2ð71275Þ im 1 sdprecim

(9)

The same was then made for the 2076–2080, 2081–2085, 2086–2090, 2091–2095 and the 2096–2100 period. Finally, A2 B2 B2 applying the calculated (mA2 im , sim ) and (mim , sim ) into Eqs. (2) and (3), we have the projected flow series that incorporate the impact of a new rainfall regime based on the A2 and B2 IPCC scenarios climate projections. The projected series of incoming flow to the 148 hydroelectric facilities of the SIN were used as input to a generation system simulation model, called SUISHI-O, developed by CEPEL (2007). It was used to assess potential variations in the Brazilian hydroelectric system’s firm power11 as a consequence of changes in the rainfall regime, as projected in the A2 and B2 scenarios. The SUISHI-O model is able to simulate the monthly operation of individual hydropower plants in interconnected hydrothermal generation systems. The model calculates the average12 and firm power of a given system configuration (i.e., a set of existing hydropower plants and transmission grid—EPE, 2006), given the monthly inflow series for each hydropower plant. The simulation carried out was ‘‘static’’, in the sense that all variables other than the natural monthly inflows to the reservoirs are kept constant throughout the period of analysis. Thus, given a set of inflow series, the storage capacity and all other technical parameters for each power plant, the SUISHI-O model estimates the highest energy demand (firm power) that could be supplied throughout the period without any deficit. Also, the model provides the energy demand the system would attend assuming the average hydrological condition.

3.2. Results Tables 1 and 2 show the results of the cross-sectional timeseries linear regression model estimated for each basin. The coefficients for Passo Fundo and Salto Santiago were insignificant for both mean and standard deviation, as for the standard deviation coefficient for Tieteˆ Basin. These were, thus, discarded and the historical mean and standard deviation were used instead 11 Firm power or power-producing capacity, in this context, corresponds to the largest energy demand that can be supplied at all times by a system of hydroelectric power plants, given a set of inflow series inputted into the model. 12 Average energy, here, corresponds to the greatest amount of energy that can be produced assuming the average hydrological condition in the set of flow series inputted into the model.

of estimates. The same was done for the basins for which precipitation data were not available. Therefore, not all basins were analyzed due to lack of historical data on precipitation. However, the studied basins represent almost 70% of the SIN’s installed hydropower capacity. This, in turn, represents some 63% of the country’s hydropower generation installed capacity (Table 3). The major Parana´ Basin is very significant to the country’s hydropower generation capacity. Together, the three minor basins (Paranaı´ba, Paranapanema and Grande) plus the Parana´ River accounted for around 42% of the hydropower capacity of the SIN, in 2007. The other analyzed basins account for some 27% of the SIN’s hydroelectric installed capacity and are located in the north–northeast region of Brazil. The estimated mean flow–mean precipitation elasticities range from 0.22 to 0.49 (non-DT period) or from 0.12 to 0.36 (DT period). These results are consistent with the average proportion of water from rain which is evaporated in the Brazilian basins estimated by ANA (2004)—around 63%. Table 3 shows the average difference in annual flow between the two scenarios and the reference projection. This measure shows the general impact on flow in the analyzed period (2071–2100) caused by alterations in the rainfall regime as projected in the two GCC scenarios. It also presents the results of the SUISHI-O model, variation in average energy produced in each of the considered basins, in relation to the reference projections. As a general result, by comparing with the reference case, the flow projections for the two GCC scenarios show a downward trend in the 2071–2100 period. In the B2 scenario, the difference is greater than in the A2 scenario. The greater impact on flow occurs in the north and northeast regions of Brazil (Parnaı´ba, Sa˜o Francisco and Tocantins–Araguaia basins). In the Parana´ Basin (which includes the Parana´ River, Paranaı´ba Basin, Paranapanema Basin and the Grande Basin) in the south–southeast of Brazil, the impacts are not so significant. The Parana´ River, Paranaı´ba Basin, Paranapanema Basin and the Grande Basin—which all belong to the major Basin of Parana´—show similar results. Besides the estimated negative average effect on flow, the seasonal variations in flow tend to be positive in the months when flow is increasing and negative in the months when it is falling. In the B2 scenario, the negative impacts are even greater. If this were the case, these power plants would face an earlier dry period, as well as an earlier start of the humid period. Given the not so relevant net annual results and the favorable seasonal pattern (higher flows in the beginning of the wet season), by adjusting the reservoir management in these existing power plants the estimated effects of GCC would be attenuated. The remaining basins all show an average negative impact on flow, especially the Sa˜o Francisco Basin, where there is an installed hydroelectric capacity of 6.8 GW (ANEEL, 2007). In that case, reservoir management would not be enough to compensate for the losses in the inflows to the hydropower plants. The Tocantins–Araguaia Basin is an important part of the SIN because of its installed capacity and because it tends to a great part of the northeast energy markets. In both scenarios, the seasonal variations tend to lower flows in the rainy season (relatively short, going from February to April) more than in the dry season. The result would be a lower capacity to accumulate water in the rainy season to manage it through the year. The Parnaı´ba Basin, although represents a very small part of the SIN, was analyzed because there was a relatively good availability of data. Still, it is representative of a poorly developed region in Brazil, and so analyzing the impacts of GCC in this basin gives an idea of the vulnerability of this area in terms of its potential energy resources. Like the results found for the

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883

Table 1 Mean monthly flow elasticities. Basin

Reference plants

Grande Marimbondo Furnas Caconde

Paranaı´ba Emborcac- a˜o Corumba´ 1 Sa˜o Sima˜o

Paranapanema

Coefficient t-Statistic Significance

Coefficient t-Statistic Significance

Capivara Chavantes

Coefficient t-Statistic Significance

Treˆs Marias Itaparica

Coefficient t-Statistic Significance

Itaipu´ Ilha Solteira

Coefficient t-Statistic Significance

Tucuruı´ Serra da Mesa

Coefficient t-Statistic Significance

Sa˜o Francisco

Parana´ Rivera

Tocantins–Araguaia

Tieteˆ Treˆs Irma˜os

Coefficient t-Statistic Significance

Boa Esperanc- a

Coefficient t-Statistic Significance

Salto Santiago

Coefficient t-Statistic Significance

Parnaı´ba

Iguac- u´

Uruguai Passo Fundo

Coefficient t-Statistic Significance

aav

bav 1

bav 2

av (bav 1 +b2 )

R2

4.642 31.830

0.369 11.300

0.133 8.490

0.236

0.8547

0.370 8.250

0.160 7.510

0.210

0.7742

0.323 7.080

0.050 5.460

0.273

0.8157

0.363

0.8003

0.276

0.7834

-0.157 -4.010

0.184

0.7267

0.424 3.820

0.072 3.700

0.352

0.7816

0.222 6.990

0.101 3.870

0.121

0.8662

0.210 0.530 –

0.036 1.300

0.245

0.1883

0.069 2.180

0.895

0.5555



5.257 26.430



5.125 23.590



5.492 22.490



7.315 23.280



5.174 20.350



4.936 9.060



5.325 40.000



5.843 2.930



0.205 0.090 –







0.494 8.080



0.388 5.790



0.341 6.230







0.825 1.710 –







0.131 3.940 

0.112 6.180 







–



–Not significant.  Significant at 1%.  Significant at 5%.  Significant at 10%. a Refers to the Parana´ River alone, not the Parana´ Basin.

Tocantins–Araguaia Basin, the projected seasonal impacts of GCC affect the rainy season more than the dry season. The energy results provided by the SUISHI-O model are shown in Table 3. For the purpose of analyzing the effects of GCC, the A2 and B2 scenario projected series were compared to the reference projections, which revealed a system’s firm power fall of 1.58% and 3.15% in the A2 and B2 scenarios, respectively. Regarding average power, in the major Parana´ Basin,13 the difference in energy generation between the A2 and B2 scenarios and the reference projections are all within 2.5%, being the difference greater in the B2 scenario. Apart from the Parana´ River and the Grande Basin, which show a slight increase in energy

13 Which includes the Parana´ River, Paranaı´ba Basin, Paranapanema Basin and the Grande Basin.

production in the A2 scenario, the results show a fall in energy production throughout the analysis period. In the basins located in the northeast and center regions (Parnaı´ba, Sa˜o Francisco and Tocantins–Araguaia), the energy results also show a decrease in generation as the consequence of the lower flow inputs to the hydroelectric plants of these basins. Just as the flow results, the Sa˜o Francisco Basin seemed to be the most affected by GCC. The decrease in energy production would reach more than 7% in the B2 scenario. The projected negative changes in flow based on the GCC scenarios should not have a proportional effect in terms of power generation because the reservoirs act as a buffer, managing the amount of water available for electricity generation. Because good reservoir management can compensate for some loss in flow, the energy results should stay within the range that goes from the projected flow figures and those of the SUISHI-O model. Results of

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Table 2 Standard deviation of monthly flow elasticities. Basin

asd

bsd 1

bsd 2

sd (bsd 1 +b2 )

R2

Coefficient t-Statistic Significance

2.069 6.900

0.780 10.530

0.140 5.940

0.640

0.8296

Coefficient t-Statistic Significance

2.761 7.040

0.580

0.7446

Coefficient t-Statistic Significance

3.457 3.920

0.537

0.4956

Capivara Chavantes

Coefficient t-Statistic Significance

3.812 8.260

0.577

0.7485

Treˆs Marias Itaparica

Coefficient t-Statistic Significance

4.457 7.710

0.729

0.7374

Itaipu´ Ilha Solteira

Coefficient t-Statistic Significance

3.173 7.790

0.452

0.7619

Tucuruı´ Serra da Mesa

Coefficient t-Statistic Significance

3.664 3.664 –

0.472

0.5161

Boa Esperanc- a

Coefficient t-statistic Significance

Salto Santiago

Reference plants

Grande Marimbondo Furnas Caconde

Paranaı´ba Emborcac- a˜o Corumba´ 1 Sa˜o Sima˜o

Paranapanema

Sa˜o Francisco

Parana´ Rivera

Tocantins–Araguaia

Tieteˆ Treˆs Irma˜os

Parnaı´ba

Iguac- u´

Uruguai Passo Fundo





0.734 8.000







0.154 5.100 

0.600 2.940

0.063 2.490

0.744 6.380

0.167 3.390

0.807 5.930

0.078 3.610

0.645 6.740

0.193 3.980

0.571 1.220

0.098 2.410

–



3.037 5.880

0.494 1.980

0.149 3.920

0.344

0.6991

Coefficient t-Statistic Significance

3.088 1.500 –

0.718 1.620

0.061 1.150

0.779

0.3699

Coefficient t-Statistic Significance

0.602 0.320 –

0.061 1.470

0.750

0.3531































–

–

0.689 1.640 –

–

–Not significant.  Significant at 1%.  Significant at 5%.  Significant at 10%. a Refers to the Parana´ River alone, not the Parana´ Basin.

Table 3 Results for hydropower (deviation from the reference projections) and relative participation of each basin in the Brazilian hydropower system. Basin

Average annual flow

Parana´ River Grande Paranaı´ba Paranapanema Parnaı´ba Sa˜o Francisco Tocantins–Araguaia Brazil (SIN) a

Average power

Firm power

%

%

A2 (%)

B2 (%)

A2 (%)

B2 (%)

A2

B2

Brazil

SINa

2.40 1.00 5.90 5.00 10.10 23.40 14.70 8.60

8.20 3.40 5.90 5.70 10.30 26.40 15.80 10.80

0.70 0.10 1.40 1.40 0.80 4.30 0.10 0.70

1.20 0.80 1.90 2.50 0.70 7.70 0.30 2.00

– – – – – – – 1.58%

– – – – – – – 3.15%

15.90 9.20 10.20 3.00 0.30 8.50 15.80 62.80

17.60 10.20 11.30 3.30 0.30 9.40 17.60 69.80

SIN—Sistema Interligado Nacional (Brazilian Interconnected electric power system).

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flow indicate a general negative trend in flow with varying seasonal impacts. However, because of the pluriannual reservoir capacities, the energy generation results do not fall as much in light of the changes induced by a new rainfall regime. On the other hand, given the increasing environmental restrictions to build new large reservoirs in Brazil, one might expect that the remaining exploitable hydroelectric potential would be mostly based on run-of-the-river hydropower plants with smaller reservoirs. Therefore, the ability to compensate for dryer rainfall regimes would be reduced as the energy system expands. In this case, the result on the likely expansion strategy based on run-of-the river hydropower plants would be the full use of the installed capacity only during the rainy season. During the dry season, these plants would need to be complemented by other power plants. The transmission capacity is also an important buffer for variations in the natural incoming flow to hydropower plants reservoirs. The national interconnected grid of the SIN allows the operation of the hydro-thermo power system to compensate for loses in certain areas, by increasing production in other. Finally, the results for energy of the SUISHI-O model indicate the amount of energy the hydroelectric system generates assuming the average hydrological conditions.14 One aspect which deeply influences a hydropower system is the occurrence of extreme climate events, such as droughts and floods. However, when analyzing such long-term projections, the impacts of GCC other than average effects are difficult to predict. As a result, the projected flow series for the reference, A2 and B2 scenario are fairly smooth, for model projections consist of values which tend to stay more close to historical average, with smaller variability, though within a great confidence interval. For this reason, i.e., the homogeneity of the projected series, the SUISHI-O model does not capture impacts related to extreme flow events. In fact, the impacts that GCC could have on the Brazilian hydropower system would come: by changing the average behaviour of flow in the energy producing river basins; or by altering the probability of occurrence of extreme events, which may lead to unforeseen climate conditions that would jeopardise the planned operation of hydropower plants and lead to bad reservoir management. This study focused on the first due to the nature of the climate projections that served as inputs to the analysis. Further analyses that incorporate the second may add to the understanding of the impacts of GCC on the Brazilian hydroelectric generation system.

4. Liquid biofuels production According to the Agriculture Ministry (MAPA, 2006), due to its territorial extension and suitable edaphoclimatic conditions, Brazil currently has 388 million hectares of fertile and potentially highly productive arable land, of which 90 million have not yet been exploited. This makes Brazil one of the most propitious countries to grow biomass both for food and energy purposes. There is also the possibility of increasing agricultural output by increasing the productivity of cattle raising, which currently takes up 220 million hectares in the country (MAPA, 2006), as long as a land-use planning policy is implemented to avoid pressure on the Amazon ecosystem. Therefore, if properly used, this land availability gives the country an advantage in producing biofuels— alcohol and biodiesel for which demand has been growing rapidly in recent years—without having to compete with other crops. 14 Similarly, it indicates the greatest demand the hydroelectric system would be able to attend given the average hydrological condition.

885

For a review of the discussion about the sustainability of ethanol production in Brazil, see Goldemberg et al. (2008). The analysis of the impact on biofuels production in Brazil was made for both ethanol and biodiesel. The ethanol produced in Brazil is derived from sugarcane, which is grown throughout the country, occupying around 6 million hectares in 2006, in which the state of Sa˜o Paulo accounts for 58%15 (IBGE, 2007). According to JBIC (2006), MAPA (2006), the area currently used to grow raw material for production of biodiesel is less than 20,000 hectares, and estimates are that 2.6 million hectares would be needed to meet demand for B5, a mixture of 5% biodiesel with petroleum diesel, which will be compulsory starting in 2013. Although the crops that will receive incentives for production of biodiesel have not yet been defined, JBIC (2006), MAPA (2006) indicate that the Agriculture Ministry, together with Embrapa (the government agricultural research agency) have been considering soybeans, dendeˆ palm nuts, castor beans, sunflower seeds and rapeseed, along with Barbados nuts (Jatropha curcas) and wild radishes (Raphanus sativus L), as possible species to produce biodiesel. This study considered soybeans, dendeˆ nuts, castor beans and sunflower seeds for biodiesel production. 4.1. Methodology This study followed the methodological procedure of recent studies published for the case of coffee, soybeans and corn, which assess the possible variations in land use, productivity, production area and water use for these crops as function of climate changes (Assad et al., 2004; Pinto et al., 2007; Junior et al., 2006). It evaluated the direct effects of GCC on the land availability for bioenergy in Brazil, but did not analyze indirect effects, such as pressure from other (non-energy) crops used to produce energy and the impacts on production cost factors (especially land costs), although they can have great bearing on the problem.16 GCC can heighten the vulnerability of tropical ecosystems, both by increased average temperatures and changes in rainfall patterns, as well as because of more intense occurrence of catastrophic events (such as storms, droughts and flooding). The process of global warming together with increased CO2 levels will directly affect many key factors, such as crop yield, agricultural distribution zones, the incidence of plant pests and diseases and the availability of lands suitable for growing some crops. However, this work presents an estimate of the impacts of GCC on the geographic distribution of sugarcane and oilseed crops, considering only the changes in the temperature range by region. Therefore, it did not consider other variables that can influence the productivity and adaptation of these crops in determined regions, such as atmospheric CO2 concentration (Souza et al., 2008) and alterations in water regimes, as well as the incidence of crop pests and diseases.17 The procedure adopted is to evaluate whether the temperature variation due to future climate change in Brazil negatively affects energy crops. If so, it is possible to affirm, ceteris paribus, that there is a probability of problems in the future supply of energy associated with the affected crop. If not, it does not necessarily mean the energy crop in question is unaffected by GCC, because factors other than temperature can also cause impacts. Given the 15 This number refers to harvest area. In this year, plantation area was, approximately, 7 million hectares. In 2007, the figures increased reaching 6, 7 and 8 million hectares, respectively. 16 Given the complexity and groundbreaking nature of the theme, these warrant separate study. 17 Although these variables can affect crop yields, of the environmental factors, temperature is probably the most significant on crop performance, especially in sugarcane production (Magalha˜es, 1987 apud Marchiori, 2004).

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Table 4 Optimal temperature for oilseed crops. Crop

1C

Castor beans Dendeˆ nuts Soybeans Sunflower seeds

20–28 X24 20–30 8–34

Source: JBIC (2006), MAPA (2006) and EMBRAPA (2008).

GCC temperature changes and the crops’ temperature tolerance, this paper analyzed the possible shifts in the geographic zones for growing energy crops, namely sugarcane and the oilseed crops with greatest potential for production of biofuels. To quantify the possible changes in the growth pattern of some crops the ‘‘degree-days’’ index was used, based on the maximum and minimum annual temperatures, in line with Siqueira et al. (2001). The results presented here, because they do not consider the beneficial effect of higher atmospheric CO2 (see Souza et al., 2008) or the negative effects of insects and plant diseases and those resulting from other climatic factors, may be too pessimistic for some crops, such as sugarcane and soybeans, and too optimistic for others. Given the projections for maximum and minimum temperature for 2080, 2090 and 2100, it was compared whether this temperature range is within the limits tolerated by sugarcane and the oilseed crops, to find out which currently productive areas might become unproductive because of GCC. Sugarcane generally tolerates high temperatures, as long as there is ample soil moisture, through irrigation or rainfall. According to Marchiori (2004), the temperature for maximum growth of the plant is between 30 and 34 1C. Below 25 and above 35 1C the growth is very slow, and virtually stops below 21 and above 38 1C. Temperature fluctuations associated with periods of moderate drought are essential for there to be greater sugar content in the stalks. So, the interval of 21–38 1C was used as optimal for sugarcane, and compared it to the minimum and maximum temperatures for 2080, 2090 and 2100 for the main producing regions and those with potential for expansion. The optimal temperatures to produce castor beans, dendeˆ nuts, soybeans and sunflower seeds range from 8 to 34 1C, as shown in Table 4 (JBIC, 2006; MAPA, 2006; EMBRAPA, 2008; Monteiro, 2007). From the optimal temperature ranges for each culture and the maximum and minimum temperature estimates for 2080, 2090 and 2100 under scenarios A2 and B2 from Ambrizzi et al. (2007) and Marengo et al. (2007), this study analyzed the impact of temperature variations on the growing zones of these crops, considering the main current producing regions and those with high potential to grow these plants.

Without considering the hydrolysis technology, industrial productivity will increase 57%, rising from 74 l/t in 2005 to 117 l/t in 2030. Assuming that 50% of the cane goes to make alcohol and 50% for sugar that year, alcohol output will climb from 16 billion to 66 billion liters in 2030 EPE (2006). In 2030, estimates are that 11% of all the bagasse and litter from cane will be used to produce alcohol, according to the projections of EPE (2006). Thus, according to the productivity estimates of 91.1 l/t, the output of alcohol from hydrolysis can reach 29.1 billion liters in 2030, resulting in a total fuel supply of 95.7 billion liters. Climate is the factor with greatest influence on sugarcane productivity. Sugarcane generally tolerates high temperatures, as long as there is ample soil moisture, through irrigation or rainfall (Marchiori, 2004). Table 5 shows the ideal temperature for sugarcane and the maximum and minimum temperatures19 projected for 2080, 2090 and 2100 (Ambrizzi et al., 2007; Marengo et al., 2007). The highlighted regions had output greater than 6 million tons in 2006, according to IBGE (2007). Our findings show that the country’s main producing regions will continue to be within the temperature limits for sugarcane. Only some states, such as Para´, Piauı´ and Tocantins, will fall outside the optimal interval. Although these states are not presently large producers, they make up the possible areas of expansion forecasts in EPE (2006). On the other hand, since the crop is grown in all the country’s regions, even if cultivation becomes unfeasible in some of these regions because of GCC, other regions can take up the slack, especially the midwest, by continuing to have a temperature range favorable to sugarcane, along with large expanses of available land. Thus, it is possible there will be shifts in the geographic distribution, with some regions becoming climatically unfavorable to growing sugarcane, but others becoming more favorable. This leads to the conclusion that even with these possible modifications in crop distribution, GCC will not significantly affect negatively the production of sugarcane ethanol in Brazil.

4.2.1. Results for sugarcane According to the projections for sugarcane production contained in EPE (2006), the area planted with sugarcane should rise 148% over the 2005–2030 period, reaching 13.9 million hectares18 and crop yield should increase by 7%, from 77 to 82 t/ha. As a result, output will expand 161%, reaching 1.14 billion tons in 2030.

4.2.2. Results for biodiesel There are various species of oilseed plants grown in the country that have potential as raw material to produce biodiesel. The standouts are soybeans, whose oil represents 90% of Brazil’s vegetable oil output, dendeˆ palm nuts and sunflower seeds, for their oil yield, and castor beans, for the plant’s resistance to drought. According to the results, the northeast and midwest regions should see a substantial increase in temperature, which can affect their capacity to produce soya. A comparison of these temperatures with the ideal for the crop shows that soybean output can fall or even become unfeasible in these regions due to the great temperature variations. On the other hand, the temperature range for growing soya in the south region is expected to improve, which can offset the negative effect of climate changes in the northeast and midwest, as pointed out by Siqueira et al. (2001). According to those authors, the projections for soybean production in the country, in a scenario of climate changes, are on the whole favorable, with estimates of growth of output of around 3.5 million tons yearly. The greatest projected impacts will be in the mid-south and south. The forecast production increases are between 5% and 34% (21% on average). Additionally, the CO2

18 It is unclear whether the projections made by EPE were based on harvest area or planted area. In this study, it was considered as harvest area, since it matches the number presented for 2005 by IBGE (2007). However, the EPE’s projection is probably underestimated, since their projection for 2010 was already achieved in 2007.

19 The climate projection data did only included average temperatures. Therefore, the maximum, and minimum temperatures were obtained based on the historical temperature standard deviation for each region (br.weather.com, 2008). This is a conservative assumption, since one would expect climate changes to increase the temperature variability.

4.2. Results

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Table 5 Ideal temperature for sugarcane and maximum and minimum temperatures projected for 2080, 2090 and 2100. Sugarcane

Scenario A2

State

Capital

AL

Maceio´

AM

Manaus

BA

Salvador

CE

Fortaleza

ES

Vito´ria

GO

ˆ nia Goia

MA

Sa˜o Luı´s

MG

Belo Horizonte

MS

Campo Grande

MT

Cuiaba´

PA

Bele´m

PB

Joa˜o Pessoa

PE

Recife

PI

Teresina

PR

Curitiba

RJ

Rio de Janeiro

RN

Natal

RS

Porto Alegre

SC

Floriano´polis

SE

Aracaju

SP

˜ o Paulo Sa

TO

Palmas

Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum

Scenario B2

Ideal

2007

2080

2090

2100

2080

2090

2100

38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00 38.00 21.00

28.67 21.33 29.00 22.92 27.92 22.58 29.58 23.08 28.25 20.92 29.67 17.25 30.17 22.92 26.67 16.33 28.58 17.58 32.33 20.50 30.00 19.92 29.08 21.92 28.92 21.58 32.83 21.83 22.42 12.25 27.25 20.92 29.92 21.58 24.58 15.50 23.92 17.33 28.08 22.75 24.75 15.17 32.58 21.17

32.23 24.90 31.78 25.69 30.76 25.43 34.18 27.68 28.23 20.90 34.09 21.67 35.32 28.07 30.87 20.54 33.79 22.79 35.85 24.02 36.20 26.12 32.63 25.47 32.61 25.28 37.87 26.87 29.46 19.29 27.07 20.74 35.80 27.47 27.02 17.94 24.52 17.94 30.34 25.01 30.42 20.84 37.40 25.98

31.83 24.50 32.94 26.86 30.97 25.63 34.75 28.25 28.75 21.41 34.82 22.40 36.28 29.03 31.53 21.20 35.08 24.08 37.04 25.21 37.61 27.53 32.78 25.62 32.61 25.27 38.84 27.84 30.71 20.54 27.55 21.21 35.99 27.66 28.15 19.07 25.65 19.07 29.87 24.53 31.20 21.61 38.26 26.84

32.87 25.53 34.80 28.72 31.76 26.43 35.28 28.78 30.24 22.90 36.46 24.04 37.17 29.92 33.43 23.10 36.91 25.91 38.64 26.81 39.31 29.23 33.47 26.30 33.46 26.13 39.49 28.49 31.82 21.66 29.26 22.93 36.43 28.09 28.35 19.26 26.24 19.66 30.90 25.57 33.09 23.51 39.49 28.07

31.74 24.41 30.58 24.49 29.14 23.81 33.09 26.59 27.08 19.75 32.88 20.46 33.72 26.47 29.83 19.49 33.31 22.31 34.77 22.94 34.72 24.64 31.81 24.64 31.80 24.47 36.10 25.10 29.61 19.44 25.49 19.16 34.87 26.53 26.65 17.56 24.58 18.00 29.09 23.76 30.08 20.50 35.18 23.77

32.68 25.35 32.77 26.68 31.34 26.01 35.29 28.79 29.04 21.70 35.24 22.82 36.48 29.23 31.77 21.44 35.26 24.26 37.07 25.24 37.52 27.43 33.58 26.42 33.56 26.23 38.82 27.82 31.45 21.28 26.33 19.99 36.74 28.41 28.56 19.48 26.88 20.29 30.09 24.76 31.55 21.96 37.96 26.54

33.75 26.42 34.52 28.44 32.13 26.80 35.95 29.45 30.84 23.50 36.89 24.47 37.45 30.20 33.75 23.42 37.12 26.12 38.67 26.83 39.41 29.32 34.30 27.14 34.34 27.01 39.57 28.57 32.50 22.34 28.15 21.82 37.14 28.81 28.70 19.61 27.42 20.83 31.20 25.87 33.40 23.82 39.30 27.89

Source: Prepared by the authors from data obtained from weather.com (2008), Ambrizzi et al. (2007) and Marengo et al. (2007).

fertilizer effect on soybeans can increase the crop yield, reducing the negative effects of temperature, as found by Siqueira et al. (2001) in their simulations. In the case of dendeˆ, the states of Para´ and Bahia have the best conditions for production of dendeˆ. Our findings indicate that the expected temperature increases will not make these areas unsuitable for growing dendeˆ palms, since the plant can withstand temperatures above 24 1C. Sunflowers, now cultivated mostly in states in the mid-south of the country, should not be affected overall by the predicted temperature rise. Some states, however, such as Mato Grosso do Sul, Mato Grosso, Piauı´ and Tocantins, may become unsuitable for growing them, while the south region can become attractive. Castor beans possibly will be the culture most affected by GCC, since it is concentrated in the northeast region, where the worst climatic scenarios are foreseen, with substantial increases in temperature and drought, according to the IPCC scenarios. No current or potential production region has a temperature range within the ideal interval for producing castor beans. This will particularly affect small farmers, and can even make the social

program to encourage small-scale production and use of biofuels unworkable. In sum, the results indicate that the production of biodiesels in the country can be affected negatively by GCC, mainly in the northeast, with a shift of suitable growing zones for oilseed crops to the south region. However, not all the crops considered here are adaptable to the edaphoclimatic conditions in that region, which can reduce the output of biodiesel in the country. This concentration of production in the south can also, in principle, reduce the positive social effects pursued under the National Biodiesel Program,20 since the north and northeast regions concentrate most small and poor family farmers. Besides this, the concentration of production in the south can cause land-use conflicts between energy and non-energy crops,

20 The stated objectives of the National Biodiesel Program are to implement the production and use of biodiesel in sustainable form, from both a technical and economic standpoint, with focus on social inclusion and regional development, through generation of employment and income, principally in the north, northeast and midwest regions.

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since this region also has the best climate conditions for many of the latter crops, something that can become more pronounced under a scenario of GCC. Discussing this issue, however, is outside the scope of this work. Finally, this simulation does not include the analysis of other factors that can influence the potential for biofuels production in the country and can, to some extent, compensate for eventual losses due to GCC. For example: increasing the crop productivity,21 genetic developments that can improve the specie’s resistance to higher levels of temperature and humidity22 and technological advances, such as the production of ethanol from lignocellulosic biomass.23

5. Concluding remarks Given the reliance of the Brazilian energy sector on renewable sources and the dependence of these on climate conditions, this paper endeavored to analyze the vulnerabilities of renewable energy production associated with GCC. However, in light of the uncertainties related to the GCC models and scenarios, the findings of this study should be viewed as a possibility rather than as a future projection. Indeed, the results of this study depend fundamentally on the quality of the climate projections on which it is based. Probably, the most important results portrayed here are the trends and directions observed for the vulnerabilities of renewable energy production in Brazil to GCC rather than the quantitative figures presented in the study. In fact, studies that investigate possible effects of GCC on the energy sector are extremely important to assess the adaptation capacity to possible, although not yet certain, impacts on the production of renewable energy. In this case, an important result of the study is the increasing vulnerability of the poorest regions of Brazil to GCC (mainly the northeast region). Both biofuels production (biodiesel) and electricity generation (hydropower) may suffer from changes in the climate of these regions. The most impacted within the analyzed basins is the Sa˜o Francisco Basin and the production of biodiesel would be mainly negatively affected in the northeast, with a shift of suitable growing zones for oilseed crops to the south region. These impacts would negatively affect the Brazilian economy in one of its most sensitive areas. The greatest uncertainties in this study are the projections of the global climate model. In fact, different global models have distinguished climate projections, especially in the Amazon and the northeast region where the impacts are most accentuated. There are no consensual projections among different global models to the future climate (INPE, 2007). The climate projections are, indeed, the ultimate driving force behind all the results found in this study. It is worth stressing that this study is a first attempt to quantify a very complex issue. Several assumptions and simplifications had to be made. For instance, the possible land competition between energy and non-energy crops was not considered in this work, but it can happen because of the reduction of areas suitable for growing certain crops. Moreover, the cost of land can rise, and 21 For instance, agricultural productivity has increased by 33% in the State of Sa˜o Paulo since 1974, when the Proalcool started, mainly due to improved agricultural practices and the development if new species (Goldemberg et al., 2008). Assuming other States can raise their productivity to the high levels of Sa˜o Paulo, the potential for biofuels production in Brazil could be greatly expanded. 22 As stressed by Goldemberg et al. (2008), genetic improvements allow cultures to be more resistant, more productive and better adapted to different conditions. This can become an important adaptation strategy to GCC in Brazil. 23 For a review of the biological and thermochemical methods that could be used to produce ethanol from lignocellulosic biomass, see Balat et al. (2008).

with this the cost of agricultural production. Similarly, the same can take place in the case of competition for water resources (competition between water for power generation and irrigation for agriculture, for instance). Nevertheless, this study concluded that the supply of some renewable energy in Brazil is vulnerable to GCC, contributing to the understanding of what are the vulnerabilities and uncertainties to which the Brazilian energy system is exposed in a GCC scenario. Therefore, it should be emphasized what is the result of the climate projections and what is the result of the modeling tools used in this study, so as to understand the real impacts of climate change on the energy production in Brazil. For example, in the hydropower generation model, the discrepancy between the flow and the energy results could be better understood by further developing the link between these two stages in the models. In the biofuels model, other factors that can influence the development of the considered cultures were not taken into account here, such as genetic innovations, changes in rainfall patterns, irrigation techniques, increased CO2 concentration and the incidence of crop pests and disease along with temperature changes. Finally, maybe the most significant vulnerability identified in this study is the poor availability of meteorological historical data in Brazil. This is especially crucial for rainfall data. If Brazil, as well as other countries, wishes to be better prepared to face GCC, it must improve the understanding of the present climate, especially through information gathering.

Acknowledgements The future climate scenarios used in this study derived mainly from the results of the project: Caracterizac- ˜ ao do Clima Atual e ´ticas para o Territo ´rio Brasileiro ao Definic- ˜ ao das Alterac- ˜ oes Clima ´c XXI, supported by PROBIO (Projeto de Conservac- a˜o e Longo do Se Utilizac- a˜o Sustenta´vel da Diversidade Biolo´gica Brasileira); MMA; BIRD; GEF; CNPq and the United Kingdom (Global Opportunity Fund—GOF) through the project Using Regional Climate Change Scenarios for Studies on Vulnerability and Adaptation in Brazil and South America. We thank Jose A. Marengo, Lincoln Alves, Roger Torres and Daniel C. Santos for the assistance in obtaining and interpreting the regional scenarios produced by INPE. We also thank the CNPq for part of the financial support for his study, and: Luiz Fernando Loureiro Legey; Roberto Arau´jo; Ricardo Dutra; Felipe Mendes Cronemberger and Thaı´s de Moraes Mattos for their help.

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