Alternative energy strategies for abatement of carbon emissions in ...

Energy Policy 1994 22 (11) 914-924

Alternative energy strategies for abatement of carbon emissions in Brazil A cost-benefit analysis E L La Rovere, L F L Legey and J D G Miguez

This paper summarizes the main results of a study on the costs of abatement of CO 2 emissions in Brazil. It discusses three possible futures for the long run (2010 and 2025) activity of the Brazilian economy and - with the help of a linear programming model for Brazil's energy sector - three scenarios for energy production and use. One of these scenarios illustrates the possibility of halving future carbon emissions originating fi'om energy generation and consumption, with relatively small increases in energy associated costs and investments. This abatement scenario would require, on the supply side of the Brazilian energy balance, increased amounts of hydropower, ethanol and bagasse fi'om sugarcane, plus wood and charcoal fi'om reforestation programs, as well as so'ong measures to promote energy conservation. Keywords: Long-run energy planning; Emissions; CO_~ abatement costs

Discussion about the greenhouse effect has been under way for a long time now. Different studies have used several different approaches to the subject. From physical and meteorological aspects to political issues, papers have been written and controversy has arisen. The extent of the greenhouse effect and the importance of its consequences are still matter of scientific dispute. This paper contributes to the discussion by presenting a study on alternative energy strategies for abatement of carbon emissions in Brazil. The importance of such a study is twofold: it can help in understanding the magnitude of greenhouse gas emissions in Brazil; and it can provide a technical basis for Brazil's viewpoint, along those of other developing countries, in the international

The authors are with the Energy Planning Program, COPPE/UFRJ, Federal University of Rio de Janeiro, Centro de Tecnologia, Bloco C, sala 21 I, C P 68565, Rio de Janeiro, RJ 21945-970, Brazil.

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debate over the causes and responsibilities involved in greenhouse gas emissions. Having within its boundaries an area over 5 million km 2 of tropical rain forests prompts Brazil to play an important role in the international forum on policies to deal with the greenhouse effect. One important issue is the question of the compensating countries that have large rain forests within their boundaries for the 'production of clean air'.l If these countries really contribute to the quality of the air people breathe in different parts of the world, then it is only fair that they receive some form of compensation - not necessarily cash, but perhaps educational grants or technological transfers - for this, just like any other commodity producer. However, that is not the only way in which Brazil is involved in the greenhouse issue. It has important experiences to share like, for example, the PRO-ALCOOL program for the use of ethanol instead of gasoline in automotive vehicles. This program is the only major effort in the world to substitute a fossil fuel with a renewable fuel. The strategic decisions on future energy and environment policies that Brazil faces indicate the appropriateness of a study such as this. Among others, these decisions include: the continuation of the PRO,/~LCOOL, in a scenario of low petroleum prices; the conservation of the Amazon tropical rain forest vis-gt-vis sustainable use of its economic potential; the utilization of biomass and related reforestation programs as a renewable energy source; a n d the implementation of programs for the rational use of energy. In respect of the Brazilian rain forests, it is urgent to find ways to preserve them, since deforestation caused essentially by the appropriation of land for agricultural purposes - is currently the main source of carbon emissions in Brazil. 2 Despite the difficulty of quantifying emissions given the lack of reliable data, deforestation related 1991 CO 2 emissions are estimated 3

0301-4215/94/11 0914- I 1 © 1994 Butterworth-Heinemann Ltd

Abating CO: emissions in Brazil: E L La Rovere et al

to be in the range of 150-220 Mt of carbon (2.2-3.1% of world emissions). Even if the annual pace of deforestation was cut by half between 1988 and 1991, the total deforested area originated by the burning of trees (queimadas) had already reached 42 635 hectares in 1991 (8.7% of the area of the Amazon region). But a strong increase in annual CO 2 emissions from deforestation is unlikely as the main governmental incentives (tax credits) for deforestation have been cut and the opposite policy of curbing the burning of the Amazon forest has now become a main source of governmental concern. On the other hand, even though CO 2 emissions from energy consumption 4 in Brazil are still relatively low, with estimates 5 in the range of 73 Mt of carbon per year in 1990, they are bound to increase and overtake in the medium term the present level of emissions from deforestation, as will be shown in this study. Adopting the correct approach towards the use of natural resources is of prime importance to the Brazilian economy, especially after the economic decline of the 1980s. To understand the magnitude of the problems faced by the Brazilian economy, we need look at the country's GDP average growth rate from 1980 to 1992, which hovered around 1.2% per year. 6 In terms of GDP per capita the situation is even more dramatic: the average growth rate is negative, ie it decreased by 0.3% a year in that period, to US$2456, in 1992. In addition, inflation rates skyrocketed, to a figure of around 35% a month, in the second semester of 1993. In one sense, a study such as this goes in the opposite direction from the free market approach, because it tries to resuscitate the idea of long-range planning. In other words, it stems from the conviction that rather than leaving the decisions about society's future to market forces, an effort should be made to forecast and then bring about some (of the more desirable) possible future outcomes. Of course, even market liberals could profit from the conclusions of the study, for example by using them as a basis for regulatory action. But, especially in a country like Brazil, where there are so many potential opportunities but, at the same time, so many threats, a look into the future is essential for positive action. Again, what is important is the revival of long-range planning, in order to plan the future with more precision. Only then will it be possible to reinforce the positive aspects of Brazilian society and minimize its shortcomings.

Methodology One of the most important aspects in the estimation of CO 2 emissions is to define the time horizon for the estimates, particularly if a normative approach is being used. In this situation, if we use a short time horizon, the time span for the implementation of the proposed mea-

Energy Policy 1994 Volume 22 Number 11

sures can be too short for their effect to be felt. On the other hand, if we look at things in a very long run, the difficulties of analyzing the future technological possibilities increase enormously. In the present paper, the time horizons used have simply followed the orientation of the group for energy studies at the Lawrence Berkeley Laboratory and the UNEP Centre at RisO National Laboratory, which have coordinated similar exercises to that presented here for other countries. The rationale for the choice of these particular time horizons was the possibility of comparing different countries with different databases. Of course, predicting what kind of country Brazil will be in the year 2025 is not easy. But then to predict the future is almost always very difficult, especially if we are talking about developing countries, where there are hardly any stable trends. To deal with this problem, international comparisons of GDP per capita were used to estimate Brazil's GDP for the year 2025. Subsequently, to find out whether it would be possible to reach the imputed GDP growth in a balanced way, the product growth of different sectors was computed by means of a model of the Brazilian economy. The purpose of this exercise was to find out which sector's growth pattern would be compatible with the overall growth of the economy. With one sector's growth available it was possible to compute - by using the energy intensities specific to each sector - the energy demand of several sectors of the economy. The methodology used was based on a technological options model - TOM - for the Brazilian energy sector. This model is in the tradition of the MARKAL model 7 for studying energy production and use, and therefore employs a linear programming type of approach. In fact, TOM is a simplified version of a MARKAL model developed for Brazil during the early 1980s. As well as TOM a model of the Brazilian economy was used, as explained in the previous paragraph, to compute different economic pictures and energy requirements for the years 2010 and 2025. 8

Technological and low carbon scenarios In terms of technological possibilities, two scenarios were constructed. The first assumes that there will be no efficiency improvements in the energy sector in Brazil, ie that the structure of the Brazilian economy for energy generation and use in 2025 (and 2010) will be the same as in 1985 - the year used as a reference for CO 2 emissions computations. The second scenario incorporates technological improvements in sectors for which available data or studies indicate how those improvements would translate into higher efficiency energy generation and use. In particular, the following possibilities were

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Abating CO2 emissions in Brazil: E L La Rovere et al Table 1. Population growth in Brazil.

1940 1990 1991 1992/1995 1996/2000 2001/2010 2011/2025 1991/2025

Population at end of period (000)

Rate of increase per year (%)

41 000 143 700 146 917 156 349 168 681 193 268 232 855 232 855

2.54 2.24 1.70 1.41 1.33 1.25 1.38

studied: the introduction of electricity saving equipment; the use of different technological methods in the steel industry; and the large-scale implementation of distillery cogeneration in the sugar and alcohol industry. In addition to two technological scenarios, a third was constructed by imposing a feasible least bound on CO 2 emissions. This bound was found by gradually reducing the level of the emissions, up to the point where the model became infeasible.

The Brazilian economy in 2025 As already mentioned in the introduction, possible future paths for the Brazilian economy were examined by comparing the present GDP per capita of different countries and by choosing two of them as proxies for Brazil's GDP per capita in 2025. For a low growth scenario, Spain's 1990 GDP per capita was chosen, while for the high growth scenario, Japan's 1990 GDP per capita was the choice. Rigorously speaking, there is no good reason why these two countries should be chosen, except that they provide a fair range of possible futures, which are compatible with the past performance of the Brazilian economy. Of course, since we are speaking in terms of GDP per capita, a projection of the Brazilian population is also

needed. The population growth rate used in these projections is the result of a demographic model 9 which forecasts a reduction of the rate of population growth in Brazil. For the period 1991-2025, the model indicates a yearly average population growth of 1.38%, while the labor force increases at an average rate of 1.63% per year. Table 1 shows the total population average growth rate for different periods. In order to project the economic sectoral growth that is consistent with the imputed overall growth of the economy and, at the same time, be able to compute future energy demands of the different sectors - by means of energy intensities specific to each sector - a macroeconomics consistency model of the Brazilian economy 1° was used. This model is based on Brazil's input-output matrix and computes the sectors' growth, given the anticipated GDP growth and the income distribution of the Brazilian population. Since the feasible range of GDP growth rates depends, among other things, on the savings rate and on capital productivity, it was necessary to adjust those parameters in the model, so as to achieve the imputed GDP growth. In addition to these adjustments, the model makes the following assumptions: (i) the growth of the productive capital stock is the result of society's capacity to save and of the efficiency of the transformation of savings into production capacity; (ii) society's propensity to save depends on the functional income distribution among households, profits, wages and government, as well as the existence of long-term mechanisms to induce voluntary household savings and, as already mentioned, the income distribution among households. As can be seen from Table 2, Brazil has the worst income distribution among the listed countries. The

Table 2. Some social indicators for selected countries.

Brazil Spain Korea Japan Mexico Turkey Argentina Colombia Iraq Iran

Life expectancy a

Gini coefficient

Relation 20% b

68.60 77.00 70.10 78.60 69.70 65.10 71.00 68.80 65.00 66.20

0.57

26.1 5.8 6.6 4.3

0.36 0.50 0.51 0.45

13.3

0.46

Average education ¢ 3.3 5.9 10.4 4.0 2.8 6.0 5.2 4.0 3.5

Expenses/GDP Social d 5.8 9.2 3.4 13.3 4.5 3.3 4.9 3.6 4.5 6.0

Military 0.9 2.3 5.2 1.0 0.6 4.9 1.5 1.0 32.0 20.0

Source: Human Development Report, Oxford University Press, 1991. aNewbom life expectancy in 1988. bRelationship between the richest 20% income and the poorest 20% of the population. CAverage number of years of education. aPublic expenditure on health and education.

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Abating CO: emissions in Brazil: E L La Rovere et al

reasons are quite complex and depend on many factors, such as education, which can only be altered in the long run. Therefore, to run the model, two 'social development' futures were used: the first assumes that the present inequality will remain, while the second considers an improvement in the income distribution to the level of 1990 Colombia. The low economic growth future - which uses the 1990 GDP per capita of Spain as a reference - corresponds to an average per capita growth of the economy of 1.7% a year, while the high growth future - which takes Japan's 1990 GDP per capita as a proxy - implies an average per capita growth of the economy of 3.4%. In order to allow for comparisons among several possible proxy countries, Table 3 shows a sample of economic and demographic indicators for selected countries.

The results of different runs of the model are summarized in Table 4, in which future A corresponds to 1990 Japan, with Brazil's 1990 income distribution; future B to 1990 Spain and Brazil's 1990 income distribution; and future C to 1990 Japan and 1990 Colombia's income distribution. It is important to observe that the main difference between futures A and C lies in the output of those sectors which are most influenced by an improvement in the income of the poorer classes. In other words, when the income distribution is more balanced, there are sectors which have to provide higher output for an increased lower-income class consumption. As can be seen in Table 4, this is exactly what happens with the sectors foodstuff and beverages and agribusiness, which show higher output in future C than in future A.

Table 3. Some economic and demographic indicators for selected countries. Economic indicators GDP Growth rates per capita 1985-88 a 1965/80 Brazil Spain Korea Japan Mexico Turkey Argentina Colombia Iraq lran

4620 8250 5680 13650 5320 3900 4360 3810 3510 3560

1980/88

6.3 4.1 7.3 5.1 3.6 3.6 1.7 3.7 0.6 2.9

1.2 4.7 7.7 4.7 - 1.4 3.0 - 1.6 1.2

Demographic indicators Inhabitants Growth rates 1990 (millions) 1960/90 144.5 (2) 39.2 42.8 123.5 88.6 55.9 32.3 33 18.9 54.6

1990/99

2.5 0.8 1.8 0.9 2.9 2.4 1.5 2.5 3.4 3.1

0.8 0.4 0.8 0.4 1.9 1.8 1.2 1.8 3.4 2.3

Source: Human Development Report, Oxford University Press, 1991 aConverted to 1988 US dollars using the purchasing power parity, according to the source's own methodology. t'Source: Energy Planning Program, COPPE/UFRJ.

Table 4. Output of different sectors of the Brazilian economy. Sector's output (in billions of 1990 US$ billion) Sector

2025 1985 a

Future A

Future B

Future C

Steel industry Other metallurgy Mining Non-metallic Chemistry Foodstuff and beverages Textile Paper and pulp Agribusiness Transport Commercial and public Energy Other

3.4 6.8 3.6 4.4 11.1 10.7 10.0 3.3 36.7 12. l 159.6 13.1 52.3

22.6 47.6 16.9 27.7 46.7 31.5 59.7 16.8 115.9 64.7 1003.1 95.0 325.6

13.4 27.2 10.7 14.7 26.2 23.3 37.9 10.8 80.8 37.8 604.9 63.6 173.2

22.2 46.6 16.6 27.0 45.8 32.1 58.8 16.6 I 17.4 64.4 977.9 94.3 316.4

Total

327.1

1873.8

1124.6

1836.2

Source." Brazilian Energy Balance and Energy Planning Program, COPPE/UFRJ (computed by using 1988 exchange rate parity and adjusting for real GDP growth and 1990 US dollars).

Energy Policy 1994 Volume 22 Number I 1

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Abating CO 2 emissions in Brazil: E L La Rovere et al Table 5. Energy intensities (sector's useful energy per output). Sector

Average intensity (toe/US$ thousand)

Steel industry Other metallurgy Mining Non-metallic Chemistry Foodstuffand beverages Textile Paper and pulp Agribusiness Transporta Commercial and public Other

3.52880 0.32530 0.17672 0.43719 0.31473 0.61059 0.10749 0.91502 0.06745 0.03267 0.01275 0.04106

aUseful energy/GDP.

Energy demand The methodology used to estimate energy demand followed the criteria put forward by the interministry group which produced a reexamination of the Brazilian energy matrix. II These criteria define energy intensities in terms of useful energy per output for each economic sector, with the exception of the transport (useful energy/GDP) and residential (useful energy per capita) sectors. By multiplying those intensities (shown in Table 5) by the value of the sector's output shown in Table 5, it was possible to obtain the useful energy demand in each sector, measured in tonnes of oil equivalent. The specific uses of energy were estimated from the useful energy demand. However, in some instances, when a particular energy source cannot be substituted by another, or when it would not be advisable to do so - as in the case, for example, of the electricity use in lighting, electric motors and electrochemistry, or the coke and charcoal used for ore reduction in steel mills - the energy demand was computed in terms of the final energy required. On the other hand, when competition among several technologies is allowed, energy demand is presented in terms of useful energy. However, in order to facilitate the modeling process, this useful energy is computed as the total of the final energy equivalent of a form of energy used as reference. With this simplification all forms of energy demand are presented to the model as final energy demand. Hence, in the case of industrial heat, for example, fuel oil was used as the energy equivalent, meaning that if all industrial heat were generated with the efficiency of boilers or furnaces fired with fuel oil, the energy demand would be presented in terms of the final energy of the fuel oil equivalent. Similarly, diesel and gasoline were used as the reference fuel for freight/bus transport and automobiles respectively. The efficiencies of competing equipment are presented in

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terms of the equipment which employs the reference energy form. Table 6 shows the energy demands used in the model, as well as those sectors in which the possibility of substitution among different energy forms was analyzed. Since the purpose of the exercise presented in this paper is to assess abatement costs of CO 2 emissions, the future demanding a larger quantity of energy - scenario A - was chosen as a reference. This future will therefore be used in the discussion that follows. According to the logic of each technological and emissions' reduction scenario, the energy demand for different sectors was treated differently. Hence, as already mentioned, when it was necessary to analyze possible substitution among distinct sources of energy, useful energy - in terms of an equivalent energy form was used. On the other hand, when no competition was allowed, the final energy requirement of the chosen energy source was used. The sections below discuss in more detail the assumptions made for each sector appearing in the disaggregation chosen to represent the energy demand of the Brazilian economy.

Steel industry

Scenario SC01 (current trends) maintained the present production structure in the steel industry: coal based integrated steel plants with a share of 66%, integrated charcoal plants with a 24% share and 10% from scrap iron electric steel mills. In scenario SC02 (efficiency) and, consequently, in scenario SC03 (low carbon), which is a derivation of scenario SC02 - it was assumed that the prevailing technology in future steel plants will be based on the energy source presenting the least cost, which is defined in terms of the cost of the whole productive chain. In addition, the computation of total energy consumption on a given steel plant considers that the energy intensity in each plant sector (ore reduction, lamination, steel mill etc) is equal to that of the most efficient similar activity performed in a plant presently in operation. Hence, there is here an implicit efficiency improvement assumption for future steel plants.

Other industries

To simplify the analysis, all industries but steel were aggregated in one block. However, the energy demand was disaggregated in different uses: heat, electricity and other specific uses. When there is no possible substitution between energy sources for a particular use, such as electricity in lighting and in electric motors, the demand was presented in final energy terms. When substitution is allowed, as in the case of process heat and


Abating CO 2 emissions in Brazil: E L La Rovere et al T a b l e 6. Projected energy d e m a n d in 2025 (in ktoe). Sector

ISX IOE IEE IOC REX REE CPX CPE AGE AGMAGC TAMTTMTOMTVM TLM TPM TXXNEX -

Energy type e

steel industry mix other industry specific uses other industry electric usesa other industry heat h residential mix residentialelectric uses~ public and commercial mix public and commercial electric usesa agricultural electricity usesa agricultural motor fuelc agricultural heat b road transport individual motor fueld road transport taxi motor fuel ~ city bus transport motor fuelc inter city bus transport motor fuel short distance freight transport motor fuelc long distance freight transport motor fuel other transport mix non-energy uses Mix

Useful Final Final Useful Final Final Final Final Final Useful Useful Useful Useful Useful Final Useful Final Final Final

Total

Future A

62.107 13.982 112.489 77.323 18.485 30.323 3.84 70.676 5.678 10.474 5.38 14.532 1.982 3.047 5.803 4.062 46.428 20.389 51.86 558.86

Future B

Future C

36.7 8.386 65.064 44.966 18.485 30.323 2.302 42.363 3.932 7.253 3.725 8.67 1.182 1.818 3.463 2.424 27.7 12.165 30.837

60.695 13.675 109.692 75.522 18.485 30.323 3.724 68.56 5.715 10.544 5.416 14.156 1.93 2.968 5.653 3.957 45.227 19.862 50.614

351.758

546.718

~0.29 toe/MWh; bfuel oil equivalent; Cdiesel equivalent; dgasoline equivalent; ewhen the energy type is useful, substitution among different forms of energy is allowed. Otherwise, the type of energy isfinal.

industrial direct heating, the d e m a n d was expressed in terms o f useful energy, c o m p u t e d in fuel oil e q u i v a l e n t by w e i g h t i n g the efficiencies o f using this fuel in the various e q u i p m e n t e m p l o y e d in different industrial sectors.

oil. Finally, the useful energy d e m a n d for generating heat for grain drying and other uses was expressed in terms o f fuel oil equivalent.

Transport Residential, commercial and public sectors In scenario SC01, the d e m a n d was presented in terms of final energy, reproducing the present structure o f energy use in those sectors in Brazil. In scenarios S C 0 2 and SC03, a penetration o f m o r e efficient electrical equipm e n t was a l l o w e d ie the m o d e l could c h o o s e b e t w e e n using m o r e efficient - but m o r e e x p e n s i v e - e q u i p m e n t and cheaper devices, which e x p e n d m o r e energy. Data on costs o f saving electricity were obtained in Geller. 12 B e c a u s e o f the lack o f information, it was not possible to introduce into the analysis the use of m o r e efficient gas stoves.

Agribusiness T h e e n e r g y d e m a n d in the agribusiness sector was d i v i d e d into three categories: the first represents the d e m a n d arising f r o m rural electrification programs, including irrigation p u m p s and e x c l u d i n g the electricity use already c o m p u t e d in the residential sector. In the r e m a i n i n g categories, c o m p e t i t i o n a m o n g different energy sources was allowed. Hence, the useful energy e m p l o y e d for m o v i n g tractors and other agricultural m a c h i n e s was estimated and expressed in terms o f diesel


Transport energy needs were disaggregated into categories. U s e f u l energy in a u t o m o b i l e transport was expressed in terms o f gasoline and c o m p e t i t i o n b e t w e e n gasoline and alcohol automobiles was allowed. T w o types of freight transport were considered: short and long distance. Useful energy d e m a n d for short-distance freight transport within cities was expressed in terms o f diesel oil and c o m p e t i t i o n a m o n g gasoline, alcohol and natural gas for fueling light trucks was allowed. L o n g distance freight transport energy d e m a n d is associated with the fuel used by intercity h e a v y trucks and was expressed in final energy terms, because of an underlying assumption that - for the 2025 planning horizon there will be no substitute for diesel oil, where highp o w e r e d engines are required. As with freight transport, two types of bus transport were considered: inner and intercity. For inner city buses, c o m p e t i t i o n b e t w e e n gasoline, alcohol and diesel was allowed, and the useful energy d e m a n d was g i v e n in terms of diesel oil. For intercity buses the d e m a n d was expressed in final energy terms, for the same reason as in intercity freight transport. The other transport category includes railway, air and water transport. The estimation of energy d e m a n d was m a d e in terms o f the final energy usually c o n s u m e d in

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Abating CO 2 emissions in Brazil: E L La Rovere et al

each of those transport modes. An additional assumption was made: for the planning horizon, there will be no modal substitution. This is quite a strong assumption and it should be reviewed in later versions of the model, since substitution among different transport modes can be an efficient way to save energy. On the other hand, this substitution usually requires heavy investment in infrastructure, making it essential that the costs of saving energy be carefully determined for each transport alternative. Finally, it is important to mention that to account for energy conservation policies, an across the board 10% efficiency improvement for all types of transport was assumed in scenarios SC02 and SC03. This assumption is based on the authors' judgment that this level of transport efficiency improvement can be easily attainable in the long run through the adoption of appropriate economic incentives and norms.

Non-energy products

Some non-energy products are quite important to the definition of the energy sector. Worth mentioning among them are petroleum products, like petrochemical naphtha, greases and lubricants, petroleum green coke, and asphalt. It was necessary to estimate the demand for these products, because from the total demand for oil products, a decision has to be made on which type of petroleum (national or imported, light or heavy) will be used in Brazilian refineries, or whether it is advisable to directly import oil products. Besides those products, the use of natural gas as raw material for the petrochemical industry or for fertilizer production was considered. To estimate the total alcohol production capacity, it was important to estimate the residual alcohol supply arising from molasses production, which derives from the need to meet future sugar demand. Additionally, the small quantity of alcohol used as spirits was also estimated. Finally, the demand for non-energy products mix was expressed in terms of final energy.

CO 2 emissions

coefficients

The coefficients used to assess total CO 2 emissions were computed according to the methodology developed by OECD. 13 Table 7 shows the coefficients used for fossil fuels. The CO 2 emissions coefficients for energy use of biomass in Brazil are specially important, given the significant share of those energy sources in the Brazilian energy balance.14 However, only in the revised version of the OECD methodology (see note 5) there appear emissions coefficients for fuel wood, charcoal and

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Table 7. CO 2 emissions coefficients. Fuel

Coefficient (tons of carbon/toe)

Oil products Domestic crude oil Natural gas Coal Domestic shale

0.902 0.823 0.690 1.172 1.024

Table 8. CO 2 emissions coefficients for biomass energy use. Fuel

Coefficient (tons of carbon/toe)

Fuelwood (direct combustion) Fuelwood (charcoal production) Charcoal Sugarcane bagasse

I. 178 0.183 1.243 0.0

sugarcane bagasse. But even in this revised version, the OECD methodology still lacks some important information such as: • the level of moisture present in the fuelwood, which greatly affects not only the calorific value but also the carbon content of the wood; • the share of the biomass in different countries to be considered as a renewable energy source, and therefore to be considered as presenting a zero net emissions coefficient; 15 and • how to deal with greenhouse gases (CO 2, methane and other hydrocarbons) in charcoal production plants. The problem of determining appropriate CO 2 emissions coefficients for biomass fuels under Brazilian conditions was therefore addressed in our initial studies 16 on this subject. Their main results are presented in Table 8.

Energy

supply

To meet energy needs in 2025, the energy production sector in Brazil will be organized in different ways, depending on the scenario being considered. Hence, in scenario SC01, the currently used and/or planned technologies will be available to meet demand. The technologies to be chosen are those that present the least total cost - including capital costs plus operational and maintenance costs. This scenario also includes the possibility of natural gas importation from Bolivia and Argentina via pipelines, as well as other countries like Peru and Venezuela or even Nigeria and the Arab countries, through the use of special tankers to transport liquefied natural gas. An indication of that possibility was the recently signed agreement with Bolivia to



supply some 16 million m3/day of natural gas to the industrialized southern region of Brazil, at a price (CIF) of US$0.9/MBTU. In scenarios SC02 and SC03, in addition to that possibility, an alternative for producing electricity by means of sugarcane bagasse cogeneration was also allowed. The use of sugarcane bagasse for cogeneration is important because it provides an economic use for a product that was formerly regarded as a waste of the alcohol production process. It also means that sugar and alcohol plants will be induced to use efficient boilers and turbines to generate electrical energy to be sold to electric utilities. This sort of technology is particularly interesting, where CO 2 emissions are concerned, because of the zero net emissions contribution of sugarcane products. The following sections present a brief description of the renewable energy sources used as abatement alternatives in scenarios SC02 and SC03: hydropower and biomass.

Hydropower Hydropower is of great importance in the Brazilian energy balance. Unlike many other countries, Brazil electricity generation is heavily based on hydroplants (around 90% of total generation in 1993). This is one of the reasons why the modeling of this sector has to be carefully considered; the other is its enormous potential for CO 2 abatement. CH 4 emissions from the biomass remaining in the dams were neglected in this first cut as our preliminary estimates have shown that they would be significant only in the case of very large dam surface/power ratios. This was the case of some plants built in the past (Balbina and Tucuru), but this can be avoided in the future. On the other hand, producing electricity from hydroplants has a serious impact on the local environment, especially when, as in the case in question, the expansion of the system has to be on rivers within rain forests. In this situation, environmental protection costs have to be considered in addition to construction and maintenance costs of the long-distance transmission lines needed to link production and consumption centers. To take into account the several thresholds of electricity generation costs and capacity, four types of electricity generation were considered in the model. Types I and lI correspond basically to the expansion of existing systems in regions closer to large consumption centers, therefore representing lower costs. Types III and IV correspond to the expansion of the system into the Amazon region, and consequently represent a higher scale of costs.

Energy Poliey 1994 Volume 22 Number 11

Biomass In Brazil, biomass is a particularly important energy source, especially where CO 2 emissions are concerned. The particular characteristics of the country, its enormous territory and tropical climate, make for competitive advantages of biomass as an energy source in Brazil. In addition, if the biomass used is planted, then the net carbon emissions are zero or close to zero in most instances. In fact, with reforestation programs, there is a real possibility of the Brazilian forests acting as a sink for carbon emissions. Brazil is a traditional fuel wood consumer and the Brazilian steel industry has been using charcoal for a long time, developing its own technology in the process. Brazil was also a pioneer in the introduction of a synthetic fuel program, through P R O - A L C O O L - the National Alcohol Program. In 1991 12.8 million m 3 of ethanol were produced in Brazil, most of which was used in automotive vehicles. There are, however, some restrictions on the use of biomass as an energy source. The most relevant are agricultural land availability; transport costs; competition with alternative land uses (eg food production); and reliability of supply. This last point is very important, since climate variations, plague infection and regional productivity differences make the planning of the sector a difficult task. In the model, two types of biomass energy sources were analyzed: fuelwood from native or planted forests; with the possibility of producing charcoal: and sugarcane, with the possibility of producing alcohol for automotive use, and, in scenarios SC02 and SC03, the utilization of sugarcane bagasse for heat production and electricity cogeneration. It should be mentioned that, at this stage, the model has not considered either the utilization of vegetable oils as an energy source (as, for instance, palm oil) or cassava alcohol production, which presently exists on a small scale. To take care of land restrictions, a maximum number of acres that could be used for sugarcane plantations or reforestation areas was established and exogenously fed into the model. For reforestation, two level of costs were established: one that assumes the forests will be near large consumption centers and another which assumes that the reforestation will occur further from consumption centers, but not more than 500 km away. The upper bound on the amount of land that could be used in reforestation programs was taken from Projeto Floram, a study conducted by the University of S'~o Paulo. x7 For sugarcane, it was assumed that the maximum alcohol production possible in 2025 would be 130 million m 3. This number was estimated by taking the 1990 total

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Abating CO 2 emissions in Brazil: E L La Rovere et al Table 9. Economic implications of reducing carbon emissions (1990 billion dollars 10% transport efficiency improvement). 1985

2010

2025

327 2505

913 4730

1874 8050

Scenario SC01 Emissions (Mt C) Primary energy (Mtoe) Cost Investment Investment/GDP (%) Foreign exchange FE/GDP (%)

82 171 44 10 5 5 2

215 346 151 57 6 12 1

394 710 419 142 8 52 3

Scenario SC02 Emissions (Mr C) Primary energy (Mtoe) Cost Investment Investment/GDP (%) Foreign exchange FE/GDP (%)

82 171 44 10 5 5 2

198 32 ! 139 51.7 6 9.6 1

375 647 384 128 7 48 3

Scenario SC03 Emissions (Mt C) Primary energy (Mtoe) Cost Investment Investment/GDP (%) Foreign exchange FE/GDP (%)

82 171 44 10 5 5 2

106 437 161 64 7 5 1

197 735 414 172 9 25 I

GDP GDP/per capita (US $)

alcohol production as a base and assuming a maximum rate of increase in the alcohol production of 5% a year. Additional assumptions for biomass energy sources are the following. Fuel wood could be extracted from native forests or planted forests and part of it could be used to produce charcoal. The use of fuelwood from native forests was allowed only in the residential sector consumption (for cooking in rural areas), while only fuelwood originated from planted forests was allowed for industrial use (heat generation). Charcoal can be used in iron ore reduction or as an energy source for industrial heat generation. Its use in small-scale cookeries was also considered. Alcohol distilleries of two types were considered: autonomous, which only produce alcohol, and annex, which are located next to sugar mills and use molasses, a byproduct of sugar production, as raw material for alcohol. Despite the fact that annex distilleries operate autonomously for part of the time, it was assumed, for modeling purposes, that they only utilize molasses as input. In other words, this technology produces alcohol only as a byproduct of the sugar operation. In addition, to model the production of anhydrous alcohol, which is used as a gasoline additive, another technology was defined. Sugarcane bagasse not used in sugar mills or distilleries was considered to be employed in other industries,

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for steam generation. In scenarios SC02 and SC03 another alternative was allowed, namely the use of sugarcane bagasse in more efficient boilers, for electricity cogeneration and steam generation.

General

results

In general, it can be said that Brazil is in a very good position with respect to reducing future CO 2 emissions. Its possibilities of using renewable energy sources, especially biomass and hydropower, are almost unique in the world. Besides that, reforestation programs could also be used as sinks for CO 2 emissions. The only restrictions, as already pointed out, are, in the case of alcohol production, the availability of agricultural land for sugarcane and competition with production of food; and, where the use of fuelwood is concerned, the distance from production to consumption centers. Another feature which is particular to Brazil is its huge reserves of shale oil (approximately 50 billion barrels in the Irati formation alone) and of high ash coal (in the Candiota mine alone the open pit reserves are estimated as 4 billion tonnes). Of course, if the goal is to limit CO 2 emissions, the use of this fossil fuel is not indicated. However, with a 2025 oil price of US$38/barrel, at least the use of some shale oil should be expected, especially in the scenarios without CO 2 emission limits.

Energy Poli~y 1994 Volume 22 Number 11


The upper bounds on the yearly production of shale oil and coal fed into the model were, respectively, 18 Mtoe and 38.4 Mtoe. Table 9 summarizes the results found in the several runs of the model, including the 2010 scenarios. As can be seen from that table, in scenario SC01, which maintains the current technologies for production and use of energy, the model indicates an overall emission of 394 Mt of carbon, in 2025, or about five times the 1985 level. The total cost associated with the production and use of energy in this scenario is, in 1990 dollars, of some US$419 billions, ie about 10 times that of 1985. Costs increase much faster than emissions mainly because of the relatively high oil price assumed and the increasing marginal costs of hydropower. Hence, if no action is taken, not only the emissions but also the costs will increase dramatically. In scenario SC02, given the possibility of an increment in the efficiency of energy production and use, there are reductions both in the costs and in carbon emissions to, respectively, US$384 billion (a 9% reduction) and 375 Mt of carbon (a 5% decrease). These improvements correspond to a no regrets type of policy, since the technologies associated with the scenario increase the efficiency of the system as a whole at a negative cost. In scenario SC03, when CO 2 emissions are limited to their lowest possible bound] 8 the costs went up to US$414 billion and emissions were reduced to a level of 197 Mt of carbon. That means that for a 47% emission reduction, as compared to scenario SC02, the costs increased only slightly (8%). When compared to scenario SC01, the results are even more striking: for a 50% emission reduction, there is only a 1% increase in costs. These results show that unlike many countries, Brazil has a large potential for curbing carbon emissions. In other words, given the enormous possibilities of using renewable energy sources - especially hydropower and biomass, which can also be used as a carbon sink Brazil is in a better position than many developed countries, which will have to base their carbon abatement policies exclusively on increments in the efficiency of producing and using energy. However, there is an important point to be aware of, namely the increase in the need for investment in the energy sector. In all scenarios, investment as a percentage of GDP increased significantly, which might indicate some inconsistency with the assumed GDP growth. To investigate this conjecture in more detail, a link between the model for the Brazilian economy and the TOM model will have to be constructed. It is also important to mention that the present work represents only a first step of a more comprehensive study, which should also contemplate the analysis of institutional and


Table 10. Primary energy shares in different scenarios (%).

Energy source Hydropower Nuclear Coal Oil Natural gas Biomass

SC01 16.9 0.1 8.8 27.5 24.8 21.8

SC02 18.5 0.1 12.9 27.8 21.0 19.7

SC03 31.3 0.1 1.8 17.5 10.4 38.9

market mechanisms to induce the use of more efficient and appropriate energy related technologies. It is interesting to note that scenario SC03 presents the largest primary energy consumption. However, as Table 10 shows, this energy comes mostly from domestic renewable sources. As a consequence, this scenario shows the lowest foreign exchange/GDP ratio and the highest amount of investment as compared to the GDP. This means that Brazil can only undertake an emissions reduction program if developed countries are willing to make the necessary technological and financial transfers.

Conclusions

There is a large scope for a positive synergy between a sustainable energy development strategy for Brazil and the preventing of global climate change. Taking into account the due precautions to minimize local and regional environmental costs, tapping the large potential of renewable energy sources will surely contribute to meeting national development objectives and simultaneously sharply limiting CO 2 emissions. The same applies to reducing Amazon deforestation, which remains a major objective of Brazilian policy in order to prevent the waste of its own natural resources while preserving the possibilities of long-term sustainable development of the region. Short-term constraints to promote sustainable development and simultaneously contribute to preventing global climate changes should not be underestimated, however. Huge difficulties of a technical, economic, financial, social, political and cultural nature arise both in the struggle against Amazon jungle-clearing and in the promotion of energy efficiency and renewables. The responsibility of governments from developing countries must be called upon. In the case of Brazil this means that government efforts should be directed not only to curb deforestation but also to promote energy conservation, afforestation programs and the use of renewables. This includes the adoption of appropriate energy pricing policies, mechanisms for information dissemination, adequate institutional building, and provision of soft funding facilities and measures to foster

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research and development efforts in this area. In this connection it should be warned that complete deregulation and privatization of energy sectors will not allow the removal of non-economic barriers to sustainable energy development strategies. On the other hand, the responsibility of industrialized countries must be invoked on two crucial points: ensuring an adequate level of energy prices and channelling an appropriate flow of technological and financial resources towards the sustainable energy development of the South. Prices of fossil fuels and nuclear energy should be kept sufficiently high to reflect their overall environmental costs and shape the orientation of technological progress in the industrialized world. A shift to more efficient end-use energy technologies and renewables in developing countries is heavily dependent on a similar move in the North. That is a major case for the adoption of a carbon tax on fossil fuels and nuclear energy in industrialized countries. Finally, the North has a major role to play in helping developing countries to overcome the short-term financial constraints to the investment in CO 2 emissions abatement strategies. The Brazilian case is a good illustration of large CO 2 emission abatement potential at reasonable cost if investment in hydropower development, ethanol production and afforestation programs were to benefit from international funding. As well as reshaping the action of financial agencies such as the World Bank, new mechanisms to fund 'small regret' sustainable energy development strategies are badly needed. In that connection, the implementation of the United Nations Convention on Global Climate Change has a crucial role to play, starting with the allocation of considerably increased amount of financial resources to the Global Environment Facility (GEF) and its use to support the viability of these strategies, in Brazil as well as in other developing countries. This paper derives from a collaborative research work among the Energy Planning Program of the Federal University of'Rio de Janeiro, the CIRED-International Research Center on Environment and Development, the UNEP Centre at Ris~ National Laboratory and the Lawrence Berkeley Laboratory. It has profited much from the criticism and suggestions from the staff of all those Institutions. Ajax Moreira, from IPEA, the Economic Research Institute of the Brazilian Planning Ministry, has computed the different futures for the Brazilian economy. Of course, any remaining errors are the authors' sole responsibility. ILuiz Legey, 'Some alternatives for achieving value change', mimeo, Global Forum of Spiritual and Parliamentary Leaders on Human Survival, Scientists Conference 1993 at Shiga, Japan. 2E La Rovere et al, UNEP Greenhouse Gas Abatement Costing

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Studies: Analysis of Abatement Costing Issues and Preparation of a Methodology, UNEP Greenhouse Gas Abatement Costing Studies, UNEP Collaborating Centre on Energy and Environment, Riso National laboratory. Roskilde, Denmark, 1993. 3E Reis, An Econometric Model of Amazon Deforestation, mimeo, IPEA, Rio de Janeiro, 1992. 4According to the Brazilian Energy Balance, total primary energy consumption in Brazil was 181 Mtoe, in 1990. Of this total, 108 Mtoe came from renewable sources - mainly as hydroelectricity (60 Mtoe) and biomass (46 Mtoe) - and 73 Mtoe from non-renewable ones. 5L Pinguelli, E L La Rovere, L F Legey et al, Emissfes de Di6xido de Carbono no Sistema Energ~tico." Estimativa da Contribui~'do Brasileira para o Efeito Estufa e Compara~'6es lnternacionais, mimeo, Energy Planning Program/COPPE/UFRJ. Rio de Janeiro, 1992. 61n 1992 Brazil's GDP, computed by using the 1988 exchange rate parity and by adjusting for real GDP growth, was 359 billion, in 1990 US dollars. 7Minist6rio das Minas e Energia, Metodologia Brasileira para Avaliaqdo de Tecnologias de Energia Utilizando o Modelo Markal, Cia Auxiliar de Empresas El6tricas Brasileiras - CAEB. Brasilia, 1983. 8In addition to the 2025 runs, the model was also used in a 2010 energy demand context. However, as the methodologies employed in the two time horizons were exactly the same - the 2010 computations were an intermediate point of the economy's trajectory towards 2025 this paper will describe in detail only the 2025 exercise, while presenting the results of the 2010 runs. 9A A Camarano, K Beltr~o and R. Neupert, S~culo XXI: A Quantas Andar6 e Onde Andar6 a Popula¢Oo Brasileira?, mimeo, IPEA/IPLAN, Brasilia, 1988. t°Ajax Moreira, Modelo Multissetorial de Consist~ncia, Discussion Paper No 217, IPEA, Rio de Janeiro, 1991. ~1C F Alvin, J D G Miguez and J A M Patusco, Metodologia do Reexame da Matriz Energ~tica Nacional, mimeo, Minist6rio de Infraestrutura (documentos de 1 a 4), Brasilia, 1990. 12Howard Geller, Efficient Electricity Use." A Development Strategy for Brazil, American Council for an Energy-Efficient Economy, Washington and Berkeley, 1991. 13OECD, Estimation of Greenhouse Gas Emissions and Sinks, Final Report from OECD Experts Meeting, 18-21 February 1991, Prepared for IPCC, revised August 1991. InMinist6rio de Minas e Energia, Balan~'o Energ~tico Nacional, Brasilia, 1992. 15This is exactly the case of sugarcane bagasse and a portion of the charcoal produced in Brazil. t6Among them, the following should be mentioned: op cit, Ref 5; and L Pinguelli, E L La Rovere, L F Legey et al, Emiss6es de Gases do Efeito Estufa na Produq(lO e Uso da Energia e em Outras Atividades lndustriais: Estimativa da Contribui~'do Brasileira, mimeo, Energy Planning Pr0gram/COPPE/UFRJ, Rio de Janeiro, 1992. 17'Projeto Floram - uma plataforma', Estudos Avan~'ados, Vol 4, No 9, Universidade de S~.o Paulo, S~.o Paulo, 1990. According to Floram, the limit of land to be used for reforestation programs is about 20.1 million hectares (or 201 000 km2). 18 This statement is not precisely correct because it would be possible to slightly reduce the amount of CO 2 emissions and maintain the feasibility of the problem; however, this would have to be done at a significant increase in the foreign exchange necessary to sustain the solution. This sort of instability is characteristic of linear programming models, when a solution jumps from one vertex of the feasible solution set to another. To know the point up to which it would be possible to reduce emissions, before the problem becomes infeasible, the model was run with the minimization of CO z emissions as the objective function.

Energy, Policy 1994 Volume 22 Number 11