129
SUPPLY AND DEMAND OF BIOMASS-BASED
ENERGY IN BRAZIL: ESTIMATES USING TIME
SERIES ANALYSIS AND CURRENT POTENTIAL
Marcelo dos Santos Guzella1, Ana Carolina de Albuquerque Santos 2
Joäo Flávio de Freitas Almeida3
Recibido: y Aceptado:
13/11/2024 - 26/6/2025
1.- marceloguzella@codemig.com.br
2.- anaorestaufv@gmail.com
3.- joao.avio@dep.ufmg.br
130
131
In this work, we developed estimates of the supply and demand of biomass-based energy in Brazil. This type
of energy is receiving increasing attention due to its benets in terms of sustainability and trade balance. We
applied time series analysis to forecast demand based on historical data and vector autoregressive models.
As regressors, we included total energy consumption, electricity prices, air temperature, population, local
stock market size, industrial growth, FDI and GDP. The energy potential was estimated based on agricultural,
livestock, urban solid waste and forestry production. The projections indicate that the demand in 2032
can reach 187 million tons of oil equivalent, which is around 41% of the 457 million tons of national energy
potential based on the production of 2022. The results show a signicant gap between the projected use
and the potential supply of this type of energy in the country. A national energy planning aimed at exploring
this gap, while considering its eects with respect to inputs, costs and other uses, may lead to a higher share
of alternative energy sources, better diversication and improved eciency.
KEYWORDS: Biomass, Energy Potential, Bioenergy, Alternative Sources, Autoregressive Models.
Overview
132
1. INTRODUCTION
Global energy demand has grown by around 69%
from 1990 to 2020, in line with a population growth
of 48% in the same period, especially in emerging
countries (Zeb et al., 2017). Most of this energy is
used for electricity generation and transportation.
Despite the increasing awareness with respect to
the harmful eects of the excessive use of fossil
fuels over the last decades, the rupture of global
chains with the Covid-19 pandemic and the war
in Ukraine have, at least temporarily, shifted the
concern to avoiding supply decits (IEA, 2022).
Nevertheless, countries participating in COP26
in 2021, including Brazil, agreed to minimize the
use of coal and other fossil fuels to reduce carbon
dioxide emissions and their eects on the climate
change, as well as human and animal health
and well-being (Wang et al., 2022). This study
seeks to contribute to this process by developing
projections of supply and demand for energy from
biomass, a resource that still accounts for only
10% of the global energy production, but which
has several advantages in terms of availability,
cost, inclusion and sustainability.
Biomass is a renewable energy source derived
from four basic sources: woody plants (timber),
non-woody plants (saccharides, cellulose, starch
and aquatic), organic waste (agricultural, industrial
and urban) and biouids (vegetable oils) (Field et
al., 2008). In Brazil, sugarcane bagasse is the
most widely used biomass resource for energy
generation, given the importance of the sugar and
alcohol sector and high levels of waste generation.
Palm oil, wood chips, food waste and even animal
manure are also used (Hofsetz & Silva, 2012). The
main biomass conversion processes are direct
combustion, in ovens and stoves; gasication, using
hot steam and air without causing combustion;
pyrolysis or carbonization; transesterication,
converting vegetable oils into glycerin or biodiesel;
anaerobic digestion, decomposing through the
action of bacteria (generating biogas and, after
purication, biomethane, equivalent to natural gas);
and fermentation, in which yeasts convert sugars
into alcohol (Hu et al., 2020). Biomass-based
generation systems can also include cogeneration
processes, in which the heat generated in the
production of electricity is incorporated into the
production process in the form of steam, saving
fuel and increasing the eciency of the system.
One of the main advantages of biomass energy
generation is its availability. All the time, we
generate organic waste in an intense and
distributed way. Almost all extraction, production,
transportation and consumption units produce
waste that can be converted into heat and
electricity. In terms of sustainability, the release
of carbon into the atmosphere from the use of
fuels from plant biomass is limited to what was
absorbed by the plants during their life cycle
(Winchester & Reilly, 2015). In addition, since the
waste generation is decentralized, transportation
costs from generation units to consumption units
tend to be lower. Biomass also does not require
the high extraction costs typical of the oil and
gas industry and can represent a supplementary
income for existing industrial units. Finally, the use
of solid waste for energy generation reduces the
volume deposited in landlls.
On the other hand, the use of biomass energy also
has disadvantages (Vassilev et al., 2015). Despite
signicant research and technological innovations,
the energy eciency of biofuels is still limited when
compared to fossil fuels. Furthermore, the use of
biomass from human or animal waste leads to an
increase in methane emissions, which are also
harmful to the environment. Pollution from burning
wood and other materials can be as harmful as
that from the use of coal and similar resources.
The biomass-based energy generation should
be combined with the development of solutions
to overcome these disadvantages, as well as
avoiding increasing levels of deforestation for the
use of wood.
A key challenge for energy supply and demand
planning is the development of projections with
adequate degrees of reliability (Moreira, 2006;
133
Senocak & Goren, 2022). Regarding biomass-
based energy, this issue is even more critical
due to the fragmentation, informality and less
regulation (Mafakheri & Nasiri, 2014). Aiming
at overcoming this problem, in this study we
developed both supply and demand projections
for biomass energy, year by year, in Brazil. Our
approach encompasses the denition of supply
and demand determinants, data collection in
previous literature, and the use of autoregressive
vector models with a bootstrapping technique to
overcome sample size problems.
The estimated supply and demand forecasts are
useful for planning and operating processes of
producers, industries, consumers and regulators.
With greater predictability, there is a tendency for
reduction in transaction costs and risk premiums,
as well as in the uncertainties of projects aimed at
increasing supply and projects that will demand
this supply (Rosillo‐Calle, 2016). Thus, despite its
limitations and room for improvements, this work
contributes to the development and improvement
of national energy plans, capturing the benets of
biomass-based supply.
We selected the main crops and sources of waste
that are inputs for the generation of biomass
energy, using production and generation data
from 2022. We collected consumption and
specic energy parameters from various sources
and estimated a potential supply of biomass-
based energy of 457 million tons of oil equivalent
(toe). Regarding consumption, our projections are
based on the time series published by the Energy
Research Company (EPE), a public company
linked to the Brazilian Ministry of Mines and
Energy that develops studies and research aimed
at supporting the planning of the energy sector.
We also used series of typical macroeconomic
determinants of energy consumption. Using data
from 2000 to 2021, we developed autoregressive
vectors that indicate that consumption may
reach 187 million toe in 2032, 41% of the current
estimated supply potential.
In the next section, we describe the data,
parameters, and methods used for the research
goals. Finally, we analyze the results and make
nal comments, presenting limitations of our study
and recommendations for future work.
134
2. METHODS
First, we estimated biomass energy consumption
in Brazil from 2022 to 2032, applying historical
data from 2000 to 2021 to VAR (vector
autoregressive) models. Historical consumption
data of total energy and of biomass-based energy
were extracted from a periodic report released
by EPE. Biomass-based energy corresponds to
the one from sugarcane bagasse, rewood, black
liquor, biogas and other recoveries, in tons of oil
equivalent (toe). Total energy comprises electricity,
ethanol, fossil fuels, solar and other renewables,
also in toe. We also collected variables that
Samuel et al. (2013) identied as determinants
of energy consumption: total country population,
real gross domestic product growth and industrial
growth, released by the IBGE (Brazilian Institute
of Geography and Statistics); market cap of
listed domestic companies and foreign direct
We then veried whether stationarity requirements
are met applying augmented Dickey-Fuller tests.
We performed log transformation and took rst
and second dierences of the series until they
become stationary, resulting in the variables
presented in Table 2. Originally, we considered
per capita real GDP, capital stock, domestic credit
to the private sector and the number of listed
domestic companies, but they did not become
stationary after the transformations.
investment, released by the World Bank (WB);
residential electricity prices, available at the CEIC
(Global Economic Data, Indicators, Charts &
Forecasts) website; and air temperature, measured
by the INMET (National Institute of Meteorology).
The variables and corresponding sources are
described in Table 1.
Table 1 - Descriptive Statistics
Note: BOE stands for barrels of oil equivalent.
135
After that, we applied autoregressive models of
order 3, since it showed better results with respect
to the Akaike information criterion (AIC). Due to
the small sample size, instead of a model with all
the variables, we combined the biomass-based
energy and the eight other regressors in 28 models
with three variables and stored the forecasted log
of the second dierence of biomass-based energy
consumption. Finally, we calculated predicted
biomass-based energy consumption based on
these forecasts. Model outcomes resulted in a
biomass-based consumption in 2032 ranging
from 39 to 187 million toe.
The second part of our analysis comprised
the estimation of the potential for production
of biomass-based energy in Brazil. Whenever
we found more than one parameter value in
the literature, we chose the lower one to have
conservative estimations. Firstly, we estimated the
potential for energy generation based on biomass
from crops in Brazil. We extracted data of the
Municipal Agricultural Production (PAM) in 2022,
released by IBGE (Brazilian Institute of Geography
and Statistics). We considered all products with
national production above one million tons in
2022, both permanent and temporary crops.
We estimated the energy in toe based on the
methodology presented by Gonzalez-Salazar
et al. (2014), which is basically the production of
the agricultural product multiplied by waste to
product ratio, adjusted by the moisture content,
and nally multiplied by the lower caloric value.
Table 2 - Results of the Augmented Dickey-Fuller Tests
Note: The alternative hypothesis of the Augmented Dickey-Fuller Test is stationarity.
Among the 27 products (that total 1.1 billion tons
in Brazil in 2022), we did not nd the parameters
only for papaya (1.1 million tons) and watermelon
(1.9 million tons). The parameters and resulting
potential of energy production for permanent and
temporary crops are presents in Tables 3 and 4,
as well as main references used to obtain these
parameters.
136
Table 3 - Inputs and Outputs for Major Permanent Crops
Table 4 - Inputs and Outputs for Major Temporary Crops
Note: 1: Algieri et al. (2019). 2: Silva et al. (2019). 3: Elauria et al. (2005). 4: Santos et al. (2020). 5: Ekinci (2011). 6:
Gonzalez-Salazar et al. (2014). 7: Yerima & Grema (2018). 8: Tahir et al. (2021). 9: Gravalos et al. (2016). 10: Citrus peel
waste. 11: Thousand tons of oil equivalent.
Note: 1: Filter cake, straw and stalks also included. 2: Silva et al. (2019). 3: Frear et al. (2005). 4: Khiari et al. (2019). 5:
Gonzalez-Salazar et al. (2014). 6: Avcıoğlu et al. (2019). 7: Veiga et al. (2016). 8: May et al. (2013). 9: Santos et al. (2018). 10:
Pinto et al. (2021). 11: Energy potential calculated based on the area intended for harvesting of 554 thousand hectares. 12:
Thousand tons of oil equivalent.
Regarding livestock biomass, we obtained
data from the Municipal Agricultural Production
(PPM) in 2022, also from IBGE. We considered
cattle, swine, poultry and equine. We estimated
the energy potential of the waste based on the
methodology also presented by Gonzalez-Salazar
et al. (2014), which considered as reference the
amount of biogas produced from each animal’s
manure through a biodigestion process. The
formula relates the number of animals to the
production of manure per animal, the yield of
biogas per manure and a lower caloric value of 17
MJ per m3. We present parameters and resulting
potential of energy production for livestock in
Table 5.
137
Table 5 - Inputs and Outputs for Major Types of Livestock
Note: 1: Parameters collected from Gonzalez-Salazar et al. (2014), which is based on a literature review.
We estimated the energy potential from forest
biomass using the survey carried out by IBGE
on the production of plant extraction and forestry
in Brazil. The production volume of charcoal
and cellulose (which can be used for bleach
production) in 2022 was 7.1 million and 25.0 million
tons, respectively. We also considered the 52.8
million and 158.3 million cubic meters of rewood
and round wood, as well. Those volumes were
converted into weight using an average density of
0.33 ton/cubic meter. Hence, we considered a by-
product to product ratio of 0.3 (1.4 for cellulose)
and a lower caloric value of 16.7 kJ/kg (12.0 kJ/
kg for cellulose). The values of those parameters
were obtained and presented by Gonzalez-
Salazar et al. (2014) and Liebel (2014), based on
a literature review. The resulting potential energy
was 854 thousand and 10,031 thousand toe (ktoe)
for charcoal and cellulose, respectively, and 2,088
thousand and 6,263 ktoe for rewood and round
wood, respectively.
Finally, with respect to the urban solid waste, we
considered the estimate made by IPEA (Brazilian
Institute of Applied Economic Research) that
approximately 160 thousand tons of waste of this
type are generated per day in Brazil, discounted by
an ideal recycling rate of 60%. We converted this
weight of 35.0 million tons into a landll volume of
2.4 billion cubic meters, using a ratio of 67.9 cubic
meter per ton, and then into energy potential using
a lower caloric values 10.2 MJ per cubic meter,
as cited by Gonzalez-Salazar et al. (2014). The
resulting potential energy was 580 ktoe.
138
3. RESULTS
According to the historical data, total biomass
consumption showed a relevant increase in its
share of the energy matrix, equivalent to 86.5%,
between 2000 and 2022 (2.9% per year), going
from 34 to 64 million toe. This energy comes
mainly from the use of sugarcane bagasse in
cogeneration systems. In line with this growth
rate, our model predictions resulted in an average
forecast of 68 million toe in 2032 (ranging from 33
to 187 million), an increase of 6.2% compared to
2022.
With respect to the annual energy potential, our
estimate was 457 million toe, 14 million based on
biomass from permanent crops, especially orange,
412 million based on biomass from temporary
crops, particularly sugar cane, soybeans and corn,
11 million from livestock farming, 19 million from
plant extraction and forestry, and 579 thousand
toe from the use of urban solid waste. Actual
biomass energy consumption in Brazil in 2022
represents 14% of this consolidated estimate of
potential generation. Average projected biomass
energy consumption in Brazil in 2032 represents
15% of this same estimate, ranging from 7% to
41%. Figure 1 compares actual and projected
forecasts of biomass-based energy consumption,
as well as the estimated production potential with
data from 2022.
Figure 1 – Biomass-based energy in Brazil (million toe)
139
4. CONCLUSIONS
Our analysis shows that there is still a considerable
gap between Brazil’s biomass-based energy
consumption and its production capacity based
on the generation of waste and co-products in
agriculture, livestock, forestry and urban activities.
Considering the advantages of this type of energy
in terms of carbon neutrality, energy security with
local production chains, and socioeconomic
development, this scenario favors the adoption
of public policies to stimulate an increase in the
production, through tax incentives and special
lines of nancing for the acquisition of machinery
and the development of both waste and co-
product supply chain and the ow of the produced
energy. Some studies about these topics show
interesting analyses (Cansino et al., 2010; Zhao et
al., 2016, Mingyuan, 2005; Khennas, 2000; Tan et
al., 2019)
Moreover, the promotion of research and innovation
initiatives to improve the eciency of waste-to-
energy conversion processes contributes to this
goal, as well as the modernization of the legal and
regulatory framework related to the use of waste
and to energy trade (Qazi et al., 2018; Banja et al.,
2019). Such policies should include an evaluation
of the eects of any stimulus in terms of the inputs
needed to intensify the production, as well as its
impact on other supply chains.
It is important to highlight that our consumption
projection method is based on the historical
growth and a limited number of determinants,
and that actual demand could be even greater
due to the contribution of supply and other
structural shocks, such as new public policies to
encourage the production and use of this type
of energy, or to reduce the use of fossil fuels.
Furthermore, our estimates of potential supply are
based on data about waste generation of 2022,
which means that it may also present a growth
projection that can be addressed in future work.
Intra-year forecasts, supply determinants, capital
expenditures and present value estimates, and
the cross-eects between biomass types are also
promising venues for future research.
140
5. REFERENCES
Algieri, A., Andiloro, S., Tamburino, V., & Zema, D. A. (2019). The potential of agricultural residues for energy
production in Calabria (Southern Italy). Renewable and Sustainable Energy Reviews, 104, 1-14.
Avcıoğlu, A. O., Dayıoğlu, M. A., & Türker, U. J. R. E. (2019). Assessment of the energy potential of agricultural
biomass residues in Turkey. Renewable Energy, 138, 610-619.
Banja, M., Sikkema, R., Jégard, M., Motola, V., & Dallemand, J. F. (2019). Biomass for energy in the EU. The
support framework. Energy Policy, 131, 215-228.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control.
John Wiley & Sons.
Cansino, J. M., Pablo-Romero, M. D. P., Román, R., & Yñiguez, R. (2010). Tax incentives to promote green
electricity: An overview of EU-27 countries. Energy policy, 38(10), 6000-6008.
Ekinci, K. (2011). Utilization of apple pruning residues as a source of biomass energy: A case study in Isparta
province. Energy Exploration & Exploitation, 29(1), 87-107.
Elauria, J. C., Castro, M. L. Y., Elauria, M. M., Bhattacharya, S. C., & Salam, P. A. (2005). Assessment of sustainable
energy potential of non-plantation biomass resources in the Philippines. Biomass and Bioenergy, 29(3), 191-198.
Field, C. B., Campbell, J. E., & Lobell, D. B. (2008). Biomass energy: the scale of the potential resource. Trends in
ecology & evolution, 23(2), 65-72.
Frear, C., Zhao, B., Fu, G., Richardson, M., Chen, S., Fuchs, M. (2005). Biomass Inventory and Bioenergy
Assessment. An Evaluation of Organic Material Resources for Bioenergy Production in Washington State.
Gonzalez-Salazar, M. A., Morini, M., Pinelli, M., Spina, P. R., Venturini, M., Finkenrath, M., & Poganietz, W. R.
(2014). Methodology for estimating biomass energy potential and its application to Colombia. Applied Energy,
136, 781-796.
Gravalos, I., Xyradakis, P., Kateris, D., Gialamas, T., Bartzialis, D., & Giannoulis, K. (2016). An experimental
determination of gross caloric value of dierent agroforestry species and bio-based industry residues. Natural
Resources, 7(1), 57-68.
Hofsetz, K., & Silva, M. A. (2012). Brazilian sugarcane bagasse: Energy and non-energy consumption. Biomass
and bioenergy, 46, 564-573.
Hu, Y., Bassi, A., & Xu, C. C. (2020). Energy from biomass. In Future Energy (pp. 447-471). Elsevier.
IEA (2022). World Energy Investment.
IBGE (2023). Pesquisa Agrícola Municipal de 2022 (PAM). Available at sidra.ibge.gov.br/pesquisa/pam.
IBGE (2023). Pesquisa da Pecuária Municipal (PPM). Available at ibge.gov.br/estatisticas/economicas/agricultura-
e-pecuaria.
IBGE (2023). Produção da Extração Vegetal e da Silvicultura de 2022 (PEVS). Available at ibge.gov.br/estatisticas/
economicas/agricultura-e-pecuaria.
141
Khennas, S. (2000). Financing models for biomass-energy; Modeles de nancement de la biomasse-energie.
Liaison Energie-Francophonie.
Khiari, B., Moussaoui, M., & Jeguirim, M. (2019). Tomato-processing by-product combustion: Thermal and kinetic
analyses. Materials, 12(4), 553.
Liebel, S. (2014). Geração Distribuída. Programa de Pós-Graduação em Bioenergia da Unicentro.
Mafakheri, F., & Nasiri, F. (2014). Modeling of biomass-to-energy supply chain operations: Applications, challenges
and research directions. Energy policy, 67, 116-126.
May, A., da Silva, D. D., & dos Santos, F. C. (2013). Cultivo do sorgo biomassa para a cogeração de energia
elétrica.
Mingyuan, W. (2005). Government incentives to promote renewable energy in the United States. Temp. J. Sci.
Tech. & Envtl. L., 24, 355.
MME (2022). Balanço Energético do Brasil. Available at epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/
balanco-energetico-nacional-2022.
Moreira, J. R. (2006). Global biomass energy potential. Mitigation and adaptation strategies for global change, 11,
313-342.
Pinto, C. W., Barth, G., Molin, R., SILVA, D. A. D., & Pauletti, V. (2021). Characterization of oat biomass for energy
production. Revista Caatinga, 34, 537-547.
Qazi, W. A., Abushammala, M. F., & Younes, M. K. (2018). Waste-to-energy technologies: a literature review. The
Journal of Solid Waste Technology and Management, 44(4), 387-409.
Rosillo‐Calle, F. (2016). A review of biomass energy–shortcomings and concerns. Journal of Chemical Technology
& Biotechnology, 91(7), 1933-1945.
Samuel, Y. A., Manu, O., & Wereko, T. B. (2013). Determinants of energy consumption: A review. International
Journal of Management Sciences, 1(12), 482-487.
Santos, M. L. G. (2018). Processo pirolítico da biomassa residual do abacaxi. Mestrado em Tecnologia ambiental.
Santos, V. O., Queiroz, L. S., Araujo, R. O., Ribeiro, F. C., Guimarães, M. N., da Costa, C. E., ... & de Souza, L. K.
(2020). Pyrolysis of acai seed biomass: kinetics and thermodynamic parameters using thermogravimetric analysis.
Bioresource Technology Reports, 12, 100553.
Senocak, A. A., & Goren, H. G. (2022). Forecasting the biomass-based energy potential using articial intelligence
and geographic information systems: A case study. Engineering Science and Technology, an International Journal,
26, 100992.
Silva, I. P. D., Lima, R. M. D. A., Ruzene, D. S., & Silva, D. P. D. (2019). Resíduos agroindustriais como biomassa
alternativa para geração de energia distribuída em comunidades rurais. Energias alternativas: tecnologias
sustentáveis para o nordeste brasileiro.
Tahir, M. H., Irfan, R. M., Cheng, X., Ahmad, M. S., Jamil, M., Karim, A., ... & Haroon, M. (2021). Mango peel as
source of bioenergy, bio-based chemicals via pyrolysis, thermodynamics and evolved gas analyses. Journal of
Analytical and Applied Pyrolysis, 155, 105066.
Tan, R. R., Aviso, K. B., & Ng, D. K. S. (2019). Optimization models for nancing innovations in green energy
technologies. Renewable and Sustainable Energy Reviews, 113, 109258.
142
Vassilev, S. V., Vassileva, C. G., & Vassilev, V. S. (2015). Advantages and disadvantages of composition and
properties of biomass in comparison with coal: An overview. Fuel, 158, 330-350.
Veiga, J. P. S., Valle, T. L., Feltran, J. C., & Bizzo, W. A. (2016). Characterization and productivity of cassava waste
and its use as an energy source. Renewable energy, 93, 691-699.
Wang, Y., Liu, Y., & Gu, B. (2022). COP26: Progress, challenges, and outlook.
Winchester, N., & Reilly, J. M. (2015). The feasibility, costs, and environmental implications of large-scale biomass
energy. Energy economics, 51, 188-203.
Yerima, I., & Grema, M. Z. (2018). The potential of coconut shell as biofuel. The Journal of Middle East and North
Africa Sciences, 4(8), 11-15.
Zeb, K., Ali, S. M., Khan, B., Mehmood, C. A., Tareen, N., Din, W., ... & Haider, A. (2017). A survey on waste heat
recovery: Electric power generation and potential prospects within Pakistan. Renewable and Sustainable Energy
Reviews, 75, 1142-1155.
Zhao, Z. Y., Chen, Y. L., & Chang, R. D. (2016). How to stimulate renewable energy power generation eectively?
China’s incentive approaches and lessons. Renewable energy, 92, 147-156.
