Modeling of Brazilian Carbon Dioxide Emissions: a Review

Pedreira, Vitor Neves; Brito, Marcos Lapa; Santos, Luiz Carlos Lobato dos; Simonelli, George

doi:10.1590/1678-4324-2022210594

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Brazilian Archives of Biology and Technology

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Sumário

Review - Engineering, Technology and Techniques • Braz. arch. biol. technol. 65 • 2022 • https://doi.org/10.1590/1678-4324-2022210594 copy

Modeling of Brazilian Carbon Dioxide Emissions: a Review

Authorship SCIMAGO INSTITUTIONS RANKINGS

HIGHLIGHTS

The most used model types and independent variables to forecasting Brazilian CO₂ emissions can be obtained by literature review.
Gray models and artificial neural networks are the most promising modeling methods.
Economic growth and energy consumption are the main independent variables to forecasting Brazilian CO₂ emissions.

Abstract:

Brazil is a signatory to the Paris Agreement and aims to reduce 43% of CO₂ emissions by 2030, compared to 2005. However, changes in energy policies are needed to achieve this goal, evaluating the produced effects on emissions. One way to predict these effects is through mathematical modeling. In this paper, we carried out a literature review to identify the most used model types and independent variables to forecasting Brazilian CO₂ emissions. The review showed that gray models and artificial neural networks are the most used ones. Furthermore, we also identified that economic growth and energy consumption are the main independent variables.

Keywords:
Brazil; Paris Agreement; Greenhouse gases; Emissions modeling

Reference	Model	Advantage	Disadvantage
[¹⁵15 Köne AI, Büke T. Forecasting of CO2 emissions from fuel combustion using trend analysis. Renew Sustain Energy Rev. 2010;14:2906-15.]	Trend analysis	A simple model that can be used with the available data	It is limited to situations of linear behavior
[¹⁶16 Pao HT, Tsai CM. Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy [Internet]. 2011;36:2450-8. Available from: http://dx.doi.org/10.1016/j.energy.2011.01.032 http://dx.doi.org/10.1016/j.energy.2011.... ]	Univariate gray prediction model	Possibility to represent complex systems with limited amounts of data	The performance of the model in comparison with other models depends on the database used
[¹⁷17 McKitrick R, Strazicich MC, Lee J. Long-term forecasting of global carbon dioxide emissions: Reducing uncertainties using a per capita approach. J Forecast. 2013;32:435-51.]	Regression for stationary samples, considering the possibility of structural breaks	The reliability of population growth predictions is high, same advantages as trend analysis	Limited to stationary situations
[¹²12 Réquia WJ, Koutrakis P, Roig HL, Adams MD. Spatiotemporal analysis of traffic emissions in over 5000 municipal districts in Brazil. J Air Waste Manag Assoc [Internet]. 2016;66:1284-1293. Available from: http://dx.doi.org/10.1080/10962247.2016.1221367 http://dx.doi.org/10.1080/10962247.2016.... ]	Top-Down approach for forecasting vehicle emissions in the period between 2001 and 2012	Alternative to obtain a more detailed emission distribution profile	Need a consistent database not only with emissions but also with generating mechanisms
[¹¹11 Schulz JR da S, Ruppenthal JE. Aplicação Da Metodologia De Box & Jenkins Para Análise Das Emissões De Dióxido De Carbono No Brasil. Reun Rev Adm Contab e Sustentabilidade. 2019;8:1-11.]	Box & Jenkins, ARIMA (0,1,2), in-sample predictions	Able to model and predict the behavior of CO₂ emissions with non-stationary data. Arima model can validate the results obtained with other models	The variable, CO₂ emissions, is explained only by past values, without taking into account the interference of other factors
[¹⁸18 Acheampong AO, Boateng EB. Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod [Internet]. 2019;225:833-56. Available from: https://doi.org/10.1016/j.jclepro.2019.03.352 https://doi.org/10.1016/j.jclepro.2019.0... ]	Artificial neural networks: Multilayer Perceptron with back-propagation	Efficient in dealing with non-linear, complex situations and with diffuse data. It is not necessary to know specific mathematical relationships between variables	Need to determine the number of neurons through trial and error
[¹⁹19 Wu W, Ma X, Zhang Y, Li W, Wang Y. A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries. Sci Total Environ [Internet]. 2020;707:135447. Available from: https://doi.org/10.1016/j.scitotenv.2019.135447 https://doi.org/10.1016/j.scitotenv.2019... ]	Conformable non-homogeneous gray fractional model	Possibility to represent complex systems with limited amounts of data	Low-performance results in cases of random, non-linear, or non-stationary data
[²⁰20 Ahmed S, Ahmed K, Ismail M. Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Qual Atmos Heal. 2020;13:1035-44.]	Gray System Model	Possibility to represent complex systems with limited amounts of data	Limited to linear systems, but there are ways to deal with this limitation

Variable	Effect on CO₂ emissions	Justification
Urbanization	Increase	The impact of urbanization is still in discussion. Some authors pointed out that modernization impacts the environment in favor of economic growth [¹⁸18 Acheampong AO, Boateng EB. Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod [Internet]. 2019;225:833-56. Available from: https://doi.org/10.1016/j.jclepro.2019.03.352 https://doi.org/10.1016/j.jclepro.2019.0... ].
Research and Development (R&D)	Increase	R&D has the potential to lead to innovations in more sustainable energy sources, but it can also cause an increase in energy consumption [¹⁸18 Acheampong AO, Boateng EB. Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod [Internet]. 2019;225:833-56. Available from: https://doi.org/10.1016/j.jclepro.2019.03.352 https://doi.org/10.1016/j.jclepro.2019.0... ].
Energy consumption	Increase	Energy consumption is one of the main factors responsible for CO₂ emissions [¹⁸18 Acheampong AO, Boateng EB. Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod [Internet]. 2019;225:833-56. Available from: https://doi.org/10.1016/j.jclepro.2019.03.352 https://doi.org/10.1016/j.jclepro.2019.0... ].
Industrialization	Decrease	Industrialization implies growth in energy consumption and is strongly linked to the consumption of fossil fuels [¹⁸18 Acheampong AO, Boateng EB. Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod [Internet]. 2019;225:833-56. Available from: https://doi.org/10.1016/j.jclepro.2019.03.352 https://doi.org/10.1016/j.jclepro.2019.0... ].
Economic growth	Decrease	Economic growth and energy consumption are related in a complex manner. Economic growth requires greater energy consumption but leads to more efficient consumption [¹⁶16 Pao HT, Tsai CM. Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy [Internet]. 2011;36:2450-8. Available from: http://dx.doi.org/10.1016/j.energy.2011.01.032 http://dx.doi.org/10.1016/j.energy.2011.... ].
Foreign investment	Decrease	It is pointed out that foreign investors promote sustainable technologies while impacting economic growth [¹⁸18 Acheampong AO, Boateng EB. Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod [Internet]. 2019;225:833-56. Available from: https://doi.org/10.1016/j.jclepro.2019.03.352 https://doi.org/10.1016/j.jclepro.2019.0... ].
Gross Domestic Product (GDP)	Unspecified individual effect	It is one of the factors considered by the empirical literature when making emission forecasts in developing countries [²⁰20 Ahmed S, Ahmed K, Ismail M. Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Qual Atmos Heal. 2020;13:1035-44.].
Consumption of fossil fuels	Increase	Energy consumption, one of the main factors considered by the empirical literature when making emission forecasts in developing countries, is divided into fossil and renewable fuels [²⁰20 Ahmed S, Ahmed K, Ismail M. Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Qual Atmos Heal. 2020;13:1035-44.].
Renewable energy	Decrease	Energy consumption, one of the main factors considered by the empirical literature when making emission forecasts in developing countries, is divided into fossil and renewable fuels [²⁰20 Ahmed S, Ahmed K, Ismail M. Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Qual Atmos Heal. 2020;13:1035-44.].
Technology patents	Increase	Technological innovation helps develop new forms of renewable energy and helps to increase the efficiency and consumption of the forms of energy already employed [²⁰20 Ahmed S, Ahmed K, Ismail M. Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Qual Atmos Heal. 2020;13:1035-44.].
Militarization	Unspecified individual effect	The military consumes large amounts of fuel due to various air, land, and sea vehicles [¹⁴14 Bildirici M. Impact of militarization and economic growth on biofuels consumption and CO2 emissions: The evidence from Brazil, China, and US. Environ Prog Sustain Energy. 2018;37:1121-31.].
Fuel price control	Situational	The transport sector is the main contributor to air pollution and emissions, resulting in the great importance of government policies to control vehicle emissions [¹³13 Lima Filho RIR, Aquino TCN, Nogueira Neto AM. Fuel price control in Brazil: environmental impacts. Environ Dev Sustain [Internet]. 2020; Available from: https://doi.org/10.1007/s10668-020-00896-7 https://doi.org/10.1007/s10668-020-00896... ].

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[1] *Correspondence: gsimonelli@ufba.br; Tel.: +55-71-32839854 (G.S.).