Ben Youssef, Slim (2025): The determinants of forest area in Brazil: Ethanol production, exports of crops and livestock, and asymmetric impact of temperature change.
Preview |
PDF
MPRA_paper_127224.pdf Download (860kB) | Preview |
Abstract
This paper evaluates the long-run impact of fuel ethanol production, exports of crops and livestock, and the asymmetric impact of temperature change on the forest area in Brazil. We use the non-linear autoregressive distributed lag model and annual data between 1990 and 2022. An increase in ethanol production or in exports of crops and livestock importantly reduces the forest area in Brazil, in the long-run. We demonstrate that while positive temperature change does reduce forest area in the long-run, falling temperatures do not guarantee the regeneration of lost forests. A temperature change increase of 1°C leads in the long term to a significant and very worrying reduction in the forest area of Brazil, of almost 9.8%. Some policy recommendations are drawn: i) To reduce GHG emissions, Brazil should encourage R&D and innovation in energy efficiency and renewable energy (e.g., solar, wave), especially in second-generation or third-generation biofuels production, through appropriate competitive credits and subsidies; ii) Brazil should encourage agricultural research to increase agricultural yields and the use of aeroponics for vegetable culture or smart agriculture, because this will lead to less pressure on agricultural lands and therefore on deforestation; iii) A strategy to preserve or even to recover the Brazilian Amazon forest should be established combined with a strategy for developing green tourism.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | The determinants of forest area in Brazil: Ethanol production, exports of crops and livestock, and asymmetric impact of temperature change |
| Language: | English |
| Keywords: | Forest area; Temperature change; Ethanol production; Exports of crops and livestock; Non-linear autoregressive distributed lag; Brazil. |
| Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes F - International Economics > F1 - Trade > F18 - Trade and Environment O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O13 - Agriculture ; Natural Resources ; Energy ; Environment ; Other Primary Products O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O54 - Latin America ; Caribbean Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q15 - Land Ownership and Tenure ; Land Reform ; Land Use ; Irrigation ; Agriculture and Environment Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q23 - Forestry Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming |
| Item ID: | 127224 |
| Depositing User: | Prof. Slim Ben Youssef |
| Date Deposited: | 21 Jan 2026 10:31 |
| Last Modified: | 21 Jan 2026 10:31 |
| References: | Aguiar, D.R.D., Taheripour, F., Silva, D.A.L., 2025. Ethanol fuel in Brazil: Policies and carbon emission avoidance. Biofuels, 16 (3), 248-258. https://doi.org/10.1080/17597269.2024.2405765. Ajanaku, B.A., Collins, A.R., 2021. Economic growth and deforestation in African countries: Is the environmental Kuznets curve hypothesis applicable? Forest Policy and Economics, 129, 102488. https://doi.org/10.1016/j.forpol.2021.102488. Angelsen, A., 2010. Policies for reduced deforestation and their impact on agricultural production. Proceedings of the National Academy of Sciences of the United States of America, 107 (46), 19639-19644. https://doi.org/10.1073/pnas.0912014107. Anwar, M.A., Nasreen, S., Tiwari, A.K., 2021. Forestation, renewable energy and environmental quality: Empirical evidence from Belt and Road Initiative economies. Journal of Environmental Management, 291. https://doi.org/10.1016/j.jenvman.2021.112684. ARGUS (2025). Climate change worsens Brazil's deforestation. https://www.argusmedia.com/en/news-and-insights/latest-market-news/2729001-climate-change-worsens-brazil-s-deforestation. Aydin, M., Cutcu, I., Cayir, B., Magdalena, R., 2024. Investigating the role of biomass energy consumption and forest products trade on the forest footprint in Finland: an environmental Kuznets curve analysis. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-05449-w. Benedek, Z., Fertő, I., 2020. Does economic growth influence forestry trends? An environmental Kuznets curve approach based on a composite Forest Recovery Index. Ecological Indicators, 112, 106067. https://doi.org/10.1016/j.ecolind.2020.106067. Ben Jebli, M., Ben Youssef, S., 2019. Combustible renewables and waste consumption, agriculture, CO2 emissions, and economic growth in Brazil. Carbon Management, 10, 309-321. https://doi.org/10.1080/17583004.2019.1605482. Bhattarai, M., Hammig, M., 2001. Institutions and the Environmental Kuznets Curve for Deforestation: A Crosscountry Analysis for Latin America, Africa and Asia. World Development, 29 (6), 995-1010. https://doi.org/10.1016/S0305-750X(01)00019-5. Brown, R.L., Durbin, J., Evans, J.M., 1975. Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society, Series B, 37 (2), 149–163. https://doi.org/10.1111/j.2517-6161.1975.tb01532.x. Carreira, I., Costa, F., Pessoa, J.P., 2024. The deforestation effects of trade and agricultural productivity in Brazil. Journal of Development Economics, 167, 103217. https://doi.org/10.1016/j.jdeveco.2023.103217. Carvalho, T.S., Domingues E.P., 2016. Economic and deforestation scenario for the Brazilian Amazon between 2006 and 2030. Nova Economia, 26 (2), 585–621. http://dx.doi.org/10.1590/0103-6351/2665. Cortez, L.A.B., 2023. Impact of Corn Ethanol Production on Land Use in Brazil. Chapter In: Evaluation and Utilization of Bioethanol Fuels. I. Edited by Ozcan Konur. Taylor & Francis Group. CRC Press. https://doi.org/10.1201/9781003226567. Costa, F., Hsiao, A., Pellegrina, H., Souza-Rodrigues, E., 2025. Deforestation. VoxDevLit, 18 (1). https://voxdev.org/sites/default/files/2025-09/Deforestation_Issue_1.pdf. Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431. Energy Information Administration, 2025. International Energy Statistics. https://www.eia.gov/international/overview/world. Faria, W.R., Almeida, A.N., 2016. Relationship between openness to trade and deforestation: Empirical evidence from the Brazilian Amazon. Ecological Economics, 121, 85-97. https://doi.org/10.1016/j.ecolecon.2015.11.014. Food and Agriculture Organisation, 2025. FAOSTAT. https://www.fao.org/faostat/en/#data. Franco, M.A., Rizzo, L.V., Teixeira, M.J. et al., 2025. How climate change and deforestation interact in the transformation of the Amazon rainforest. Nature Communications, 16, 7944. https://doi.org/10.1038/s41467-025-63156-0. Gatti, L.V., Basso, L.S., Miller, J.B. et al., 2021. Amazonia as a carbon source linked to deforestation and climate change. Nature, 595, 388–393. https://doi.org/10.1038/s41586-021-03629-6. Govoni, C., Zhuo, L., Marchioni, D.M., Rulli, M.C., 2025. China's animal-protein-rich diets are increasingly reliant on Brazil's land and water resources. Nature Food, 6, 954-967. https://doi.org/10.1038/s43016-025-01238-4. Halkos, G., Skouloudis, A., 2020. Revisiting the dynamics of forest area change: A panel data assessment. Journal of Forest Economics, 35 (2-3), 107–127. http://dx.doi.org/10.1561/112.00000511. Kauano, E.E., Silva, J.M.C., Filho, J.A.F.D., Michalski, F., 2020. Do protected areas hamper economic development of the Amazon region? An analysis of the relationship between protected areas and the economic growth of Brazilian Amazon municipalities. Land Use Policy, 92, 104473. https://doi.org/10.1016/j.landusepol.2020.104473. Kröger, M., 2017. Inter-sectoral determinants of forest policy: the power of deforesting actors in post-2012 Brazil. Forest Policy and Economics, 77, 24-32. https://doi.org/10.1016/j.forpol.2016.06.003. Lapola, D. M., Schaldach, R., Alcamo, J., Bondeau, A., Msangi, S., Priess, J.A., Silvestrini, R., Soares-Filho, B.S., 2011. Impacts of climate change and the end of deforestation on land use in the Brazilian Legal Amazon. Earth Interactions, 15, 1–29. https://doi.org/10.1175/2010EI333.1. Munday, G., Jones, C.D., Steinert, N.J. et al., 2025. Risks of unavoidable impacts on forests at 1.5 °C with and without overshoot. Nature Climate Change, 15, 650–655. https://doi.org/10.1038/s41558-025-02327-9. Pablo-Romero, M.P., Sánchez-Braza, A., Gil-Pérez, J., 2023. Is deforestation needed for growth? Testing the EKC hypothesis for Latin America. Forest Policy and Economics, 148, 102915. https://doi.org/10.1016/j.forpol.2023.102915. Pereira, O., Bernasconi, P., 2025. Brazilian beef exports and deforestation. Trase. https://trase.earth/insights/brazilian-beef-exports-and-deforestation-2025. Pesaran, M.H., Pesaran, B., 1997. Working With Microfit 4.0: Interactive Econometric Analysis. Oxford University Press, Oxford. Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326. https://doi.org/10.1002/jae.616. Pesaran, M.H., Smith, R.P., 1998. Structural analysis of cointegrating VARs. Journal of Economic Surveys, 12, 471–505. https://doi.org/10.1111/1467-6419.00065. Pratzer, M., Fernández-Llamazares, Á., Meyfroidt, P., et al., 2023. Agricultural intensification, Indigenous stewardship and land sparing in tropical dry forests. Nature Sustainability, 6, 671–682. https://doi.org/10.1038/s41893-023-01073-0. Raihan, A., Begum, R.A., Nizam, M., Said M, Pereira, J.J., 2022. Dynamic impacts of energy use, agricultural land expansion, and deforestation on CO2 emissions in Malaysia. Environmental and Ecological Statistics, 29, 477–507. https://doi.org/10.1007/s10651-022-00532-9. Raihan, A., Tuspekova, A., 2022. Dynamic impacts of economic growth, energy use, urbanization, tourism, agricultural value-added, and forested area on carbon dioxide emissions in Brazil. Journal of Environmental Studies and Sciences, 12, 794–814. https://doi.org/10.1007/s13412-022-00782-w. Shin, Y., Yu, B., Greenwood-Nimmo, M., 2014. Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: R.C. Sickles and W.C. Horrace (eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications. Springer, New York, pp. 281–314. https://link.springer.com/chapter/10.1007/978-1-4899-8008-3_9. Silva, R.F.B., Moran, E.F., Millington, J.D.A., Vina, A., Liu, J., 2023. Complex relationships between soybean trade destination and tropical deforestation. Scientific Reports, 13, 11254. https://doi.org/10.1038/s41598-023-38405-1. Souza, H., Barbosa, G., 2025. Testing Kuznets’ environmental hypothesis for the Legal Amazon: A nonlinear approach. Environment and Development Economics, 1-18. https://doi.org/10.1017/S1355770X25000063. Sousa, W. L., Irffi, G., Asevedo, M. D. G., 2022. Deforestation of the Atlantic Forest in the state of Ceará: Analysis of the Environmental Kuznets curve from panel data, 2011 to 2017. Revista de Economia e Sociologia Rural, 60(1), e229884. https://doi.org/10.1590/1806-9479.2021.229884. Souza, R.M., Ribeiro, J.C., 2025. The impact of Brazil’s agricultural export boom on domestic food security. Law and Economy, 4 (1), 38 – 47. https://www.paradigmpress.org/le. Tan, K.T., Lee, K.T., Mohamed, A.R., 2008. Role of energy policy in renewable energy accomplishment: The case of second-generation bioethanol. Energy Policy, 36 (9), 3360-3365. https://doi.org/10.1016/j.enpol.2008.05.016. Tan, J., Tachibana, S., 2025. Socioeconomic factors influencing forest area changes in China: Regional panel autoregressive distributed lag modeling approach. Japan Agricultural Research Quarterly, 59 (2), 175-186. https://doi.org/10.6090/jarq.24J05. World Bank, 2025. World Development Indicators. Accessed at: https://databank.worldbank.org/source/world-development-indicators. Zivot, E., Andrews, D., 1992. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10 (3), 251-70. https://doi.org/10.1080/07350015.1992.10509904. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/127224 |

