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Brazilian sugarcane ethanol as an expandable green alternative to crude oil use

Matters Arising to this article was published on 25 February 2019

Abstract

Reduction of CO2 emissions will require a transition from fossil fuels to alternative energy sources. Expansion of Brazilian sugarcane ethanol1,2 provides one near-term scalable solution to reduce CO2 emissions from the global transport sector. In contrast to corn ethanol, the Brazilian sugarcane ethanol system may offset 86% of CO2 emissions compared to oil use, and emissions resulting from land-use change to sugarcane are paid back in just 2–8 years3,4. But, it has been uncertain how much further expansion is possible given increasing demand for food and animal feed, climate change impacts and protection of natural ecosystems. We show that Brazilian sugarcane ethanol can provide the equivalent of 3.63–12.77 Mb d−1 of crude oil by 2045 under projected climate change while protecting forests under conservation5 and accounting for future land demand for food and animal feed production. The corresponding range of CO2 offsets is 0.55–2.0 Gigatons yr−1. This would displace 3.8–13.7% of crude oil consumption and 1.5–5.6% of net CO2 emission globally relative to data for 20146,7.

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Figure 1: The fraction of land available for sugarcane expansion by 2045 in each of the legally defined micro-regions of Brazil under the three land-use scenarios considered in this study.
Figure 2: Yield change map of harvested stem yield (wet basis) to demonstrate the spatial distribution of climate change impact on sugarcane production by 2045.

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References

  1. De Souza, A. P., Grandis, A., Leite, D. C. C. & Buckeridge, M. S. Sugarcane as a bioenergy source: history, performance, and perspectives for second-generation bioethanol. Bioenerg. Res. 7, 24–35 (2014).

    Article  Google Scholar 

  2. Somerville, C., Youngs, H., Taylor, C., Davis, S. C. & Long, S. P. Feedstocks for lignocellulosic biofuels. Science 329, 790–792 (2010).

    Article  CAS  Google Scholar 

  3. Searchinger, T. et al. Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319, 1238–1240 (2008).

    Article  CAS  Google Scholar 

  4. Mello, F. F. C. et al. Payback time for soil carbon and sugarcane ethanol. Nat. Clim. Change 4, 605–609 (2014).

    Article  CAS  Google Scholar 

  5. Sparovek, G., Barretto, A. G. O. P., Matsumoto, M. & Berndes, G. Effects of governance on availability of land for agriculture and conservation in Brazil. Environ. Sci. Technol. 49, 10285–10293 (2015).

    Article  CAS  Google Scholar 

  6. BP Statistical Review of World Energy (BP, 2016)https://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-full-report.pdf

  7. Olivier, J. G. J., Janssens-Maenhout, G., Muntean, M. & Peters, J. A. H. W. Trends in Global CO2 Emission: 2015 Report, The Hague (PBL Netherlands Environmental Assessment Agency; Ispra: European Commission, Joint Research Centre, 2015).

    Google Scholar 

  8. GeoSpacial Library - Agroecological Zoning (Brazilian Agricultural Research Corporation, 2014); http://geo.cnpma.embrapa.br/projeto_en.aspx

  9. Magrin, G. O. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Barros, V. R. et al.) 1499–1566 (IPCC, Cambridge Univ. Press, 2014).

    Google Scholar 

  10. Ferreira-Leitão, V. et al. Biomass residues in Brazil: availability and potential uses. Waste Biomass Valorization 1, 65–76 (2010).

    Article  Google Scholar 

  11. Marin, F. R., Thorburn, P. J., Nassif, D. S. P. & Costa, L. G. Sugarcane model intercomparison: structural differences and uncertainties under current and potential future climates. Environ. Model. Softw. 72, 372–386 (2015).

    Article  Google Scholar 

  12. Marin, F. R., Ribeiro, R. V. & Marchiori, P. E. R. How can crop modeling and plant physiology help to understand the plant responses to climate change? A case study with sugarcane. Theor. Exp. Plant Physiol. 26, 49–63 (2014).

    Article  CAS  Google Scholar 

  13. Miguez, F. E., Zhu, X., Humphries, S., Bollero, G. A. & Long, S. P. A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus × giganteus: description, parameterization and validation. Glob. Change Biol. Bioenergy 1, 282–296 (2009).

    Article  Google Scholar 

  14. Cooper, M., Mendes, L. M. S., Silva, W. L. C. & Sparovek, G. A national soil profile database for Brazil available for international scientists. Soil Sci. Soc. Am. J. 69, 649–652 (2005).

    Article  CAS  Google Scholar 

  15. Sugarcane Production and Procesing per Harvesting (Brazilian sugarcane industry association (UNICA), 2016)http://www.unicadata.com.br/historico-de-producao-e-moagem.php?idMn=32&tipoHistorico=4

  16. Bodirsky, B. L. et al. Global food demand scenarios for the 21st century. PLoS ONE 10, e0139201 (2015).

    Article  Google Scholar 

  17. Nelson, G. C. et al. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).

    Article  CAS  Google Scholar 

  18. Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    Article  CAS  Google Scholar 

  19. Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).

    Article  CAS  Google Scholar 

  20. Loiola, M. L. & Souza, F. Statistics on irrigation in Brazil according to the 1995–1996 agricultural census. Rev. Bras. Eng. Agr. Amb. 5, 171–180 (2001).

    Article  Google Scholar 

  21. Oliveira, M. E. D., Vaughan, B. E. & Rykiel, E. J. Ethanol as fuel: energy, carbon dioxide balances, and ecological footprint. BioScience 55, 593–602 (2005).

    Article  Google Scholar 

  22. Galdos, M., Cantarella, H., Hastings, A., Hillier, J. & Smith, P. Advances of Basic Science for Second Generation Bioethanol from Sugarcane (eds Buckeridge, M. S. & De Souza, A. P.) 177–195 (Springer, 2017).

    Book  Google Scholar 

  23. Watanabe, M. D. B. et al. Hybrid input-output life cycle assessment of first- and second-generation ethanol production technologies in Brazil. J. Ind. Ecol. 20, 764–774 (2016).

    Article  CAS  Google Scholar 

  24. Le Mer, J. & Roger, P. Production, oxidation, emission and consumption of methane by soils: a review. Eur. J. Soil Biol. 37, 25–50 (2001).

    Article  CAS  Google Scholar 

  25. Otto, R. et al. Nitrogen use efficiency for sugarcane-biofuel production: what is next? Bioenerg. Res. 9, 1272–1289 (2016).

    Article  CAS  Google Scholar 

  26. Crutzen, P. J., Mosier, A. R., Smith, K. A. & Winiwarter, W. Paul J. Crutzen: A Pioneer on Atmospheric Chemistry and Climate Change in the Anthropocene (eds Crutzen, P. J. & Brauch, H. G.) 227–238 (Springer, 2016).

    Book  Google Scholar 

  27. Projections of Agribusinesses. Brazil 2013/14 to 2023/24. Long-Term Projections. Report No. MAPA/ACS, 1–98 (Ministry of Agriculture, Livestock and Food Supply, 2014).

  28. Electric Power Transmission and Distribution Losses (World Bank, 2014); http://data.worldbank.org/indicator/EG.ELC.LOSS.ZS

  29. Losordo, Z. et al. Cost competitive second-generation ethanol production from hemicellulose in a Brazilian sugarcane biorefinery. Biofuels Bioprod. Biorefin. 10, 589–602 (2016).

    Article  CAS  Google Scholar 

  30. Tao, L., Schell, D., Tan, E. C. & Elander, R. NREL 2012 Achievement of Ethanol Cost Targets: Biochemical Ethanol Fermentation via Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover (National Renewable Energy Laboratory, 2014); http://www.nrel.gov/docs/fy14osti/61563.pdf

    Book  Google Scholar 

  31. Leahy, J. Brazilian ethanol producer sees boost from plant waste. Financial Times (25 July 2017); https://www.ft.com/content/366b86e4-5933-11e7-9bc8-8055f264aa8b

    Google Scholar 

  32. Lee, H. Turning the focus to solutions. Science 350, 1007 (2015).

    Article  CAS  Google Scholar 

  33. Larsen, S., Jaiswal, D., Bentsen, N. S., Wang, D. & Long, S. P. Comparing predicted yield and yield stability of willow and miscanthus across Denmark. Glob. Change Biol. Bioenergy 8, 1061–1070 (2016).

    Article  Google Scholar 

  34. Miguez, F. E., Maughan, M., Bollero, G. A. & Long, S. P. Modeling spatial and dynamic variation in growth, yield, and yield stability of the bioenergy crops Miscanthus × giganteus and Panicum virgatum across the conterminous United States. Glob. Change Biol. Bioenergy 4, 509–520 (2012).

    Article  Google Scholar 

  35. Wang, D. et al. A physiological and biophysical model of coppice willow (Salix spp.) production yields for the contiguous USA in current and future climate scenarios. Plant Cell Env. 38, 1850–1865 (2015).

    Article  Google Scholar 

  36. Collatz, G., Ribas-Carbo, M. & Berry, J. Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Funct. Plant Biol. 19, 519–538 (1992).

    Article  Google Scholar 

  37. Leakey, A. D. B. et al. Photosynthesis, productivity, and yield of maize are not affected by open-air elevation of CO2 concentration in the absence of drought. Plant Physiol. 140, 779–790 (2006).

    Article  CAS  Google Scholar 

  38. LeBauer, D. S. et al. BETYdb: a yield, trait, and ecosystem service database applied to second-generation bioenergy feedstock production. Glob. Change Biol. Bioenergy http://dx.doi.org/10.1111/gcbb.12420 (2017).

  39. University of São Paulo. Brazil Soil Database (accessed 27 March 2014); http://www.esalq.usp.br/gerd

  40. Reichert, J. M. et al. Estimation of water retention and availability in soils of Rio Grande do Sul. Rev. Bras. Cienc. Solo. 33, 1547–1560 (2009).

    Article  Google Scholar 

  41. Braga, R. L. C. Jr, Oliveira, I. A., Souza Andrade, F. & Nardy, V. Censo Varietal e de Produtividade em 2012 (CTC, 2012).

    Google Scholar 

  42. Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction - the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).

    Article  Google Scholar 

  43. Nychka, D., Furrer, R., Paige, J., Sain, S. & Nychka, M. D. R Package ‘fields’ (2017); http://www.image.ucar.edu/fields

    Google Scholar 

  44. ESRI 2011, ArcGIS Desktop. Release 10 (Environmental Systems Research Institute, 2011).

  45. Macedo, I. C., Leal, M. R. L. V. & Hassuani, S. J. Sugarcane residues for power generation in the sugar/ethanol mills in Brazil. Energy Sustain. Dev. 1, 77–82 (2001).

    Article  Google Scholar 

  46. Schogor, A. L. B. et al. Losses in sugarcane submitted to different harvesting methods. Revista Brasileira de Zootecnia 38, 1443–1450 (2009).

    Article  Google Scholar 

  47. Long, S. P. et al. Bioenergy and Sustainability: Bridging the Gaps (eds Souza, G. M., Victoria, R. L., Joly, C. A. & Verdade, L. M.) (SCOPE/72, 2015).

  48. Brazilian Institute of Geography and Statistics (IBGE) Table 1612 - Planted Area, Area Harvested, Quantity Produced, Average Yield and Production Value of Temporary Crops (2014); https://sidra.ibge.gov.br/Tabela/1612

  49. Brazilian Institute of Geography and Statistics (IBGE) Table 73 - Effective of Herds, by Type of Herd (Series Closed) (2014); https://sidra.ibge.gov.br/Tabela/73

  50. Brazilian Institute of Geography and Statistics (IBGE) Table 264 - Area of Agricultural Establishments by Land Use - Historical Series (1970/2006) (2014); https://sidra.ibge.gov.br/Tabela/264

  51. Brazilian Institute of Geography and Statistics (IBGE) Table 281 - Effective of Animals in Agricultural Establishments by Type of Herd - Historical Series (1970/2006) (2014); https://sidra.ibge.gov.br/Tabela/281

  52. De Souza, A. P. Photosynthetic mechanism and source-sink relationship in sugarcane grown in elevated CO2. PhD thesis, Univ. São Paulo (2011).

  53. Patzek, T. W. & Pimentel, D. Thermodynamics of energy production from biomass. Crit. Rev. Plant Sci. 24, 327–364 (2006).

    Article  Google Scholar 

  54. Pereira, S. C., Maehara, L., Machado, C. M. M. & Farinas, C. S. 2G ethanol from the whole sugarcane lignocellulosic biomass. Biotechnol. Biofuels 8, 1–16 (2015).

    Article  Google Scholar 

  55. Preston, T. R. Nutritive value of sugarcane for ruminants. Trop. Anim. Prod. 2, 125–142 (1977).

    Google Scholar 

  56. Annual Sugar Report (International Sugar Organization, 2009); http://www.isosugar.org

  57. Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision (Food and Agriculture Organization of the United Nations, 2012).

    Google Scholar 

  58. Vasconcelos, J. N., Lopes, C. E. & Franca, F. P. Continuous ethanol production using yeast immobilized on sugar-cane stalks. Braz. J. Chem. Eng. 21, 357–365 (2004).

    Article  Google Scholar 

  59. Miller, K. Solid–liquid separation technologies in the conversion of bagasse to liquid fuel. MS thesis, Louisiana State Univ. (2010).

  60. Dwivedi, P. et al. Cost of abating greenhouse gas emissions with cellulosic ethanol. Environ. Sci. Technol. 49, 2512–2522 (2015).

    Article  CAS  Google Scholar 

  61. Jaiswal, D. et al. Brazilian Sugarcane Ethanol as an expandable green alternative to crude oil use. Dryad Digital Repositoryhttp://dx.doi.org/10.5061/dryad.222j0.

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Acknowledgements

D.J. and S.P.L. acknowledge the financial support of the Energy Bioscience Institute and the Center for Advanced Bioenergy and Bioproducts Innovation, both within the University of Illinois. D.J. acknowledges advice from D. Tanjore of Lawrence Berkeley National Lab on calculations related to second-generation ethanol production and J. R. Soares of University of Campinas on issues related to nitrogen in Brazilian sugarcane operation. S.P.L. acknowledges the support of the Newton-Abrahams Visiting Professorship at the University of Oxford, UK.

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D.J. and S.P.L. led the study and analysis. D.J., A.P.D.S. and S.P.L. drafted the manuscript with support from S.L., D.S.L., F.E.M., G.S., G.B. and M.S.B. S.L. and G.S. assisted with soil, land-use data and developing land-use change model. A.P.D.S. and M.S.B. collected data from the Brazilian literature and databases for evaluating model performance and current status of ethanol industry in Brazil. D.S.L. assisted in obtaining climate data to perform simulations. D.J., F.E.M., G.B. and S.L. contributed to the development of the model to project sugarcane production.

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Correspondence to Stephen P. Long.

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The authors declare no competing financial interests.

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Jaiswal, D., De Souza, A., Larsen, S. et al. Brazilian sugarcane ethanol as an expandable green alternative to crude oil use. Nature Clim Change 7, 788–792 (2017). https://doi.org/10.1038/nclimate3410

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