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Economic Feasibility and Decarbonization Incentives of Sugarcane Biogas Production Pathways

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  • Flavio Eduardo Fava

    (Graduate Program on Agricultural Systems Engineering, “Luiz de Queiroz” College of Agriculture, University of Sao Paulo, Av. Padua Dias, 11, Postal Box 9, Piracicaba CEP 13418-900, SP, Brazil)

  • Lucílio Rogério Aparecido Alves

    (Department of Economics, Administration and Sociology, “Luiz de Queiroz” College of Agriculture, University of Sao Paulo, Av. Padua Dias, 11, Postal Box 9, Piracicaba CEP 13418-900, SP, Brazil)

  • Thiago Libório Romanelli

    (Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture, University of Sao Paulo, Av. Pádua Dias, 11, Postal Box 9, Piracicaba CEP 13418-900, SP, Brazil)

Abstract

Challenges in investment decisions for new fuels remain due to uncertain scenarios regarding profitability. There is also a challenge to improve production efficiency and waste utilization, either for biomass or by-products. This study evaluates the economic potential of biomethane production within sugarcane biorefineries through the principles of the circular economy and economic feasibility. To obtain price data for CBios, Brent crude oil, and natural gas, stochastic models based on GBM and Monte Carlo simulations were applied to project prices and assess revenue potential over a 10-year horizon. Price data were incorporated to assess market correlations and revenue scenarios. Key findings reveal that biomethane’s price stability, driven by its strong correlation with oil markets, supports its viability as a renewable energy source, while CBio presents a weak correlation and limited price predictability with present challenges for long-term planning. Economic modeling indicates high investment returns, with IRR values surpassing 35% in conservative scenarios and payback periods from 2 to 6 years. These results highlight biomethane’s potential for energy efficiency, carbon emission reduction, and the creation of new revenue through waste use. We conclude that targeted investments in biomethane infrastructure, coupled with policy and market support, are essential for achieving global sustainability goals.

Suggested Citation

  • Flavio Eduardo Fava & Lucílio Rogério Aparecido Alves & Thiago Libório Romanelli, 2025. "Economic Feasibility and Decarbonization Incentives of Sugarcane Biogas Production Pathways," Agriculture, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:4:p:380-:d:1588600
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    References listed on IDEAS

    as
    1. Milão, Raquel de Freitas Dias & Carminati, Hudson B. & Araújo, Ofélia de Queiroz F. & de Medeiros, José Luiz, 2019. "Thermodynamic, financial and resource assessments of a large-scale sugarcane-biorefinery: Prelude of full bioenergy carbon capture and storage scenario," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Robert N. Stavins, 2022. "The Relative Merits of Carbon Pricing Instruments: Taxes versus Trading," Review of Environmental Economics and Policy, University of Chicago Press, vol. 16(1), pages 62-82.
    3. Kirchherr, Julian & Reike, Denise & Hekkert, Marko, 2017. "Conceptualizing the circular economy: An analysis of 114 definitions," Resources, Conservation & Recycling, Elsevier, vol. 127(C), pages 221-232.
    4. Keogh, Niamh & Corr, D. & O'Shea, R. & Monaghan, R.F.D., 2022. "The gas grid as a vector for regional decarbonisation - a techno economic case study for biomethane injection and natural gas heavy goods vehicles," Applied Energy, Elsevier, vol. 323(C).
    5. Parameswaran Gopikrishnan & Martin Meyer & Luis A Nunes Amaral & H Eugene Stanley, 1998. "Inverse Cubic Law for the Probability Distribution of Stock Price Variations," Papers cond-mat/9803374, arXiv.org, revised May 1998.
    6. P. Gopikrishnan & M. Meyer & L.A.N. Amaral & H.E. Stanley, 1998. "Inverse cubic law for the distribution of stock price variations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 3(2), pages 139-140, July.
    7. Vandenberghe, L.P.S. & Valladares-Diestra, K.K. & Bittencourt, G.A. & Zevallos Torres, L.A. & Vieira, S. & Karp, S.G. & Sydney, E.B. & de Carvalho, J.C. & Thomaz Soccol, V. & Soccol, C.R., 2022. "Beyond sugar and ethanol: The future of sugarcane biorefineries in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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