IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v152y2021ics1364032121008893.html
   My bibliography  Save this article

Framework for improving agro-industrial efficiency in renewable energy: Examining Brazilian bioenergy companies

Author

Listed:
  • Lemos, S.V.
  • Salgado Junior, A.P.
  • Rebehy, P.C.P.W.
  • Carlucci, F.V.
  • Novi, J.C.

Abstract

Transitioning to a low-carbon economy, a goal referenced in the United Nations Framework Convention on Climate Change, is possible through strategies: improving energy efficiency, using low-carbon fuels, using geoengineering approaches, capturing and storing CO2, and renewable energy sources. The objective of this study was to propose best practices for improving agricultural and industrial efficiency in production of sugarcane. This study's quali-quantitative method included a two-stage data envelopment analysis and a multi-case study on Brazilian bioenergy companies. A two-stage Data Envelopment Analysis (DEA) was applied to select efficient cases and identify variables to be addressed during a multi-case study. The variables were associated with production process of 29 Brazilian bioenergy companies during 2006–2016 harvests. Multiple cases were studied by evaluating these variables among the most efficient sugarcane mills. Fifteen best practices were identified: (i) Agricultural, industrial, and managerial dimensions affect mill performance; managerial dimension is a new contribution. (ii) Each best practice affects variables with some intersections among them. (iii) Correlation between variables and productive chain indicates mill performance depends on field sector. (iv) Climate change affects the field stage as a result of uncontrollable environmental factors, such as rainfall and temperature. The study also verified benchmark values for indicators that support mill management: total reducing sugars must be higher than 16.46 %; dextran should be lower than 665.58 mg/L brix; sugarcane borers must be under 2.29 %; rod-shaped bacteria must be less than 2.53 × 105/mL; impurities should be lower than 0.275 %; and sucrose must be higher than 14.98 %.

Suggested Citation

  • Lemos, S.V. & Salgado Junior, A.P. & Rebehy, P.C.P.W. & Carlucci, F.V. & Novi, J.C., 2021. "Framework for improving agro-industrial efficiency in renewable energy: Examining Brazilian bioenergy companies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
  • Handle: RePEc:eee:rensus:v:152:y:2021:i:c:s1364032121008893
    DOI: 10.1016/j.rser.2021.111613
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032121008893
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2021.111613?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Savin, N Eugene & White, Kenneth J, 1977. "The Durbin-Watson Test for Serial Correlation with Extreme Sample Sizes or Many Regressors," Econometrica, Econometric Society, vol. 45(8), pages 1989-1996, November.
    2. Klein, Bruno Colling & Chagas, Mateus Ferreira & Watanabe, Marcos Djun Barbosa & Bonomi, Antonio & Maciel Filho, Rubens, 2019. "Low carbon biofuels and the New Brazilian National Biofuel Policy (RenovaBio): A case study for sugarcane mills and integrated sugarcane-microalgae biorefineries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Juliana Quintanilha da Silveira & João Carlos Correia Baptista Soares de Mello & Lidia Angulo-Meza, 2019. "Input redistribution using a parametric DEA frontier and variable returns to scale: The parabolic efficient frontier," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 751-759, May.
    4. Karl Widerquist, 2018. "The Bottom Line," Exploring the Basic Income Guarantee, in: A Critical Analysis of Basic Income Experiments for Researchers, Policymakers, and Citizens, chapter 0, pages 93-98, Palgrave Macmillan.
    5. Ana J. Righetto & Thiago G. Ramires & Luiz R. Nakamura & Pedro L. D. B. Castanho & Christel Faes & Taciana V. Savian, 2019. "Predicting weed invasion in a sugarcane cultivar using multispectral image," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(1), pages 1-12, January.
    6. Odeck, James, 2009. "Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers," Omega, Elsevier, vol. 37(5), pages 1007-1017, October.
    7. Goto, Mika & Tsutsui, Miki, 1998. "Comparison of Productive and Cost Efficiencies Among Japanese and US Electric Utilities," Omega, Elsevier, vol. 26(2), pages 177-194, April.
    8. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    9. Fuseini Issaka & Zhen Zhang & Zhong-Qiu Zhao & Evans Asenso & Jiu-Hao Li & Yong-Tao Li & Jin-Jin Wang, 2019. "Sustainable Conservation Tillage Improves Soil Nutrients and Reduces Nitrogen and Phosphorous Losses in Maize Farmland in Southern China," Sustainability, MDPI, vol. 11(8), pages 1-13, April.
    10. Oliveira, Dener M.S. & Cherubin, Maurício R. & Franco, André L.C. & Santos, Augusto S. & Gelain, Jaquelini G. & Dias, Naissa M.S. & Diniz, Tatiana R. & Almeida, Alexandre N. & Feigl, Brigitte J. & Dav, 2019. "Is the expansion of sugarcane over pasturelands a sustainable strategy for Brazil's bioenergy industry?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 346-355.
    11. de Moraes Dutenkefer, Raphael & de Oliveira Ribeiro, Celma & Morgado Mutran, Victoria & Eduardo Rego, Erik, 2018. "The insertion of biogas in the sugarcane mill product portfolio: A study using the robust optimization approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 729-740.
    12. Filoso, Solange & Carmo, Janaina Braga do & Mardegan, Sílvia Fernanda & Lins, Silvia Rafaela Machado & Gomes, Taciana Figueiredo & Martinelli, Luiz Antonio, 2015. "Reassessing the environmental impacts of sugarcane ethanol production in Brazil to help meet sustainability goals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1847-1856.
    13. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    14. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    15. Lemos, Stella Vannucci & Salgado, Alexandre Pereira & Duarte, Alexandre & de Souza, Marco Antonio Alves & de Almeida Antunes, Fernanda, 2019. "Agroindustrial best practices that contribute to technical efficiency in Brazilian sugar and ethanol production mills," Energy, Elsevier, vol. 177(C), pages 397-411.
    16. Pereira, L.G. & Cavalett, O. & Bonomi, A. & Zhang, Y. & Warner, E. & Chum, H.L., 2019. "Comparison of biofuel life-cycle GHG emissions assessment tools: The case studies of ethanol produced from sugarcane, corn, and wheat," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 1-12.
    17. de Carvalho, Ariovaldo Lopes & Antunes, Carlos Henggeler & Freire, Fausto, 2016. "Economic-energy-environment analysis of prospective sugarcane bioethanol production in Brazil," Applied Energy, Elsevier, vol. 181(C), pages 514-526.
    18. De Clercq, Djavan & Wen, Zongguo & Caicedo, Luis & Cao, Xin & Fan, Fei & Xu, Ruifei, 2017. "Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China," Applied Energy, Elsevier, vol. 205(C), pages 1231-1243.
    19. Jamasb, T. & Pollitt, M., 2000. "Benchmarking and regulation: international electricity experience," Utilities Policy, Elsevier, vol. 9(3), pages 107-130, September.
    20. R. W. Farebrother, 1990. "The Distribution of a Quadratic Form in Normal Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(2), pages 294-309, June.
    21. Huang, Wei & Eling, Martin, 2013. "An efficiency comparison of the non-life insurance industry in the BRIC countries," European Journal of Operational Research, Elsevier, vol. 226(3), pages 577-591.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hamed, Mohammad M. & Mohammed, Ali & Olabi, Abdul Ghani, 2023. "Renewable energy adoption decisions in Jordan's industrial sector: Statistical analysis with unobserved heterogeneity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Wei, Yu & Zhang, Jiahao & Chen, Yongfei & Wang, Yizhi, 2022. "The impacts of El Niño-southern oscillation on renewable energy stock markets: Evidence from quantile perspective," Energy, Elsevier, vol. 260(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pereira de Souza, Marcus Vinicius & Souza, Reinaldo C. & Pessanha, José Francisco M. & da Costa Oliveira, Carlos Henrique & Diallo, Madiagne, 2014. "An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities," Socio-Economic Planning Sciences, Elsevier, vol. 48(3), pages 169-174.
    2. Lemos, Stella Vannucci & Salgado, Alexandre Pereira & Duarte, Alexandre & de Souza, Marco Antonio Alves & de Almeida Antunes, Fernanda, 2019. "Agroindustrial best practices that contribute to technical efficiency in Brazilian sugar and ethanol production mills," Energy, Elsevier, vol. 177(C), pages 397-411.
    3. Canabarro, N.I. & Silva-Ortiz, P. & Nogueira, L.A.H. & Cantarella, H. & Maciel-Filho, R. & Souza, G.M., 2023. "Sustainability assessment of ethanol and biodiesel production in Argentina, Brazil, Colombia, and Guatemala," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    4. Ogunrinde, Olawale & Shittu, Ekundayo, 2023. "Efficiency and productivity of renewable energy technologies: Evidence from U.S. investor-owned utilities across regional markets," Utilities Policy, Elsevier, vol. 82(C).
    5. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.
    6. Nakano, Makiko & Managi, Shunsuke, 2008. "Regulatory reforms and productivity: An empirical analysis of the Japanese electricity industry," Energy Policy, Elsevier, vol. 36(1), pages 201-209, January.
    7. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    8. Thomas Sargent & Noah Williams & Tao Zha, 2006. "Shocks and Government Beliefs: The Rise and Fall of American Inflation," American Economic Review, American Economic Association, vol. 96(4), pages 1193-1224, September.
    9. Francesco Bianchi, 2013. "Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(2), pages 463-490.
    10. Munechika Katayama & Kwang Hwan Kim, 2018. "Intersectoral Labor Immobility, Sectoral Comovement, and News Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 77-114, February.
    11. Queijo, Virginia, 2005. "How Important are Financial Frictions in the U.S. and Euro Area?," Seminar Papers 738, Stockholm University, Institute for International Economic Studies.
    12. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
    13. Hibiki Ichiue & Takushi Kurozumi & Takeki Sunakawa, 2013. "Inflation Dynamics And Labor Market Specifications: A Bayesian Dynamic Stochastic General Equilibrium Approach For Japan'S Economy," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 273-287, January.
    14. Ippei Fujiwara & Yasuo Hirose & Mototsugu Shintani, 2011. "Can News Be a Major Source of Aggregate Fluctuations? A Bayesian DSGE Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 1-29, February.
    15. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    16. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    17. Zheng Liu & Daniel F. Waggoner & Tao Zha, 2009. "Sources of the Great Moderation: shocks, frictions, or monetary policy?," FRB Atlanta Working Paper 2009-03, Federal Reserve Bank of Atlanta.
    18. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    19. Kleijn, R.H. & van Dijk, H.K., 2001. "A Bayesian analysis of the PPP puzzle using an unobserved components model," Econometric Institute Research Papers EI 2001-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Pablo Guerrón-Quintana & Alexey Khazanov & Molin Zhong, 2023. "Financial and Macroeconomic Data Through the Lens of a Nonlinear Dynamic Factor Model," Finance and Economics Discussion Series 2023-027, Board of Governors of the Federal Reserve System (U.S.).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:152:y:2021:i:c:s1364032121008893. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.