Biogas and Biomethane Technologies: An AHP Model to Support the Policy Maker in Incentive Design in Italy

  • Donatella Banzato
  • Rubina CanesiEmail author
  • Chiara D’Alpaos
Conference paper
Part of the Green Energy and Technology book series (GREEN)


Over the past six years, biogas production in Italy has experienced an economic boom: investments of more than 4.5 billion euros and production of about 2 billion normal m2 of natural gas equivalent. By contrast, biomethane production in Italy is not widespread. This limited spread substantially results from the lack of effective government incentives for biomethane production. In the near future, the Italian government is expected to fix new feed-in tariff (FIT) schemes for energy production from renewable energy sources (RES). In this context, it is fundamental for the policy maker to determine whether it will be preferable to introduce more generous FITs to support biogas production for electric-power generation or biomethane production through biogas upgrading. In this paper, we propose a multicriteria decision model to support the policy maker in the definition of sustainable development policies for biogas and biomethane production. Specifically we provide an Analytic Hierarchy Process (AHP) model to multicriteria prioritization of incentives paid to biogas versus biomethane. In accord with group decision-making approaches, we selected a pool of experts that structured the decision problem and disaggregated it into a hierarchy by identifying quantitative and qualitative criteria and subcriteria to evaluate each technology. The model results reveal that biomethane production plants are preferred to biogas production plants, independently of their size, whereas larger biomethane installations are ranked higher than smaller ones. Under stringent public budget constraints, it might be de facto inefficient and not cost-effective to introduce incentive mechanisms for biogas-production plants.


Biogas Biomethane RES support policy Incentive design AHP 


  1. Auer, J., Resch, G., Haas, R., Held, A., & Ragwitz, M. (2009). Regulatory instruments to deliver the full potential of renewable energy sources of efficiently. European Review of Energy Markets, 3(2), 91–124.Google Scholar
  2. Bana e Costa, C., & Vansnick, J. (2008). A critical analysis of the eigenvalue method used to derive priorities in AHP. European Journal of Operational Research, 187(3), 1422–1428.MathSciNetCrossRefGoogle Scholar
  3. Banzato, D. (2015). The incentive system for the production of electricity and thermal energy from anaerobic digestion in Italy and Europe: A comparison. Valori e Valutazioni, 15, 43–53.Google Scholar
  4. Banzato, D. (2016). The use of the digestate from anaerobic digestion: A comparison with the EU countries. Valori e Valutazioni, 17, 73–80.Google Scholar
  5. Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: An integrated approach. Dordrecht: Kluwer Academic Publisher.CrossRefGoogle Scholar
  6. Billig, E., & Thrän, D. (2016). Evaluation of biomethane technologies in Europe—Technical concepts under the scope of a delphi-survey embedded in a multi-criteria analysis. Energy, 114, 1176–1186.CrossRefGoogle Scholar
  7. Canesi, R., D’Alpaos, C., & Marella, G. (2016a). Forced sale values vs. market values in Italy. Journal of Real Estate Literature, 24(2), 377–401.Google Scholar
  8. Canesi, R., D’Alpaos, C., & Marella, G. (2016b). Foreclosed homes market in Italy: Bases of value. International Journal for Housing Science and Its Applications, 40(3), 201–209.Google Scholar
  9. Cavallaro, F., & Ciraolo, L. (2005). A multicriteria approach to evaluate wind energy plants on an Italian island. Energy Policy, 33(2), 235–244.CrossRefGoogle Scholar
  10. CIB. (2016). Piattaforma Biometano. Documento programmatico. Accessed at April 18, 2017, from
  11. Couture, T. D., Cory, K., Kreycik, C., & Williams, E. (2010). Policymaker’s guide to feed-in tariff policy design (No. NREL/TP-6A2-44849). Golden, CO: National Renewable Energy Laboratory (NREL).Google Scholar
  12. Couture, T., & Gagnon, Y. (2010). An analysis of feed-in tariff remuneration models: Implications for renewable energy investment. Energy Policy, 38(2), 955–965.CrossRefGoogle Scholar
  13. D’Alpaos C., & Canesi R. (2014). MCDM approaches in property investments: An AHP model for risk assessment. In Proceedings of the International Symposium of the Analytic Hierarchy Process (ISAHP), June 29–July 2, 2014. Washington DC, USA.Google Scholar
  14. De Felice, F., & Petrillo, A. (2013). Absolute measurement with analytic hierarchy process: A case study for Italian racecourse. International Journal of Applied Decision Sciences, 6(3), 209–227.CrossRefGoogle Scholar
  15. Dinca, C., Badea, A., Rousseaux, P., & Apostol, T. (2007). A multi-criteria approach to evaluate the natural gas energy systems. Energy Policy, 35(11), 5754–5765.CrossRefGoogle Scholar
  16. EBA. (2016). Statistical report 2016: Annual statistical report of the European biogas association. Brussels: EBA. Accessed at: April 18, 2017, from
  17. Energy & Strategy Group. (2016). Renewable energy report. Politecnico di Milano, Milano. Accessed at: April 18, 2017, from
  18. Faúndez, P. (2008). Renewable energy in a market-based economy: How to estimate its potential and choose the right incentives. Renewable Energy, 33(8), 1768–1774.CrossRefGoogle Scholar
  19. Ferreira, F. A., Santos, S. P., & Dias, V. M. (2014). An AHP-based approach to credit risk evaluation of mortgage loans. International Journal of Strategic Property Management, 18(1), 38–55.CrossRefGoogle Scholar
  20. Fouquet, D., & Johansson, T. B. (2008). European renewable energy policy at crossroads: Focus on electricity support mechanisms. Energy Policy, 36(11), 4079–4092.CrossRefGoogle Scholar
  21. Georgiou, D., Mohammed, E. S., & Rozakis, S. (2015). Multi-criteria decision making on the energy supply configuration of autonomous desalination units. Renewable Energy, 75, 459–467.CrossRefGoogle Scholar
  22. Grafakos, S., Flamos, A., & Enseñado, E. M. (2015). Preferences matter: A constructive approach to incorporating local stakeholders’ preferences in the sustainability evaluation of energy technologies. Sustainability, 7(8), 10922–10960.CrossRefGoogle Scholar
  23. Grošelj, P., & Zadnik Stirn, L. (2012). Acceptable consistency of aggregated comparison matrices in analytic hierarchy process. European Journal of Operational Research, 223(2), 417–4201.MathSciNetCrossRefGoogle Scholar
  24. GSE. (2017). Rapporto statistico, Energia e fonti rinnovabili in Italia Anno 2015. Accessed at: April 18, 2017, from
  25. Guerrero-Liquet, G. C., Sánchez-Lozano, J. M., García-Cascales, M. S., Lamata, M. T., & Verdegay, J. L. (2016). Decision-making for risk management in sustainable renewable energy facilities: A case study in the Dominican Republic. Sustainability, 8(5), 455.CrossRefGoogle Scholar
  26. Haas, R., Resch, G., Panzer, C., Busch, S., Ragwitz, M., & Held, A. (2011). Efficiency and effectiveness of promotion systems for electricity generation from renewable energy sources—Lessons from EU countries. Energy, 36(4), 2186–2193.CrossRefGoogle Scholar
  27. IEA. (2008). Deploying renewables: Principles for effective policies. Paris: IEA Publications. ISBN 978-92-64-04220-9. Accessed at: April 18, 2017, from
  28. Jacobsson, S., Bergek, A., Finon, D., Lauber, V., Mitchell, C., Toke, D., et al. (2009). EU renewable energy support policy: Faith or facts? Energy policy, 37(6), 2143–2146.CrossRefGoogle Scholar
  29. Jankowski, J., Michalski, R., Bródka, P., Kazienko, P., & Utz, S. (2015). Knowledge acquisition from social platforms based on network distributions fitting. Computers in Human Behavior, 51, 685–693.CrossRefGoogle Scholar
  30. Klessmann, C., Held, A., Rathman, M., & Ragwitz, M. (2011). Status and perspectives of renewable energy policy and deployment in the European Union—What is needed to reach the 2020 targets? Energy Policy, 39(12), 7637–7657.CrossRefGoogle Scholar
  31. Laffont, J. J., & Martimort, D. (2002). The theory of incentives: The principal-agent model. Princeton NJ: Princeton University Press.Google Scholar
  32. Mardani, A., Jusoh, A., Zavadskas, E. K., Cavallaro, F., & Khalifah, Z. (2015). Sustainable and renewable energy: An overview of the application of multiple criteria decision making techniques and approaches. Sustainability, 7(10), 13947–13984.CrossRefGoogle Scholar
  33. Maskin, E., Laffont, J. J., & Hildenbrand, W. (1982). The theory of incentives: An overview. In: Advances in economic theory (invited lectures from the 4th World Congress of the Econometric Society) (pp. 31–94). Cambridge: Cambridge University Press.Google Scholar
  34. Ministero dello Sviluppo Economico. (2013). Strategia Energetica Nazionale: per un’energia più competitiva e sostenibile. Accessed at: April 18, 2017, from
  35. Nigim, K., Munier, N., & Green, J. (2004). Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources. Renewable Energy, 29(11), 1775–1791.CrossRefGoogle Scholar
  36. Peniwati, K. (2006). Criteria for evaluating group decision-making methods. International Series in Operations Research and Management Science, 95, 251–273.CrossRefGoogle Scholar
  37. Ragwitz, M., Held, A., Resch, G., Faber, T., Haas, R., Huber, C., et al. (2007). Assessment and optimisation of renewable energy support schemes in the European electricity market. Germany: Fraunhofer IRB Verlag.Google Scholar
  38. Rao, B., Mane, A., Rao, A. B., & Sardeshpande, V. (2014). Multi-criteria analysis of alternative biogas technologies. Energy Procedia, 54, 292–301.CrossRefGoogle Scholar
  39. Reiche, D., & Bechberger, M. (2004). Policy differences in the promotion of renewable energies in the EU member states. Energy policy, 32(7), 843–849.CrossRefGoogle Scholar
  40. REN21. (2006). Renewables global status report: 2006 update. Paris, Washington, DC: REN21 Secretariat and Worldwatch Institute. Accessed at: April 18, 2017, from
  41. REN21. (2016). Renewables global status report: 2016. Paris, Washington, DC: REN21 Secretariat and Worldwatch Institute. Accessed at: April 18, 2017, from
  42. Rickerson, W., & Grace, R. C. (2007). The debate over fixed price incentives for renewable electricity in Europe and the United States: Fallout and future directions. A white paper prepared for the Heinrich Böll Foundation. Accessed at: April 18, 2017, from
  43. Saaty, T. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw-Hill.zbMATHGoogle Scholar
  44. Saaty, T. L. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process (Vol. 6). Pittsburgh: RWS Publications.Google Scholar
  45. Saaty, T. L., & Peniwati, K. (2012). Group decision making: Drawing out and reconciling differences. Pittsburgh: RWS Publications.Google Scholar
  46. Scannapieco, D., Naddeo, V., & Belgiorno, V. (2014). Sustainable power plants: A support tool for the analysis of alternatives. Land Use Policy, 36, 478–484.CrossRefGoogle Scholar
  47. Senge, P. M. (2006). The fifth discipline: The art & practice of the learning organization. New York: Currency Doubleday.Google Scholar
  48. Sindhu, S., Nehra, V., & Luthra, S. (2017). Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renewable and Sustainable Energy Reviews, 73, 496–511.CrossRefGoogle Scholar
  49. Spyridaki, N., Banaka, S., & Flamos, A. (2016). Evaluating public policy instruments in the greek building sector. Energy Policy, 88, 528–543.CrossRefGoogle Scholar
  50. Toke, D. (2008). The EU Renewables Directive—What is the fuss about trading? Energy Policy, 36(8), 3001–3008.CrossRefGoogle Scholar
  51. Väisänen, S., Mikkilä, M., Havukainen, J., Sokka, L., Luoranen, M., & Horttanainen, M. (2016). Using a multi-method approach for decision-making about a sustainable local distributed energy system: A case study from Finland. Journal of Cleaner Production, 137, 1330–1338.CrossRefGoogle Scholar
  52. Wątróbski, J., & Sałabun, W. (2016a). Green supplier selection framework based on multi-criteria decision-analysis approach. In R. Setchi, R. Howlett, Y. Liu, & P. Theobald (Eds.), Sustainable design and manufacturing 2016 (pp. 361–371). Cham: Springer.CrossRefGoogle Scholar
  53. Wątróbski, J., & Sałabun, W. (2016b). The characteristic objects method: A new intelligent decision support tool for sustainable manufacturing. In R. Setchi, R. Howlett, Y. Liu, & P. Theobald (Eds.), Sustainable design and manufacturing 2016 (pp. 349–359). Cham: Springer.CrossRefGoogle Scholar
  54. Wątróbski, J., Ziemba, P., Jankowski, J., & Zioło, M. (2016). Green energy for a green city—A multi-perspective model approach. Sustainability, 8(8), 702.CrossRefGoogle Scholar
  55. Xu, Z. (2000). On consistency of the weighted geometric mean complex judgement matrix in AHP. European Journal of Operational Research, 126(3), 683–687.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Donatella Banzato
    • 1
  • Rubina Canesi
    • 2
    Email author
  • Chiara D’Alpaos
    • 1
  1. 1.Department of Civil, Environmental and Architectural Engineering and Interdepartmental Centre Giorgio Levi Cases for Energy Economics and TechnologyUniversity of PadovaPaduaItaly
  2. 2.Department of Civil, Environmental and Architectural EngineeringUniversity of PadovaPaduaItaly

Personalised recommendations