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Modeling the bio-refinery industry in rural areas: A participatory approach for policy options comparison

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  • Lopolito, Antonio
  • Nardone, Gianluca
  • Prosperi, Maurizio
  • Sisto, Roberta
  • Stasi, Antonio

Abstract

The development of bio-refineries has become a relevant topic in the EU's agenda. However, the promotion of a new industry in rural areas is typically hindered by the scarcity of human capital, lack of information, infrastructures, and competing interests. In this context, public support is unavoidable to assist promotion of this innovative sector. The various policy options reveal some strengths and drawbacks, posing the problem of finding the best trade-off to public decision makers. In this paper we aim at developing a methodology to support policy decision making within the biorefinery framework, with the purpose of determining a way to identify the most suitable policy option given the actual uncertainty in developing the bio-refinery industry in rural areas. The empirical experiment, based on a simulation of the enforcement of four identified policy instruments, highlights that, although subsidies and incentives to profitability of dedicated crops appear to have the greatest effects on the development of bio-refinery, the best performances are exhibited by technological innovation and information options.

Suggested Citation

  • Lopolito, Antonio & Nardone, Gianluca & Prosperi, Maurizio & Sisto, Roberta & Stasi, Antonio, 2011. "Modeling the bio-refinery industry in rural areas: A participatory approach for policy options comparison," Ecological Economics, Elsevier, vol. 72(C), pages 18-27.
    • Handle: RePEc:eee:ecolec:v:72:y:2011:i:c:p:18-27
    DOI: 10.1016/j.ecolecon.2011.09.010
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    2. Antonio Baselice & Mariarosaria Lombardi & Maurizio Prosperi & Antonio Stasi & Antonio Lopolito, 2021. "Key Drivers of the Engagement of Farmers in Social Innovation for Marginalised Rural Areas," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    3. Sacchelli, S. & Fabbrizzi, S., 2015. "Minimisation of uncertainty in decision-making processes using optimised probabilistic Fuzzy Cognitive Maps: A case study for a rural sector," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 31-40.
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    5. Pramod K Singh & Harpalsinh Chudasama, 2017. "Pathways for drought resilient livelihoods based on people’s perception," Climatic Change, Springer, vol. 140(2), pages 179-193, January.
    6. Sisto, Roberta & Fernández-Portillo, Luis A. & Yazdani, Morteza & Estepa-Mohedano, Lorenzo & Torkayesh, Ali Ebadi, 2022. "Strategic planning of rural areas: Integrating participatory backcasting and multiple criteria decision analysis tools," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    7. Falcone, Pasquale Marcello & Lopolito, Antonio & Sica, Edgardo, 2019. "Instrument mix for energy transition: A method for policy formulation," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    8. Falcone, Pasquale Marcello & De Rosa, Salvatore Paolo, 2020. "Use of fuzzy cognitive maps to develop policy strategies for the optimization of municipal waste management: A case study of the land of fires (Italy)," Land Use Policy, Elsevier, vol. 96(C).
    9. Chinnici, Gaetano & D’Amico, Mario & Rizzo, Marcella & Pecorino, Biagio, 2015. "Analysis of biomass availability for energy use in Sicily," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1025-1030.
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