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Periurban Agriculture: do the Current EU Agri-environmental Policy Programmes Fit with it?

Author

Listed:
  • Linda Arata

    (Università Cattolica del Sacro Cuore)

  • Gianni Guastella

    (Università Cattolica del Sacro Cuore and Fondazione ENI Enrico Mattei)

  • Stefano Pareglio

    (Università Cattolica del Sacro Cuore and Fondazione ENI Enrico Mattei)

  • Riccardo Scarpa

    (University of Durham, University of Verona and University of Waikato)

  • Paolo Sckokai

    (Università Cattolica del Sacro Cuore)

Abstract

In the European Union (EU) periurban agriculture is under the same agri-environmental policy regime designed for general agriculture. We argue that the specific needs of periurban agriculture may justify ad hoc agri-environmental policy measures. We present results from a Choice Experiment (CE) performed on a sample of 600 people living in the municipality of Milan, which was designed to assess the willingness to pay (WTP) for ecological benefits generated by four agri-environmental practices implementable in the periurban area and already included in the Rural Development Programmes of the Lombardy region. Results suggest that a large population share is willing to pay to support an increase in the use of the agricultural practices studied with an average WTP ranging between 5.6 to 16.3 euro/person/year, according to the type of practice. These results are in contrast with their current low level of adoption. The sub-optimal uptake rate is likely due to an insufficient per hectare compensating payment, which is too low to cover the income foregone consequent to the adoption of sustainable agriculture measures in this area. The mismatch between the low uptake rate and the high social benefits generated by the four agri-environmental agricultural practices sheds light on the need to design agri-environmental policy programmes specifically targeted to periurban areas, where the costs of compliance with AEMs are high and the social benefits of their adoption are large.

Suggested Citation

  • Linda Arata & Gianni Guastella & Stefano Pareglio & Riccardo Scarpa & Paolo Sckokai, 2018. "Periurban Agriculture: do the Current EU Agri-environmental Policy Programmes Fit with it?," Working Papers 2018.16, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2018.16
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    References listed on IDEAS

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    1. Chabé-Ferret, Sylvain & Subervie, Julie, 2013. "How much green for the buck? Estimating additional and windfall effects of French agro-environmental schemes by DID-matching," Journal of Environmental Economics and Management, Elsevier, vol. 65(1), pages 12-27.
    2. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    3. Daniel Kahneman & Jack L. Knetsch & Richard H. Thaler, 1991. "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 193-206, Winter.
    4. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    5. Riccardo Scarpa & John M. Rose, 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 253-282, September.
    6. Riccardo Scarpa & Mara Thiene & Kenneth Train, 2008. "Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 994-1010.
    7. Tiziana de-Magistris & Azucena Gracia & Rodolfo M. Nayga, 2013. "On the Use of Honesty Priming Tasks to Mitigate Hypothetical Bias in Choice Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(5), pages 1136-1154.
    8. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
    9. Dan Marsh & Lena Mkwara & Riccardo Scarpa, 2011. "Do Respondents’ Perceptions of the Status Quo Matter in Non-Market Valuation with Choice Experiments? An Application to New Zealand Freshwater Streams," Sustainability, MDPI, vol. 3(9), pages 1-23, September.
    10. Scarpa, R. & Thiene, M. & Train, K., 2008. "Appendix to Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps," American Journal of Agricultural Economics APPENDICES, Agricultural and Applied Economics Association, vol. 90(4), pages 1-9, January.
    11. Hensher,David A. & Rose,John M. & Greene,William H., 2015. "Applied Choice Analysis," Cambridge Books, Cambridge University Press, number 9781107465923, October.
    12. Riccardo Scarpa & Kenneth G. Willis & Melinda Acutt, 2007. "Valuing externalities from water supply: Status quo, choice complexity and individual random effects in panel kernel logit analysis of choice experiments," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 50(4), pages 449-466.
    13. Mara Thiene & Riccardo Scarpa, 2009. "Deriving and Testing Efficient Estimates of WTP Distributions in Destination Choice Models," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 44(3), pages 379-395, November.
    14. Andrew Daly & Stephane Hess & Kenneth Train, 2012. "Assuring finite moments for willingness to pay in random coefficient models," Transportation, Springer, vol. 39(1), pages 19-31, January.
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    More about this item

    Keywords

    Periurban Agriculture; Agri-environmental Policy; Choice Experiment; Random Parameter Logit Model; Error Component; WTP Space;
    All these keywords.

    JEL classification:

    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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