A Latent Class Nested Logit Model for Rank-Ordered Data with Application to Cork Oak Reforestation

A Latent Class Nested Logit Model for Rank-Ordered Data with Application to Cork Oak Reforestation We analyze stated ranking data collected from recreational visitors to the Alcornocales Natural Park (ANP) in Spain. The ANP is a large protected area which comprises mainly cork oak woodlands. The visitors ranked cork oak reforestation programs delivering different sets of environmental (reforestation technique, biodiversity, forest surface) and social (jobs and recreation sites created) outcomes. We specify a novel latent class nested logit model for rank-ordered data to estimate the distribution of willingness-to-pay for each outcome. Our modeling approach jointly exploits recent advances in discrete choice methods. The results suggest that prioritizing biodiversity would increase certainty over public support for a reforestation program. In addition, a substantial fraction of the visitor population are willing to pay more for the social outcomes than the environmental outcomes, whereas the existing reforestation subsidies are often justified by the environmental outcomes alone. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental and Resource Economics Springer Journals

A Latent Class Nested Logit Model for Rank-Ordered Data with Application to Cork Oak Reforestation

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Economics; Environmental Economics; Environmental Law/Policy/Ecojustice; Political Economy/Economic Policy; Economics, general; Environmental Management
ISSN
0924-6460
eISSN
1573-1502
D.O.I.
10.1007/s10640-016-0058-7
Publisher site
See Article on Publisher Site

Abstract

We analyze stated ranking data collected from recreational visitors to the Alcornocales Natural Park (ANP) in Spain. The ANP is a large protected area which comprises mainly cork oak woodlands. The visitors ranked cork oak reforestation programs delivering different sets of environmental (reforestation technique, biodiversity, forest surface) and social (jobs and recreation sites created) outcomes. We specify a novel latent class nested logit model for rank-ordered data to estimate the distribution of willingness-to-pay for each outcome. Our modeling approach jointly exploits recent advances in discrete choice methods. The results suggest that prioritizing biodiversity would increase certainty over public support for a reforestation program. In addition, a substantial fraction of the visitor population are willing to pay more for the social outcomes than the environmental outcomes, whereas the existing reforestation subsidies are often justified by the environmental outcomes alone.

Journal

Environmental and Resource EconomicsSpringer Journals

Published: Aug 3, 2016

References

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