Spatially explicit demand for afforestation

J Sagebiel, K Glenk, J Meyerhoff

Research output: Contribution to journalArticle

10 Citations (Scopus)
1 Downloads (Pure)

Abstract

Afforestation is a stated goal in European Union policy and several member states have already implemented schemes to extend forest cover. However, little is known about the magnitude of non-market benefits of afforestation and how these benefits spatially differ. In this article, we propose a novel method to spatially explicitly predict marginal willingness to pay for afforestation. The approach is illustrated with data from a discrete choice experiment on local land use changes in Germany. GIS data on the respondent's place of residence allows inferring their current endowment with forest, which enters the utility specification of each respondent's status quo alternative. Marginal willingness to pay estimates therefore represent the value of changes in local forest cover relative to the observed status quo. This relationship can be utilized to predict willingness to pay at the county level. We find that marginal willingness to pay decreases as the current endowment with forest increases. The estimated optimal share of forest based on the average respondent's preferences is between 50 and 60%. The associated county level predictions of marginal and total willingness to pay can be used to inform national, regional and local policies that aim to increase forest cover.
Original languageEnglish
Pages (from-to)190 - 199
Number of pages10
JournalForest Policy and Economics
Volume78
Early online date12 Feb 2017
DOIs
Publication statusFirst published - 12 Feb 2017

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willingness to pay
afforestation
forest cover
land use change
European Union
GIS
demand
prediction
experiment
policy
county

Bibliographical note

1030762

Keywords

  • Cultural ecosystem services
  • Discrete choice experiment
  • Land use changes
  • Spatial preference heterogeneity
  • Stated preferences
  • Willingness to pay

Cite this

Sagebiel, J ; Glenk, K ; Meyerhoff, J. / Spatially explicit demand for afforestation. In: Forest Policy and Economics. 2017 ; Vol. 78. pp. 190 - 199.
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Spatially explicit demand for afforestation. / Sagebiel, J; Glenk, K; Meyerhoff, J.

In: Forest Policy and Economics, Vol. 78, 12.02.2017, p. 190 - 199.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Spatially explicit demand for afforestation

AU - Sagebiel, J

AU - Glenk, K

AU - Meyerhoff, J

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PY - 2017/2/12

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N2 - Afforestation is a stated goal in European Union policy and several member states have already implemented schemes to extend forest cover. However, little is known about the magnitude of non-market benefits of afforestation and how these benefits spatially differ. In this article, we propose a novel method to spatially explicitly predict marginal willingness to pay for afforestation. The approach is illustrated with data from a discrete choice experiment on local land use changes in Germany. GIS data on the respondent's place of residence allows inferring their current endowment with forest, which enters the utility specification of each respondent's status quo alternative. Marginal willingness to pay estimates therefore represent the value of changes in local forest cover relative to the observed status quo. This relationship can be utilized to predict willingness to pay at the county level. We find that marginal willingness to pay decreases as the current endowment with forest increases. The estimated optimal share of forest based on the average respondent's preferences is between 50 and 60%. The associated county level predictions of marginal and total willingness to pay can be used to inform national, regional and local policies that aim to increase forest cover.

AB - Afforestation is a stated goal in European Union policy and several member states have already implemented schemes to extend forest cover. However, little is known about the magnitude of non-market benefits of afforestation and how these benefits spatially differ. In this article, we propose a novel method to spatially explicitly predict marginal willingness to pay for afforestation. The approach is illustrated with data from a discrete choice experiment on local land use changes in Germany. GIS data on the respondent's place of residence allows inferring their current endowment with forest, which enters the utility specification of each respondent's status quo alternative. Marginal willingness to pay estimates therefore represent the value of changes in local forest cover relative to the observed status quo. This relationship can be utilized to predict willingness to pay at the county level. We find that marginal willingness to pay decreases as the current endowment with forest increases. The estimated optimal share of forest based on the average respondent's preferences is between 50 and 60%. The associated county level predictions of marginal and total willingness to pay can be used to inform national, regional and local policies that aim to increase forest cover.

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