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Estimating the Impact of Climate Change on Landscape Value

Estimating the Impact of Climate Change on Landscape Value Aliza Fleischer 1 , Denise Fouks 1 and Marcelo Sterenberg 2 1 Hebrew University of Jerusalem 2 Tel Aviv University. GLOWA – Jordan River. Objective.

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Estimating the Impact of Climate Change on Landscape Value

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  1. Estimating the Impact of Climate Change on Landscape Value Aliza Fleischer1, Denise Fouks1 and Marcelo Sterenberg2 1Hebrew University of Jerusalem 2Tel Aviv University GLOWA – Jordan River

  2. Objective Evaluating the impact of climate change on the economic value of different natural landscape with an emphasis on the Galilee

  3. Economic Impact of Global Climate Change on Grazing Land

  4. ~ 480 km The Climatic Gradient Humid Mediterranean - 780 mm עין יעקב Topography South-facing slopes with stony and shallow soil (Terra rossa to desert lithosol on hard limestone and chalk) Temperature Mean annual temperature 140C-230C Rainfall Mainly winter - 5 summer months with no rainfall Range North-South: 780 to 90 mm מטע Mediterranean - 540 mm להב שדה בוקר Semiarid – 300 mm Arid – 90 mm

  5. Methodology • A stated preference survey was designed to ask respondents to choose their preferable program to reduce the ecological impacts from 5 sets of five programs. • The alternative that has been chosen represent the maximum utility for the respondent. • Let Uij be the utility for the ith individual from alternative j. X= attribute [1,k] β= coefficient of the attribute = random error term i.i.d

  6. a

  7. Multinomial Logit Model The utility an individual gets from alternative j is: This probability an individual will choose this alternative is: In the Multi Logit Model (MNL) the probability is:

  8. The IIA Assumption • The MNL is subject to the independence of irrelevant alternatives (IIA) property. • IIA= the odds ratios are independent of the other probabilities. • IIA test = if an alternative is irrelevant omitting it from the model will not change parameter estimates systematically. • We rejected the hypothesis and thus had to use a model not subjected to IIA.

  9. Random Parameters Logit Model The utility function associated with the model is from the general form: is a random term with zero mean iid over alternatives and does not depend on parameters or data.

  10. Random Parameters Logit Model In the RPL model the probability of choice can be simulated as: This model is not subjected to the IIA.

  11. Alternatives Description • Each alternative contains all four attributes. • Alternatives differ in the levels of attributes.

  12. Econometric Analysis - Specification The indirect utility function V is specified as linear in parameters. “Price” enters linearly. One dummy variable for “forestry”, two for “abatement” (in this estimation we only used one due to singularity) and three dummy variable for “landscape changes” The indirect utility function would look as follow:

  13. Econometric Results Biomass *** significant at 1% ** significant at 5% * significant at 10%

  14. Econometric Results - WTP The willingness to pay in order to prevent landscape changes was calculated as the coefficient of the “landscape change” divided by the coefficient of “price”. is the utility differences between the state and without the programs to prevent landscape change

  15. Welfare from landscape loss ($/ha) as a function of biomass loss predicted real

  16. Income Loss as a Result of Decreases in Grassy Biomass $/ha Loss of grassy bio mass

  17. Income Loss as a Result of Decreases in Grassy Biomass $/ha Loss of grass bio mass


  19. Conclusions • Local community is eliciting utility from landscape and is willing to pay for government mitigation measures. • Loss of welfare for recreation services is larger than grazing services • The higher the conceived landscape loss the higher is the payment. • Policy makers have the public consent for taxing this generation in order for future generations to enjoy the landscape.

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