Abstract
Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions.
Highlights
Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape
We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing Choice Experiments (CE) studies and the general integral functions of willingness to pay (WTP) or willingness to accept (WTA) against wind farm attributes are useful for future spatial modelling and benefit transfer studies
Analysis was focused on the estimated WTP and WTA for three wind farm attributes, namely the distance from the wind farm to residential or coastal areas, the size of wind farm and turbine height
Summary
Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. We propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. Unlike the multi-variate meta-regression, this new method performs separate analysis on individual wind farm attributes, i.e. the distance from the wind farm to residential/coastal areas, the size of wind farm (namely the number of wind turbines) and turbine height This new analysis method involves 1) deriving the WTP or WTA functions for each of the three attributes from individual valuation studies, 2) calculating the average derivatives of the WTP or WTA functions from different studies for each attribute range that has been evaluated by different numbers of studies, 3) fitting the average derivative function across the whole evaluated attribute range, and lastly 4) integrating the fitted average derivative function to derive a general integral function which describes the general relationship between the attribute and estimated WTP or WTA across all studies. Due to the procedures of deriving the derivative and integral functions, we call this new analysis method a “calculus method”
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