Do turbines in the vicinity of respondents residences influence choices among programmes for future wind power generation?

Transcription

1 Do turbines in the vicinity of respondents residences influence choices among programmes for future wind power generation? Jürgen Meyerhoff Technische Universität Berlin, Institute for Landscape Architecture and Environmental Planning

2 Motivation and objectives Data are from the project Strategies for sustainable land use in the context of wind power generation Expansion of renewable energy is a central element of German climate and energy policy Target for 2020 was to produce 30% of electricity from renewables now we are after Fukushima How to allocate turbines in the landscape taking production costs and externalities into account? This paper contributes to an emerging literature on accounting for spatial characteristics in environmental valuation. Other results are reported in: Meyerhoff et al. (2010) Landscape externalities from on-shore wind power. Energy Policy Drechsler et al. (2011) Combining spatial modeling and choice experiments for the optimal spatial allocation of wind turbines. Energy Policy

3 Method Choice Experiments Non-market valuation: hypothetical market is created through surveys Utility is derived from the attributes of the good in question Respondents choose among alternatives CE are useful to value multidimensional changes because they provide information on trade-off

4 Choice set Wind power generation in Westsachsen until 2020 Size of the wind farms Height of the turbines Effect on red kite population Minimum distance to settlement Surcharge to energy bill per month Program A Program B Program C large farms small farms large farms 200 meter 110 meter 110 meter 10% 5% 10% 750 meter meter meter 0 6,- 1,- I choose * * * Future Status quo 2020

5 Attributes and levels Attribute 1 Size of wind farms Level small: 4 bis 6 mills medium:10 bis 12 mills large: 16 bis 18 mills Maximum height of turbines Effect on red kite population Minimum distance to settlements Monthly surcharge to energy bill 200m / 150m / 110m 5% / 10% / 15% 750m / 1.100m/ 1.500m 0 / 1 / 2,5 / 4 / 6 => Experimentel design to combine attributes to chocie sets

6 Study region Westsachsen 150 km southwest of Berlin Turbines with 235 MW capacity in 2007 Survey interviews - Phone/mail interviews May/ June Each respondents faced five choice sets

7 Spatial characteristics and valuation Q1: Do people value larger distances to turbines positively? Distance as a choice attribute => Studies suggest that larger distance is positively valued Q2: Do turbines in respondent s vicinity affect their choices? Actual exposure as a choice determinant => Those who live closer might rather be against it (distance-decay) Q3: Do people living close to each other value exposure to turbines similarly? Spatial autocorrelation => Tobler s law near things are more related than distant things suggests correlation

8 Respondents exposure to turbines mean sd med min max Characteristics of nearest turbine Turbine distance (in km) Height turbines (metres) Rotor diameter (metres) Power (KW) Years in operation Part of a wind farm (1 = yes) Turbines in nearest wind farm Turbines in 5 km surrounding Number of turbines Number of wind farms heterogeneity

9 Econometric approach Random Utility Theory Conditional Logit Model U in = V(X in, β)+ε in P in = exp(µv ) in (µv jn ) j C Assumes that disturbances are independent and identically distributed (IID) Assumes homogenous preferences Assumes no correlation among choices done by the same respondent (panel structure of the data)

10 Extensions of Conditional Logit Error Component Logit (ECL) Error component for buy-alternatives (heterogeneity among alternatives) Continuous mixture models (RPL) Taste heterogeneity is recognised Attributes normal random / cost (constrained triangular) Interactions with wind power characteristics Finite mixture models (Latent class) Taste heterogeneity by classes Wind power characteristics in membership function

11 WTP and spatial autocorrelation Individual-specific WTP-estimates RPL and LC models (Panel specification) accommodate the estimation of individual-specific preferences based on their known choices Spatial autocorrelation Gobal and local measures Moran s I/Geary s c (hot-spot analysis) Basic utility function U (PA) = ASC pa + ß 1 FarmSize + ß 2 Height + ß 3 RedKite + ß 4 MinDis + β 5 Cost + ß 6 ASC pa *TurD U (PB) = ASC pb + ß 1 FarmSize + ß 2 Height + ß 3 RedKite + ß 4 MinDis + β 5 Cost + E BC U (PC) = ß 1 FarmSize + ß 2 Height + ß 3 RedKite + ß 4 MinDis + β 5 Cost + E BC

12 Estimation results

13 Individual WTP-estimates # only the standard deviation is statistically significant, * parameter not statistically significant

14 Spatial autocorrelation Moran s I: 0 = random spatial pattern ; > 0 positive corr. ; < 0 negative corr. Geary s c: 0 = no spatial autocorrealtion; < 1 positive; > 1 negative

15 Latent class in space Advocates Moderates Opponents Hot-spot analysis indicates only few small clusters; circles mark clusters with low WTP estimates (cold-spot) for FarmSize from LC model..

16 Main results Q1: People prefer larger distances to turbines but LC shows that one group (advocates) does not care Q2: Exposure to turbines influences choices but not in the way it was expected: those who live at larger distance are more likely to choose constrained alternatives (fits to NIMBY-literature) Q3: Spatial autocorrelation is weak at best, i.e. people with similar exposure do not value externalities similarly; however these measures are strongly susceptible to model specifications

17 Discussion Wind power use in the study region is rather modest; thus, applying this approach to a region with a higher level of wind power could reveal different results Spatial sampling might be a next step to investigate influence of exposure to renewables more appropriate New project in BMBF research initiative Climate change and economics concerning externalities of renewables applying the overall project approach to Germany as a whole and and also to solar and biomass as renewables

18 The End! Funding for this research, which was part of the project Strategies for sustainable land use in the context of wind power generation, was provided by the Federal Ministry of Education and Research in Germany and is gratefully acknowledged (Fkz. 01UN0601B).