LOCAL DIFFERENCES IN SUSTAINABILITY TRANSITIONS: THE CASE OF CAR-SHARING IN THE NETHERLANDS Toon Meelen

THE PUZZLE Why do sustainability transitions happen in some places but not in others? EV in The Netherlands (RWO, own calculations,2013) PV in The Netherlands (Peer, own calculations, 2013) PV in Germany (Dewald & Truffer, 2012)

THE CASE: CAR SHARING IN THE NETHERLANDS Peer-to-peer Traditional

GEOGRAPHY OF SUSTAINIBILITY TRANSITIONS Space & Scale (Coenen et al. 2012) local factors influencing transitions (e.g. local institutions, urban policies) (Hansen & Coenen, 2014)

Truffer Dewald (2012) “Market formation” Sine Lee (2011) environmental movements Longhurst (2015) “alternative milieu” provides epistemological + ontological security Focus on market/users

METHODOLOGY Data on number of shared cars in the Netherlands via the peer-to-peer and traditional model per neighbourhood (n=10,421 cars, n=4047 neighbourhoods) Data on geographical and socio-demographic characteristics of neighbourhoods from Dutch statistical offices + car-sharing policy from all municipalities Zero inflated Negative Binomial Model

ADOPTION PER NEIGHBOUR HOOD Zero inflated part: Probability that zero cars are shared in a neighbourhood Traditional car sharingPeer-to-peer car sharing Model 1Model 2Model 1Model 2 Constant (8.610) (7.920) (4.186) (4.737) Number of cars (x1000) (0.149) (0.198) *** (0.305) *** (0.350) Population density (x1000) (0.100) (0.089) (0.103) (0.144) Distance to facilities (0.113) (0.113) (0.048) (0.051) Income (x1000) (0.098) (0.085) (0.045) (0.050) % Vocational education (0.094) (0.077) (0.032) (0.035) % college, university education * (0.093) (0.070) * (0.031) (0.035) % one person households ** (0.067) ** (0.058) (0.031) (0.031) % age (0.162) (0.119) (0.068) (0.075) % age (0.134) (0.113) (0.054) (0.060) % age * (0.125) (0.088) (0.046) (0.048) % Member environmental organization ** (0.394) * (0.420) (0.163) (0.181) % Western immigrants (0.072) (0.068) (0.035) (0.039) Municipal policy (Information) (0.633) (0.752) Municipal policy (Parking) (0.785 ) (1.503) Spatial lags indep var yes Mcfadden adj R N Non-zero observations ***: sign. < **: sign. < 0.01 *: sign. < 0.05

ADOPTION PER NEIGHBOUR HOOD Number of cars shared in neighbourhoods in which it is likely that cars are shared Traditional car sharing Peer-to-peer car sharing Model 1Model 2Model 1Model 2 Constant * (1.495) * (1.522) (0.624) (0.663) Number of cars (x1000) 0.298*** (0.043) 0.220*** (0.043) 0.238*** (0.013) 0.238*** (0. 014) Population density (x1000) (0.016) (0.016) 0.033*** (0.008) 0.042*** (0.009) Distance to facilities ** (0.031) * (0.032) * (0.010) * (0.010) Income (x1000) (0.020) (0.020) (0.007) (0.007) % Vocational education *** (0.014) ** (0.014) *** (0.006) *** (0.006) % college, university education (0.016) (0.015) (0.005 ) (0.006) % one person households 0.038** (0.011) ** (0.011) 0.014** (0.004 ) 0.013** (0.004) % age * (0.017) (0.017 ) (0.008) (0.009) % age (0.018) (0.019) *** (0.008) *** (0.009) % age (0.012) (0.013 ) ** (0.006 ) ** (0.006) % Member environmental organization 0.414*** (0.070 ) *** (0.079) 0.321*** (0.029) 0.316*** (0.034 ) % Western immigrants * (0.018) 0.110*** (0.020) * (0.007) * (0.007) Municipal policy (Information) 0.618*** (0.169) ** (0.061) Municipal policy (Parking) 0.510* (0.234) (0.071) Spatial lags indep var yes Mcfadden adj R N Non-zero observations ***: sign. < **: sign. < 0.01 *: sign. < 0.05

Conclusions: Peer-to-peer car-sharing occurs everywhere, traditional car-sharing occurs in places with people that are environmentally aware Policy mixed results Environmental awareness strong predictor of number of shared cars Peer-to-peer car-sharing for younger, traditional car-sharing for older people

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