Household Adoption of Green Technologies: The Case of Chicago Rain Barrels

Transcription

1 Household Adoption of Green Technologies: The Case of Chicago Rain Barrels Amy W. Ando and Luiz Freitas Dept. of Ag. and Consumer Econ. University of Illinois, Urbana- Champaign 1

2 The Rain Barrel Program (1) Connects to residential rooftop gutter downspout and collects rainwater for later use. Run by Chicago Dept. of Environment Part of a multiple effort to reduce municipal water consumption, enhance groundwater recharge, and improve storm water management. Distribution: 2004, 2006, 2007 Image: Reference: City of Chicago Rain Barrel Program Online Description 2

3 Year The Rain Barrel Program (2) Barrels Distributed Cost to City ( per barrel) Cost to residents (per barrel) $70 $ $78 $ $78 $ Addresses available Rough estimates of benefits from 4,000 barrels (NPV at 3%): 1,450,000 gal. of water conserved and prevented from entering the sewer system. Sewer avoidance: $62,118 Water conservation: $73,950 Environmental quality and flood prevention benefits:? Lower bound on total benefits: $136,068 3

4 Data Sources & Assembly Provided by Chicago directly: Basement flooding data ( ). Rain barrel buyers addresses (2004, 2006, and 2007). Green Alley locations. Distribution point locations. Obtained elsewhere: U.S. Census data for year 2000 from factfinder.census.gov ArcMap shape files of Chicago census tracts, wards, and voting districts gubernatorial election data by voting district. Assembled and merged using ArcMap. 4

5 Data: Summary Statistics Variable Mean Std. Dev. Min Max Total rain barrels Dummy, tract has at least one barrel Median population age Housing units occupied Housing units occupied by owner % pop. voted Republican % pop. voted Green Median household income ($1,000) % pop. living in detached building % pop. living in 10+ unit building % pop. with at least a bachelor s degree % households who do not own a car Average number of basement floods Average dist. to nearest distr. center (1,000 m) Tract has a Green Alley N=897 5

6 Data: Spatial Distribution, All Years N=2775 6

7 Local Index of Spatial Association (LISA) Map Clustering suggests spatial autocorrelation among rain barrel adoptions. 7

8 Econometric Framework Logit regression estimates factors that affect probability of adoption Binary dependent variable: 1 if tract has at least one barrel; 0 otherwise. Negative binomial regression estimates factors that affect number of barrels adopted. Both regressions also estimated with ward dummies. True spatial probit model run in MATLAB. 8

9 Regression Results (Selected Variables) Logit Neg. bin. Logit Neg. bin. Spatial Probit Ward dummies - - Significant Significant ρ (autocorr. coeff.) *** (0.010) % living in 10+ unit building *** *** * *** *** (0.005) (0.003) (0.007) (0.003) (0.106) Median HH income 0.112*** 0.069*** 0.105*** 0.065*** 1.213*** (0.024) (0.013) (0.028) (0.013) (0.329) Median HH income squared *** *** ** -<0.001*** *** (<0.001) (<0.001) (<0.001) (<0.001) (0.283) Average number of basement 0.025** *** 0.011** 0.195*** floods (0.011) (0.003) (0.016) (0.004) (0.087) Avg. distance to distribution *** *** *** *** center (0.027) (0.016) (0.077) (0.035) (0.065) Tract has a Green Alley * *** 0.080* (0.418) (0.151) (0.472) (0.136) (0.068) Log likelihood (Pseudo) R-squared *** significant at 99%; ** significant at 95%; *significant at 90%. 9

10 Logit Marginal Effects 10+ housing: 10% increase over median (9.6%) -3.7% Change (Prob. of adoption) Income: 10 over mean (38.2) Income squared: 10 over sqrt. mean (42.6) +9.8% Floods: Zero basement floods versus median (3.66) -2.2% Distance: 1 mi. from distr. center versus mean distance (4.1 mi.) +14.3% Alleys: Has Alley versus no Alley +10.4% *** significant at 99%; ** significant at 95%; *significant at 90%. 10

11 Econometric Conclusions Environmental awareness Some evidence for the signaling role suggested for Green Alleys. Link between human behavior and environment Strong evidence (but small effect) for role of basement flooding. Convenience Strong evidence for proximity to distribution center. 11

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13 Data: Spatial Distribution By Year 2004 N= N= N=

14 Data: Income Distribution 14

15 Data: Voting Distribution 15

16 Data: Basement Floods 16

17 Data: Pop. In 10+ Unit Housing 17

18 Regression Results Logit Neg. bin. Logit Neg. bin. Spatial Probit Significant Significant Ward dummies - - b c ρ (autocorr. coeff.) *** Median age *** < Housing units 0.001*** <0.001*** 0.001*** <0.001*** 0.653*** occupied Housing units ownerocc ** <0.001** < ** % voted Republican % voted Green Median HH income 0.112*** 0.069*** 0.105*** 0.065*** 1.213*** (Median HH income) *** *** ** -<0.001*** *** % in detached <0.001 < building in 10+ unit building *** *** * *** *** % with bachelor s * *** degree % do not own a car ** 0.015*** # of basement floods 0.025** *** 0.011** 0.195*** Distance to distr *** *** *** *** center Has Green Alley * *** 0.080* *** significant at 99%; ** significant at 95%; *significant at 90%. 18

19 Demographic controls Theory Census and voting variables. Link between human behavior and environment feedback Basement flooding. Convenience of green technology adoption Distance from distribution point and vehicle ownership. Exposure to other green technologies Green Alleys. 19

20 Related Literature Technology diffusion and learning Foster & Rozenzeweig (1995), Bandiera & Rasul (2006) on new crop adoption. Residential sorting Tiebout (1956), Waldfogel (2006) on consumers who live in areas with others who share their preferences. Green consumerism Kotchen & Moore (2007) & Kahn (2007) on green goods. Other factors Ando & Gosselin (2005) on convenience. Carig et al (2006), Uitenbroek et al (2000) on education campaigns. Ashraf et al (2007) on implications of full subsidy. Gatzlaff et al (1998) on residential principal-agent issues. 20