If your neighborhood adopts greener, energy-efficient residential heating, ventilating, and air conditioning (HVAC) systems, will your proenvironmental behavior become contagious, spilling over into adjacent neighborhoods’ HVAC adoptions? Objective data on more than 300,000 detailed single-family house sale records in the Greater Chicago area from 1992 to 2004 are aggregated to census block-group neighborhoods to answer that question. Spatial lag regression models show that spatial dependence or “contagion” exists for neighborhood adoption of energy-efficient HVACs. Specifically, if 625 of 726 homes in a demonstration neighborhood upgraded to green HVAC, data of this study predict that at least 98 upgrades would occur in adjacent neighborhoods, more than doubling their baseline adoption rates. This spatial multiplier substantially magnifies the effects of factors affecting adoption rates. These results have important policy implications, especially in the context of new standards for neighborhood development, such as Leadership in Energy and Environmental Design (LEED) or Low-Impact Development standards.
AbrahamseW.StegL.VlekC.RothengatterT. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25, 273-291.
AnselinL. (2001). Spatial effects in econometric practice in environmental and resource economics. American Journal of Agricultural Economics, 83, 705-710.
4.
AnselinL. (2003). Spatial externalities. International Regional Science Review, 26, 147-152.
5.
ArdehaliM. M.SmithT. F. (1996). Evaluation of variable volume and temperature HVAC system for commercial and residential buildings. Energy Conversion and Management, 37, 1469-1479.
6.
ArrowK. (1962). The economic implications of learning by doing. Review of Economic Studies, 29, 155-173.
7.
BaerenklauK. A. (2005). Toward an understanding of technology adoption: Risk, learning, and neighborhood effects. Land Economics, 81, 1-19.
8.
BandieraO.RasulI. (2006). Social networks and technology adoption in Northern Mozambique. Economic Journal, 116, 869-902.
9.
ChandlerS.BrownM. (2009). Meta-review of efficiency potential studies and their implications for the south (Working Paper No. 51). Atlanta: Georgia Institute of Technology, School of Public Policy.
10.
FosterA.RosenzweigM. (1995). Learning by doing and learning from others: Human capital and technical change in agriculture. Journal of Political Economy, 103, 1176-1209.
11.
FragkiasM.SetoK. C. (2007). Modeling urban growth in data-sparse environments: A new approach. Environment and Planning B: Planning and Design, 34, 858-883.
12.
GoolsbeeA.KlenowP. J. (2002). Evidence on learning and network externalities in the diffusion of home computers. Journal of Law & Economics, 45, 317-343.
13.
GowrisankaranG.StavinsJ. (2004). Network externalities and technology adoption: Lessons from electronic payments. RAND Journal of Economics, 35, 260-276.
14.
HekkertM. P.SuursR. A. A.NegroS. O.KuhlmannS.SmitsR. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting & Social Change, 74, 413-432.
15.
IoannidesY. M.ZabelJ. E. (2003). Neighbourhood effects and housing demand. Journal of Applied Econometrics, 18, 563-584.
16.
JaberJ. O.MamlookR.AwadW. (2005). Evaluation of energy conservation programs in residential sector using fuzzy logic methodology. Energy Policy, 33, 1329-1338.
17.
KimC. W.PhippsT. T.AnselinL. (2003). Measuring the benefits of air quality improvement: A spatial hedonic approach. Journal of Environmental Economics and Management, 45, 24-39.
18.
KokN.McGrawM.QuigleyJ. M. (2011). The diffusion of energy efficiency in building. American Economic Review, 101, 77-82.
19.
KrauseR. (2009). Energy efficiency and conservation. Paper presented at The Search for Wise Energy Policy Conference, Washington, DC.
20.
KühnI.BiermanS. M.DurkaW.KlotzS. (2006). Relating geographical variation in pollination types to environmental and spatial factors using novel statistical methods. New Phytologist, 172, 127-139.
LongleyP. A.TobónC. (2004). Spatial dependence and heterogeneity in patterns of hardship: An intra-urban analysis. Annals of the Association of American Geographers, 94, 503-519.
23.
MenanteauP.LefebvreH. (2000). Competing technologies and the diffusion of innovations: The emergence of energy-efficient lamps in the residential sector. Research Policy, 29, 375-389.
24.
MillsB. F.SchleichJ. (2010). Why don’t households see the light? Explaining the diffusion of compact fluorescent lamps. Resource and Energy Economics, 32, 363-378.
25.
MlecnikE. (2010). Adoption of highly energy-efficient renovation concepts. Open House International, 35, 39-48.
26.
NairG.GustavssonL.MahapatraK. (2010). Factors influencing energy efficiency investments in existing Swedish residential buildings. Energy Policy, 38, 2956-2963.
27.
NiemeyerS. (2010). Consumer voices: Adoption of residential energy-efficient practices. International Journal of Consumer Studies, 34, 140-145.
28.
OsbaldistonR.SchottJ. P. (2011). Environmental sustainability and behavioral science: Meta-analysis of proenvironmental behavior experiments. Environment & Behavior. Advance online publication. doi:10.1177/0013916511402673
29.
OsterE.ThorntonR. (2009). Determinants of technology adoption: Private value and peer effects in menstrual cup take-up (NBER Working Paper No. 14828). Cambridge, MA: National Bureau of Economic Research.
30.
RogersE. M. (1995). Diffusion of innovations (4th ed.). New York, NY: Free Press.
31.
SchotJ.GeelsF. W. (2008). Strategic niche management and sustainable innovation journeys: Theory, findings, research agenda, and policy. Technology Analysis & Strategic Management, 20, 537-554.
32.
SovacoolB. K. (2009). The cultural barriers to renewable energy and energy efficiency in the United States. Technology in Society, 31, 365-373.
33.
SternP. C. (2011). Contributions of psychology to limiting climate change. American Psychologist, 66, 303-314.
34.
TseR. Y. C.WebbJ. R. (1999). Property tax and housing returns. Review of Urban & Regional Development Studies, 11, 114-126.
