The Effect of Electricity Consumption Incentives on Demand: An Investigation with Meta Analysis

Abstract

Controlling energy consumption is a serious environmental problem due to global warming and pollution. Feedback on electricity consumption is used as a tool for consumers to better control their consumption and ultimately save energy. As a matter of fact, limited motivation and lack of knowledge about consumption are the main obstacles for households to act on smart meter incentives. A comprehensive analysis of household-scale interventions and their emission reduction potential was found to be lacking, although policy decisions addressing climate change and all mitigation options for information were evaluated. Therefore, in the study, meta-analysis method was conducted using data from 43 studies to obtain precise estimates of the effect of different incentives on housing consumption. In the literature, it has been observed that monetary, informative and behavioral incentives can have an effect between 6.4% and 7.4% on the amount of consumption. The results of the meta-analysis show that personal feedback and monetary information are more effective in reducing consumption than real-time feedback and monetary incentives.

Keywords

• Behavioral incentive
• feedback
• meta analysis

Elektrik Tüketim Teşviklerinin Talep Miktarı Üzerindeki Etkisi: Meta Analizi ile Bir İnceleme

Abstract

Enerji tüketiminin kontrol edilmesi, küresel ısınma ve kirlilik nedeniyle ciddi bir çevre sorunudur. Elektrik tüketimine ilişkin geribildirim tüketicilerin tüketimlerini daha iyi kontrol etmeleri ve nihayetinde enerji tasarrufu yapmaları için araç olarak kullanılmaktadır. Nitekim sınırlı motivasyon ile tüketime ilişkin bilgi eksikliği, hanelerin akıllı sayaç teşviklerine göre hareket etmesinin önündeki başlıca engellerdir. İklim değişikliğini ele alan politika kararları ve bilgilendirmeye yönelik tüm azaltma seçenekleri değerlendirilmesine rağmen hane ölçeğindeki müdahalelerin kapsamlı bir analizi ve bunların emisyon azaltma potansiyelinin eksik olduğu görülmüştür. Bu nedenle çalışmada farklı teşviklerin konut tüketimi üzerindeki etkisinin kesin tahminlerini elde etmek için 43 çalışmaya ilişkin veriler kullanılarak meta analiz yöntemi yapılmıştır. Literatürde parasal, bilgilendirici ve davranışsal teşviklerin tüketim miktarı üzerinde %6,4 ile %7,4 arasında bir etki doğurabileceği görülmüştür. Yapılan meta analiz sonuçları ise kişisel geribildirim ve parasal bilgilerin tüketimi azaltmada gerçek zamanlı geribildirim ve parasal teşviklerden daha etkili olduğunu göstermektedir.

Keywords

• Davranışsal teşvik
• geribildirim
• meta analizi

References

1. Abrahamse, W., Wokje, A., Steg, L., Vlek, C. ve Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3), 273–291.
2. Alcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95(9-10), 1082–1095.
3. Allen, D. ve Kathryn B. Janda(2006). The effects of household characteristics and energy use consciousness on the effectiveness of real-time energy use feedback: A pilot study. Proceedings from ACEEE ’92. American Council for an Energy Efficient Economy Summer Study on Energy Efficiency in Buildings, 111–5.
4. Arvola, A., Anttila,U.ve Arvola,A. (1993).Billing feedback as a means to encourage household electricity conservation: A field experiment in Helsinki. In R. Ling and H. Wilhite (Eds.), The energy efficiency challenge for Europe.
5. Ata, O. ve Erdoğan, M. (2020). Geri bildirim ve sosyal normların hanehalkı elektrik enerjisi tasarrufuna etkileri: Deneysel bir çalışma. Tüketici ve Tüketim Araştırmaları Dergisi, 12(1), 1-32.
6. Ayres, I., Raseman, S. ve Shih, A. (2013). Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage. Journal of Law, Economics, and Organization, 29, 992– 1022.
7. B.Huedo-Medina,T., Sa´nchez-Meca, J.ve Marı´n-Martı´nez, F. (2006). Assessing Heterogeneity in Meta-Analysis: Q Statistic or I 2 Index. Psychological Methods,193-206.
8. Battalio, R. C., Kagel, J., Winkler, R, ve Winett, R.A. (1979). Residential electricity demand: An experimental study. The Review of Economics and Statistics, 61, 180 -189.

9. Bittle, R. G., Valesano, R. ve Thaler, G. (1979). The effects of daily feedback on residential electricity usage as a function of usage level and type of feedback information. Journal of EnvironmentalSystems, 9, 275–287
10. Bittle, R. G., Valesano, R. ve Thaler,G. (1979 –1980). The effects of daily cost feedback on residential electricity consumption. Behavior Modification, 3, 187–202.
11. Borenstein, M., Hedges, L. V., Higgins, J. P. T., ve Rothstein, H. R. (2009). Introduction to Meta-Analysis. Chichester, UK: John Wiley & Sons, Ltd.
12. Brandon, G. ve Lewis, A. (1999). Reducing household energy consumption: A qualitative and quantitative field study. Journal of Environmental Psychology, 19, 75–85.
13. Buchanan, K., Russo,R. ve Anderson,B. (2014).Feeding back about eco-feedback: How do consumers use and respond to energy monitors?.Energy Policy,138-146.
14. Costa, D. ve E.Kahn, M. (2010).Energy Conservation “Nudges” and Environmentalist Ideology: Evidence from a Randomized Residential Electricity Field Experiment. NBER Working Paper, 15939.
15. Darby, S. (2006). The Effectiveness of Feedback On Energy Consumption A Review For Defra of The Literature on Metering, Billing and Direct Displays. Environmental Change Institute Universty Of Oxford: 486.
16. DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological Methods, 2, 292–306.
17. Delmas, M. A., Fischlein, M., ve Asensio, O. I. (2013). Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012. Energy Policy, 61:729-739.
18. Dobson, J. K., J.D. Griffin,A. ve Hydro,O. (1992). Conservation effect of immediate electricity cost feedback on residential consumption behavior. Proceedings from ACEEE ‘92: American Council for an Energy Efficient Economy Summer Study on Energy Efficiency in Buildings. Pacific Grove, CA: American Council for an Energy-Efficient Economy.
19. Duval, S.ve Tweedie, R. (2000). Trim and Fill: A Simple Funnel-Plot-Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis, Biometrics,56(2),455-63.
20. Egger, M., Smith,G.D., Schneider, M.ve Minder, C. (1997). Bias İn Meta-Analysis Detected By A Simple, Graphical Test.BMJ, 315, 629–34.
21. Ehrhardt-Martinez, K., Donnelly, K. A., ve Laitner, S. (2010). Advanced metering initiatives and residential feedback programs: A meta-review for household electricity-saving opportunities. American Council for an Energy-E cient Economy Washington, DC.
22. Faruqui, A. Sergici,S. ve Akaba,L. (2012).Dynamic Pricing of Electricty for Residential Customers:The evidence from Michigan.Technical report, The Brattle Group, Energy Efficiency, 6(3).
23. Fischer, C. (2008). Feedback on household electricity consumption: A tool for saving energy? Energy Efficiency, 1(1), 79-104.
24. Fuller, R.J. ve Robert. H. Crawford (2011). Impact of past and future residential housing development patterns on energy demand and related emissions. Journal of Housing and the Built Environment, 26(2), 165–183.
25. Gusenbauer, M.ve Haddaway, N.R. (2020).Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesıs Methods,181-217.
26. Haakana, M., Sillanpaeae, L. ve Talsi, M. (1997). The effect of feedback and focused advice on household energy consumption. Proceedings from ECEEE ’97: European Council for an Energy Efficient Economy Summer Study on Energy Efficiency in Buildings. Toulon/Hyères, France: European Council for an Energy Efficient Economy.
27. Hather GJ, Haynes W, Higdon R, Kolker N, Stewart EA, Arzberger, P.ve diğerleri (2010) The United States of America and Scientific Research. PLoS ONE 5(8): e12203.doi:10.1371/journal.pone.0012203.
28. Hayes, S.C. ve John, D. Cone. (1981). Reduction of residential consumption of electricity through simple monthly feedback. Journal of Applied Behavior Analysis, 14,81– 88.
29. Heinonen, J. (2012). The Impacts of Urban Structure and the Related Consumption Patterns on the Carbon Emissions of an Average Consumer (Ph.D. Thesis). Aalto University, Helsinki, Finland.
30. Heinonen, J.ve Junnila, S. (2014). Residential energy consumption patterns and the overall housing energy requirements of urban and rural households in Finland, Energy and Buildings, 295 303.
31. Hess, B., Olejnik, S. ve Huberty, C. J. (2001). The efficacy of two improvement-over-chance effect sizes for two-group univariate comparisons under variance heterogeneity and nonnormality. Educational & Psychological Measurement, 61, 909–936.
32. Huovila, P., Juusela,M., Melchert,L. ve Pouffary,S. (2007). Buildings and Climate Change: Status, challenges and Opportunities. United Nations Environment Programme, Paris,
33. Hutton, R. B., Mauser, G.A.ve Filiatrault,P (1986). Effects of cost-related feedback on consumer knowledge and consumption behavior: A field experimental approach. Journal of Consumer Research, 13, 327–336.
34. Hutton, R. B. (1982). Advertising and the Department of Energy’s campaign for energy conservation. Journal of Advertising, 11, 27–39.
35. Ippolito, M., Riva Sanseverino,E. ve Zizzo,G. (2014). Impact of building automation control systems and technical building management systems on the energy performance class of residential buildings: an Italian case study, Energy and Buildings, 69, 33–40.
36. Katzev, R., Cooper, L.ve Fisher,P. (1980 1981). The effect of feedback and social reinforcement on residential electricity consumption. Journal of Environmental Systems, 10, 215–227.
37. Kasulis, J. J., Huettner,D.A. ve Dikeman,N.J. (1981). The feasibility of changing electricity consumption patterns. Journal of Consumer Research. 8, 279–290.
38. Kurz, T., Donaghue,N. ve Walker,L. (2005). Utilizing a social-ecological framework to promote water and energy conservation: A field experiment. Journal of Applied Social Psychology, 35: 1281–1300.
39. Matsukawa, I. (2004). The effects of information on residential demand for electricity. The Energy Journal (Cambridge, Mass.),25,1–17.
40. Mathur, M.B. ve Vander Weele, T.J. (2020). Sensitivity analysis for publication bias in meta‐analyses, Journal of the Royal Statistical Socıety,Serıes C,Applıed Statıstıcs,69(5),1091-1119.
41. Midden, C. J., Joanne F.Meter, Weenig,M.H.ve Zieverink,H.J.A. (1983). Using feedback, reinforcement and information to reduce energy consumption in households: A field-experiment. Journal of Economic Psychology, 3,65–86.
42. Mountain, D. C. (2007). Real-time feedback and residential electricity consumption: British Columbia and Newfoundland and Labrador Pilots. Ontatio, Canada: Mountain Economic Consulting.
43. Nexus Energy Software. (2006). California bill analysis pilot final report. San Francisco, CA: Calmac.
44. Nilsson, A., Bergstad, C. J., Thuvander, L., Andersson, D., Andersson, K. ve Meiling, P. (2014). Effects of continuous feedback on households' electricity consumption: Potentials and barriers. Applied Energy, 122:17-2.
45. Owie, E., Ademola, E.O. ve Adams, D. (2017). Reality of Human Decision-Making: An Analysis, Humanties, Management, Arts, Education &The Social Sciences Journal, 5(2) (sayfa no).
46. Lin, L., Chu,H., Murad, M.H., Hong, Qu, Z., Cole,S.R. ve diğerleri(2018).Empirical Comparison of Publication Bias Tests in Meta-Analysis, Journal of General Internal Medicine,33,1260-1267.
47. Pallak, M. S. ve Cummings, W. (1976). Commitment and voluntary energy conservation. Personality and Social Psychology Bulletin, 2, 27–30.
48. Parker, D. S., Maria D. Mazzara ve Sherwin,J. (1996). Monitored energy use patterns in low-income housing in a hot and humid climate. Proceedings from IBSHHC ’96: Tenth Symposium on Improving Building Systems inHot Humid Climates. Fort Worth.
49. Robinson, J. (2007). The effect of electricity-use feedback on residential consumption: A case study of customers with smart meters in Milton, Ontario (Master’s thesis). University of Waterloo, Waterloo, Canada.
50. Schultz P.W., Jessica M. Nolan,. Cialdini,R.B.,Goldstein,N.J.ve Griskevicius,V. (2007).The Constructive, Destructive, and Reconstructive Power of Social Norms. Psychological Science, 18(5)
51. Seligman, C., Darley, J. M. ve Becker, L. J. (1978). Behavioral approaches to residential energy conservation. Energy and Building, 1, 325–337.
52. Sipe, B. ve Castor, S. (2009). The net impact of home energy feedback devices. Proceedings from IEPEC ’09: International Energy Program Evaluation Conference, Portland, OR: IEPEC.
53. Snyder,H. (2019). Literature review as a research methodology: An overview and guidelines, Journal of Business Research,104,333-339.
54. Simmonds, M. (2015). Quantifying the risk of error when interpreting funnel plots,Systematic Reviews.
55. Sovacool, B. (2016). How long will it take? Conceptualizing the temporal dynamics of energy transitions, Energy Research & Social Science,13,202-215.
56. Thaler, Richard H. ve Cass R. Sunstein (2008). Nudge Improving Decisions About Health, Wealth and Happiness. New Haven ve London: Yale University.
57. Weber, C. ve Perrels, A. (2000).Modelling lifestyle effects on energy demand and related emissions. Energy Policy, 28(8), 549–566.
58. Wilhite, H. ve Ling, R. (1995). Measured energy savings from a more informative energy bill. Energy and Building, 22, 145–155.
59. Wiedenhofer, D., Lenzen,M. ve Steinberger ,J. (2013). Energy requirements of consumption: urban form, climatic and socio-economic factors, rebounds and their policy implications. Energy Policy.