Energy Efficiency

, Volume 7, Issue 2, pp 217–242 | Cite as

Increasing energy- and greenhouse gas-saving behaviors among adolescents: a school-based cluster-randomized controlled trial

  • Marilyn Cornelius
  • K. Carrie Armel
  • Kathryn Hoffman
  • Lindsay Allen
  • Susan W. Bryson
  • Manisha Desai
  • Thomas N. Robinson
Original Article

Abstract

Individual behavior change can serve as a key strategy for reducing energy use to mitigate greenhouse gas (GHG) emissions and improve energy security. A theory-based, school-based intervention to promote energy- and GHG-saving behaviors was developed by applying strategies and approaches from prior successful work in health behavior change. The focus was on changing behaviors rather than increasing knowledge, awareness, and attitudes, making extensive use of experimentally validated behavioral theory and principles. The intervention was evaluated in a cluster-randomized controlled trial. Public high school students (N = 165) in a required course were randomized by teacher to receive a 5-week, five-lesson behavior change curriculum promoting changes to reduce home electricity-, transportation-, and food-related energy use and GHG emissions or their usual coursework. Students reported their energy- and GHG-saving behaviors at baseline and 6 weeks later (1 week after the completion of the curriculum for the treatment group students). Effects were tested with hierarchical linear models to account for potential clustering within classrooms. Students randomized to receive the curriculum statistically significantly increased their total energy- and GHG-saving behaviors compared to controls [adjusted difference = 0.43 on a scale from 0 to 6 behavioral categories, 95 % confidence interval (CI) = 0.07 to 0.80, p = 0.02; number needed to treat (NNT) = 4.1]. The largest effects occurred in hang drying clothing (adjusted difference = 0.098, 95 % CI 0.028 to 0.165, NNT = 4.1) and shutting off appliances and other energy-using devices when not in use (adjusted difference = 0.095; 95 % CI 0.055 to 0.135; NNT 3.5). These results indicate that a theory-driven, school-based classroom intervention can increase energy- and GHG-saving behaviors among adolescents.

Keywords

Residential Energy Climate change Greenhouse gas Social cognitive theory School Behavior change Barriers Intervention Cluster-randomized controlled trial 

References

  1. Abrahamse, W. L., Steg, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25, 273–291.CrossRefGoogle Scholar
  2. Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2007). The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology, 27(4), 265–276.CrossRefGoogle Scholar
  3. Allcott, H., & Mullainathan, S. (2010). Behavioral science and energy policy. Science, 327, 1204–1205.CrossRefGoogle Scholar
  4. American Physical Society. (2008). Energy future: think efficiency. College Park: American Physical Society.Google Scholar
  5. Armel, K. C., Yan, K., Todd, A., & Robinson, T. N. (2011). Validation of the Stanford Climate Change Behavior (SCCB) Survey: Assessing greenhouse gas emissions-related behaviors in individuals and populations. Climatic Change, 109, 671–694.Google Scholar
  6. Ayres, I., Raseman, S., & Shih, A. (2009). Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage. Working Paper, Yale University.Google Scholar
  7. Bandura, A. (1982). Self-efficacy mechanism in human agency. The American Psychologist, 37, 122–147.CrossRefGoogle Scholar
  8. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs: Prentice-Hall.Google Scholar
  9. Bandura, A. (1991). Social cognitive theory of moral thought and action. In W. M. Kurtines & J. L. Gewirtz (Eds.), Handbook of moral behavior and development: Theory, research and applications (Vol. 1, pp. 71–129). Hillsdale, NJ: Erlbaum.Google Scholar
  10. Bandura, A. (1994). Self-efficacy. In V.S. Ramachaudran (Ed.) Encyclopedia of human Behavior (Vol. 4, pp. 71–81). New York, NY: Academic Press.Google Scholar
  11. Bandura, A. (1997). Self-efficacy: the exercise of control. New York: Freeman and Company.Google Scholar
  12. Bandura, A., & Adams, N. E. (1977). Analysis of self-efficacy theory of behavioral change. Cognitive Therapy and Research, 1, 287–310.CrossRefGoogle Scholar
  13. Bandura, A., Bandura, A., & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality and Social Psychology, 41, 586–598.CrossRefGoogle Scholar
  14. Barnett, W. S. (1996). Lives in the balance: age-27 benefit-cost analysis of the High/Scope Perry Preschool Program. Monographs Number Eleven. Ypsilanti: High/Scope Educational Research Foundation.Google Scholar
  15. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.CrossRefGoogle Scholar
  16. Barratt, A., Wyer, P. C., Hatala, R., McGinn, T., Dans, A. L., Keitz, S., Moyer, V., Barratt, A., Wyer, P. C., Hatala, R., McGinn, T., Dans, A. L., Keitz, S., Moyer, V., & Guyatt, G. (2004). Tips for learners of evidence-based medicine: 1. Relative risk reduction, absolute risk reduction and number needed to treat. Canadian Medical Association Journal. doi:10.1503/cmaj.1021197.Google Scholar
  17. Beatty, S., & Talpade, S. (1994). Adolescent influence in family decision making: a replication with extension. The Journal of Consumer Research, 21, 332–341.CrossRefGoogle Scholar
  18. Becker, L. (1978). Joint effect of feedback and goal setting on performance: a field study of residential energy conservation. Journal of Applied Psychology, 63, 428–433.CrossRefGoogle Scholar
  19. Begel, A. D., Garcia, D., & Wolfman, S. (2004). Kinesthetic learning in the classroom. Norfolk: SIGCSE.Google Scholar
  20. Belch, G. E., Belch, M. A., & Ceresino, G. (1985). Parental and teenage child influences in family decision making. Journal of Business Research, 13, 163–176.CrossRefGoogle Scholar
  21. Bickman, L. (1972). Environmental attitudes and actions. The Journal of Social Psychology, 87, 323–324.CrossRefGoogle Scholar
  22. Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across and adolescent transition: a longitudinal study and an intervention. Child Development, 78, 246–263.CrossRefGoogle Scholar
  23. Bravata, D. M., Smith-Spangler, C., Sundaram, V., Gienger, A. L., Lin, N., Lewis, R., Stave, C. D., Olkin, I., & Sirard, J. R. (2007). Using pedometers to increase physical activity and improve health: a systematic review. Journal of the American Medical Association, 298, 2296–2304.CrossRefGoogle Scholar
  24. Canning, P., Ainsley, C., Huang, S., Polenske, K, & Waters, A. (2010). Energy use in the U.S. Food System. Economic research report number 94. Washington, DC: US Department of Agriculture Economic Research Service.Google Scholar
  25. Chatellier, G., Zapletal, E., Lemaitre, D., Menard, J., & Degoulet, P. (1996). The number needed to treat: a clinically useful nomogram in its proper context. British Medical Journal, 312, 7028.CrossRefGoogle Scholar
  26. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357, 370–379.CrossRefGoogle Scholar
  27. Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: compliance and conformity. Annual Review of Psychology, 55, 591–621.CrossRefGoogle Scholar
  28. Clasen, D. R., & Brown, B. B. (1985). The multidimensionality of peer pressure in adolescence. Journal of Youth and Adolescence, 14, 451–468.Google Scholar
  29. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum.MATHGoogle Scholar
  30. Cohen, G. L., Garcia, J., Purdie-Vaughns, V., Apfel, N., & Brzustoski, P. (2009). Recursive processes in self-affirmation: intervening to close the minority achievement gap. Science, 324, 400–403.CrossRefGoogle Scholar
  31. Corner, A., & Randall, A. (2011). Selling climate change? The limitations of social marketing as a strategy for climate change public engagement. Global Environmental Change, 21, 1005–1014.CrossRefGoogle Scholar
  32. Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: a handbook for research on interactions. New York: Irvington.Google Scholar
  33. Darby, S. (2006a). Social learning and public policy: lessons from an energy-conscious village. Energy Policy, 34(17), 2929–2940.CrossRefGoogle Scholar
  34. Darby, S. (2006b). The effectiveness of feedback on energy consumption: a review for DEFRA of the literature on metering, billing and direct displays. Oxford: Environmental Change Institute, University of Oxford.Google Scholar
  35. De Young, R. (2000). Expanding and evaluating motives for environmentally responsible behavior. Journal of Social Issues, 56, 509–526.CrossRefGoogle Scholar
  36. Ehrhardt-Martinez, K. (2010). The persistence of feedback–induced energy savings in the residential sector: evidence from a meta–review. [Power point slides] Oral presentation at the Behavior Energy Climate Change (BECC) Conference, Sacramento, CA. http://www.stanford.edu/group/peec/cgi-bin/docs/events/2010/becc/presentations/3D_KarenEhrhardt-Martinez.pdf. Accessed 20 November 2011.
  37. Elley, C. R. (2008). Review: use of pedometers increases physical activity in adults. British Medical Journal, 13(3), 72.Google Scholar
  38. Energy Information Administration (2001). End-use consumption of electricity 2001. http://www.eia.gov/emeu/recs/recs2001/enduse2001/enduse2001.html. Accessed 21 April, 2012.
  39. Energy Information Administration. (2008). Annual energy review 2007, report DOE/EIA-0384(2007). Washington, DC: Office of Energy Markets and End Use, US Department of Energy.Google Scholar
  40. Farhar, B. C., & Fitzpatrick, C. (1989). Effects of feedback on residential electricity consumption: a literature review, SERI/TR-254-3386. Golden: Solar Energy Research Inst.CrossRefGoogle Scholar
  41. Farquhar, J. W., Maccoby, N., Wood, P. D., Alexander, J. K., Breitrose, H., Brown, B. et al. (1977). Community education for cardiovascular health. Lancet, 1, 1192–1195.Google Scholar
  42. Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. Journal of Abnormal and Social Psychology, 58, 203–210.CrossRefGoogle Scholar
  43. Finney, J. W., & Mitchell, R. E. (1984). Methodological issues in estimating main and interactive effects. Journal of Health and Social Behavior, 25, 85–98.CrossRefGoogle Scholar
  44. Foxman, E. R., Tansuhaj, P. S., & Ekstrom, K. M. (1989). Family members’ perceptions of adolescents’ influence in family decision making. The Journal of Consumer Research, 15, 482–491.CrossRefGoogle Scholar
  45. Frese, M., & Zapf, D. (1994). Action as the core of work psychology: A German approach. In H. C. Triandis, M. D. Dunnette, & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (Vol. 4, pp. 271–340). Palo Alto: Consulting Psychologists.Google Scholar
  46. Fuller, M., Kunkel, C., Zimring, M., Hoffman, I., Soroye, K.L., & Goldman, C. (2010). Driving demand for home energy improvements. LBNL-3960E.Google Scholar
  47. Gardner, G. T., & Stern, P. C. (2008). The short list: the most effective actions US households can take to curb climate change. Environment: Science and Policy for Sustainable Development, 50, 12–25.CrossRefGoogle Scholar
  48. Geller, E. S. (1981). Evaluating energy conservation programs: is verbal report enough? The Journal of Consumer Research, 8, 331–335.CrossRefGoogle Scholar
  49. Geller, E. S., Erickson, J. B., & Buttram, B. A. (1983). Attempts to promote residential water conservation with educational, behavioral and engineering strategies. Population and Environment, 6, 96–112.CrossRefGoogle Scholar
  50. Gleick, P. H., Coole, H. S., Gleick, P. H., & Coole, H. S. (2009). Energy implications of bottled water. Environ. Res. Lett. doi:10.1088/1748-9326/4/1/014009. 4 014009 (6pp).Google Scholar
  51. Hekler, E. B., Gardner, C. D, & Robinson, T. N. (2010). Effects of a college course about food and society on students' eating behaviors. American Journal of Preventive Medicine, 38(5), 543–547.Google Scholar
  52. Hirst, E. (1984). Household energy conservation: a review of the federal residential conservation service. Public Administration Review, 44, 421–430.Google Scholar
  53. Hogan, J. E., Lemon, K. N., & Libai, B. (2004). Quantifying the ripple: word-of-mouth and advertising effectiveness. Journal of Advertising Research, 2004, 271–280.CrossRefGoogle Scholar
  54. Holden, G. (1991). The relationship of self-efficacy appraisals to subsequent health related outcomes: a meta-analysis. Social Work in Health Care, 16, 53–93.CrossRefGoogle Scholar
  55. Hunecke, M., Haustein, S., Böhler, S., & Grischkat, S. (2010). Attitude-based target groups to reduce the ecological impact of daily mobility behavior. Environment and Behavior, 42(1), 3–43.CrossRefGoogle Scholar
  56. IPCC. (2007). Climate change 2007—mitigation of climate change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Google Scholar
  57. Janiszewski, C. H., Noel, H., & Sawyer, A. G. (2003). A meta-analysis of the spacing effect in verbal learning: Implications for research on advertising repetition and consumer memory. The Journal of Consumer Research, 30(1), 138–149.CrossRefGoogle Scholar
  58. Jones, S.E. (1964). Attitude changes of public speakers during the investigative and expressive stages of advocacy. Ph.D. dissertation (Public Address and Group Communication). Retrieved 14 March 2001, from WorldCat database.Google Scholar
  59. Katona, C., Katona, C., Livingston, G., & Livingston, G. (2002). How well do antidepressants work in older people? A systematic review of number needed to treat. Journal of Affective Disorders, 69(1−3), 47–52.CrossRefGoogle Scholar
  60. Klemmer, S. R., Hartmann, B., & Takayama, L. (2006). How bodies matter: five themes for interaction design. New York: ACM.Google Scholar
  61. Kraemer, H. C., & Kupfer, D. J. (2006). Size of treatment effects and their importance to clinical research and practice. Biological Psychiatry, 59, 990–996.CrossRefGoogle Scholar
  62. Kraemer, H. C., & Thiemann, S. (1987). How many subjects? Statistical power analysis in research. Newbury Park: Sage.Google Scholar
  63. Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001). How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. The American Journal of Psychiatry, 158(6), 848–856.CrossRefGoogle Scholar
  64. Kraemer, H. C., Wilson, G. T., Fairburn, C. G., & Agras, W. S. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883.CrossRefGoogle Scholar
  65. Laitner, J.A., Ehrhardt-Martinez, K., McKinney, V. (2009). Examining the scale of the behaviour energy efficiency continuum. American Council for an Energy-Efficient Economy, paper ID 1367. Presented at the European Council for an Energy Efficient Economy Conference, 6/1/09, Cote d’Azur, France.Google Scholar
  66. Latham, G. P., & Saari, L. M. (1979). Importance of supportive relationships in goal setting. Journal of Applied Psychology, 64(2), 151–156.CrossRefGoogle Scholar
  67. Leighty, W., & Meier A. (2010). Short-term electricity conservation in Juneau, Alaska: technology and behavioral change in persistence. Institute of Transportation Studies, University of California, Davis, research report UCD-ITS-RR-10-26.Google Scholar
  68. Lepper, M. R., Master, A., & Yow, W. Q. (2008). Intrinsic motivation in education. In M. L Maehr, S. A. Karabenick, & T. C. Urdan (Eds.), Advances in motivation and achievement, volume 15: social psychological perspectives (pp. 521–555). Bingley: Emerald.Google Scholar
  69. Locke, E. A., Shaw, K. N., Saari, L. M., & Latham, G. P. (1981). Goal setting and task performance: 1969–1980. Psychological Bulletin, 90, 125–152.CrossRefGoogle Scholar
  70. Maccoby, N., & Farquhar, J. W. (1975). Communication for health: unselling heart disease. The Journal of Communication, 25, 114–126.CrossRefGoogle Scholar
  71. Mastrandrea, M., Leurs, A., & Schneider, S. (2006). Changing perceptions of changing risks: climate change and the California public. Stanford: Woods Institute for the Environment, Stanford University.Google Scholar
  72. McCalley, L. T., & Midden, C. J. H. (2002). Energy conservation through product-integrated feedback: the roles of goal-setting and social orientation. Journal of Economic Psychology, 23, 589–603.CrossRefGoogle Scholar
  73. McKenzie-Mohr, D. (2002). New ways to promote proenvironmental behavior: promoting sustainable behavior: an introduction to community-based social marketing. Journal of Social Issues, 56(3), 543–554.CrossRefGoogle Scholar
  74. McKenzie-Mohr, D., & Smith, W. (1999). Fostering sustainable behavior: an introduction to community-based social marketing. Gabriola Island: New Society.Google Scholar
  75. McKinsey & Company. (2009). Unlocking energy efficiency in the U.S. economy. McKinsey global energy and materials. Washington, DC: McKinsey & Company.Google Scholar
  76. McQuay, H. J., Carroll, D., Jadad, A. R., Wiffen, P. J., & Moore, R. A. (1995). Anticonvulsant drugs for management of pain: a systematic review. British Medical Journal, 311, 1047.CrossRefGoogle Scholar
  77. Miller, R. L., Brickman, P., & Bolen, D. (1975). Attribution versus persuasion as a means for modifying behavior. Journal of Personality and Social Psychology, 31(3), 430–442.CrossRefGoogle Scholar
  78. O’Keefe, D. J. (2002). Persuasion: theory and research. Newbury Park: Sage.Google Scholar
  79. Overall, J. E., Lee, D. M., & Hornick, C. W. (1981). Comparisons of two strategies for analysis of variance in nonorthogonal designs. Psychological Bulletin, 90, 367–375.CrossRefGoogle Scholar
  80. Parker, D., D. Hoak, A. Meier, and R. Brown. (2006). How much energy are we using? Potential of residential energy demand feedback devices. Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings. http://tinyurl.com/5mdfkp (or http://www2.fsec.ucf.edu/en/publications/pdf/FSEC-CR-1665-06.pdf).
  81. Petersen, J. E., Shunturov, V., Janda, K., Platt, G., & Weinberger, K. (2007). Dormitory residents reduce electricity consumption when exposed to real-time visual feedback and incentives. International Journal of Sustainability in Higher Education, 8, 16–33.CrossRefGoogle Scholar
  82. Poortinga, W., Steg, L., & Vlek, C. (2004). Values, environmental concern, and environmental behavior: a study into household energy use. Environment and Behavior, 36(1), 70–93.CrossRefGoogle Scholar
  83. Premack, D. (1965). “Reinforcement theory1”. Current theory and research in motivation, 123.Google Scholar
  84. Reeves, B., Malone, T. W., et al. (2008). Leadership’s online labs. Harvard Business Review, 86(5), 58.Google Scholar
  85. Robinson, T. N. (1999). Reducing children's television viewing to prevent obesity. Journal of the American Medical Association, 282, 1561–1567.Google Scholar
  86. Robinson, T. N. (2001). Population-based obesity prevention for children and adolescents. In F. E. Johnston & G. D. Foster (Eds.), Obesity, Growth and Development (Vol. 3, pp. 129–141). London, UK: Smith-Gordon and Company Limited.Google Scholar
  87. Robinson, T. N. (2010) Stealth interventions for obesity prevention and control: motivating behavior change. In L. Dube, A. Bechara, A. Dagher, A. Drewnowski, J. LeBel, P. James, D. Richard & R. Yada (Eds.), Obesity Prevention: The Role of Brain and Society on Individual Behavior (pp. 319–327). New York: Elsevier, Inc.Google Scholar
  88. Robinson, T. N., & Borzekowski, D. L. G. (2006). Effects of the SMART classroom curriculum to reduce child and family screen time. Journal of Communication, 56, 1–26.Google Scholar
  89. Robinson, T. N., & Sirard J. R. (2005). Preventing Childhood Obesity: A Solution-Oriented Research Paradigm. American Journal of Preventive Medicine, 28, 194–201.Google Scholar
  90. Robinson, T. N., Saphir, M. N., Kraemer, H. C., Varady, A., & Haydel, K. F. (2001a). Effects of reducing television viewing on children's requests for toys: A randomized controlled trial. Journal of Developmental and Behavioral Pediatrics, 22(3), 179–184.Google Scholar
  91. Robinson, T. N., Wilde, M. L., Navratil, L. C., Haydel, K. F., & Varady, A. (2001b). Effects of reducing children's television and video game use on aggressive behavior: A randomized controlled trial. Archives of Pediatrics and Adolescent Medicine, 155(1), 17–23.Google Scholar
  92. Robinson, T. N., Killen, J. D., Kraemer, H. C., Wilson, D. M., Matheson, D. M., Haskell, W. L., et al. (2003). Dance and reducing television viewing to prevent weight gain in African-American girls: The Stanford GEMS pilot study. Ethnicity and Disease, 13(S1), s65–s77.Google Scholar
  93. Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88, 307–321.CrossRefGoogle Scholar
  94. Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology, 14, 32–38.CrossRefGoogle Scholar
  95. Schachter, S., & Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review, 69(5), 379–399.CrossRefGoogle Scholar
  96. Schultz, P. W. (2002). Knowledge, information, and household recycling: examining the knowledge-deficit model of behavior change. In T. Dietz & P. C. Stern (Eds.), New tools for environmental protection: education, information, and voluntary measures (pp. 67–82). Washington, DC: National Academy.Google Scholar
  97. Sibbald, B., & Roland, M. (1998). Understanding controlled trials: why are randomized controlled trials important? British Medical Journal, 316, 201.CrossRefGoogle Scholar
  98. Skumatz, L. A. (2009). Lessons learned and next steps in energy efficiency measurement and attribution: energy savings, net to gross, non-energy benefits, and persistence of energy efficiency behavior. Report prepared for the CIEE Behavior and Energy Program. http://uc-ciee.org/downloads/EEM_A.pdf. Accessed 27 September 2011.
  99. Stern, P. C. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407–424.CrossRefGoogle Scholar
  100. Stern, P. C., & Gardner, G. T. (1981). Psychological research and energy policy. The American Psychologist, 36(4), 329–342.CrossRefGoogle Scholar
  101. Strecher, V., McEvoy DeVellis, B., Becker, M., & Rosenstock, I. (1986). The role of self-efficacy in achieving health behavior change. Health Education & Behavior, 1(13), 73–92. http://hdl.handle.net/2027.42/68171.
  102. Sullivan, M. J. (2009). Using experiments to foster innovation and improve the effectiveness of energy efficiency programs. White paper prepared for CIEE Behavior and Energy Program. http://uc-ciee.org/downloads/exp_design_wp.pdf. Accessed 9 September 2011.
  103. Sweeney, J. (2007). Energy efficiency overview. Snowmass Workshop.Google Scholar
  104. Thompson, B. H., Jr. (2000). Tragically difficult: the obstacles to governing the commons. Environmental Law, 30, 241–278.Google Scholar
  105. Tukey, J. W. (1962). The future of data analysis. Annals of Mathematical Statistics, 33(1), 1–67.MATHMathSciNetCrossRefGoogle Scholar
  106. UNEP. (2010). Assessing the environmental impacts of consumption and production—priority products and materials. Nairobi: United Nations Environment Program.Google Scholar
  107. US Department of Education, Institute of Education Sciences. (2003). Identifying and implementing educational practices supported by rigorous evidence: a user-friendly guide. http://ies.ed.gov/ncee/wwc/references/idocviewer/Doc.aspx?docId=14&tocId=4. Accessed 31 August 2011.
  108. Vandenbergh, M. P., Barkenbus, J., et al. (2008). Individual carbon emissions: the low hanging fruit. UCLA Law Review, 55.Google Scholar
  109. Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. Berlin: Springer.MATHGoogle Scholar
  110. Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447–1451. doi:10.1126/science.1198364.CrossRefGoogle Scholar
  111. Wood, W. (2000). Attitude change: persuasion and social influence. Annual Review of Psychology, 51, 539–570.CrossRefGoogle Scholar
  112. Zelezny, L. C. (1999). Educational interventions that improve environmental behaviors: a meta-analysis. The Journal of Environmental Education, 31(1), 5–14.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Marilyn Cornelius
    • 1
  • K. Carrie Armel
    • 2
  • Kathryn Hoffman
    • 3
  • Lindsay Allen
    • 3
  • Susan W. Bryson
    • 4
  • Manisha Desai
    • 5
  • Thomas N. Robinson
    • 6
  1. 1.Emmett Interdisciplinary Program in Environment and Resources (E-IPER), Stanford UniversityD.cipher Pathways LLCHonokaaUSA
  2. 2.Precourt Energy Efficiency CenterStanford UniversityStanfordUSA
  3. 3.Stanford UniversityStanfordUSA
  4. 4.Department of Psychiatry and Behavioral Medicine and Stanford Prevention Research CenterStanford University School of MedicineStanfordUSA
  5. 5.Quantitative Sciences Unit, Department of MedicineStanford University School of MedicineStanfordUSA
  6. 6.Division of General Pediatrics, Department of Pediatrics and Stanford Prevention Research Center, Department of MedicineStanford University School of MedicineStanfordUSA

Personalised recommendations