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Adapting neuroeconomics for environmental and energy policy

  • NIK SAWE (a1)


Neuroimaging methods provide insight into the neural mechanisms underlying the decision process, characterizing choice at the individual level and, in a growing number of contexts, predicting national- and market-level behavior. This dual capacity to examine heterogeneity while forecasting aggregate choice is particularly beneficial to those studying environmental decision-making. To effectively reduce residential energy usage and foster other pro-environmental behaviors, policy-makers must understand the effects of information frames and behavioral nudges across individuals who hold a diverse array of attitudes toward the environment and face a broad range of barriers to action. This paper articulates the potential of neuroeconomic methods to aid environmental policy-makers interested in behavior change, especially those interested in closing the energy efficiency gap. Investigation into the roles of affect, eco-labeling and social norms will be discussed, as well as personal identity and climate change beliefs. Combining neuroimaging with behavioral economics experiments can inform the development of effective messaging, characterize the influence of individual differences on the decision process and aid in forecasting the efficacy of policy interventions at scale.

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Ajzen, I. (1991), ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes, 50: 179211. doi:10.1016/0749-5978(91)90020-T.
Allcott, H. (2011a), ‘Consumers' perceptions and misperceptions of energy costs’, American Economic Review, 101: 98104. doi:10.1257/aer.101.3.98.
Allcott, H. (2011b), ‘Social norms and energy conservation’, Journal of Public Economics, 95: 10821095. doi:10.1016/j.jpubeco.2011.03.003.
Attari, S. Z., DeKay, M. L., Davidson, C. I. and Bruine de Bruin, W. (2010), ‘Public perceptions of energy consumption and savings’, Proceedings of the National Academy of Sciences, 107: 1605416059. doi:10.1073/pnas.1001509107.
Ayres, I., Raseman, S. and Shih, A. (2013), ‘Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage’, The Journal of Law, Economics, and Organization, doi:10.1093/jleo/ews020.
Ballard, K. and Knutson, B. (2009), ‘Dissociable neural representations of future reward magnitude and delay during temporal discounting’, Neuroimage, 45: 143150. doi:10.1016/j.neuroimage.2008.11.004.
Bartra, O., McGuire, J. T. and Kable, J. W. (2013), ‘The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value’, Neuroimage, 76: 412427.
Berns, G. S., Bell, E., Capra, C. M., Prietula, M. J., Moore, S., Anderson, B., Ginges, J. and Atran, S. (2012), ‘The price of your soul: neural evidence for the non-utilitarian representation of sacred values’, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367: 754–62.
Berns, G. S. and Moore, S. E. (2011), ‘A neural predictor of cultural popularity’, Journal of Consumer Psychology, 17. doi:10.1016/j.jcps.2011.05.001.
Bollinger, B. and Gillingham, K. (2012), ‘Peer Effects in the Diffusion of Solar Photovoltaic Panels’, Marketing Science, 31: 900912.
Bonini, N., Ritov, I. and Graffeo, M. (2008), ‘When does a referent public problem affect financial and political support for public action?’, Journal of Behavioral Decision Making, doi:10.1002/bdm.580.
Bradley, M. M. and Lang, P. J. (1999), ‘Affective norms for English words (ANEW): Instruction manual and affective ratings’, Univ. Florida Cent. Res. Psychophysiol.
Camerer, C. and Weber, M. (1992), ‘Recent developments in modeling preferences: Uncertainty and ambiguity’, Journal of Risk and Uncertainty, 5: 325370. doi:10.1007/BF00122575.
Cleveland, M., Kalamas, M. and Laroche, M. (2005), ‘Shades of green: linking environmental locus of control and pro-environmental behaviors’, Journal of Consumer Marketing, 22: 198212. doi:10.1108/07363760510605317.
Clithero, J. A. and Rangel, A. (2013), ‘Informatic parcellation of the network involved in the computation of subjective value’, Social Cognitive and Affective Neuroscience, 9: 12891302.
Cohen, J. D. (2005), ‘The Vulcanization of the Human Brain: A Neural Perspective on Interactions Between Cognition and Emotion’, Journal of Economic Perspectives, 19: 324. doi:10.1257/089533005775196750.
Daily, G. C., Polasky, S., Goldstein, J., Kareiva, P. M., Mooney, H. A., Pejchar, L., Ricketts, T. H., Salzman, J. and Shallenberger, R. (2009), ‘Ecosystem services in decision making: time to deliver’, Frontiers in Ecology and the Environment, 7: 2128. doi:10.1890/080025.
Denny, B. T., Kober, H., Wager, T. D. and Ochsner, K. N. (2012), ‘A Meta-analysis of Functional Neuroimaging Studies of Self- and Other Judgments Reveals a Spatial Gradient for Mentalizing in Medial Prefrontal Cortex’, Journal of Cognitive Neuroscience, doi:10.1162/jocn_a_00233.
Diekhof, E. K., Falkai, P. and Gruber, O. (2008), ‘Functional neuroimaging of reward processing and decision-making: A review of aberrant motivational and affective processing in addiction and mood disorders’, Brain Research Reviews, 59: 164184. doi:10.1016/j.brainresrev.2008.07.004.
Dolnicar, S. and Grun, B. (2009), ‘Environmentally Friendly Behavior: Can Heterogeneity Among Individuals and Contexts/ Environments Be Harvested for Improved Sustainable Management?’, Environment and Behavior, 41: 693714. doi:10.1177/0013916508319448.
Doré, B. P., Zerubavel, N. and Ochsner, K. N. (2015), ‘Social cognitive neuroscience: A review of core systems’, APA Handbook of Personality and Social Psychology. Volume 1: Attitudes and Social Cognition, 1: 693720. doi:10.1037/14341-022.
Dunlap, R. E., Van Liere, K. D., Mertig, A. G. and Jones, R. E. (2000), ‘Measuring Endorsement of the New Ecological Paradigm: A Revised NEP Scale’, Journal of Social Issues, 56: 425442. doi:10.1111/0022-4537.00176.
Ellsberg, D. (1961), ‘Risk, Ambiguity, and the Savage Axioms’, The Quarterly Journal of Economics, doi:10.2307/1884324.
Falk, E., O'Donnel, M., Tompson, S., Gonzalez, R., Cin, S., Strecher, V., Cummings, K. and An, L. (2015), ‘Functional brain imaging predicts public health campaign success’, Social Cognitive and Affective Neuroscience. e-ahead of, 111. doi:10.1093/scan/nsv108.
Falk, E. B., Berkman, E. T. and Lieberman, M. D. (2012), ‘From Neural Responses to Population Behavior: Neural Focus Group Predicts Population-Level Media Effects’, Psychological Science, 23: 439–45. doi:10.1177/0956797611434964.
Gardner, G. T. and Stern, P. C. (2002), Environmental Problems and Human Behavior, 2nd Ed. ed. Pearson Custom Publishing, Boston, MA.
Gattig, A. and Hendrickx, L. (2007), ‘Judgmental Discounting and Environmental Risk Perception: Dimensional Similarities, Domain Differences, and Implications for Sustainability’, Journal of Social Issues, 63: 2139. doi:10.1111/j.1540-4560.2007.00494.x.
Genevsky, A. and Knutson, B. (2015), ‘Neural Affective Mechanisms Predict Market-Level Microlending’, Psychological Science.
Genevsky, A. and Knutson, B. (2015), ‘Neural affective mechanisms predict market-level microlending’, Psychological Science, 26: 14111422. doi:10.1177/0956797615588467.
Genevsky, A., Yoon, C. and Knutson, B. (2017), ‘When brain beats behavior: Neuroforecasting crowdfunding outcomes’, Journal of Neuroscience, 37: 1633–16. doi:10.1523/JNEUROSCI.1633-16.2017.
Gigerenzer, G. and Selten, R. (Eds.) (2002), Bounded Rationality: The Adaptive Toolbox, MIT Press.
Gillingham, K. and Palmer, K. (2014), ‘Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence’, Review of Environmental Economics and Policy, 8: 1838. doi:10.1093/reep/ret021.
Goldstein, N. J., Cialdini, R. B. and Griskevicius, V. (2008), ‘A Room with a Viewpoint: Using Social Norms to Motivate Environmental Conservation in Hotels’, Journal of Consumer Research, 35: 472482. doi:10.1086/586910.
Gowdy, J. M. (2008), ‘Behavioral economics and climate change policy’, Journal of Economic Behavior & Organization, 68: 632644. doi:10.1016/j.jebo.2008.06.011.
Grabenhorst, F. and Rolls, E. T. (2011), ‘Value, pleasure and choice in the ventral prefrontal cortex’, Trends in Cognitive Sciences, 15: 5667. doi:10.1016/j.tics.2010.12.004.
Granade, H. C., Creyts, J., Derkach, A., Farese, P., Nyguist, S. and Ostrowski, K. (2009), ‘Unlocking Energy Efficiency in the U.S. Economy’, McKinsey Global Energy and Materials, 1165. doi:10.1016/j.enbuild.2006.06.009.
Hardisty, D. J. and Weber, E. U. (2009), ‘Discounting future green: money versus the environment’, Journal of Experimental Psychology, 138: 329–40. doi:10.1037/a0016433.
Hausman, D. M. and Welch, B. (2010), ‘Debate: To nudge or not to nudge’, Journal of Political Philosophy, 18: 123136. doi:10.1111/j.1467-9760.2009.00351.x.
Hausman, J. A. (1979), ‘Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables’, Bell Journal of Economics, 10: 3354. doi:10.2307/3003318.
Heimlich, J. and Ardoin, N. (2008), ‘Understanding behavior to understand behavior change: a literature review’, Environmental Education Research, 14: 215237. doi:10.1080/13504620802148881.
Houde, S. (2011), ‘How Consumers Respond to Product Certification: A Welfare Analysis of the Energy Star Program’, Work. Pap.
Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., Camerer, C. F., Adolphus, R., Tranel, D. and Camerer, C. F. (2005), ‘Neural systems responding to degrees of uncertainty in human decision-making’, Science 310: 16801683. doi:10.1126/science.1115327.
Huettel, S. and McCarthy, G. (2001), ‘The effects of single-trial averaging upon the spatial extent of fMRI activation’, Neuroreport, 12: 24112416.
Jaffe, A. B. and Stavins, R. N. (1994), ‘The energy-efficiency gap What does it mean?’, Energy Policy, 22: 804810. doi:10.1016/0301-4215(94)90138-4.
Jones, R. N. (2000), ‘Managing uncertainty in climate change projections–issues for impact assessment’, Climatic Change, doi:10.1016/j.urology.2013.09.061.
Kahan, D. M., Peters, E., Braman, D., Slovic, P., Wittlin, M., Ouellette, L. L. and Mandel, G. (2011), ‘The Tragedy of the Risk-Perception Commons: Culture Conflict, Rationality Conflict, and Climate Change’, Cult. Cogn. Proj. Work. Pap.
Kahneman, D. (2003), ‘Maps of Bounded Rationality: Psychology for Behavioral Economics’, American Economic Review, 93: 14491475.
Kahneman, D., Ilana, R. and Schkade, D. (1999), ‘Economic Preferences or Attitude Expressions?: An Analysis of Dollar Responses to Public Issues’, Journal of Risk and Uncertainty, 19: 203235.
Kahneman, D. and Ritov, I. (1994), ‘Determinants of stated willingness to pay for public goods: A study in the headline method’, Journal of Risk and Uncertainty, doi:10.1007/BF01073401.
Kang, M. J. and Camerer, C. F. (2013), ‘FMRI evidence of a hot-cold empathy gap in hypothetical and real aversive choices’, Frontiers in Neuroscience, 7: 104. doi:10.3389/fnins.2013.00104.
Karmarkar, U. R. and Yoon, C. (2016), ‘Consumer neuroscience: Advances in understanding consumer psychology’, Current Opinion in Psychology, 10: 160165. doi:10.1016/j.copsyc.2016.01.010.
Kaufmann, L., Wood, G., Rubinsten, O. and Henik, A. (2011), ‘Meta-analyses of developmental fMRI studies investigating typical and atypical trajectories of number processing and calculation’, Developmental Neuropsychology, 36: 763787. doi:10.1080/87565641.2010.549884.
Knutson, B. and Greer, S. M. (2008), ‘Anticipatory affect: neural correlates and consequences for choice’, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363: 37713786.
Knutson, B., Katovich, K. and Suri, G. (2014), ‘Inferring affect from fMRI data’, Trends in Cognitive Sciences, 18: 422428.
Knutson, B., Rick, S., Wimmer, G. E., Prelec, D. and Loewenstein, G. (2007), ‘Neural Predictors of Purchases’, Neuron, 53: 147156.
Knutson, B., Taylor, J., Kaufman, M., Peterson, R. and Glover, G. (2005), ‘Distributed neural representation of expected value’, Journal of Neuroscience, 25: 48064812.
Kühn, S., Strelow, E. and Gallinat, J. (2016), ‘Multiple “buy buttons” in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI’, Neuroimage, doi:10.1016/j.neuroimage.2016.05.021.
Lane, B. and Potter, S. (2007), ‘The adoption of cleaner vehicles in the UK: exploring the consumer attitude-action gap’, Journal of Cleaner Production, 15: 10851092. doi:10.1016/j.jclepro.2006.05.026.
Leard, B. (2013), Consumer Heterogeneity and the Energy Paradox.
Lee, D. (2008), ‘Game theory and neural basis of social decision making’, Nature Neuroscience, 11: 404409. doi:10.1038/nn2065.
Lee, J. H., Durand, R., Gradinaru, V., Zhang, F., Goshen, I., Kim, D.-S., Fenno, L. E., Ramakrishnan, C. and Deisseroth, K. (2010), ‘Global and local fMRI signals driven by neurons defined optogenetically by type and wiring’, Nature, 465: 788792. doi:10.1038/nature09108.
Leiserowitz, A. (2006), ‘Climate change risk perception and policy preferences: The role of affect, imagery, and values’, in Climatic Change, 4572. doi:10.1007/s10584-006-9059-9.
Lieberman, M. D. (2007), ‘Social cognitive neuroscience: a review of core processes’, Annual Review of Psychology, 58: 259–89.
Lim, S.-L., O'Doherty, J. P. and Rangel, A. (2011), ‘The Decision Value Computations in the vMPFC and Striatum Use a Relative Value Code That is Guided by Visual Attention’, Journal of Neuroscience, 31: 1321413223. doi:10.1523/JNEUROSCI.1246-11.2011.
Linder, N. S., Uhl, G., Fliessbach, K., Trautner, P., Elger, C. E. and Weber, B. (2010), ‘Organic labeling influences food valuation and choice’, Neuroimage, 53: 215220. doi:10.1016/j.neuroimage.2010.05.077.
McClure, S. M. (2004), ‘Separate Neural Systems Value Immediate and Delayed Monetary Rewards’, Science (80-.), 306: 503507. doi:10.1126/science.1100907.
McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G. and Cohen, J. D. (2007), ‘Time discounting for primary rewards’, Journal of Neuroscience, 27: 57965804.
Montague, P. R., King-Casas, B. and Cohen, J. D. (2006), ‘Imaging valuation models in human choice’, Annual Review of Neuroscience, 29: 417448. doi:10.1146/annurev.neuro.29.051605.112903.
Newell, R. G. and Siikamäki, J. V. (2013), ‘Nudging Energy Efficiency Behavior: The Role of Information Labels’, Journal of the Association of Environmental and Resource Economists, 1: 555598. doi:10.1016/j.yexmp.2014.03.001.
Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J. and Griskevicius, V. (2008), ‘Normative Social Influence is Underdetected. Personal’, Personality and Social Psychology Bulletin, 34: 913923. doi:10.1177/0146167208316691.
Peysakhovich, A. and Karmarkar, (2015), ‘Asymmetric Effects of Favorable and Unfavorable Information on Decision Making Under Ambiguity’, Management Science.
Plassmann, H. and O'Doherty, J., Rangel, A. (2007), ‘Orbitofrontal Cortex Encodes Willingness to Pay in Everyday Economic Transactions’, Journal of Neuroscience, doi:10.1523/JNEUROSCI.2131-07.2007.
Preuschoff, K., Quartz, S. R. and Bossaerts, P. (2008), ‘Human Insula Activation Reflects Risk Prediction Errors As Well As Risk’, Journal of Neuroscience, 28: 27452752. doi:10.1523/JNEUROSCI.4286-07.2008.
Ritov, I., Kahneman, D. (1997), ‘How people value the environment: Attitudes versus economic values’, in Psychological Perspectives to Environmental and Ethical Issues.
Sahoo, A. and Sawe, N. (2015), ‘The Heterogeneous Effects of Eco-Labels on Internalities and Externalities’.
Sallee, J. M. (2014), ‘Rational inattention and energy efficiency’, The Journal of Law and Economics, 57: 781820. doi:10.1017/CBO9781107415324.004.
Samanez-Larkin, G. R. and Knutson, B. (2015), ‘Decision making in the ageing brain: changes in affective and motivational circuits’, Nature Reviews Neuroscience, 16: 278289. doi:10.1038/nrn3917.
Sanfey, A. G. (2007), ‘Social decision-making: insights from game theory and neuroscience’, Science, 318: 598602.
Sawe, N., 2017. ‘Using neuroeconomics to understand environmental valuation’, Ecological Economics, 135. doi:10.1016/j.ecolecon.2016.12.018.
Sawe, N. and Knutson, B. (2015), ‘Neural valuation of environmental resources’, Neuroimage, 122: 8795. doi:10.1016/j.neuroimage.2015.08.010.
Scholz, C., Baek, E. C., Brook, M., Donnell, O., Suk, H., Cappella, J. N. and Falk, E. B. (2017), ‘A neural model of valuation and information virality’, Proceedings of the National Academy of Sciences of the United States of America, 114: 28812886. doi:10.1073/pnas.1615259114.
Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J. and Griskevicius, V. (2007), ‘The constructive, destructive, and reconstructive power of social norms: Research article’, Psychological Science, doi:10.1111/j.1467-9280.2007.01917.x.
Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K. and Lehman, D. R. (2002), ‘Maximizing versus satisficing: Happiness is a matter of choice’, Journal of Personality and Social Psychology, 83: 11781197. doi:10.1037/0022-3514.83.5.1178.
Shogren, J. F. and Taylor, L. O. (2008), ‘On behavioral-environmental economics’, Review of Environmental Economics and Policy, 2: 2644. doi:10.1093/reep/rem027.
Train, K. (1985), ‘Discount rates in consumers’ energy-related decisions: A review of the literature’, Energy, doi:10.1016/0360-5442(85)90135-5.
Tremblay, S., Sharika, K. M. and Platt, M. L. (2017), ‘Social Decision-Making and the Brain: A Comparative Perspective’, Trends in Cognitive Sciences, 21: 265276. doi:10.1016/j.tics.2017.01.007.
Ungemach, C., Camilleri, A. R., Johnson, E. J., Larrick, R. P. and Weber, E. U. (2017), ‘Translated Attributes as Choice Architecture: Aligning Objectives and Choices Through Decision Signposts’, Management Science, 115. doi:10.1287/mnsc.2016.2703.
US Environmental Protection Agency (2016), ‘Energy Star Overview of 2015 Achievements’.
Venkatachalam, L., (2008), ‘Behavioral economics for environmental policy’, Ecological Economics, 67: 640645. doi:10.1016/j.ecolecon.2008.01.018.
Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., Hershfield, H. E., Ishihara, M., Winer, R. S., Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K. and Hampton, W. (2015), ‘Predicting Advertising Success Beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling’, Journal of Marketing Research, LII: 141218074858006. doi:215.204.1409.
Wallis, J. D. (2012), ‘Cross-species studies of orbitofrontal cortex and value-based decision-making’, Nature Neuroscience, 15: 13–9. doi:10.1038/nn.2956.
Ward, D. O., Clark, C. D., Jensen, K. L., Yen, S. T. and Russell, C. S. (2011), ‘Factors influencing willingness-to-pay for the ENERGY STAR® label’, Energy Policy, 39: 14501458. doi:10.1016/j.enpol.2010.12.017.
Weber, E. U. (2006), ‘Experience-Based and Description-Based Perceptions of Long-Term Risk: Why Global Warming does not Scare us (Yet)’, Climatic Change, 77: 103120. doi:10.1007/s10584-006-9060-3.
Wilson, C. and Dowlatabadi, H. (2007), ‘Models of Decision Making and Residential Energy Use’, Annual Review of Environment and Resources, 32: 169203. doi:10.1146/
Wu, C. C., Samanez-Larkin, G. R., Katovich, K. and Knutson, B. (2014), ‘Affective traits link to reliable neural markers of incentive anticipation’, Neuroimage, 84: 279289. doi:10.1016/j.neuroimage.2013.08.055.
Wu, H., Luo, Y. and Feng, C. (2016), ‘Neural signatures of social conformity: A coordinate-based activation likelihood estimation meta-analysis of functional brain imaging studies’, Neuroscience and Biobehavioral Review, 71: 101111. doi:10.1016/j.neubiorev.2016.08.038.
Young, W., Hwang, K., Mcdonald, S. and Oates, C. J. (2010), ‘Sustainable Consumption: Green Consumer Behaviour when Purchasing Products’, Sustainable Development, 18: 2031.


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