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Can competing demands affect pro-environmental behaviour: a study of the impact of exposure to partly related sequential experiments

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  • Amaris, Gloria
  • Vesely, Stepan
  • Hess, Stephane
  • Klöckner, Christian A.

Abstract

The study of human behaviour is central to the development of appropriate policies for sustainability. We argue that mathematical models of human choice behaviour may produce biased results if they fail to account for the possibility of spillover effects, in particular the possibility that individual behaviour may change as a result of interventions along with competing demands (multiple demands), such as in the sequential exposure to partly related choice contexts. Using a sample of 751 individuals and a carefully constructed experiment, we develop mathematical models that jointly explain the choice between different pro-environmental actions and the willingness to donate money for environmental causes, and at the same time, allow us to test the indirect effect of exposure to multiple demands. We find that the strength of preferences for behavioural changes leading to greater CO2 reductions is (causally) shaped by participants previously considering other similar behavioural changes. The kind of spillover effects we find are relatively complex and often subtle, and thus warrant further replication studies. Care was taken to account for variation of tastes, formatting, order and learning effects, thus reducing the risk of the spillover-related results being influenced by differences across individuals in environmental preferences. Our study demonstrates the existence of a specific type of spillover effects, namely how prior exposure to related choice contexts may affect behaviour in subsequent settings and showcased the effectiveness of discrete choice models to test for it. Given our results, we believe that spillover effects need to be taken into account in the broader choice modelling literature, and at the same time we showcase a useful experimental framework for environmental psychologists and economists.

Suggested Citation

  • Amaris, Gloria & Vesely, Stepan & Hess, Stephane & Klöckner, Christian A., 2024. "Can competing demands affect pro-environmental behaviour: a study of the impact of exposure to partly related sequential experiments," Ecological Economics, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:ecolec:v:216:y:2024:i:c:s0921800923002860
    DOI: 10.1016/j.ecolecon.2023.108023
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    References listed on IDEAS

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    1. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
    2. John M. Rose & Michiel C.J. Bliemer, 2014. "Stated choice experimental design theory: the who, the what and the why," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 7, pages 152-177, Edward Elgar Publishing.
    3. Giovanna d’Adda & Yu Gao & Massimo Tavoni, 2022. "A randomized trial of energy cost information provision alongside energy-efficiency classes for refrigerator purchases," Nature Energy, Nature, vol. 7(4), pages 360-368, April.
    4. Aistė Čapienė & Aušra Rūtelionė & Manuela Tvaronavičienė, 2021. "Pro-Environmental and Pro-Social Engagement in Sustainable Consumption: Exploratory Study," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    5. Thijs Dekker & Paul Koster & Roy Brouwer, 2014. "Changing with the Tide: Semiparametric Estimation of Preference Dynamics," Land Economics, University of Wisconsin Press, vol. 90(4), pages 717-745.
    6. David Cooper & John Kagel, 2008. "Learning and transfer in signaling games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 34(3), pages 415-439, March.
    7. Alexander Maki & Amanda R. Carrico & Kaitlin T. Raimi & Heather Barnes Truelove & Brandon Araujo & Kam Leung Yeung, 2019. "Meta-analysis of pro-environmental behaviour spillover," Nature Sustainability, Nature, vol. 2(4), pages 307-315, April.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, September.
    9. Rupert Sausgruber & Jean-Robert Tyran, 2005. "Testing the Mill hypothesis of fiscal illusion," Public Choice, Springer, vol. 122(1), pages 39-68, January.
    10. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    11. Ho, Chinh Q. & Mulley, Corinne & Hensher, David A., 2020. "Public preferences for mobility as a service: Insights from stated preference surveys," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 70-90.
    12. Giovanni B Concu, 2009. "Measuring Environmental Externality Spillovers through Choice Modelling," Environment and Planning A, , vol. 41(1), pages 199-212, January.
    13. Soregaroli, Claudio & Ricci, Elena Claire & Stranieri, Stefanella & Nayga, Rodolfo M. & Capri, Ettore & Castellari, Elena, 2021. "Carbon footprint information, prices, and restaurant wine choices by customers: A natural field experiment," Ecological Economics, Elsevier, vol. 186(C).
    14. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    15. Thijs Dekker & Paul Koster & Roy Brouwer, 2014. "Changing with the Tide: Semiparametric Estimation of Preference Dynamics," Land Economics, University of Wisconsin Press, vol. 90(4), pages 717-745.
    16. Ayelet Gneezy & Alex Imas & Amber Brown & Leif D. Nelson & Michael I. Norton, 2012. "Paying to Be Nice: Consistency and Costly Prosocial Behavior," Management Science, INFORMS, vol. 58(1), pages 179-187, January.
    17. Seth Wynes & Jiaying Zhao & Simon D. Donner, 2020. "How well do people understand the climate impact of individual actions?," Climatic Change, Springer, vol. 162(3), pages 1521-1534, October.
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