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Leveraging neuroscience for climate change research

Abstract

Anthropogenic climate change poses a substantial threat to societal living conditions. Here, we argue that neuroscience can substantially contribute to the fight against climate change and provide a framework and a roadmap to organize and prioritize neuroscience research in this domain. We outline how neuroscience can be used to: (1) investigate the negative impact of climate change on the human brain; (2) identify ways to adapt; (3) understand the neural substrates of decisions with pro-environmental and harmful outcomes; and (4) create neuroscience-based insights into communication and intervention strategies that aim to promote climate action. The paper is also a call to action for neuroscientists to join broader scientific efforts to tackle the existential environmental threats Earth is currently facing.

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Fig. 1: Reciprocal relationships between the brain and a changing environment.

References

  1. IPCC Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).

  2. Amel, E., Manning, C., Scott, B. & Koger, S. Beyond the roots of human inaction: fostering collective effort toward ecosystem conservation. Science 356, 275–279 (2017).

    Article  CAS  Google Scholar 

  3. Addressing the Climate Crisis: An Action Plan for Psychologists (APA Task Force on Climate Change, 2022).

  4. van der Linden, S. & Weber, E. U. Editorial overview: can behavioral science solve the climate crisis? Curr. Opin. Behav. Sci. 42, iii–viii (2021).

    Article  Google Scholar 

  5. Aoki, R., Ito, A., Izuma, K. & Saijo, T. How can neuroscience contribute to the science of intergenerational sustainability? Preprint at https://econpapers.repec.org/RePEc:kch:wpaper:sdes-2020-11 (2020).

  6. Sawe, N. & Chawla, K. Environmental neuroeconomics: how neuroscience can inform our understanding of human responses to climate change. Curr. Opin. Behav. Sci. 42, 147–154 (2021). A succinct outline of how neuroeconomics and neuroforecasting can be used to help understand human behaviour.

    Article  Google Scholar 

  7. Wang, S. & van den Berg, B. Neuroscience and climate change: how brain recordings can help us understand human responses to climate change. Curr. Opin. Psychol. 42, 126–132 (2021).

    Article  CAS  Google Scholar 

  8. Leeuwis, N., van Bommel, T. & Alimardani, M. A framework for application of consumer neuroscience in pro-environmental behavior change interventions. Front. Hum. Neurosci. 16, 886600 (2022).

    Article  Google Scholar 

  9. Berman, M. G., Kardan, O., Kotabe, H. P., Nusbaum, H. C. & London, S. E. The promise of environmental neuroscience. Nat. Hum. Behav. 3, 414–417 (2019). This comment briefly motivates and highlights the utility of the field of environmental neuroscience.

    Article  Google Scholar 

  10. Berman, M. G., Stier, A. J. & Akcelik, G. N. Environmental neuroscience. Am. Psychol. 74, 1039–1052 (2019).

    Article  Google Scholar 

  11. Hebb, D. O. The Organization of Behavior: a Neuropsychological Theory (Wiley, 1949).

  12. Blakemore, C. & Cooper, G. F. Development of the brain depends on the visual environment. Nature 228, 477–478 (1970).

    Article  CAS  Google Scholar 

  13. Hackman, D. A., Farah, M. J. & Meaney, M. J. Socioeconomic status and the brain: mechanistic insights from human and animal research. Nat. Rev. Neurosci. 11, 651–659 (2010).

    Article  CAS  Google Scholar 

  14. Peterson, B. S. et al. Effects of prenatal exposure to air pollutants (polycyclic aromatic hydrocarbons) on the development of brain white matter, cognition, and behavior in later childhood. JAMA Psychiatry 72, 531–540 (2015).

    Article  Google Scholar 

  15. Pace, A., Luo, R., Hirsh-Pasek, K. & Golinkoff, R. M. Identifying pathways between socioeconomic status and language development. Annu. Rev. Linguist. 3, 285–308 (2017).

    Article  Google Scholar 

  16. Neville, H. J. et al. Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proc. Natl Acad. Sci. USA 110, 12138–12143 (2013).

    Article  CAS  Google Scholar 

  17. Nielsen, K. S., Nicholas, K. A., Creutzig, F., Dietz, T. & Stern, P. C. The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions. Nat. Energy 6, 1011–1016 (2021).

    Article  Google Scholar 

  18. Doell, K. C., Pärnamets, P., Harris, E. A., Hackel, L. M. & Van Bavel, J. J. Understanding the effects of partisan identity on climate change. Curr. Opin. Behav. Sci. 42, 54–59 (2021). This review summarizes social psychology and cognitive neuroscience research, outlining how partisan identities impact climate action.

    Article  Google Scholar 

  19. Brosch, T. Affect and emotions as drivers of climate change perception and action: a review. Curr. Opin. Behav. Sci. 42, 15–21 (2021). This review article examines recent findings and emerging trends in the role of affect and emotion in climate change perceptions, and their potential to drive sustainable actions.

    Article  Google Scholar 

  20. Hickman, C. et al. Climate anxiety in children and young people and their beliefs about government responses to climate change: a global survey. Lancet Planet. Health 5, e863–e873 (2021).

    Article  Google Scholar 

  21. Hsiang, S. M., Burke, M. & Miguel, E. Quantifying the influence of climate on human conflict. Science 341, 1235367 (2013).

  22. Ruszkiewicz, J. A. et al. Brain diseases in changing climate. Environ. Res. 177, 108637 (2019).

    Article  CAS  Google Scholar 

  23. Mora, C., Counsell, C. W. W., Bielecki, C. R. & Louis, L. V. Twenty-seven ways a heat wave can kill you: deadly heat in the era of climate change. Circ. Cardiovasc. Qual. Outcomes 10, e004233 (2017).

    Article  Google Scholar 

  24. Gifford, R. The dragons of inaction: psychological barriers that limit climate change mitigation and adaptation. Am. Psychol. 66, 290–302 (2011).

    Article  Google Scholar 

  25. Sawe, N. Using neuroeconomics to understand environmental valuation. Ecol. Econ. 135, 1–9 (2017).

  26. Berkman, E. & Falk, E. Beyond brain mapping: using neural measures to predict real-world outcomes. Curr. Dir. Psychol. Sci. 22, 45–50 (2013). A perspective article that discusses the integration of neuroscience with traditional psychological methods to predict long-term behaviour, highlighting the potential for bridging the gap between laboratory research and real-world applications.

    Article  Google Scholar 

  27. Karmarkar, U. R. & Yoon, C. Consumer neuroscience: advances in understanding consumer psychology. Curr. Opin. Psychol. 10, 160–165 (2016).

    Article  Google Scholar 

  28. Brevers, D. et al. Brain mechanisms underlying prospective thinking of sustainable behaviours. Nat. Sustain. 4, 433–439 (2021).

    Article  Google Scholar 

  29. Doell, K. C., Conte, B. & Brosch, T. Interindividual differences in environmentally relevant positive trait affect impacts sustainable behavior in everyday life. Sci. Rep. 11, 20423 (2021).

    Article  CAS  Google Scholar 

  30. Falk, E. B., Berkman, E. T. & Lieberman, M. D. From neural responses to population behavior: neural focus group predicts population-level media effects. Psychol. Sci. 23, 439–445 (2012).

    Article  Google Scholar 

  31. Kühn, S. et al. Spend time outdoors for your brain – an in-depth longitudinal MRI study. World J. Biol. Psychiatry 23, 201–207 (2021).

    Article  Google Scholar 

  32. Tost, H. et al. Neural correlates of individual differences in affective benefit of real-life urban green space exposure. Nat. Neurosci. 22, 1389–1393 (2019).

    Article  CAS  Google Scholar 

  33. Martin, L. et al. Nature contact, nature connectedness and associations with health, wellbeing and pro-environmental behaviours. J. Environ. Psychol. 68, 101389 (2020).

    Article  Google Scholar 

  34. Zuo, J. et al. Impacts of heat waves and corresponding measures: a review. J. Clean. Prod. 92, 1–12 (2015).

    Article  Google Scholar 

  35. Hsiang, S. M., Meng, K. C. & Cane, M. A. Civil conflicts are associated with the global climate. Nature 476, 438–441 (2011).

    Article  CAS  Google Scholar 

  36. Zammit, C., Torzhenskaya, N., Ozarkar, P. D. & Calleja Agius, J. Neurological disorders vis-à-vis climate change. Early Hum. Dev. 155, 105217 (2021).

    Article  CAS  Google Scholar 

  37. Orru, H., Ebi, K. L. & Forsberg, B. The interplay of climate change and air pollution on health. Curr. Environ. Health Rep. 4, 504–513 (2017).

    Article  CAS  Google Scholar 

  38. O'Dell, K. et al. Estimated mortality and morbidity attributable to smoke plumes in the United States: not just a western US problem. GeoHealth 5, e2021GH000457 (2021).

    Article  Google Scholar 

  39. Tsai, T. L. et al. Fine particulate matter is a potential determinant of Alzheimer’s disease: a systemic review and meta-analysis. Environ. Res. 177, 108638 (2019).

    Article  CAS  Google Scholar 

  40. Wu, J. et al. Association between ambient air pollution and MRI-defined brain infarcts in health examinations in China. Int. J. Environ. Res. Public Health 18, 4325 (2021).

    Article  CAS  Google Scholar 

  41. Debette, S. et al. Association of MRI markers of vascular brain injury with incident stroke, mild cognitive impairment, dementia, and mortality. Stroke 41, 600–606 (2010).

    Article  Google Scholar 

  42. Clayton, S. Climate anxiety: psychological responses to climate change. J. Anxiety Disord. 74, 102263 (2020).

    Article  Google Scholar 

  43. Clayton, S. & Karazsia, B. T. Development and validation of a measure of climate change anxiety. J. Environ. Psychol. 69, 101434 (2020).

    Article  Google Scholar 

  44. Beaglehole, B. et al. Psychological distress and psychiatric disorder after natural disasters: systematic review and meta-analysis. Br. J. Psychiatry 213, 716–722 (2018).

    Article  Google Scholar 

  45. Boccia, M. et al. Different neural modifications underpin PTSD after different traumatic events: an fMRI meta-analytic study. Brain Imaging Behav. 10, 226–237 (2016).

    Article  Google Scholar 

  46. Cohen, S., Janicki-Deverts, D. & Miller, G. E. Psychological stress and disease. J. Am. Med. Assoc. 298, 1685–1687 (2007).

    Article  CAS  Google Scholar 

  47. Sudimac, S., Sale, V. & Kühn, S. How nature nurtures: amygdala activity decreases as the result of a one-hour walk in nature. Mol. Psychiatry 27, 4446–4452 (2022). An empirical paper that helps to highlight the utility of leveraging neuroscience methodologies to understand the acute impact of urban versus green spaces on the brain.

    Article  Google Scholar 

  48. Dadvand, P. et al. The association between lifelong greenspace exposure and 3-dimensional brain magnetic resonance imaging in Barcelona schoolchildren. Environ. Health Perspect. 126, 027012 (2018).

    Article  Google Scholar 

  49. Litleskare, S., Macintyre, T. E. & Calogiuri, G. Enable, reconnect and augment: a new era of virtual nature research and application. Int. J. Environ. Res. Public Health 17, 1738 (2020).

    Article  Google Scholar 

  50. Yeo, N. L. et al. What is the best way of delivering virtual nature for improving mood? An experimental comparison of high definition TV, 360° video, and computer generated virtual reality. J. Environ. Psychol. 72, 101500 (2020).

    Article  CAS  Google Scholar 

  51. Mostajeran, F., Krzikawski, J., Steinicke, F. & Kühn, S. Effects of exposure to immersive videos and photo slideshows of forest and urban environments. Sci. Rep. 11, 3994 (2021).

    Article  CAS  Google Scholar 

  52. Markowitz, D. M. & Bailenson, J. N. Virtual reality and the psychology of climate change. Curr. Opin. Psychol. 42, 60–65 (2021).

    Article  Google Scholar 

  53. Meijers, M. H. C., Torfadóttir, R. H., Wonneberger, A. & Maslowska, E. Experiencing climate change virtually: the effects of virtual reality on climate change related cognitions, emotions, and behavior. Environ. Commun. https://doi.org/10.1080/17524032.2023.2229043 (2023).

  54. Wolfe, U. & Lindeborg, H. Neuroscience and sustainability: an online module on ‘environmental neuroscience’. J. Undergrad. Neurosci. Educ. 17, A20–A25 (2018).

    Google Scholar 

  55. Lange, F. & Dewitte, S. Measuring pro-environmental behavior: review and recommendations. J. Environ. Psychol. 63, 92–100 (2019). A review of diverse pro-environmental behaviour measurement methods, many of which can be adaptable for neuroscience.

    Article  Google Scholar 

  56. Sawe, N., Srirangarajan, T., Sahoo, A., Tang, G. S. & Knutson, B. Neural responses clarify how ecolabels promote sustainable purchases. NeuroImage 263, 119668 (2022).

    Article  Google Scholar 

  57. Brosch, T., Stussi, Y., Desrichard, O. & Sander, D. Not my future? Core values and the neural representation of future events. Cogn. Affect. Behav. Neurosci. 18, 476–484 (2018).

    Article  Google Scholar 

  58. Baumgartner, T., Langenbach, B. P., Gianotti, L. R. R., Müri, R. M. & Knoch, D. Frequency of everyday pro-environmental behaviour is explained by baseline activation in lateral prefrontal cortex. Sci. Rep. 9, 9 (2019).

    Article  Google Scholar 

  59. Vezich, I. S., Gunter, B. C. & Lieberman, M. D. The mere green effect: an fMRI study of pro-environmental advertisements. Soc. Neurosci. 12, 400–408 (2017).

    Article  Google Scholar 

  60. Guizar Rosales, E., Baumgartner, T. & Knoch, D. Interindividual differences in intergenerational sustainable behavior are associated with cortical thickness of the dorsomedial and dorsolateral prefrontal cortex. NeuroImage https://doi.org/10.1016/j.neuroimage.2022.119664 (2022).

  61. Nash, K., Gianotti, L. R. R. & Knoch, D. A neural trait approach to exploring individual differences in social preferences. Front. Behav. Neurosci. 8, 458 (2014).

    Google Scholar 

  62. Sparkman, G. & Walton, G. M. Dynamic norms promote sustainable behavior, even if it is counternormative. Psychol. Sci. 28, 1663–1674 (2017).

    Article  Google Scholar 

  63. de Bruin, D., van Baar, J. M., Rodríguez, P. L. & FeldmanHall, O. Shared neural representations and temporal segmentation of political content predict ideological similarity. Sci. Adv. 9, eabq5920 (2023).

    Article  Google Scholar 

  64. Cacioppo, J. T., Cacioppo, S. & Petty, R. E. The neuroscience of persuasion: a review with an emphasis on issues and opportunities. Soc. Neurosci. 13, 129–172 (2018).

    Article  Google Scholar 

  65. Kühn, S., Strelow, E. & Gallinat, J. Multiple ‘buy buttons’ in the brain: forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI. NeuroImage 136, 122–128 (2016).

    Article  Google Scholar 

  66. Knutson, B. & Genevsky, A. Neuroforecasting aggregate choice. Curr. Dir. Psychol. Sci. 27, 110–115 (2018). An excellent review about how neuroforecasting can be used to understand aggregate choice.

    Article  Google Scholar 

  67. Genevsky, A., Yoon, C. & Knutson, B. When brain beats behavior: neuroforecasting crowdfunding outcomes. J. Neurosci. 37, 8625–8634 (2017).

    Article  CAS  Google Scholar 

  68. Boksem, M. A. S. & Smidts, A. Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. J. Mark. Res. 52, 482–492 (2015).

    Article  Google Scholar 

  69. Langenbach, B. P., Savic, B., Baumgartner, T., Wyss, A. M. & Knoch, D. Mentalizing with the future: electrical stimulation of the right TPJ increases sustainable decision-making. Cortex 146, 227–237 (2022). An empirical paper demonstrating how neuroscience methodologies can be applied to answer questions related to sustainable behaviour.

    Article  Google Scholar 

  70. Lamm, C., Bukowski, H. & Silani, G. From shared to distinct self-other representations in empathy: evidence from neurotypical function and socio-cognitive disorders. Phil. Trans. R. Soc. B 371, 20150083 (2016).

    Article  CAS  Google Scholar 

  71. Langenbach, B. P., Baumgartner, T., Cazzoli, D., Müri, R. M. & Knoch, D. Inhibition of the right dlPFC by theta burst stimulation does not alter sustainable decision-making. Sci. Rep. 9, 13852 (2019).

    Article  Google Scholar 

  72. MAGNETOM Prisma: Environmental Product Declaration (Siemens Healthcare GmbH, 2020).

  73. Greenhouse Gas Equivalencies Calculator (US EPA, 2015); https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator

  74. van Ewijk, S. & Hoekman, P. Emission reduction potentials for academic conference travel. J. Ind. Ecol. 25, 778–788 (2021).

    Article  Google Scholar 

  75. Capstick, S. et al. Civil disobedience by scientists helps press for urgent climate action. Nat. Clim. Change 12, 773–774 (2022).

    Article  Google Scholar 

  76. Rae, C. L., Farley, M., Jeffery, K. J. & Urai, A. E. Climate crisis and ecological emergency: why they concern (neuro)scientists, and what we can do. Brain Neurosci. Adv. 6, 239821282210754 (2022). This paper suggests different actions that scientists, especially neuroscientists, can take to make their professions more sustainable.

    Article  Google Scholar 

  77. Lange, F. et al. Beyond self-reports: a call for more behavior in environmental psychology. J. Environ. Psychol. https://doi.org/10.1016/j.jenvp.2023.101965 (2023).

  78. Doell, K. C. Megastudies to test the efficacy of behavioural interventions. Nat. Rev. Psychol. 2, 263–263 (2023).

    Article  Google Scholar 

  79. ENIGMA-Environment (2023); https://enigma.ini.usc.edu/ongoing/enigma-environment/

  80. Weisberg, D. S., Taylor, J. C. V. & Hopkins, E. J. Deconstructing the seductive allure of neuroscience explanations. Judgm. Decis. Mak. 10, 429–441 (2015).

    Article  Google Scholar 

  81. LeDoux, J. The amygdala. Curr. Biol. 17, R868–R874 (2007).

    Article  CAS  Google Scholar 

  82. Behbehani, M. M. Functional characteristics of the midbrain periaqueductal gray. Prog. Neurobiol. 46, 575–605 (1995).

    Article  CAS  Google Scholar 

  83. Schacter, D. L. et al. The future of memory: remembering, imagining, and the brain. Neuron 76, 677–694 (2012).

    Article  CAS  Google Scholar 

  84. Ballard, K. & Knutson, B. Dissociable neural representations of future reward magnitude and delay during temporal discounting. NeuroImage 45, 143–150 (2009).

    Article  Google Scholar 

  85. Hare, T., Hakimi, S. & Rangel, A. Activity in dlPFC and its effective connectivity to vmPFC are associated with temporal discounting. Front. Neurosci. 8, 50 (2014).

    Article  Google Scholar 

  86. Canessa, N. et al. The functional and structural neural basis of individual differences in loss aversion. J. Neurosci. 33, 14307–14317 (2013).

    Article  CAS  Google Scholar 

  87. Levin, I. et al. A neuropsychological approach to understanding risk-taking for potential gains and losses. Front. Neurosci. 6, 15 (2012).

    Article  Google Scholar 

  88. Schurz, M., Aichhorn, M., Martin, A. & Perner, J. Common brain areas engaged in false belief reasoning and visual perspective taking: a meta-analysis of functional brain imaging studies. Front. Hum. Neurosci. 7, 712 (2013).

    Article  Google Scholar 

  89. Knutson, B. & Greer, S. M. Anticipatory affect: neural correlates and consequences for choice. Phil. Trans. R. Soc. B 363, 3771–3786 (2008).

    Article  Google Scholar 

  90. Ruff, C. C. & Fehr, E. The neurobiology of rewards and values in social decision making. Nat. Rev. Neurosci. 15, 549–562 (2014).

    Article  CAS  Google Scholar 

  91. Baxter, M. G. & Murray, E. A. The amygdala and reward. Nat. Rev. Neurosci. 3, 563–573 (2002).

    Article  CAS  Google Scholar 

  92. Kahnt, T., Heinzle, J., Park, S. Q. & Haynes, J.-D. Decoding different roles for vmPFC and dlPFC in multi-attribute decision making. NeuroImage 56, 709–715 (2011).

    Article  Google Scholar 

  93. Prévost, C., Pessiglione, M., Météreau, E., Cléry-Melin, M.-L. & Dreher, J.-C. Separate valuation subsystems for delay and effort decision costs. J. Neurosci. 30, 14080–14090 (2010).

    Article  Google Scholar 

  94. Lopez-Gamundi, P. et al. The neural basis of effort valuation: a meta-analysis of functional magnetic resonance imaging studies. Neurosci. Biobehav. Rev. 131, 1275–1287 (2021).

    Article  Google Scholar 

  95. Niendam, T. A. et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn. Affect. Behav. Neurosci. 12, 241–268 (2012).

    Article  Google Scholar 

  96. Badre, D. & Nee, D. E. Frontal cortex and the hierarchical control of behavior. Trends Cogn. Sci. 22, 170–188 (2017).

    Article  Google Scholar 

  97. Poldrack, R. Can cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10, 59–63 (2006).

    Article  Google Scholar 

  98. Environmental Psychology: An Introduction (John Wiley & Sons, 2018).

  99. Alcock, I., White, M. P., Pahl, S., Duarte-Davidson, R. & Fleming, L. E. Associations between pro-environmental behaviour and neighbourhood nature, nature visit frequency and nature appreciation: evidence from a nationally representative survey in England. Environ. Int. 136, 105441 (2020).

    Article  Google Scholar 

  100. Mertens, S., Herberz, M., Hahnel, U. J. J. & Brosch, T. The effectiveness of nudging: a meta-analysis of choice architecture interventions across behavioral domains. Proc. Natl Acad. Sci. USA 119, e2107346118 (2022).

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported in part by a grant from the Swiss National Science Foundation to K.C.D. (grant no. P400PS_190997), a National Science Foundation Smart and Connected Communities grant (grant no. CNS-1952050) to M.G.B., and grants from the John Templeton Foundation and Jigsaw to J.J.V.B. We would like to thank B. Todorova for her assistance with editing and formatting the manuscript.

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Doell, K.C., Berman, M.G., Bratman, G.N. et al. Leveraging neuroscience for climate change research. Nat. Clim. Chang. (2023). https://doi.org/10.1038/s41558-023-01857-4

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