Climatic Change

, Volume 150, Issue 3–4, pp 447–456 | Cite as

Using Google search data to inform global climate change adaptation policy

  • Carla L. Archibald
  • Nathalie Butt


The well-being of human societies in many parts of the world is threatened by climate change. While climate change is global, impacts are local and regional, and vulnerability varies widely across communities, countries, and regions. Climate change awareness has been related to how willingly communities adapt to climate change; thus, identifying communities’ awareness could help to gain insights into communities’ willingness to adopt climate change policy. In this study, we use culturomics to analyze big data from Google™ search queries to group countries based on their awareness, potential willingness, and potential capacity to deal with climate change. We demonstrate that culturomics can be used to allocate countries along a typology gradient, ranging from high-risk and high awareness to low-risk and low awareness, to climate change. Furthermore, we identify a positive correlation between countries’ climate vulnerability and awareness of climate change. As the Paris Agreement establishes a global goal to “enhance adaptive capacity, strengthen resilience and reduce vulnerability to climate change,” identifying countries’ potential adaptive capacity to climate impacts is critical. Pairing culturomics insights with climate vulnerability is a novel approach to facilitate international climate change adaptation.



CA is supported through an Australian Postgraduate Award and a top-up scholarship through the ARC Centre of Excellence for Environmental Decisions. NB is supported by ARC Grant DE150101552. The authors would like to thank Dr. Jason Samson for providing climate vulnerability data and Dr. Megan Evans, Blake Alexander Simmons, Felicia Jane Runting, Dr. Morena Mills, and Associate Professor Jonathan Rhodes for reviewing and providing useful comments during the drafting process of the manuscript.

Supplementary material

10584_2018_2289_MOESM1_ESM.docx (6 mb)
ESM 1 (DOCX 6154 kb)
10584_2018_2289_MOESM2_ESM.xlxs (42 kb)
ESM 2 (XLXS 42 kb)


  1. Adger WN, Barnett J, Brown K et al (2012) Cultural dimensions of climate change impacts and adaptation. Nat Clim Chang 3:112–117. CrossRefGoogle Scholar
  2. Althor G, Watson JEM, Fuller RA (2016) Global mismatch between greenhouse gas emissions and the burden of climate change. Sci Rep 6:20281. CrossRefGoogle Scholar
  3. Burler D (2013) When Google got flu wrong. Nature 494:5–6. CrossRefGoogle Scholar
  4. Cavanagh P, Lang C, Li X et al (2016) Searching for the determinants of climate change interest. Geogr J 2014:1–8. CrossRefGoogle Scholar
  5. Cinner JE, Adger WN, Allison EH et al (2018) Building adaptive capacity to climate change in tropical coastal communities. Nat Clim Chang 8. CrossRefGoogle Scholar
  6. Correia RA, Jepson P, Malhado ACM, Ladle RJ (2017) Internet scientific name frequency as an indicator of cultural salience of biodiversity. 78:549–555. CrossRefGoogle Scholar
  7. Haddad BM (2003) Property rights, ecosystem management, and John Locke’s labor theory of ownership. Ecol Econ 46:19–31. CrossRefGoogle Scholar
  8. Hidasi-Neto J, Loyola R, Cianciaruso MV (2015) Global and local evolutionary and ecological distinctiveness of terrestrial mammals: identifying priorities across scales. Divers Distrib 21:548–559. CrossRefGoogle Scholar
  9. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. CrossRefGoogle Scholar
  10. IPCC (2013) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. (eds Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V. CambridgeGoogle Scholar
  11. Knight KW (2017) Public awareness and perception of climate change: a quantitative cross-national study. Environ Sociol 2:101–113. CrossRefGoogle Scholar
  12. Ladle RJ, Correia RA, Do Y et al (2016) Conservation culturomics. Front Ecol Environ 14:269–275. CrossRefGoogle Scholar
  13. Lang C (2014) Do weather fluctuations cause people to seek information about climate change? Clim Chang 125:291–303. CrossRefGoogle Scholar
  14. Lang C, Ryder JD (2016) The effect of tropical cyclones on climate change engagement. Clim Chang 135:625–638. CrossRefGoogle Scholar
  15. Lassen NB, Madsen R, Vatrapu R (2014) Predicting iPhone sales from iPhone tweets. Proc IEEE 18th Int Enterp Distrib object Comput Conf 2014–Decem:81–90.
  16. Lee TM, Markowitz EM, Howe PD et al (2015) Predictors of public climate change awareness and risk perception around the world. Nat Clim Chang 5. CrossRefGoogle Scholar
  17. Lineman M, Do Y, Kim JY, Joo G-J (2015) Talking about climate change and global warming. PLoS One 10:e0138996. CrossRefGoogle Scholar
  18. Marshall NA, Park S, Howden SM et al (2013) Climate change awareness is associated with enhanced adaptive capacity. Agric Syst 117:30–34. CrossRefGoogle Scholar
  19. Mills M, Mutafoglu K, Adams VM et al (2016) Perceived and projected flood risk and adaptation in coastal Southeast Queensland, Australia. Clim Change. CrossRefGoogle Scholar
  20. Muccione V, Allen SK, Huggel C, Birkmann J (2017) Differentiating regions for adaptation financing: the role of global vulnerability and risk distributions. Wiley Interdiscip Rev Clim Chang 8(2). Google Scholar
  21. Proulx R, Massicotte P, Pépino M (2014) Googling trends in conservation biology. Conserv Biol 28:44–51. CrossRefGoogle Scholar
  22. R Core Team (2015) R: A language and environment for statistical computing.Google Scholar
  23. Ripberger JT (2011) Capturing curiosity: using internet search trends to measure public attentiveness. Policy Stud J 39:239–259. CrossRefGoogle Scholar
  24. Sachs JD, Baillie JEM, Sutherland WJ et al (2009) Biodiversity conservation and the millennium development goals. Science 325:1502–1503. CrossRefGoogle Scholar
  25. Samson J, Berteaux D, Mcgill BJ, Humphries MM (2011) Geographic disparities and moral hazards in the predicted impacts of climate change on human populations. Glob Ecol Biogeogr 20:532–544. CrossRefGoogle Scholar
  26. Sisco MR, Bosetti V, Weber EU (2017) When do extreme weather events generate attention to climate change? Clim Chang 143:227–241. CrossRefGoogle Scholar
  27. Stadelmann M, Persson Å, Ratajczak-Juszko I, Michaelowa A (2014) Equity and cost-effectiveness of multilateral adaptation finance: are they friends or foes? Int Environ Agreements Polit Law Econ 14:101–120. CrossRefGoogle Scholar
  28. Stephens-davidowitz S (2014) The cost of racial animus on a black candidate: evidence using Google search dataF. J Public Econ 118:26–40. CrossRefGoogle Scholar
  29. The World Bank. (2016) GNI per capita, PPP (current international $).
  30. UNFCCC (2015) Paris Agreement. Conf Parties its twenty-first Sess 21932:32. FCCC/CP/2015/L.9/Rev.1Google Scholar
  31. Wilde GR, Pope KL (2013) Worldwide trends in fishing interest indicated by internet search volume. Fish Manag Ecol 20:211–222. CrossRefGoogle Scholar
  32. World Resources Institute (2014) World resources institute, climate analysis indicators tool: WRI’s climate data explorer. In: Available

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.School of Earth and Environmental SciencesThe University of QueenslandSt LuciaAustralia
  2. 2.Australian Research Council Centre of Excellence for Environmental DecisionsThe University of QueenslandSt LuciaAustralia
  3. 3.School of Biological SciencesThe University of QueenslandSt LuciaAustralia

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