A scoping review on climate change education

Escalating climate impacts predicted in the past decades are now a reality almost everywhere on the planet, and the time-critical dimension of the climate crisis means that the coming years will be instrumental in securing a climate resilient future for generations to come. Education is central to promoting climate action, yet the role that climate change education plays in advancing climate awareness, action and advocacy, and helping to enhance resiliency for young generations and the public at large is poorly understood. Here, we provide a first-of-its kind mapping of the literature on climate change education to better understand topic relationships and spatial distribution, and highlight potential new avenues for research on climate education. Machine learning methods including semantic analysis, geoparsing and topic modeling are used to support our study. Topic modeling shows that climate change education is a very interdisciplinary field of research well embedded in key climate change research topics including climate change adaptation, disaster risks and education, mitigation and sustainability, with the bulk of the literature situated in social science research, followed by topics on agricultural and adaptation, and education topics including methodologies, paradigm shifts, and research methods. Central to climate change education is the methodological dimension of teaching and educating either through formal or informal methods. Topic clustering reveals that topics including energy, renewable energy, fossil fuel and emissions are visibly far from topics school, teacher and science. As expected, social research lies in the middle and overlaps at the periphery with most other topic clusters, except with topics of energy mitigation, disaster risk, and medical health. Through geoparsing, country mentions and case studies are largely skewed towards the

[1]  S. Lane,et al.  The climate change research that makes the front page: Is it fit to engage societal action? , 2023, Global Environmental Change.

[2]  Markus Leippold,et al.  chatClimate: Grounding Conversational AI in Climate Science , 2023, Communications Earth & Environment.

[3]  Vandana Singh How to teach climate change in a physics classroom , 2023, Nature Reviews Physics.

[4]  Isyaku Hassan,et al.  Analysis of climate change disinformation across types, agents and media platforms , 2023, Information Development.

[5]  Stefan Ruseti,et al.  Counteracting French Fake News on Climate Change Using Language Models , 2022, Sustainability.

[6]  Shupei Yuan,et al.  More aggressive, more retweets? Exploring the effects of aggressive climate change messages on Twitter , 2022, New Media & Society.

[7]  Jin Chen,et al.  Fear emotion reduces reported mitigation behavior in adolescents subject to climate change education , 2022, Climatic Change.

[8]  C. Frantz To create serious movement on climate change, we must dispel the myth of indifference , 2022, Nature Communications.

[9]  Jan Činčera,et al.  What Triggers Climate Action: The Impact of a Climate Change Education Program on Students’ Climate Literacy and Their Willingness to Act , 2022, Sustainability.

[10]  Bianka Plüschke-Altof,et al.  Speaking of a ‘climate crisis’: Fridays for Future's attempts to reframe climate change , 2022, Innovation: The European Journal of Social Science Research.

[11]  Kai Niebert,et al.  The (Un)political Perspective on Climate Change in Education—A Systematic Review , 2022, Sustainability.

[12]  J. Stötter,et al.  Rethinking Quality Science Education for Climate Action: Transdisciplinary Education for Transformative Learning and Engagement , 2022, Frontiers in Education.

[13]  Risto Kunelius,et al.  Voices of a generation the communicative power of youth activism , 2021, Climatic Change.

[14]  C. Brick,et al.  To strike or not to strike? an investigation of the determinants of strike participation at the Fridays for Future climate strikes in Switzerland , 2021, PloS one.

[15]  A. Corner,et al.  Evaluating effective public engagement: local stories from a global network of IPCC scientists , 2021, Climatic Change.

[16]  B. Moore,et al.  Transformations for climate change mitigation: A systematic review of terminology, concepts, and characteristics , 2021, WIREs Climate Change.

[17]  K. Mach,et al.  Global evidence of constraints and limits to human adaptation , 2021, Regional Environmental Change.

[18]  H. King,et al.  The ‘web of conditions’ governing England’s climate change education policy landscape , 2021, Journal of Education Policy.

[19]  J. Minx,et al.  Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies , 2021, Nature Climate Change.

[20]  J. Minx,et al.  Systematic mapping of global research on climate and health: a machine learning review , 2021, The Lancet. Planetary health.

[21]  V. Symeonidis,et al.  FridaysForFuture as an Enactive Network: Collective Agency for the Transition Towards Sustainable Development , 2021, Frontiers in Education.

[22]  M. McKenzie Climate change education and communication in global review: tracking progress through national submissions to the UNFCCC Secretariat , 2021 .

[23]  C. Adler,et al.  Climate Change Adaptation in European Mountain Systems: A Systematic Mapping of Academic Research , 2021, Mountain Research and Development.

[24]  A. Sanson,et al.  Children and youth in the climate crisis , 2021, BJPsych Bulletin.

[25]  Iqbal H. Sarker Machine Learning: Algorithms, Real-World Applications and Research Directions , 2021, SN Computer Science.

[26]  A. Hadjichambis,et al.  Teachers’ Perceptions on Environmental Citizenship: A Systematic Review of the Literature , 2021, Sustainability.

[27]  K. Aikens,et al.  A comparative analysis of environment and sustainability in policy across subnational education systems , 2021 .

[28]  Neal R Haddaway,et al.  The Global Adaptation Mapping Initiative (GAMI): Part 1 – Introduction and overview of methods , 2021 .

[29]  Kate Winfield,et al.  Open Data Challenges in Climate Science , 2020, Data Sci. J..

[30]  Michael L. Waskom,et al.  Seaborn: Statistical Data Visualization , 2021, J. Open Source Softw..

[31]  Tet Hin Yeap,et al.  Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis , 2020, Frontiers in Artificial Intelligence.

[32]  R. Leichenko,et al.  Teaching climate change in the Anthropocene: An integrative approach , 2020, Anthropocene.

[33]  C. Howarth,et al.  Effectively Communicating Climate Science beyond Academia: Harnessing the Heterogeneity of Climate Knowledge , 2020, One Earth.

[34]  J. Bohr “Reporting on climate change: A computational analysis of U.S. newspapers and sources of bias, 1997–2017” , 2020, Global Environmental Change.

[35]  Piers M. Forster,et al.  A topography of climate change research , 2020, Nature Climate Change.

[36]  I. Otto,et al.  Social tipping dynamics for stabilizing Earth’s climate by 2050 , 2020, Proceedings of the National Academy of Sciences.

[37]  Josef Spidlen,et al.  Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets , 2019, Nature Communications.

[38]  D. Istance,et al.  Learning to Leapfrog: Innovative Pedagogies to Transform Education. , 2019 .

[39]  M. Monroe,et al.  Identifying effective climate change education strategies: a systematic review of the research , 2019 .

[40]  David Rousell,et al.  Education for what? Shaping the field of climate change education with children and young people as co-researchers , 2019 .

[41]  Rachelle K. Gould,et al.  Exploring connections between environmental learning and behavior through four everyday-life case studies , 2018, Environmental Education Research.

[42]  T. C. Tai,et al.  Enhancing Climate Change Research With Open Science , 2018, Front. Environ. Sci..

[43]  E. Cordero,et al.  The role of climate change education on individual lifetime carbon emissions , 2018, bioRxiv.

[44]  Lisa Zaval,et al.  Effective education and communication strategies to promote environmental engagement : The role of social-psychological mechanisms , 2017 .

[45]  S. Juhola,et al.  A systematic review of dynamics in climate risk and vulnerability assessments , 2017 .

[46]  Shaowen Yao,et al.  An overview of topic modeling and its current applications in bioinformatics , 2016, SpringerPlus.

[47]  Kai Petersen,et al.  Guidelines for conducting systematic mapping studies in software engineering: An update , 2015, Inf. Softw. Technol..

[48]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[49]  James D. Ford,et al.  Systematic review approaches for climate change adaptation research , 2015, Regional Environmental Change.

[50]  Michael Röder,et al.  Exploring the Space of Topic Coherence Measures , 2015, WSDM.

[51]  Wolfgang Lutz,et al.  Universal education is key to enhanced climate adaptation , 2014, Science.

[52]  Gilles Louppe,et al.  Independent consultant , 2013 .

[53]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[54]  Kai Petersen,et al.  Systematic Mapping Studies in Software Engineering , 2008, EASE.

[55]  Lars Kai Hansen,et al.  Mining the posterior cingulate: Segregation between memory and pain components , 2005, NeuroImage.

[56]  A. Kollmuss,et al.  Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? , 2002 .

[57]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[58]  A. Ruane,et al.  SYNTHESIS REPORT OF THE IPCC SIXTH ASSESSMENT REPORT (AR6) , 2023 .

[59]  J. Cook Understanding and Countering Misinformation About Climate Change , 2019, Advances in Media, Entertainment, and the Arts.

[60]  J. Tracey,et al.  SDG 13 Climate Action , 2019, Science for Sustainable Societies.

[61]  M. Kubát An Introduction to Machine Learning , 2017, Springer International Publishing.

[62]  E. Hertwich,et al.  The climate mitigation gap : education and government recommendations miss the most effective individual actions , 2017 .

[63]  N. Nakicenovic,et al.  Summary for policymakers , 2012 .

[64]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[65]  John C. Donovan,et al.  The policy makers , 1970 .