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Scaffolding, software and scenarios: Applying Bruner's learning theory to energy scenario development with the public

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  • Upham, Paul
  • Carney, Sebastian
  • Klapper, Rita

Abstract

While there is a literature on public and stakeholder engagement in environmental research and scenario development, less attention has been given to the individual learning processes that take place in these contexts. We present public perceptions of emission contraction scenarios for the UK city of Manchester and discuss this in terms of learning theory developed by Lev Vygotsky and Jerome Bruner. A key theme of this was the combination of three learning tools: scaffolding techniques, scenario building and backcasting. Overall, participants had little trouble envisaging a city-scale 41% CO2 emissions reduction by 2020 relative to a 2005 baseline. However envisaging a 90% CO2 emissions reduction for 2050 was found much more difficult, inducing discussion of whether some forms of compulsion might be justifiable. Despite detailed discussion and real-time, modelled feedback on the emissions implications of various energy technology scenarios, participants largely retained their original attitudes towards individual technologies and demand reduction options.

Suggested Citation

  • Upham, Paul & Carney, Sebastian & Klapper, Rita, 2014. "Scaffolding, software and scenarios: Applying Bruner's learning theory to energy scenario development with the public," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 131-142.
    • Handle: RePEc:eee:tefoso:v:81:y:2014:i:c:p:131-142
    DOI: 10.1016/j.techfore.2013.05.001
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    References listed on IDEAS

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    Cited by:

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    2. Giles Thomson & Henrik Ny & Varvara Nikulina & Sven Borén & James Ayers & Jayne Bryant, 2020. "‘Rapid Scenario Planning’ to Support a Regional Sustainability Transformation Vision: A Case Study from Blekinge, Sweden," Sustainability, MDPI, vol. 12(17), pages 1-26, August.
    3. Papachristos, George & Adamides, Emmanuel, 2016. "A retroductive systems-based methodology for socio-technical transitions research," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 1-14.

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