Abstract
On global as well as national levels, top-down approaches to reduce climate-related energy consumption have not been successful and effective at all until now. Thus, a bottom-up approach is recommended: citizens role should change from consumers to prosumers, to (co-)decision-makers regarding energy-related behaviour, and to co-investors in the new energy infrastructure. However behavioural change is difficult to realize in face of the ‘knowledge-behaviour gap’, ‘value-action gap’ or ‘attitude-behaviour gap’. Thus, the CODALoop project collected data with its platform Energanz and developed the socio-cognitive model for energy-related behaviour. In the new planned IF4E project that model becomes an integrated module of a process model for energy-related behavioural change with real-time feedback loops, real-time energy consumption data, and a user’s Personal Energy Profile (PEP) which is updated continuously. The challenges and risks in investigating and implementing the proposed process model in real settings for changing climate-related energy consumption behaviour are discussed. This position paper aims receiving feedback of experts regarding the planned project before its realization.
Alphabetic order of authors; equal amount of authors contributions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
United Nations Environment Programme (UNEP) (2019). The Emissions Gap Report 2019. A UN Environment Synthesis Report. Series: The Emissions Gap Report, 10th Edition, 26th November 2019. https://www.unenvironment.org/resources/emissions-gap-report-2019; Executive summary (in Japanese): https://www.iges.or.jp/jp/publication_documents/pub/policyreport/jp/10436/UN_Emissions+Gap+Report_2019_J.pdf.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
European Commission (2018) Mission-oriented R&I policies: Case Study Report Energiewende (DE). Available at: http://europa.eu/!md89DM.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
- 19.
- 20.
- 21.
- 22.
- 23.
- 24.
- 25.
- 26.
- 27.
- 28.
- 29.
- 30.
- 31.
- 32.
- 33.
- 34.
References
Albert, D., Lukas, J. (eds.): Knowledge Spaces: Theories, Empirical Research, and Applications. Lawrence Erlbaum Associates, Mahwah, USA (1999)
Bedek, M., Albert, D.: Modelling the psychosocial dimension of energy consumption and behaviour. In: Savini, F., Pineda Revilla, B., Pfeffer, K., Bertolini, L. (eds.) From Efficiency to Reduction, Tackling Energy Consumption in a Cross Disciplinary Perspective, pp. 67–88 (2019)
Courtenay-Hall, P., Rogers, L.: Gaps in mind: problems in environmental knowledge-behaviour modelling research. Environ. Educ. Res. 8(3), 283–297 (2002)
Doignon, J.P., Falmagne, J.C.: Spaces for the assessment of knowledge. Int. J. Man-Mach. Stud. 23, 175–196 (1985)
Doignon, J.P., Falmagne, J.C.: Knowledge Spaces. Springer, Berlin (1999)
Düntsch, I., Gediga, G.: On query procedures to build knowledge structures. J. Math. Psychol. 40(2), 160–168 (1996)
Pflügler, C., Schreieck, M., Hernandez, G., Wiesche, M., Krcmar, H.: Referenzmodell einer Mobilitätsplattform. In: Wiesche, M., Sauer, P., Krimmling, J., Krcmar, H. (eds.) Management digitaler Plattformen. Informationsmanagement und digitale Transformation, Springer Gabler, Wiesbaden (2018)
Savini, F., Pineda Revilla, B., Pfeffer, K., & Bertolini, L. (eds.): From Efficiency to Reduction: Tackling Energy Consumption in a Cross Disciplinary Perspective (2019)
Schrepp, M.: ITA 2.0: a program for classical and inductive item tree analysis. J. Statist. Softw. 16(10), 1–14 (2006)
Schrepp, M.: Extracting knowledge structures from observed data. Br. J. Math. Statist. Psychol. 52(2), 213–224 (1999)
Schrepp, M.: A method for the analysis of hierarchical dependencies between items of a questionnaire. Methods Psychol. Res. 19, 43–79 (2003)
Weng, D., Gan, X., Chen, W., Ji, S., Lu, Y (2020) A new DGNSS positioning infrastructure for android smartphones. Sensors 20(487), 1–14
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Albert, D., Bedek, M.A., Horn, W.A. (2021). Reducing Energy Consumption by Behavioural Change. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-8944-7_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8943-0
Online ISBN: 978-981-15-8944-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)