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The behavioural aspect of green technology investments: a general positive model in the context of heterogeneous agents

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  • F. Knobloch
  • J. -F. Mercure

Abstract

Studies report that firms do not invest in cost-effective green technologies. While economic barriers can explain parts of the gap, behavioural aspects cause further under-valuation. This could be partly due to systematic deviations of decision-making agents' perceptions from normative benchmarks, and partly due to their diversity. This paper combines available behavioural knowledge into a simple model of technology adoption. Firms are modelled as heterogeneous agents with different behavioural responses. To quantify the gap, the model simulates their investment decisions from different theoretical perspectives. While relevant parameters are uncertain at the micro-level, using distributed agent perspectives provides a realistic representation of the macro adoption rate. The model is calibrated using audit data for proposed investments in energy efficient electric motors. The inclusion of behavioural factors reduces significantly expected adoption rates: from 81% using a normative optimisation perspective, down to 20% using a behavioural perspective. The effectiveness of various policies is tested.

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  • F. Knobloch & J. -F. Mercure, 2016. "The behavioural aspect of green technology investments: a general positive model in the context of heterogeneous agents," Papers 1603.06888, arXiv.org.
    • Handle: RePEc:arx:papers:1603.06888
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    2. Busra Agan & Mehmet Balcilar, 2022. "On the Determinants of Green Technology Diffusion: An Empirical Analysis of Economic, Social, Political, and Environmental Factors," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    3. Michael Grubb & Jean-Francois Mercure & Pablo Salas & Rutger-Jan Lange & Ida Sognnaes, 2018. "Systems Innovation, Inertia and Pliability: A mathematical exploration with implications for climate change abatement," Working Papers EPRG 1808, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Roberts, Ruby & Flin, Rhona & Millar, David & Corradi, Luca, 2021. "Psychological factors influencing technology adoption: A case study from the oil and gas industry," Technovation, Elsevier, vol. 102(C).
    5. Frank W. Geels & Jonatan Pinkse & Dimitri Zenghelis, 2021. "Productivity opportunities and risks in a transformative,low-carbon and digital age," Working Papers 009, The Productivity Institute.
    6. Mehdi Bensouda & Mimoun Benali, 2023. "From Fairly Good to Optimal Energy Efficiency Practices within the Moroccan Manufacturing Sector: Are Financial Resources Sufficient?," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 478-488, May.
    7. Odenweller, Adrian, 2022. "Climate mitigation under S-shaped energy technology diffusion: Leveraging synergies of optimisation and simulation models," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    8. Knobloch, Florian & Pollitt, Hector & Chewpreecha, Unnada & Lewney, Richard & Huijbregts, Mark A.J. & Mercure, Jean-Francois, 2021. "FTT:Heat — A simulation model for technological change in the European residential heating sector," Energy Policy, Elsevier, vol. 153(C).
    9. Florian Knobloch & Hector Pollitt & Unnada Chewpreecha & Vassilis Daioglou & Jean-Francois Mercure, 2017. "Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C," Papers 1710.11019, arXiv.org, revised May 2018.

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