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Diffusion of Water-Saving Technologies in Private Households: The Innovation Module of DeepHousehold

  • Nina SchwarzEmail author
  • Silke Kuhn
  • Roman Seidl
  • Andreas Ernst

Abstract

The Innovation model is a supplementary module in the DeepHousehold model within the Environmental Psychology subproject. It focuses on water saving through increased efficiency, manifested by the purchase of newer, more efficient technologies. The factors that influence the extent of adopting water-saving innovations were investigated, and an estimate was calculated as to how the diffusion of these innovations in the near future might look. Three innovations were chosen: shower heads, toilet flushes and rainwater use. The Innovation model simulates the decision process of domestic households based on two empirical studies: a quantitative, written study and supplementary telephone interviews. The five Household actors generally decide on the adoption or rejection of new technologies using two different decision algorithms: a deliberative decision rule, based on “Theory of Planned Behavior”, and the so-called take-the-best heuristic. If no clear decision is made, the Household actors imitate the behaviour of their social network. Three innovation scenarios were developed in contrast to a baseline: an information campaign, subsidies and regulation. Simulation runs show that the regulation has the greatest impact on all three innovations. However, information campaigns also led to a spread of the water use innovations. Financial incentives had no additional impact on the spread for either water-saving shower heads or for 2-volume toilet flush; just like the Baseline scenario, they led to a moderate increase. In the case of rainwater usage devices, both the Information scenario and the Subsidies scenario led to a notable effect in approximately equal size.

Keywords

Innovation diffusion Water use Agent-based modelling GLOWA-Danube 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Nina Schwarz
    • 1
    Email author
  • Silke Kuhn
    • 2
  • Roman Seidl
    • 3
  • Andreas Ernst
    • 2
  1. 1.Department of Computational Landscape EcologyHelmholtz-Centre for Environmental Research – UFZLeipzigGermany
  2. 2.Center for Environmental Systems Research (CESR)University of KasselKasselGermany
  3. 3.Institute for Environmental DecisionsETH ZürichZürichSwitzerland

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