2017
DOI: 10.1080/09613218.2017.1362919
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Participatory system dynamics modelling for housing, energy and wellbeing interactions

Abstract: The built environment is a key target of decarbonization policies. However, such policies often have a narrow objective and narrow focus, resulting in 'policy-resistance' and unintended consequences. The literature attributes these unintended consequences to a narrow financial focus, adverse incentives, and inadequate handling of knowledge, skills, communication and feedback gaps, but it provides little advice on how these complex interactions can be captured. This paper illustrates the development and applica… Show more

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Cited by 80 publications
(59 citation statements)
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“…A study protocol for the workshops and data collection was reviewed and approved by the University of Colorado's Institutional Review Board (IRB # 17-0292) and all participants gave their informed consent for inclusion before they participated in the workshops. Building upon participatory system dynamic methods [55][56][57][58], this work used a 'Factor Mapping' workshop format to elicit knowledge about factor interaction and dynamics as developed by Walters et al [59][60][61][62]. The GMB approach provided a structured process for engaging knowledgeable stakeholders in discussions around complex problems [46,63,64].…”
Section: Methodsmentioning
confidence: 99%
“…A study protocol for the workshops and data collection was reviewed and approved by the University of Colorado's Institutional Review Board (IRB # 17-0292) and all participants gave their informed consent for inclusion before they participated in the workshops. Building upon participatory system dynamic methods [55][56][57][58], this work used a 'Factor Mapping' workshop format to elicit knowledge about factor interaction and dynamics as developed by Walters et al [59][60][61][62]. The GMB approach provided a structured process for engaging knowledgeable stakeholders in discussions around complex problems [46,63,64].…”
Section: Methodsmentioning
confidence: 99%
“…The model analysis stage is essential in establishing confidence in the model correctness and usefulness before it can be used for decision making, scenario runs and policy analysis [12,13]. Undoubtedly, it is important to understand why and how certain model output emerged.…”
Section: Stage 4: Model Analysismentioning
confidence: 99%
“…Both, quantitative and qualitative data is used throughout the modelling procedure. Nevertheless, incorporating qualitative inputs into SD models is very challenging [13]. However, reliable empirical data is often not available to support causal relationships for many of the elements contained within SD models.…”
Section: Review Of Participatory Sd Modelling Approachesmentioning
confidence: 99%
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