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Updated: Feb 7, 2026

Design and Construction of an Urban Runoff Research Facility
Published on: August 8, 2014
A framework to diagnose factors influencing proenvironmental behaviors in water-sensitive urban design
Jacki Schirmer1,2, Fiona Dyer3
1Institute for Applied Ecology, University of Canberra, Canberra, ACT 2617, Australia; jacki.schirmer@canberra.edu.au.
Abstract:
The ongoing challenge of maintaining and improving the quality of water that leaves urban stormwater systems is often addressed using technical rather than social solutions. The need for investment in often expensive water infrastructure can be reduced through better investing in promoting human behaviors that protect water quality as part of water-sensitive urban design (WSUD) initiatives. Successfully achieving this requires understanding factors that influence adoption of proenvironmental behaviors. We review past studies examining this topic and identify that factors influencing adoption of proenvironmental behaviors relevant to WSUD commonly fall into four domains: proenvironmental values and norms, awareness and knowledge of environmental problems and the actions that can address them, proximity and place-based identity, and life-stage and lifestyle factors. We propose the VAIL (values, awareness, identify, lifestyle) framework, based on these four domains and able to be contextualized to specific water-quality problems and individual communities, to assist in diagnosing factors influencing adoption of proenvironmental behaviors. We demonstrate the applicability of the framework in a case study examining adoption of gardening practices that support water quality in Canberra, Australia. We developed 22 locally relevant VAIL indicators and surveyed 3,334 residents to understand engagement in four water-friendly gardening behaviors that help improve water quality in local lakes. In regression modeling, the indicators explained a significant amount of variance in these behaviors and suggested avenues for supporting greater adoption of these behaviors. Predictor variables across all four VAIL domains were significant, highlighting the importance of a multidomain framework.
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