Incorporating visual attention measures to reveal homeowners’ preferences for environmentally friendly urban landscapes

Sustainable urban planning practices can help reduce the urban heat island effect, promote green roofs and walls, and encourage the preservation of natural habitats within urban areas. Cities can enhance their resilience to climate change and create healthier, more attractive living spaces for their inhabitants. Resource-efficient or environmentally friendly urban landscapes can support local ecosystems and biodiversity and require fewer external inputs, such as irrigation water and nutrients. To mitigate the increase in population and water demand in urbanizing areas, the options include voluntary landscape water conservation programs, sensor-based irrigation technology, regulations, and low-input landscape plant breeding programs. An in-depth understanding of homeowners' preferences for environmentally friendly landscapes is essential for understanding the potential impact of landscape conservation initiatives and policies on homeowners' adoption intentions. This study employs data from laboratory choice experiments to investigate Floridian homeowners’ preferences for eco-friendly residential landscapes, using an innovative visually inferred attribute non-attendance (ANA) method to better understand their choice decisions. The findings suggest that homeowners can be classified into distinct groups based on their prioritization of various landscape attributes. Designs featuring 100 ​% non-turfgrass ornamental plant coverage were less favored compared to those with a traditional mix of 25 ​% plants and 75 ​% turfgrass designs. Furthermore, financial factors such as cost and rebate incentives could motivate homeowners to focus on cost-related attributes to transition from conventional turfgrass landscaping to environmentally friendly alternatives. The findings of this study have a two-fold implication. For sustainable urban planning, particularly in the context of energy-efficient urban design, by understanding homeowners' preferences for environmentally friendly landscapes, policymakers and urban planners can develop data-driven strategies to promote green infrastructure and mitigate the urban heat island effect. Machine learning and data-based methods, such as the visually inferred attribute non-attendance (ANA) approach employed in this study, can help identify key factors influencing homeowners' adoption of eco-friendly landscapes. This information can be integrated into urban planning models to optimize the design of green spaces, reduce water consumption, and enhance the resilience of urban ecosystems to climate change. The study's results can inform the development of targeted incentives and policies to encourage the adoption of environmentally friendly landscapes, ultimately contributing to creating more sustainable and livable cities. • Visual attention measures revealed homeowners' sustainable landscape preference. • The visually inferred method was used to control for attribute non-attendance and reveal sustainable landscape choices. • Homeowners were analytically classified into distinct groups based on prioritizing various landscape attributes. • 100 ​% plant coverage was less favored, while cost and rebate incentives could motivate adoption.