Related Staff : Ashley Kelly
Given Hong Kong’s unremitting development pressures, both pro-development and pro-conservation groups are now calling for ways to evaluate sites for development based on environmental metrics and new conservation agreements. However, for the built-environment disciplines in Hong Kong, sustainability discourse is predominantly aligned with economic and urban sustainability, rather than the new forms of conservation that contend to use environmental modeling to justify the conversion of conservation uses. For urban and landscape resilience, we must ensure the critical and innovative deployment of conservation and impact assessment instruments and tools while fully aware of the territory’s increasing politics of sustainability. During the term, students were immersed in and experimented with methods and tools of other disciplines engaging development, including: 1) Landscape and biodiversity modeling techniques for measuring connectivity, fragmentation, and species richness that questioned issues of data quality, scientific bias, reductive methodologies, and disciplinary blindspots; and 2) Anthropological cases on public participation and environmental advocacy, including issues of expertise, evidence, discourse analysis, counter-knowledge, and universal values. These exercises were complemented by seminars on Hong Kong’s legal, planning and assessment tools related to conservation, as well as discussions on disciplinary boundaries of sustainability sciences to help students better articulate their own expertise as landscape architects and planners. For the remainder of the design studio, students raised critical issues through creating scenarios of development, such as: Redrawing ecological baselines from a more thorough understanding of specific sites’ environmental histories; Problematizing the timescales of ecology and development planning (e.g., public participation, judicial review, ecological assessment) at sites of past land conversion; and Salvaging science and challenging transparency through scenarios of environmental data uncertainty.