Beyond City-Level Canopy Cover

A Contextualized Tree Equity Score (Tes) Framework for Greening Indian Cities

Abstract

Urban trees provide critical ecosystem services that enhance thermal comfort, public health, and urban livability. However, tree cover equity—the fair distribution of tree-based benefits across socio-economic groups—remains weakly addressed in Indian urban policy. Existing greening initiatives, such as the Smart Cities Mission and Nagar Van Yojana, primarily emphasize the quantity of green space or carbon sequestration within designated parks, often overlooking neighbourhood-scale disparities in everyday exposure to shade and cooling. This study proposes a contextualized Tree Equity Score (TES) framework for Indian cities by modifying the conventional TES methodology to better reflect local land constraints. Using Bhubaneswar as a case study, the analysis integrates existing tree cover, heat exposure, and socio-economic vulnerability at the ward scale. The conventional TES, which defines canopy targets based on biome classification and building density, identifies only a small number of inequitable wards, largely due to suppressed canopy targets in high-density areas. To address this limitation, the modified framework recalibrates canopy targets using ward-level plantable area derived from land-cover classification, while retaining the original weighting structure for vulnerability and heat exposure. Results show that incorporating plantable area does not uniformly lower equity scores but selectively reveals inequities in dense Old Town wards, environmentally constrained residential areas, and transitional peripheral zones that were masked under the conventional approach. Planned neighbourhoods and forest-adjacent wards remain stable across both frameworks, indicating genuinely favourable greening conditions. The findings demonstrate that canopy target formulation strongly shapes equity diagnostics and that plantable-area-based targets offer a more actionable basis for prioritizing urban greening in land-constrained Indian cities.