ANNOUNCEMENTS
The city of Chennai, located along the south-eastern coast of India, is a city of contrasts—its bustling urban centres coexist with a rich ecological realm, shaped by its long coastline, rivers, and wetlands. But now, research has asserted 7.29% of the Chennai Metropolitan Area is to face inundation by 2040 (CSTEP, 2024). One such area at high risk from climate change-induced sea-level rise (SLR) is the Pallikaranai Marshland, a critical ecological asset to the city. This study assesses the coastal vulnerability of the marsh, emphasizing its rich biodiversity, groundwater recharge capacity and, flood-mitigation services. It also introduces a multi-dimensional framework using the IPCC AR6 SLR projections and the Dynamic Interactive Vulnerability Assessment (DIVA) model to derive a Coastal Segment Vulnerability Score (CSVS), which accounts for exposure, sensitivity, and adaptive capacity. Three Representation Concentration Pathways (RCP) scenarios (2.6, 4.5, 8.5) were analyzed, all showing high impact values. The research further applies a Contingent Valuation Method (CVM) with discrete choice and bidding formats to arrive at Willingness to Pay (WTP) across four resilience scenarios (A–D), with multiple payment vehicles such as entry fee, water tax increments and, lump-sum donations.
A primary survey of 91 respondents—predominantly youth and middle-income group with 75% from four high-risk localities was conducted. The results revealed zero support for the status quo (Scenario A), with majority of them preferring moderate improvement (Scenario B) over C and D scenarios. Further, an econometric analysis of results using logit model showed significant price sensitivity, with proximity to the Marsh as the strongest positive predictor and gender as a negative predictor for choosing improvement scenarios. Despite better outcomes, support declined sharply at higher WTP levels.
Keywords: Sea Level Rise, Dynamic Interactive Vulnerability Assessment, Contingent Valuation Method, logit model, Pallikaranai Marsh.
