The impacts of climate change are of particular concern to the coastal region of tropical countries like India, which are exposed to cyclones, floods, tsunami, seawater intrusion, etc. Climate-change adaptation presupposes comprehensive assessment of vulnerability status. Studies so far relied either on remote sensing-based spatial mapping of physical vulnerability or on certain socio-economic aspects with limited scope for upscaling or replication. The current study is an attempt to develop a holistic and robust framework to assess the vulnerability of coastal India at different levels. We propose and estimate cumulative vulnerability index (CVI) as a function of exposure, sensitivity and adaptive capacity, at the village level, using nationally comparable and credible datasets. The exposure index (EI) was determined at the village level by decomposing the spatial multi-hazard maps, while sensitivity (SI) and adaptive capacity indices (ACI) were estimated using 23 indicators, covering social and economic aspects. The indicators were identified through the literature review, expert consultations, opinion survey, and were further validated through statistical tests. The socio-economic vulnerability index (SEVI) was constructed as a function of sensitivity and adaptive capacity for planning grassroot-level interventions and adaptation strategies. The framework was piloted in Sindhudurg, a coastal district in Maharashtra, India. It comprises 317 villages, spread across three taluks viz., Devgad, Malvan and Vengurla. The villages in Sindhudurg were ranked based on this multi-criteria approach. Based on CVI values, 92 villages (30%) in Sindhudurg were identified as highly vulnerable. We propose a decision tool for identifying villages vulnerable to changing climate, based on their level of sensitivity and adaptive capacity in a two-dimensional matrix, thus aiding in planning location-specific interventions. Here, vulnerability indicators are classified and designated as 'drivers' (indicators with significantly high values and intervention priority) and 'buffers' (indicators with low-to-moderate values) at the village level. The framework provides for aggregation or decomposition of CVI and other sub-indices, in order to plan spatial contingency plans and enable swift action for climate adaptation.

An emphasis on sustainable marine fish production has paved the way for the enactment of Marine Fisheries Regulation Acts and subsequent regulations in India. A closed season ban on fishing has been one of the very few successful regulatory measures since its introduction in 2001 in Tamil Nadu. Since 2017, the fishing ban period has been extended from 45 to 60 days for the east coast of India, for ecosystem‐based marine fisheries management. This study analyses the socioeconomic impacts of change in the fishing ban (closed season) across the four coastal zones of Tamil Nadu. For the past 10 years, CPUE has been stable or increased marginally. Among the four coasts, the relative change in employment and labour income loss was highest for Palk Bay (36.84%). Transaction costs of implementation of the ban caused an additional expense of Rs 496.5 million to the Government in 2017 and the overall total labour income loss has leaped from Rs 1,638.2 to 2,100.2 million due to the extension of the ban. The fishing ban has aided in the recovery of habitat and regeneration of stock through recruitment. Training and capacity building on alternative livelihood options are highly recommended to enable the workers to cope with the ban period.

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Identification of spatial gradient in the vulnerability of white leg shrimp production to climate change is imperative in the formulation and implementation of suitable adaptive measures. A composite vulnerability index was computed by employing 36 variables pertaining to exposure (11), sensitivity (11) and adaptive capacity ( 14) dimensions to map the extent of vulnerability in white leg shrimp production across Indian states. Based on its magnitude, the vulnerability index was categorized into three groups, namely low, moderate and high. Results showed that the mean composite vulnerability index was 0.65 and ranged from 0.34 to 0.99 indicating that there was a strong spatial pattern. Among the nine states, Goa (0.99), Kerala (0.84) and Odisha (0.77) were highly vulnerable; Gujarat (0.75), Karnataka (0.57) and West Bengal (0.56) were moderately vulnerable; and Tamil Nadu (0.54), Andhra Pradesh (0.46) and Maharashtra (0.34) were less vulnerable to shrimp production. About one-fourth of the production and culture area of white leg shrimp were in moderate and highly vulnerable regions. The impact of climate change on shrimp production is diverse but can be reduced by implementing adaptive measures-suitable policies and investment plans-which should be region-specific.