Paper: GS – III, Subject: Environment, Ecology and Disaster Management, Topic: Disaster Management, Issue: Population Bias in Disaster Funding: A Policy Flaw.
Context:
Despite being the most disaster-prone state with the highest hazard score and strong preparedness, Odisha has faced the largest reduction in disaster funding share under the 16th Finance Commission. This highlights a deeper issue: disaster risk is being mismeasured leading to systematic underfunding of highly vulnerable states like Odisha, Kerala, Andhra Pradesh, and Assam.
Key Takeaways:
BACKGROUND
- Disaster risk is the interaction of hazard, exposure, and vulnerability.
- As per the United Nations and IPCC Sixth Assessment Report, exposure means presence of people in hazard-prone areas, not total population.
- Finance Commission under Article 280 allocates disaster funds through SDRF.
- The State Disaster Response Fund (SDRF), constituted under Section 48(1)(a) of the Disaster Management Act, 2005, is the primary fund available to State Governments for immediate relief during notified disasters. It is funded 75% by the Centre (90% for NE/Himalayan states) and 25% by the State, covering calamities like floods, earthquakes, and landslides.
- The 16th Finance Commission uses Disaster Risk Index = Hazard × Exposure × Vulnerability to distribute disaster funds.
- Climate change is increasing cyclone intensity, extreme rainfall, and drought risks across India. So there is urgent need to provide properly measured funding.
CORE ANALYSIS:
- India’s disaster funding framework reveals a structural flaw where risk-based allocation is distorted by population bias. The formula used (Disaster Risk Index = Hazard × Exposure × Vulnerability) is conceptually sound but operationally flawed due to incorrect measurement of exposure and vulnerability.
- Exposure is calculated using total population, ignoring spatial distribution of risk. For Example: A large inland population faces lower risk than a smaller coastal population exposed to cyclones. Result: Uttar Pradesh and Bihar gain due to size, while Odisha loses despite highest hazard exposure.
- Odisha’s hazard score is highest, yet low population weight reduces its Disaster Risk Index, leading to lower funding.
- Vulnerability is proxied using per capita Net State Domestic Product (NSDP), which reflects income, not disaster susceptibility. For Example: Kerala floods (2018) caused about Rs 31,000 crore losses, yet vulnerability score remains low due to higher income.
- Jharkhand also shows high vulnerability but still loses due to population disadvantage.
- The system rewards demographic size rather than actual risk, undermining fiscal equity and preparedness incentives.
- A rational reform framework requires:
- Measuring exposure using hazard-zone population such as coastal belts, floodplains, seismic regions. Creating multidimensional vulnerability index including housing quality, health infrastructure, agricultural dependence, insurance coverage, early warning reach.
- Using datasets from BMTPC Vulnerability Atlas, Census, NFHS, PMFBY, NHM, IMD.
- Mandating NDMA to publish a State Disaster Vulnerability Index.
WAY FORWARD
- Replace total population with hazard-based exposure metrics.
- Shift from income-based to multidimensional vulnerability assessment.
- Integrate granular, district-level data for allocation.
- Institutionalise annual vulnerability indexing under NDMA.
- Align disaster finance with climate resilience and cooperative federalism.
SYLLABUS LINKAGE– GS3 (Disaster management)
Source: (The Hindu)
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