India is one of the most disaster-prone countries in the world, with varying levels of vulnerability across different States. Odisha stands out due to its long coastline and frequent exposure to severe cyclones. Over the past two decades, Odisha has significantly improved its disaster preparedness by investing in early warning systems, evacuation mechanisms, and resilient infrastructure, reducing cyclone-related deaths to nearly zero.
However, despite this progress and its high exposure to natural hazards, the 16th Finance Commission has reduced Odisha’s share in disaster funding.
The Revised Disaster Risk Framework
The Finance Commission introduced a new Disaster Risk Index (DRI) based on a multiplicative formula: Hazard × Exposure × Vulnerability. This marks a shift from the additive model used earlier. The approach aligns with global frameworks such as those proposed by the Intergovernmental Panel on Climate Change, as it reflects that disasters occur when hazards intersect with exposed and vulnerable populations.
At the same time, the Commission increased the total allocation to State Disaster Response Funds to ₹2,04,401 crore, which is a significant rise.
Key Flaws in the Allocation Formula
Misrepresentation of Exposure
The Commission measures exposure based on the total population of a State, which is scaled linearly. This is problematic because exposure should refer only to populations living in hazard-prone areas, not the entire population. As a result, highly populous States receive higher scores even if large parts of their population are relatively safe.
Impact on High-Risk but Smaller States
States like Odisha, despite having high hazard exposure, receive lower risk scores because of their smaller population. This demonstrates that the formula prioritizes demographic size over real vulnerability, leading to unfair outcomes.
Oversimplified Measurement of Vulnerability
Vulnerability is calculated using per capita Net State Domestic Product (NSDP), where poorer States are ranked as more vulnerable. While this reflects fiscal capacity, it ignores critical factors such as housing quality, healthcare infrastructure, early warning systems, and livelihood dependence on climate-sensitive sectors. The important point is that economic indicators alone cannot capture true disaster vulnerability.
Case Study: Kerala
Kerala experienced devastating floods in 2018, yet it receives a relatively low vulnerability score due to its higher per capita income. economic averages can mask real disaster risks and ground realities.
Case Study: Jharkhand
Jharkhand, which faces significant structural vulnerabilities and poverty, loses funding share because its smaller population reduces its overall risk score. the formula disadvantages genuinely vulnerable but less populous States.
Bias Toward Population Size
The multiplicative formula amplifies the influence of population, resulting in larger States receiving disproportionately higher allocations. Consequently, nearly twenty States have lost funding share, despite facing real disaster risks. the current system contradicts the objective of risk-based allocation.
Consequences of the Current Framework
The flaws in the formula lead to several serious consequences. These include misallocation of disaster funds, neglect of high-risk but smaller States, and failure to capture intra-state inequalities. Ultimately, disaster risk assessment becomes a population-based exercise rather than a scientific evaluation of actual risk and vulnerability.
Proposed Reforms
Redefining Exposure
Exposure should be redefined as the population living in hazard-prone areas, such as coastal cyclone zones, floodplains, and earthquake-prone regions. This can be achieved using data from sources like the Vulnerability Atlas and Census records.
Developing a Composite Vulnerability Index
A more comprehensive vulnerability index should include multiple indicators such as housing conditions, healthcare access, agricultural dependence, insurance coverage, and early warning systems. This would provide a more realistic and multidimensional assessment of vulnerability.
Institutionalising Risk Assessment
The National Disaster Management Authority should be mandated to develop a standardized Disaster Vulnerability Index. This would ensure consistency, transparency, and scientific accuracy in disaster funding allocation.
Conclusion
As climate change increases the frequency and intensity of disasters, the need for a fair and accurate disaster funding framework becomes critical. States like Odisha, which face high risks and have invested in preparedness, should not be penalized due to flawed methodologies.