Relevance: GS Paper III (Disaster Management) & GS Paper II (Centre-State Relations) | Source: The Hindu

Think of Odisha as a sincere student who prepared perfectly for an exam, but the examiner changed the marking system at the last minute and gave them fewer marks.

Odisha faces terrible cyclones almost every year. But over the last 20 years, they built early warning systems and cyclone shelters, bringing deaths down to zero. Yet, the 16th Finance Commission recently cut down Odisha’s share of disaster relief funds.

Why did this happen? Here is a simple breakdown of this flawed policy.

1. The New Funding Formula

The 16th Finance Commission increased the total disaster relief money for states to over ₹2 lakh crore. To distribute this money fairly, they used a new, scientific-sounding formula:

• Disaster Risk = Hazard × Exposure × Vulnerability
• The Logic: A cyclone hitting an empty beach is not a disaster. A disaster only happens when danger (hazard) hits people (exposure) who are weak and unprepared (vulnerability).
• While the logic sounds perfect, the way the government actually calculated these terms is deeply flawed.

2. The Big Mistakes in the Formula

The formula failed because of two major calculation errors:

• Error 1: ‘Exposure’ simply meant ‘Total Population’
  • Instead of counting how many people actually live in danger zones (like coasts or flood plains), the Commission simply looked at the total population of the state.
  • The Result: Uttar Pradesh got the highest disaster score simply because it has the most people. Odisha got a much lower score, even though it faces far more cyclones. The formula just became a “headcount.”
• Error 2: ‘Vulnerability’ simply meant ‘Average Income’
  • The Commission decided that poorer states are more vulnerable. So, they used Average Income to calculate weakness.
  • The Result: This punished states like Kerala. Kerala suffered massive floods in 2018, but because the state’s average income is high, the formula assumed they were “safe” and gave them fewer funds. Average income does not save you from a flood; strong houses and good hospitals do.

3. How to Fix This?

To make disaster funding truly fair, we need to change the math:

• Real Exposure: Count only the people living in actual danger zones using satellite data, not the whole state.
• Real Vulnerability: Stop looking only at income. Instead, check the number of kutcha (mud) houses, the lack of hospitals, and how many farmers have crop insurance.

4. Quick Basics: India’s Disaster Management

Connect this news with your static UPSC syllabus:

• NDMA: National Disaster Management Authority (Headed by the Prime Minister).
• SDMA: State Disaster Management Authority (Headed by the Chief Minister).
• DDMA: District Disaster Management Authority (Headed by the District Magistrate).
• The Funds: The State Disaster Response Fund (SDRF) is the main purse for states. The Centre gives 75% of this money. If a disaster is massive, the Centre unlocks the National Disaster Response Fund (NDRF).

One Line Wrap (/Conclusion)

Disaster funds should be given to states based on the real climate dangers they face, not just on how large their population is.

“The recent disaster funding formula by the Finance Commission highlights a paradox where proactive disaster preparation is penalized.” Discuss the flaws in calculating disaster risk and suggest simple reforms. (15 Marks, 250 Words)

Mains Answer hint:

• Intro: Mention the 16th Finance Commission and the “Odisha Paradox” (where Odisha got fewer funds despite high cyclone risks).
• Body: * The Formula: Write down the formula (Risk = Hazard × Exposure × Vulnerability).
  • The Flaws: Explain how “Exposure” wrongly relies on total population (benefiting UP) and “Vulnerability” wrongly relies on average income (punishing Kerala).
  • The Fix: Suggest counting people only in hazard zones and measuring vulnerability through kutcha housing and health infrastructure.
• Conclusion: Conclude that the NDMA must step in to create an accurate State Vulnerability Index so India can fight climate change fairly and effectively.