Subject: Geography

Mentor’s Comment

There is a critical flaw in India’s disaster financing architecture, the shift from risk-based assessment to population-based allocation. The issue is in the news due to concerns over the 16th Finance Commission’s disaster risk funding formula, which paradoxically allocates higher funds to States with larger populations rather than those with greater disaster exposure. This marks a sharp departure from earlier approaches and undermines decades of progress in disaster preparedness. The scale of the problem is significant, States like Odisha, with the highest hazard score (12), receive less effective consideration than States like Bihar (224.2) and Uttar Pradesh (413.2) due to population weighting.

What structural flaw exists in the disaster funding formula?

1. Multiplicative Risk Formula: Uses Disaster Risk Index (DRI = Hazard × Exposure × Vulnerability), but distorts outcomes due to flawed exposure metrics.
2. Population-Based Exposure: Defines exposure as total population (scaled 1-25), ignoring actual hazard-prone zones.
3. Bias Toward Larger States: Ensures States like Uttar Pradesh receive higher weight despite lower hazard intensity.
4. Departure from Previous Approach: Replaces additive model of 15th Finance Commission, which treated hazard and vulnerability separately.
5. Outcome Distortion: Rewards demographic size rather than disaster risk, contradicting risk-based allocation principles.

Why is ‘exposure’ measurement scientifically flawed?

1. Incorrect Definition: Uses total population instead of hazard-zone population.
2. IPCC Standard Ignored: Defines exposure as people in hazard-prone areas, not administrative boundaries.
3. Misleading Comparisons: Inland plateau populations treated equal to cyclone-prone coastal populations.
4. Example: Odisha’s high-risk coastline equated with safer inland regions in other States.
5. Result: Artificial inflation of exposure scores for populous but less vulnerable States.

How does vulnerability measurement misrepresent actual risk?

1. Income-Based Proxy: Uses per capita NSDP, which measures fiscal capacity, not vulnerability.
2. Multidimensional Nature Ignored: Overlooks housing quality, health infrastructure, and early warning access.
3. Kerala Case Study: Despite ₹31,000 crore flood damages (2018), receives low vulnerability score (1.073).
4. Hidden Inequality: Average income masks intra-state disparities and disaster susceptibility.
5. Outcome: Underestimates real vulnerability in disaster-prone but relatively richer States.

Why does the formula penalize disaster-prone States?

1. Population Bias: Prioritizes demographic size over risk intensity.
2. Funding Paradox: Odisha (highest hazard score) loses out due to lower population score.
3. Disproportionate Allocation: Bihar (224.2) and UP (413.2) overshadow Odisha despite lower hazard exposure.
4. Kerala’s Loss: Loses 0.78 percentage points despite high vulnerability ranking.
5. Systemic Inequity: Smaller, disaster-prone States receive inadequate fiscal support.

What are the implications for disaster governance in India?

1. Misallocation of Resources: Funds diverted away from high-risk zones.
2. Reduced Preparedness: States with higher hazard exposure face fiscal constraints.
3. Climate Risk Escalation: Cyclones, floods, and droughts increasing in intensity and frequency.
4. Regional Inequality: Coastal and northeastern States disproportionately affected.
5. Policy Credibility Issue: Undermines objective of risk-based disaster financing.

What reforms are required in disaster risk assessment?

1. Hazard-Zone Mapping: Measures exposure based on population in disaster-prone areas.
2. Composite Vulnerability Index: Includes housing, health, agriculture, and infrastructure indicators.
3. Use of Data Systems: Integrates Building Materials and Technology Promotion Council (BMTPC) Vulnerability Atlas, National Family Health Survey-5 (NFHS-5), Pradhan Mantri Fasal Bima Yojana (PMFBY) database, National Health Mission (NHM) facility surveys, and India Meteorological Department (IMD) monitoring records. 
4. Institutional Mechanism: Mandates NDMA to publish annual State Disaster Vulnerability Index.
5. Policy Continuity: Institutionalizes methodology across Finance Commissions. 

Conclusion

A population-based approach to disaster funding undermines the principle of risk-sensitive governance. A shift toward hazard-specific exposure mapping and multidimensional vulnerability assessment is essential to ensure equitable and effective disaster resilience in India.

Mentor’s Comment

The rollback of the Bureau of Indian Standards (BIS) revision of India’s earthquake zoning framework has revived debate over seismic risk assessment. The proposed revision sought to replace the simplified fixed seismic zoning model with probabilistic seismic hazard assessment, a method widely used globally. It also introduced a new high-risk Zone VI covering vulnerable regions such as Kashmir and the Himalayan belt. However, stricter zoning raised economic concerns, as construction costs could increase by about 20% with a one-zone rise and nearly one-third with two zones. 

Why does India require a revised earthquake zoning framework?

1. Urban Expansion and Risk Exposure: Rapid urbanisation increases population and infrastructure in seismically vulnerable areas. Large infrastructure such as metro systems, dams, highways, and power stations require updated seismic design standards.
2. Disaster Preparedness: Accurate zoning enables safer city planning, infrastructure design, and disaster management strategies. It reduces casualties and economic losses during earthquakes.
3. Climate and Disaster Resilience: Earthquake-resilient infrastructure contributes to broader climate-resilient development and sustainable cities.
4. Infrastructure Protection: Critical infrastructure projects must incorporate seismic design standards to prevent catastrophic failure during earthquakes.

What is the current earthquake zoning system in India?

1. Fixed Zoning Model: India currently uses a simplified seismic zoning map, dividing the country into fixed categories based on historical seismic activity.
2. Seismic Zones: India’s seismic classification includes Zones II, III, IV and V, with Zone V representing the highest risk areas.
3. Limitations of Fixed Zoning: Fixed zones rely heavily on past earthquake records and may not fully capture future seismic probabilities or micro-level risk variations.
4. Urban Planning Integration: These zones influence building codes, infrastructure design standards, and urban planning guidelines.

What changes were proposed in the BIS revision?

1. Probabilistic Seismic Hazard Assessment (PSHA): Introduces probability-based simulations to estimate earthquake intensity and frequency rather than relying solely on historical data.
2. Introduction of Zone VI: Adds a new highest-risk seismic zone, covering Kashmir, parts of the Himalayan belt, Kutch in Gujarat, and the northeast.
3. Improved Risk Modelling: Uses dynamic modelling of ground motion probabilities to improve earthquake preparedness.
4. Alignment with Global Practice: Aligns India’s seismic risk assessment methodology with advanced economies and seismically active regions worldwide.

Why did the proposed revision face opposition?

1. Economic Cost: Construction costs could rise significantly.
   1. One-zone increase: Costs may rise by around 20%.
   2. Two-zone increase: Costs may rise by nearly one-third.
2. Infrastructure Cost Escalation: High-value projects such as metro systems, dams, and power stations may face substantially higher structural design costs.
3. Development Concerns: Urban planners fear stricter zoning could slow infrastructure development in economically fragile regions.
4. Housing Informality: Nearly 80% of India’s housing stock lies in the informal sector, raising concerns that stricter regulations may increase unregulated construction.

What are the broader governance and policy challenges?

1. Institutional Coordination: The proposal faced resistance from multiple agencies including Ministry of Housing and Urban Affairs, Home Affairs, Central Water Commission, and National Dam Safety Authority.
2. Policy Consultation Gap: Large regulatory changes require extensive consultation across government agencies, industry stakeholders, and technical experts.
3. Balancing Safety and Affordability: Stricter building standards improve safety but increase construction costs and housing affordability pressures.
4. Implementation Capacity: Enforcement challenges remain significant due to informal housing markets and limited regulatory capacity.

How does the debate intersect with climate and sustainability goals?

1. Construction Sector Emissions: The construction sector is among the largest dispersed sources of carbon emissions in India.
2. Infrastructure Lifecycle: Seismic-resilient structures reduce the need for reconstruction after disasters, lowering long-term carbon and economic costs.
3. Resilient Urban Development: Disaster-proof infrastructure supports climate adaptation strategies and sustainable urbanisation.

Conclusion

Revising India’s earthquake zoning framework remains essential for ensuring disaster-resilient urban growth and infrastructure safety. However, scientific improvements must be accompanied by broad institutional consultation, economic feasibility assessments, and strong implementation mechanisms. A balanced framework that integrates advanced risk modelling with practical governance capacity is critical for strengthening India’s long-term disaster resilience.