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GS Paper: GS3

  • The dual impact of Artificial Intelligence on the finance industry

    Why in the News?

    AI is rapidly becoming central to financial systems, marking a shift from human-driven processes to algorithm-based decision-making. Nearly 75-97% of financial leaders report active AI adoption, while fraud risks are also scaling, AI-enabled financial fraud losses in the U.S. could reach $40 billion by 2027.

    How is AI transforming operational efficiency in finance?

    1. Automation of Processes: Ensures faster data processing and decision-making; example, credit scoring, portfolio management, algorithmic trading.
    2. Cost Reduction: Reduces operational expenses through automation of repetitive tasks such as data entry and routine analysis.
    3. Real-time Analytics: Enables processing of vast datasets instantly, improving accuracy in financial decisions.

    How has AI improved risk management and fraud detection?

    1. Predictive Analytics: Identifies anomalies and potential threats before materialization.
    2. Fraud Detection Efficiency: Reduces investigation time by 70% in major U.S. banks.
    3. Loss Reduction: Decreases fraud losses by 54% in organizations adopting AI-based systems.
    4. High-volume Monitoring: Analyses millions of transactions per second, improving detection accuracy over traditional systems.

    How is AI reshaping customer experience and financial services delivery?

    1. Personalization: Enables tailored financial services based on individual behavior and preferences.
    2. 24/7 Support Systems: Chatbots and virtual assistants ensure continuous customer engagement.
    3. Client Retention: Improves satisfaction and loyalty through data-driven recommendations.

    What are the employment implications of AI adoption in finance?

    1. Job Displacement: Automates repetitive roles such as data entry and customer service; up to 800,000 jobs in the U.S. could be automated by 2030.
    2. Job Creation: Generates new roles in digital risk analysis, compliance, and AI system management; 1.3 million jobs expected globally.
    3. Net Impact: Anticipates both displacement (1.1 million jobs) and creation, indicating structural workforce transition.
    4. Skill Shift: Requires analytical thinking, digital literacy, and AI management capabilities.

    What ethical and security challenges arise from AI in finance?

    1. Algorithmic Bias: Perpetuates biases present in training data, leading to discriminatory outcomes in lending decisions.
    2. Cybersecurity Risks: Increases vulnerability as AI systems become targets of sophisticated cyberattacks.
    3. Governance Deficit: Necessitates regulatory oversight to ensure market integrity and consumer protection.

    How is the financial workforce adapting to AI-driven transformation?

    1. Reskilling Imperative: Requires continuous learning and workforce adaptation to new roles.
    2. Institutional Partnerships: Promotes collaboration with educational institutions to bridge skill gaps.
    3. Employment Growth: Projects 16% growth in financial analyst and data science roles (2024-2030).

    What do market trends and projections indicate about AI in finance?

    1. Adoption Rate: 60% of U.S. financial firms have implemented or plan to implement AI solutions.
    2. Market Expansion: Global AI in finance market projected to reach $64.03 billion by 2030.
    3. Growth Rate: Expands at a CAGR of 23.7%, indicating rapid technological penetration.

    Conclusion

    AI in finance represents a dual-edged transformation, enhancing efficiency, accuracy, and innovation while introducing risks related to employment, ethics, and security. Sustainable integration depends on balancing technological advancement with governance, transparency, and workforce adaptation.

    PYQ Relevance

    [UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

    Linkage: AI in finance and healthcare reflects the broader theme of technology-driven transformation of critical sectors, relevant to GS-III (S&T and Economy). Issues of data privacy, algorithmic bias, and regulation directly link to ethical governance and cybersecurity concerns in AI-enabled systems.

  • The discrepancies in India’s new GDP data

    Why in the News?

    India’s newly revised GDP series has again brought the issue of ‘discrepancies’ into focus, with their share in GDP rising sharply to ~1.5% in 2025-26, compared to 0.4% in 2022-23, a nearly 4-fold increase. This is significant because discrepancies directly affect the credibility of GDP estimates, and their resurgence contrasts with expectations that improved data systems would reduce them.

    What is the New Revised GDP Series?

    Base Year Revision: Reflects Current Economic Structure

    1. Updated Base Year (2011-12): Aligns GDP calculation with a more recent economic structure, replacing older bases like 2004-05 and 1999-2000.
    2. Better Representation: Captures changes such as rise of services, digital economy, and consumption patterns.
    3. Purpose: Ensures GDP estimates remain relevant and comparable over time.

    Methodological & Data Improvements: Expands Coverage

    1. Wider Data Sources: Incorporates GST data, corporate filings (MCA-21), digital transactions.
    2. Improved Measurement: Better estimation of private consumption, corporate sector output, and formal economy activities.
    3. Enhanced Deflators: Uses 600+ price indices (earlier ~180) for more accurate real GDP calculation.

    Reasons for Revision: Improves Accuracy and Credibility

    1. Structural Changes: Accounts for shift from agriculture to services and formalisation of economy.
    2. Data Availability: Utilises new datasets and improved statistical systems.
    3. Global Alignment: Brings methodology closer to international standards (UN System of National Accounts).

    What was the controversy in the old GDP series?

    1. Overstatement of GDP Growth: The new GDP series (base year 2011-12) indicated average GDP growth of ~7.5% (2012-16), while many macro indicators did not support such high growth, raising concerns of overestimation.
    2. Nominal vs Real Growth Inconsistency: The article highlights that nominal GDP grew at ~8%, while real GDP growth was estimated at 7.4%, implying an inflation (deflator) of only ~0.6%. This is highly unrealistic in the Indian context.
    3. Inflation Measurement Issue: An implied inflation of ~0.6% was far lower than actual price trends, suggesting deflators were underestimated, which in turn artificially inflated real GDP growth figures.

    What are ‘discrepancies’ in GDP estimation and why do they arise?

    1. Definition of Discrepancy: Represents the gap between GDP estimates derived from production (GVA) and expenditure methods (GDP).
      1. Nature of Discrepancy: In practice, these two estimates do not match exactly, creating a residual called ‘discrepancy’, which is added to reconcile the accounts.
      2. Accounting Identity: GDP = GVA + Taxes – Subsidies + Discrepancy; Discrepancy ensures the final GDP number balances despite differences in estimation.
    2. Statistical Residual: Acts as a balancing figure when both methods do not match exactly due to data gaps or estimation issues.
    3. Theoretical Expectation: Ideally, discrepancies should be minimal or near zero, indicating robust statistical systems.
    4. Practical Reality: Occurs due to timing differences, incomplete data, and proxy-based estimation, especially in informal sectors.

    What explains GDP growth and where does the mismatch arise?

    The main components of GDP from the expenditure side are: 

    1. Private Final Consumption Expenditure (PFCE):
      1. Represents money spent by individuals/households on goods and services.
      2. Includes food, clothes, rent, services etc.
      3. Largest contributor (~60% of GDP)
    2. Gross Fixed Capital Formation (GFCF):
      1. Represents investment by businesses and government in creating assets.
      2. Includes factories, machinery, equipment, infrastructure
      3. Contributes ~30% of GDP
    3. Government Final Consumption Expenditure (GFCE):
      1. Represents government spending on day-to-day functioning
      2. Salaries, pensions, fuel, administration
      3. Contributes ~10% of GDP
    4. Other Components:
      1. Net Exports (X-M)
      2. Change in Stocks (Inventory changes)

    If these explain GDP, then where is the problem?

    1. Coverage of Components:
       PFCE + GFCF + GFCE together account for ~98% of GDP
    2. Growth Reality:
      1. GDP Growth = 7.2% (FY24)
      2. But these 3 components grew only = 5.7%
    3. Logical Contradiction:
      1. If 98% of the economy grows at 5.7%, then the question arises as to how is GDP growing at 7.2%?

    What fills this unexplained gap?

    1. Discrepancy as Residual:
      1. The gap between 5.7% and 7.2% is captured as “discrepancy”
      2. Magnitude:
        1. ₹0 (FY23) to ₹1 lakh crore+ (FY24)
        2. +230% increase in FY25 (~₹3.5 lakh crore)
        3. ~₹4.9 lakh crore (FY26)
      3. Additional Factor: Change in stocks increased by 116%, adding to statistical distortion

    Why is the rise in discrepancies in the new GDP series significant?

    1. Sharp Increase: Discrepancies rose from 0.4% (FY23) to 1.2% (FY24) to 1.5% (FY26).
    2. Growth Contribution: Accounted for ~23% of GDP growth in FY25, indicating disproportionate influence.
    3. Credibility Concerns: High discrepancies weaken confidence in headline GDP numbers.
    4. Historical Contrast: Earlier expectation with improved data systems was declining discrepancies, but trend has reversed.

    What structural changes in the new GDP series influence discrepancies?

    1. Base Year Revision: Shift from 2011-12 base year, incorporating updated economic structure.
    2. Data Source Expansion: Increased reliance on digital transactions, GST data, and corporate filings.
    3. Measurement Complexity: Larger informal sector and evolving consumption patterns complicate estimation.
    4. Deflator Issues: Use of 600+ deflators (earlier ~180) affects real GDP calculation accuracy.

    How do discrepancies reflect underlying economic trends?

    1. Consumption Weakness Signal: Positive discrepancies imply actual consumption weaker than production estimates.
    2. Statistical Overestimation Risk: Negative discrepancies suggest consumption stronger than production estimates.
    3. Recent Trend Insight: Rising discrepancies indicate growth not fully supported by core demand components.
    4. Component Imbalance: Real GDP growth (~7.2%) exceeds sum of major components (~6.1%), gap filled by discrepancies.

    What are the implications for policy and economic analysis?

    1. Policy Uncertainty: Weakens reliability of GDP as a basis for monetary and fiscal decisions.
    2. Investment Signals: Distorts perception of economic momentum for investors.
    3. Credibility Risk: Raises questions on statistical integrity and transparency.
    4. Need for Reform: Calls for strengthening data collection, methodology, and reconciliation processes.

    Why is India’s GDP estimation particularly prone to discrepancies?

    1. Informal Sector Dominance: Large share of economic activity lacks real-time measurable data.
    2. Proxy-based Estimation: Use of indicators like corporate data to estimate informal output.
    3. Diverse Economy: Wide variation across sectors complicates uniform data capture.
    4. Data Lag: Delays in availability of high-frequency, reliable datasets.

    Conclusion

    The rising discrepancies in India’s GDP estimates highlight a structural statistical challenge rather than a mere technical issue. While GDP growth remains robust on paper, the increasing reliance on discrepancies signals data inconsistencies and potential overestimation risks, necessitating urgent improvements in statistical systems to maintain credibility.

    PYQ Relevance

    [UPSC 2021] Explain the difference between computing methodology of India’s Gross Domestic Product (GDP) before the year 2015 and after the year 2015.

    Linkage: This question tests understanding of GDP methodology changes, including base year, data sources, and deflators in GS-3. It links to current concerns on GDP credibility and discrepancies, especially mismatch in PFCE, GFCF, and growth.

  • Rare ‘Dual-Sex’ Crab Discovered in Silent Valley

    Why in the News

    A rare freshwater crab species, Vela carli, found in Silent Valley National Park, has been discovered showing both male and female biological traits.

    What was discovered?

    • Scientists found three crabs exhibiting dual sexual characteristics
    • The condition is called Gynandromorphy
    • Example in this case:
    • Some body parts had male reproductive organs
    • Others had female features (gonopores)

    What is Gynandromorphy?

    • A rare biological condition where: An organism shows both male and female characteristics.
    • Usually caused by: Errors during cell division (chromosomal distribution). 
    • Important distinction:
      • Not hermaphroditism (where both reproductive organs function normally)
      • Instead, it is a mosaic of male and female tissues

    About Vela Carli

    • Vela carli is a freshwater crab species endemic to India, specifically found in the Central Western Ghats, one of the world’s richest biodiversity hotspots.
    [2013] Consider the following pairs: National Park : River flowing through the Park Corbett National Park : Ganga Kaziranga National Park : Manas Silent Valley National Park : Kaveri Which of the above pairs is/are correctly matched? (a) 1 and 2 (b) 3 only (c) 1 and 3 (d) None

  • National Quantum Mission: Govt Clears 23 Institutions for Quantum Labs

    Why in the News

    The Government has approved 23 institutions to set up quantum teaching laboratories under the National Quantum Mission, with around 100 more proposals under evaluation.

    About National Quantum Mission (NQM)

    • Approved in 2023 with an outlay of ₹6003.65 crore
    • Duration: 2023–2031
    • Aim: Position India as a global leader in quantum technologies

    Key Objectives

    • Quantum Computing: Develop 50–1,000 qubit quantum computers
      • Higher qubits = more computational power
    • Quantum Communication: Build satellite-based secure communication systems.
      • Enable hack-proof encryption (quantum key distribution)
    • Quantum Sensing & Materials: 
      • Develop: High-precision sensors (defence, navigation) and Advanced quantum materials.

    What are Quantum Labs?

    • Teaching and research facilities in universities
    • Focus on:
      • Training students in quantum technologies
      • Building skilled manpower
    • Help bridge India’s quantum skill gap

    Significance of the Move

    • Capacity Building: Creates a pipeline of skilled researchers and engineers
      • Strengthens India’s R&D ecosystem
    • Strategic Importance: Quantum tech has applications in:
      • Defence (secure communication)
      • Cybersecurity
      • Space & navigation
    • Helps India compete with:
      • United States
      • China
    • Economic Potential: Quantum technologies expected to drive: Next-gen computing and Innovation-led growth. 
    [2022] Which one of the following is the context in which the term “qubit” is mentioned? (a) Cloud Services (b) Quantum Computing (c) Visible Light Communication Technologies (d) Wireless Communication Technologies

  • [16th March 2026] The Hindu OpED: Building India’s climate resilience with water at the core

    PYQ Relevance[UPSC 2017] Climate Change is a global problem. How India will be affected by climate change? How Himalayan and coastal states of India will be affected by climate change?Linkage: Climate change in India largely manifests through water stress, floods, glacial melt, and sea-level rise. The article links these impacts to Himalayan river instability and coastal aquifer salinisation, highlighting regional climate vulnerability.

    Why in the News?

    The COP30 Climate Summit in Belém (Brazil, 2025) introduced the first global adaptation indicators integrating Water, Sanitation and Hygiene (WASH) systems into climate accountability frameworks. Now there is a major shift in global climate governance: water systems are emerging as the central pillar of climate resilience. The outcomes of the UN Climate Conference COP30 and the Belém Adaptation Indicators place water management, sanitation, and hydrological governance at the core of adaptation strategies.

    How does climate change manifest primarily through water systems in India?

    1. Hydrological Disruptions: Climate change alters rainfall patterns, leading to extreme floods and prolonged droughts affecting urban and rural economies.
    2. Glacial Melt Impact: Himalayan glacier retreat destabilizes river systems, affecting long-term water availability for major rivers like the Ganga and Brahmaputra.
    3. Saline Intrusion: Rising sea levels cause salinisation of coastal aquifers, contaminating freshwater sources in coastal regions.
    4. Agricultural Vulnerability: Agriculture contributes ~40% of anthropogenic methane emissions, particularly from rice cultivation, livestock systems, and organic waste.
    5. Food Security Threats: Erratic monsoon cycles disrupt crop productivity and irrigation systems.

    What are Belém Adaptation Indicators?

    1. The Belém Adaptation Indicators are a set of 59-60 voluntary, global measures adopted at the COP30 climate summit in Belém, Brazil (scheduled for November 2025) to track how well countries are adapting to climate change. 
    2. Developed through a two-year UN process under the UAE-Belém Work Programme, they aim to provide a shared, practical language for monitoring resilience against climate impacts like floods, droughts, and heatwaves.

    Key Features of the Belém Adaptation Indicators are as follows:

    1. Purpose: To monitor progress toward the Global Goal on Adaptation (GGA) adopted under the Paris Agreement, focusing on whether communities are becoming safer and better able to cope with climate threats
    2. Focus Areas: The measures look at essential sectors such as water security, food systems, health, housing, early warning systems, ecosystems, and local economies
    3. Scope: The indicators emphasize protecting vulnerable populations, including women, indigenous groups, and people with disabilities
    4. Voluntary Nature: They are designed to be flexible rather than a rigid top-down mandate, allowing countries to adapt them to their national circumstances.

    How do Belém Adaptation Indicators redefine climate governance?

    1. Climate-Resilient Water Systems: Focus on reducing water scarcity and increasing resilience against floods and droughts.
    2. Universal Drinking Water Access: Ensures safe drinking water availability for all communities.
    3. Climate-Resilient Sanitation Infrastructure: Strengthens sanitation systems capable of functioning during extreme climate events.
    4. Multi-Hazard Early Warning Systems: Establishes universal early warning coverage by 2027.
    5. Hydrometeorological Capacity: Strengthens meteorological monitoring and national vulnerability assessments by 2030.

    How is India strengthening water governance to build climate resilience?

    1. Institutional Consolidation: Establishment of the Ministry of Jal Shakti (2019) integrates water governance across sectors.
    2. Water Vision 2047: Aligns national water policy with sustainability, equity, and climate resilience goals.
    3. Aquifer Mapping Programme: National Aquifer Mapping and Management Programme (NAQUIM 2.0) advances aquifer-level planning based on hydrogeological data.
    4. River Rejuvenation: National Mission for Clean Ganga (NMCG) expands focus beyond sewage treatment to biodiversity restoration and river basin management.
    5. Integrated Water Management: Encourages linking scientific hydrology with policy planning.

    What systemic risks threaten India’s climate-water resilience?

    1. Unequal Water Distribution: Water scarcity remains acute and unevenly distributed across regions.
    2. Water-Linked Disasters: Most climate disasters in India are water-related (floods, droughts, cyclones).
    3. Fragile Adaptation Finance: Global climate finance pathways remain uncertain despite projections of $1.3 trillion annually by 2035.
    4. Recovery Bias: Lack of predictable finance shifts focus toward post-disaster recovery rather than long-term resilience planning.
    5. Infrastructure Stress: Water supply systems require climate stress testing and diversification of water sources.

    Why is digital fragmentation a challenge for climate-water governance?

    1. Fragmented Data Systems: Hydrological and meteorological datasets remain distributed across institutions without integration.
    2. Limited AI-Driven Decision Support: Despite large datasets, real-time AI integration in governance remains weak.
    3. Planning Disconnect: Water data is rarely linked to budgeting, crop advisories, insurance mechanisms, or disaster response systems.
    4. Need for Interoperable Platforms: Integration of hydrological data, crop advisory systems, insurance frameworks, and financial flows is essential.

    How can India lead global climate adaptation through water governance?

    1. Policy Convergence: Align national missions such as drinking water coverage, irrigation efficiency, and urban water reforms with climate adaptation.
    2. Digital Public Infrastructure: Utilize India’s strength in digital governance systems to integrate climate-water datasets.
    3. Operational Adaptation: Shift from infrastructure creation to functional system resilience.
    4. Global South Leadership: Demonstrate scalable climate adaptation models applicable to other developing countries.

    Conclusion

    Water systems are emerging as the operational backbone of climate adaptation. India possesses strong institutional foundations, including water governance reforms, digital infrastructure, and river restoration programmes. However, translating policy ambition into measurable climate resilience requires integrating hydrological data, strengthening climate finance, and ensuring equitable water distribution. By aligning national missions with global adaptation frameworks, India can emerge as a leader in climate-resilient water governance for the Global South.

  • Ice patches on melting glaciers greater threat than thought: ISRO scientists

    Why in the News

    A new study by scientists from the Indian Space Research Organisation has identified exposed ice patches on retreating Himalayan glaciers as a key precursor to flash floods. The study examined the August 5, 2025 Dharali flash flood in Uttarakhand that killed nine people and devastated settlements along the Bhagirathi river valley. Satellite imagery revealed exposed ice patches in the nivation zone of the Srikanta glacier shortly before the disaster. (Nivation is defined as the erosion of the ground beneath and around a snow bank, primarily resulting from the processes of alternate freezing and thawing.) This indicates accelerated deglaciation and unstable cryosphere conditions. This finding marks an important shift in understanding Himalayan hazards: disasters may originate not only from glacial lake outburst floods (GLOFs) but also from smaller, previously overlooked cryospheric instabilities linked to warming temperatures.

    What are exposed ice patches?

    1. Exposed ice patches are areas of ancient, stable ice that have become visible on the surface of a glacier or mountain slope after their protective covering of seasonal snow and firn (intermediate ice) has thinned or melted away. 
    2. Unlike the main body of a glacier, which flows like a slow-moving river, these patches are often stationary and act as “prehistoric freezers”

    Reasons for their formation are as follows:

    1. Thinning Insulation: Warmer temperatures reduce the layers of snow and firn that normally insulate the deeper ice.
    2. Ablation: During the ablation period (when a glacier loses more ice/snow than it gains), these patches may emerge on steep, shaded slopes, particularly in nivation hollows where snow traditionally lingers year-round.
    3. Wind Scouring: In some regions, like Antarctica, strong winds can strip away top layers to reveal bright blue patches of older, denser ice.

    How do exposed ice patches signal accelerated glacier retreat in the Himalayas?

    1. Deglaciation indicator: Exposed ice patches in the Srikanta glacier’s ablation zone indicate thinning seasonal snow and firn cover due to rising temperatures.
    2. Satellite evidence: Pre-event satellite imagery showed persistent exposed ice patches on north-northeast facing slopes where snow normally accumulates.
    3. Cryosphere instability: Loss of insulating snow layers accelerates melting and structural weakening of glaciers.
    4. Regional warming effect: Similar processes have been documented in other warming cryosphere regions including the Canadian Arctic and Greenland.

    What role did nivation processes play in triggering the Dharali flash flood?

    1. Nivation process: Erosion of ground beneath snowbanks caused by alternate freezing and thawing cycles.
    2. Formation of nivation hollows: Repeated snow accumulation creates depressions which deepen over time.
    3. Structural instability: In steep Himalayan terrain, nivation hollows accumulate ice, meltwater, and debris.
    4. Trigger mechanism: Collapse of an exposed ice patch within the nivation zone of the Srikanta glacier released meltwater and debris.
    5. Result: Sudden downstream debris flow triggered the Dharali flash flood.

    Why are Himalayan glaciers increasingly vulnerable to cryosphere hazards?

    1. Rapid glacier retreat: Himalayan glaciers are losing ice due to rising regional temperatures.
    2. Snow and firn thinning: Seasonal snow cover that stabilizes glaciers is shrinking.
    3. Steep mountain terrain: High relief areas amplify instability and debris flow risks.
    4. Glacier fragmentation: Smaller unstable ice masses form as glaciers shrink.
    5. Emerging hazard types: Hazards now include not only GLOFs but also ice collapses, debris flows, and cryosphere mass movements.

    How do satellite observations improve early warning systems for glacier disasters?

    1. Pre-event detection: Satellite imagery identified exposed ice patches before the Dharali flood.
    2. Landscape monitoring: Remote sensing helps track glacier retreat and unstable cryosphere zones.
    3. Hazard reconstruction: Earth observation data reconstructs sequences leading to disasters.
    4. Early warning potential: Monitoring exposed ice patches could provide advance signals of possible cryosphere hazards.

    Why must disaster monitoring extend beyond glacial lakes to smaller cryosphere instabilities?

    1. Focus shift: Traditional monitoring emphasizes glacial lake outburst floods.
    2. Overlooked hazards: Small-scale cryosphere instabilities can trigger similar destructive floods.
    3. Regional prevalence: Similar geomorphological conditions exist across much of the Himalayan arc.
    4. Policy implication: Disaster risk assessment must include nivation zones and exposed ice patches.

    Conclusion

    Rapid glacier retreat in the Himalayas is generating new cryosphere hazards beyond traditional glacial lake outburst floods. The Dharali flash flood demonstrates how exposed ice patches and nivation-zone instability can trigger sudden disasters in high-mountain regions. Strengthening satellite monitoring, hazard mapping, and climate-resilient disaster management systems is essential to reduce risks and protect vulnerable Himalayan communities.

    PYQ Relevance

    [UPSC 2024] What is disaster resilience? How is it determined? Describe various elements of a resilience framework. Also mention the global targets of the Sendai Framework for Disaster Risk Reduction (2015-2030).

    Linkage: The Dharali flash flood from glacier ice-patch collapse highlights the need for disaster resilience in fragile Himalayan regions facing climate-induced hazards. It underlines the importance of Sendai Framework goals like risk monitoring, early warning systems, and satellite-based glacier surveillance.

  • Thorium and India’s 100 GWe Nuclear Power Mission by 2047

    Why in the News

    Experts have highlighted the importance of thorium-based nuclear energy in achieving India’s target of 100 gigawatts electric (GWe) nuclear power capacity by 2047, especially after the passage of the SHANTI Act 2025.

    Why Thorium is Important for India

    1. Largest Thorium Reserves

    • India possesses the world’s largest thorium reserves.
    • Thorium deposits are mainly found in monazite sands along the Indian coastline.

    2. Energy Security

    • India’s current nuclear programme relies heavily on imported uranium because domestic uranium ores are low-grade.
    • A nuclear capacity of 100 GWe would require 18,000–20,000 tonnes of uranium annually, which may become difficult to secure globally.

    3. Reduced Nuclear Proliferation Risk

    • Thorium fuel cycles produce less weapons-usable material, reducing proliferation risks compared to conventional uranium cycles.

    India’s Three-Stage Nuclear Power Programme

    The programme was designed to use India’s large thorium resources.

    Stage 1: Thermal Reactors

    • Pressurised Heavy Water Reactor
    • Uses natural uranium as fuel.

    Stage 2: Fast Breeder Reactors (FBRs)

    • Convert plutonium and fertile materials into more fuel.
    • India’s Prototype Fast Breeder Reactor (500 MWe) is nearing completion.

    Stage 3: Thorium-Based Reactors

    • Thorium is converted into uranium-233, which becomes the main fuel.

    Key Technologies for Thorium Deployment

    • Fast Breeder Reactors: Essential to generate uranium-233 from thorium.
    • Thorium Molten Salt Reactors (TMSR): Advanced reactors designed for thorium fuel cycles.
    • Small Modular Reactors (SMRs): Compact reactors that can produce electricity and green hydrogen.
    • HALEU Fuel: High-Assay Low-Enriched Uranium
      • Can be combined with thorium in existing reactors to accelerate the thorium fuel cycle.

    Role of Nuclear Fuel Recycling

    • Nuclear recycling can increase the energy potential of fuel 50–100 times.
    • Countries such as France, Russia, and India already use such technologies
    [2012] To meet its rapidly growing energy demand, some opine that India should pursue research and development on thorium as the future fuel of nuclear energy. In this context, what advantage does thorium hold over uranium? Thorium is far more abundant in nature than uranium. On the basis of per unit mass of mined mineral, thorium can generate more energy compared to natural uranium. Thorium produces less harmful waste compared to uranium. Select the correct answer using the code given below: (a) 1 only (b) 2 and 3 only (c) 1 and 3 only (d) 1, 2 and 3

  • LPG Consumption in India

    Why in the News

    Recent data from the Petroleum Planning and Analysis Cell shows that although India has over 34 crore LPG consumers, the average household consumption is only about half a cylinder per month, especially in rural areas.

    Key Data Highlights

    LPG Consumers in India

    • Total LPG consumers: ≈33.37 crore households
    • Connections under Pradhan Mantri Ujjwala Yojana: 10.56 crore

    Growth in LPG Consumption

    • LPG consumption increased six-fold: 446 TMT in 1998–99 and 2,754 TMT in 2025–26
    • Major growth occurred during the 2000s and 2010s (8–11% annually).
    • A sharp rise happened in 2016–17 after the launch of PMUY.

    Household Consumption Pattern

    Average LPG Use per Household

    • Delhi (mostly urban): ~ 11.4 kg per month
    • Bihar (mostly rural): ~ 6.7 kg per month
    • Uttar Pradesh: ~ 7.7 kg per month

    States with Highest LPG Consumers

    • Uttar Pradesh – 4.87 crore consumers (highest)
    • Maharashtra – 3.2 crore
    • West Bengal – 2.72 crore
    • Tamil Nadu – 2.4 crore
    • Bihar – 2.33 crore

    Key Insight

    • Urban households rely almost entirely on LPG, leading to higher monthly usage.
    • Rural households often combine LPG with traditional fuels, resulting in lower consumption despite having connections.
    [2009] With which one of the following has the B.K. Chaturvedi Committee dealt? (a) Review of Centre-States relation (b) Review of Delimitation Act (c) Tax reforms and measures to increase revenues (d) Price reforms in the oil sector

  • Supreme Court Urges Crop Diversification Towards Pulses

    Why in the News

    The Supreme Court of India asked the Union Government to reconsider agricultural policies and encourage farmers to shift from wheat and paddy cultivation to pulses.

    Key Observations by the Court

    • A Bench headed by Surya Kant stressed the need for agricultural diversification, particularly in North India where paddy cultivation dominates.
    • The Court noted that excessive cultivation of paddy may not be necessary except for export needs, and land could instead be used for pulses.

    Directions to the Government

    1. Review the policy framework to promote pulse cultivation.
    2. Convene a meeting of stakeholders, including experts and farmer representatives.
    3. Address issues affecting pulse farmers such as:
      • Lack of adequate Minimum Support Price (MSP) incentives.
      • Ensuring guaranteed and timely procurement of pulses.
      • Fixing the cost price of imported yellow peas so imports do not harm domestic producers.

    Relevant ministries expected to participate include:

    • Ministry of Agriculture and Farmers Welfare
    • Ministry of Commerce and Industry
    • Ministry of Consumer Affairs Food and Public Distribution

    Context

    • The case was filed by the farmers’ organisation Kisan Mahapanchayat, which sought restrictions on yellow pea imports to protect domestic pulse farmers.

    Significance

    • Promoting pulses could:
      • Improve soil health through nitrogen fixation.
      • Reduce water-intensive paddy cultivation.
      • Enhance India’s nutritional security, as pulses are a major protein source.
      • Reduce dependence on pulse imports.
    [2020] With reference to pulse production in India, consider the following statements: Black gram can be cultivated as both kharif and rabi crop. Green-gram alone accounts for nearly half of pulse production. In the last three decades, while the production of kharif pulses has increased, the production of rabi pulses has decreased. Which of the statements given above is/are correct? (a) 1 only (b) 2 and 3 only (c) 2 only (d) 1, 2 and 3

  • Economic Stabilisation Fund to Tackle Global Headwinds

    Why in the News

    The Government of India has created an Economic Stabilisation Fund of ₹57,381 crore through the Second Supplementary Demand for Grants to manage economic shocks arising from global crises such as the West Asia conflict and rising oil prices.

    Key Highlights

    1. Supplementary Demand for Grants

    • The Lok Sabha approved the Second Supplementary Demand for Grants.
    • Gross additional expenditure: about ₹2.81 lakh crore.
    • Estimated savings and receipts: around ₹80,000 crore.
    • Net additional cash outgo: about ₹2.01 lakh crore.

    2. Economic Stabilisation Fund

    • Allocation: ₹57,381 crore.
    • Purpose: Provide fiscal space to address global economic uncertainties, including
      • Oil price shocks
      • Supply chain disruptions
      • External economic crises
      • Sector-specific shocks.
    • According to Nirmala Sitharaman, the fund will help the government respond quickly to unexpected global developments.

    3. Context: Global Economic Pressures

    • Rising crude oil prices (around $100 per barrel).
    • Disruptions due to West Asia conflict affecting energy supply chains.
    • Risk of broader global economic instability.

    4. Fiscal Deficit Assurance

    • The government reiterated that India’s fiscal deficit target for FY 2025–26 will remain at 4.4% of GDP, even after these additional expenditures.

    Significance

    • Acts as a buffer mechanism against external economic shocks.
    • Enhances fiscal flexibility for emergency responses.
    • Helps maintain macroeconomic stability without deviating from the fiscal consolidation roadmap.
    [2012] Which of the following are the methods of Parliamentary control over public finance in India? 1. Placing Annual Financial Statement before the Parliament. 2. Withdrawal of moneys from Consolidated Fund of India only after passing the Appropriation Bill. 3. Provisions of supplementary grants and vote-on-account. A periodic or at least a mid-year review of programmes of the Government against macroeconomic forecasts and expenditure by a Parliamentary Budget Office. Introducing Finance Bill in the Parliament. Select the correct answer using the code given below: (a) 1, 2, 3 and 5 only (b) 1, 2 and 4 only (c) 3, 4 and 5 only (d) 1, 2, 3, 4 and 5