💥Join UPSC 2027,2028 Mentorship (July Batch) + XFactor Notes & Microthemes PDF
Talk to Mentor

Civilsdaily

Login

Type: Explained

These Newscards correspond to the explained section of various newspapers. They become immensely important for both prelims and mains and special attention needs to be paid to them

  • AI Use by the Judiciary: SC’s Draft AI Regulations, 2026

    Why in the News?

    The Supreme Court released the Draft Regulations for Use of Artificial Intelligence in Courts, 2026 last month, inviting public comments till July 15. The draft permits AI for administrative and research functions in courts but places an absolute, non-derogable bar on any AI role in decisions affecting bail, recidivism (a critical metric used to measure the effectiveness of the justice and rehabilitation systems.), witness credibility, or personal liberty.

    What does the Draft Regulations permit AI to do in courts?

    1. Administrative and assistive functions: AI use is permitted for case management, transcription, translation, legal research, document summarisation, accessibility, and court administration.
    2. Approval requirement: Every permitted use requires prior written approval from the Apex Body for the Supreme Court, or the AI Committee of the concerned High Court or tribunal.
    3. Human supervision: Officers nominated by the court must supervise and verify AI-assisted outputs before use.
    4. Scope boundary: Permission covers efficiency-enhancing functions only. It does not extend to any function that produces or contributes to a judicial outcome.

    Why has the SC opted for a staggered, court-wise implementation instead of a uniform rollout?

    1. SC-specific notification: Provisions apply to the Supreme Court only from a date notified by the Chief Justice of India.
    2. High Court autonomy: Provisions for High Courts and the courts and tribunals under their jurisdiction come into force separately, on dates notified by the respective High Court Chief Justice.
    3. Provision-wise phasing: Different provisions can be brought into force on different dates within the same court.
    4. Rationale: Phasing allows each court to adopt AI at a pace suited to its own infrastructure, caseload, and readiness.

    Why is human judicial authority made non-negotiable in adjudicative outcomes?

    1. Categorical bar on algorithmic outcomes: No judicial outcome can be reached through algorithmic decision-making alone, or solely on the basis of AI-generated information.
    2. Determinative human authority: Human judicial authority is determinative in all adjudicative decisions, regardless of AI input.
    3. Advisory-only role: Where AI is used anywhere in a decision-making process, its role is only advisory.
    4. Independent evaluation mandate: Any AI-assisted input is subject to independent human judicial evaluation before use.

    What functions has the SC placed beyond regulatory reach altogether, and why?

    1. Risk scoring barred: AI cannot be used for ‘risk scoring’ to assess flight risk.
    2. Recidivism prediction barred: AI cannot be used to predict recidivism.
    3. Bail eligibility barred: AI cannot be used to evaluate bail eligibility.
    4. Witness credibility barred: AI cannot be used to determine the credibility of witnesses.
    5. Profiling barred: AI cannot be used to predict, profile, or infer the future conduct or behaviour of parties, accused persons, witnesses, or legal representatives.
    6. Undisclosed AI evidence barred: AI-generated output cannot be submitted as independent evidence without full disclosure of its AI-generated character.
    7. Blackbox AI barred in liberty matters: Unexplainable AI systems cannot be used in matters affecting personal liberty.
    8. Non-derogable status: These prohibitions are absolute. No authority can permit them later under the Regulations.

    Does the disclosure mechanism for litigants adequately safeguard their right to know?

    1. Material assistance trigger: Litigants must be informed only when an AI tool “materially assists” case management, document analysis, or judicial administration.
    2. Timely and accessible disclosure: Disclosure to litigants and their counsel must be made in a timely and accessible manner.
    3. Threshold-based, not blanket disclosure: Litigants are not informed of every instance of AI use in their case, only instances that meet the material assistance standard.
    4. Undefined threshold: The Regulations do not define what constitutes “material assistance,” leaving the disclosure trigger to case-by-case determination by courts.

    What institutional architecture will govern AI use in courts?

    1. Apex Body: An Apex Body at the Supreme Court will set minimum mandatory standards for AI systems and issue implementation guidelines.
    2. Composition: The Apex Body comprises sitting Supreme Court and High Court judges, an official of the Ministry of Electronics and Information Technology, and experts in finance and cybersecurity.
    3. Specialised committees: The Apex Body will function through five specialised committees.
    4. Court-level AI Committees: The Supreme Court and each High Court will constitute their own AI Committees, backed by an AI Secretariat.
    5. Dedicated research body: The Centre of Research and Excellence on Artificial Intelligence (CoRE-AI) will evaluate AI tools and track technological developments to support the Apex Body.

    How are private AI vendors regulated to prevent capture of judicial data and infrastructure?

    1. Prior written approval: Private companies can supply AI tools only with written approval from the relevant court authority.
    2. Mandatory contract terms: Vendor agreements must include a mandatory list of contract terms set out by the Regulations.
    3. Data ownership and access: Contracts must specify ownership of, and access rights to, court data and AI outputs.
    4. Bar on sensitive data use: Vendors are barred from using sensitive judicial data.
    5. No unauthorised model training: Vendors cannot retain or fine-tune models using court data without the AI Committee’s written approval.
    6. IP restriction: Vendors cannot claim exclusive intellectual property rights over tools built substantially using public resources.

    Conclusion

    The Draft Regulations construct a two-tier framework for judicial AI: broad permission for administrative efficiency, and an absolute prohibition on AI’s role in outcome-determinative and liberty-affecting functions. This boundary, not the list of permitted uses, is the framework’s operative safeguard against algorithmic opacity compromising due process. The undefined “material assistance” threshold for litigant disclosure remains its weakest link, leaving courts significant discretion over what litigants get to know. Effective implementation will depend on how the Apex Body and CoRE-AI operationalise this boundary as AI adoption scales across courts.

    PYQ Relevance

    [UPSC 2024] Explain the reasons for the growth of public interest litigation in India. As a result of it, has the Indian Supreme Court emerged as the world’s most powerful judiciary?”

    Linkage: The PYQ discusses expansion of judicial power through institutional self-assertion. The Draft AI Regulations are another instance of the SC using its institutional authority to self-regulate its own processes.

  • Why Weekly Diabetes Shot Could Reshape Treatment

    Why in the News?

    Novo Nordisk launched Awiqli (insulin icodec), the world’s first once-a-week insulin injection, in India, cutting required insulin shots from 365 to 52 a year at Rs 261 per week. The launch targets India’s exceptionally large and growing diabetic population, but insulin use in India has long lagged clinical need because of reluctance among both patients and doctors to initiate insulin therapy.

    How does icodec technically reduce insulin’s dosing burden without changing its clinical effect?

    1. Albumin-binding depot: A fatty acid chain added to the insulin molecule increases its affinity for albumin, a blood protein. Delivered under the skin, the drug binds reversibly to albumin, forming an inactive depot that releases insulin into the bloodstream through the week.
    2. Reduced receptor affinity: Three amino acid substitutions lower the molecule’s affinity for insulin receptors. This slows the rate at which released insulin is used up, without reducing its potency.
    3. Injection frequency reduction: The two modifications together cut insulin injections from 365 days a year to 52 days, making icodec the world’s first long-acting weekly insulin shot.
    4. Clinical equivalence, not clinical superiority: Physicians state icodec’s blood sugar-lowering effect is similar to other insulins. The advance lies in reduced dosing frequency, expected to improve compliance rather than glucose control itself.
    5. Position in insulin’s evolution: Icodec is a genetically engineered insulin analogue (Insulin analogue: a modified version of human insulin engineered to alter how long it stays active or how it is absorbed), part of a line of modifications that extend how long insulin stays active in the body.

    Why does India’s insulin gap persist despite insulin’s proven superiority over oral therapy?

    1. Patient reluctance despite clinical failure of pills: Type 2 diabetics who have failed to control blood glucose even on the highest doses of oral medicines remain unwilling to switch to insulin shots, despite the risk of organ, nerve, and eye damage from delay.
    2. Physician-side reluctance: Doctors themselves show reluctance to initiate insulin treatment in patients, delaying transition even when maximal oral therapy has failed.
    3. Insulin’s undeserved stigma: Novo Nordisk India’s managing director states insulin is a drug that is never abused and is highly effective, yet patients avoid it, indicating the barrier is perceptual rather than clinical.
    4. Scale of underuse: Only six million people are currently on insulin in India, a number industry estimates should be at least double, given the population that clinically needs it.
    5. Gendered burden compounding avoidance: Women on multiple daily insulin doses report needing to adjust doses during menstruation, a flexibility burden not addressed by frequency reduction alone.

    Which patient groups does icodec target, and why does the clinical logic differ between type 1 and type 2 diabetes?

    1. First target group: treatment-failed type 2 diabetics: Patients with eight to ten years of diabetes whose pills can no longer control blood glucose are the primary intended users, to prevent further organ and nerve damage from delay.
    2. Second target group: background insulin for type 1 diabetics: Type 1 diabetics need a long-acting basal dose (Basal dose: a steady, long-acting insulin dose that manages blood glucose between meals) alongside meal-time bolus doses (Bolus dose: a fast-acting insulin dose taken around mealtimes based on calorie intake); icodec would add a fourth weekly dose without significantly raising treatment burden.
    3. Why type 2 is the better clinical fit: Type 1 diabetics already take three daily doses, and their blood glucose fluctuates more, requiring frequent dose adjustment that weekly dosing cannot accommodate.
    4. Loss of flexibility as a trade-off: A physician-run survey found women needed to adjust insulin doses during menstruation, a flexibility that a fixed weekly dose foreclosed for type 1 patients.
    5. Type 2’s larger untapped pool: Since 25% to 30% of type 2 diabetics eventually require insulin despite most managing initially on pills, this is the segment with the largest late-stage conversion potential.

    Does icodec’s safety and cost profile remove the practical objections to insulin therapy?

    1. Hypoglycemia risk unchanged: The most common side effect, hypoglycemia (Hypoglycemia: a condition where blood glucose levels fall too low), affects about one in ten people on icodec, matching the risk seen with other daily insulin shots.
    2. Why hypoglycemia appears more noticeable on insulin: Blood glucose is controlled for the first time once insulin is started, making hypoglycemic episodes more apparent; pills can cause hypoglycaemia too, but uncontrolled high glucose on pills masks the comparison.
    3. Weekly cost undercuts existing insulin analogues: Icodec costs Rs 261 a week, compared to Rs 345 to Rs 453 a week for existing insulin analogues, working out to about Rs 50 a day.
    4. Pricing structure: The drug is sold in two pre-filled pen sizes, a 700 ml unit priced at Rs 2,611 and a 2,100 ml unit priced at Rs 7,883, with a typical patient needing around 70 units a week depending on requirement.
    5. Combination potential with weight-loss drugs: Icodec becomes more effective when combined with GLP-1 drugs (GLP-1 drugs: a class of medicines that lower blood glucose and are also used for weight loss), since abdominal obesity reduces insulin sensitivity and raises the insulin needed to process the same amount of sugar.

    Does convenience alone close India’s insulin treatment gap?

    1. Scale of the underlying burden: India currently has 101 million people living with diabetes and 136 million with pre-diabetes, one of the largest such populations in the world.
    2. Projected insulin need over time: Industry estimates suggest 5% to 10% of diabetics would need insulin after five years of pill-based management, rising to 20% to 30% after ten years.
    3. Conservative estimate still implies a large gap: Even at a conservative 20% requirement, the number needing insulin would stand at around 20 million, more than three times the current six million on insulin.
    4. Convenience as the stated lever for closing this gap: Industry framing ties the drug’s adoption prospects explicitly to convenience and comparable cost, not to any claimed improvement in glucose control.
    5. Unaddressed question: Whether reduced dosing frequency by itself overcomes the reluctance documented among both patients and doctors, distinct from cost or frequency, is not established by the launch itself.

    Conclusion

    Icodec’s weekly dosing and competitive pricing directly target the practical barriers of frequency and cost that have long deterred insulin use in India. The deeper barrier is behavioural: both patients and physicians delay insulin initiation despite its established superiority over maximal oral therapy, driven by stigma and reluctance rather than price or frequency alone. Reducing shots from 365 to 52 a year does not by itself address this psychological resistance. Whether convenience translates into earlier insulin initiation, and closes the gap between India’s 101 million diabetics and the roughly 20 million projected to eventually need insulin, will depend on physician-driven behavioural change as much as on the drug’s technical advance.

  • Landslides: The Need for Early Warning Systems

    Why in the News?

    Recent landslides across the Western Ghats and other parts of India have revived the debate on installing early warning systems (EWS) for landslides. The renewed discussion exposes a gap between what landslide-prediction technology has already proven capable of and the absence of any single, scaled system deploying it nationally.

    Why has landslide prediction returned to the policy conversation, and does the science actually work?

    1. Trigger: Recent landslides in the Western Ghats and other parts of India reignited discussion on installing EWS for such events.
    2. Proven feasibility: Landslides can be predicted in high-risk zones. The 2024 Wayanad landslide killed more than 300 people, illustrating the human cost when prediction is absent.
    3. Working precedent: Two weeks before the Wayanad disaster, landslides in Munnar caused no fatalities. The Idukki district administration evacuated residents on the advice of an Amrita University research team, led by Maneesha Vinodini Ramesh, that was testing an EWS.
    4. Global validation: EWS already operates effectively in multiple countries, establishing that the underlying approach is proven rather than experimental.

    What are the two competing methodologies India is currently developing for landslide early warning?

    1. Amrita University approach: Deploys a network of on-site sensors, tilt meters, pressure gauges, accelerometers, at high-risk slopes to measure vibration and ground movement.
    2. Threshold-based alerts: When sensor readings cross well-defined thresholds, an automated warning is issued, allowing the administration to act.
    3. IIT Mandi approach: Professor Dericks Praise Shukla’s team uses probabilistic forecasting instead of physical sensors, currently being validated against ongoing landslide events in the Himalayan region.
    4. Satellite-based mapping: The IIT Mandi team has mapped vulnerable spots across the Himalayan region using a satellite-based database of past landslide events.
    5. Multi-factor modelling: The probabilistic model factors in localised rainfall forecasts along with soil conditions, rock stability, extent of slope, and population density.

    Why does neither current methodology, on its own, deliver a complete early warning solution?

    1. Sensor method’s blind spot: Amrita’s sensor network reports data only for the specific slope where instruments are installed. Neighbouring slopes remain unmonitored, even though landslides are highly localised events.
    2. Rainfall model’s lead-time constraint: Shukla’s probabilistic model depends on rainfall forecasts, but highly localised forecasts are currently available only for the day of the event or one day earlier, giving very little lead time.
    3. Trade-off exposed: The sensor method provides adequate lead time but incomplete geographic coverage. The probabilistic method provides wider coverage but insufficient lead time.
    4. Scale limitation: Both methods remain validated only at pilot or regional scale. Neither is currently integrated into a single nationwide operational system.

    What must change before India moves from pilot-scale projects to a comprehensive national system?

    1. Precondition 1: high-risk zone identification: A comprehensive system first requires identifying high-risk areas where landslides are frequent, before sensors or models can be meaningfully deployed at scale.
    2. Risk zones already flagged: Shukla identifies the north-western Himalayan region and parts of Manipur and Mizoram as highly vulnerable. Sikkim is relatively less vulnerable due to a less dense road network, which implies greater slope stability.
    3. Precondition 2: higher-resolution rainfall forecasting: The probabilistic method’s lead-time limitation can only be resolved once the India Meteorological Department develops higher-resolution rainfall forecasts, which is currently in progress.
    4. Timeline and resourcing: A comprehensive and effective landslide EWS can be built in about two years if resources and effort are properly dedicated to it, according to Shukla.
    5. Sequencing: The stated roadmap identifies high-risk zones nationally first, and installs sensors at selected sites only afterward, mapping precedes instrumentation, not the reverse.

    Conclusion

    Landslide early warning technology is scientifically proven and has already prevented casualties in India, as seen in Munnar in 2024. No standardised national system exists, however; current efforts are split between a sensor-based method and a rainfall-probability-based method, each constrained by a different limitation, localised coverage in one case, short lead time in the other. Scaling to a comprehensive national system depends on two preconditions currently absent: systematic identification of high-risk zones across India, and higher-resolution rainfall forecasting infrastructure from the India Meteorological Department. Until both are in place, early warning capability will remain confined to isolated pilot projects rather than a nationwide shield.

    PYQ Relevance

    [UPSC 2021] Describe the various causes and the effects of landslides. Mention the important components of the National Landslide Risk Management Strategy.

    Linkage: The PYQ examines India’s institutional approach to landslide risk reduction through the National Landslide Risk Management Strategy (NLRMS) and disaster preparedness. The article directly complements this PYQ by highlighting early warning systems, sensor networks, vulnerability mapping, localized rainfall forecasting, and timely evacuation, all of which are core components of proactive landslide risk management envisaged under the NLRMS.

  • Why is the centre revising the NFSA 

    Why in the News?

    The Union Food and Public Distribution Department has published a draft amendment to the National Food Security Act (NFSA), 2013 converting the Antyodaya Anna Yojana (AAY) entitlement from a household-based to a per-capita formula. Tamil Nadu and Kerala have objected, arguing the change will cut monthly foodgrain allocations for smaller households even though it is framed as an equity correction. The dispute revives a food-politics fault line between the Centre and these two States that traces back to the NFSA’s 2013 enactment.

    What has the Centre proposed, and what does it claim to fix?

    1. Current rule: Every Antyodaya Anna Yojana (AAY) household receives 35 kg of foodgrains per month, regardless of household size.
    2. Proposed rule: Each person in an AAY household is entitled to 7 kg per month, subject to a ceiling of 35 kg per household.
    3. Legal provision amended: The first provision to Section 3(1) of the NFSA, which governs the right to subsidised foodgrains for eligible households.
    4. Stated rationale: The F&PD Department says the household-based system causes intra-category inequity. Smaller households get a higher per-capita share. Larger households get a lower per-capita share that can fall below what priority households receive.
    5. Stated objective: The amendment aims to make allocation more rational and align entitlements with nutritional norms.
    6. Consultation window: Public comments were invited till July 13, 2026.
    7. Gap in the amendment: The draft does not address inclusion of ineligible persons as beneficiaries. This problem remains a State-level issue.

    Why have Tamil Nadu and Kerala historically treated food policy as high-stakes politics?

    1. Kerala’s PDS legacy: Kerala traces informal food distribution mechanisms to the erstwhile princely State of Travancore and launched a formal Public Distribution System (PDS) in 1962, three years before the Food Corporation of India (FCI) was established.
    2. Tamil Nadu’s political precedent: Incumbent governments lost power in 1952 and 1967 over failure to manage rice shortages, making rice policy a lasting political sensitivity.
    3. Kerala’s resistance to the 2013 NFSA: The Congress-led UDF government, despite the Congress-led UPA pushing the law at the Centre, resisted implementation. It argued the law would drop a large number of poor families and impose a heavy financial burden on the State.
    4. Delayed Kerala rollout: Chief Minister Oommen Chandy committed to enforcing the NFSA, but the formal decision was taken only under his successor, Pinarayi Vijayan.
    5. Tamil Nadu’s universal rice policy: Chief Minister Jayalalithaa opposed the NFSA after her government began distributing free rice to all ration cardholders in 2011, regardless of economic status.
    6. Concession extracted in 2013: Tamil Nadu secured a Central guarantee that its then-existing allocation levels would be legally protected under the NFSA.
    7. Delayed adoption: Both southern States joined the rest of the country in implementing the NFSA only in November 2016.

    Why does a per-capita formula built on a household ceiling disadvantage southern States?

    1. Mechanical effect of the formula: A household with fewer than five members receives less than 35 kg under the per-capita rule, since 7 kg multiplied by fewer than five persons falls short of the existing ceiling.
    2. Kerala’s structural exposure: Kerala’s Food Minister has argued that States characterised by nuclear families will lose out, since Kerala took the position in 2013 that AAY cardholders deserved “special consideration,” a stance it maintains.
    3. Tamil Nadu’s quantified loss: The State’s monthly allocation is projected to fall from 65,261 tonnes to 42,040 tonnes under the new formula.
    4. Scale of exposure in Tamil Nadu: Of 18.64 lakh AAY households, 15.75 lakh have fewer than five members, covering 58.51 lakh of the State’s 69.27 lakh AAY beneficiaries.
    5. Non-substitutability argument: Rice is a staple across all three daily meals for AAY cardholders and cannot be replaced with market purchases without significant out-of-pocket cost.
    6. North-South divide argument: Right to Food Campaign functionary Anuradha Talwar has argued that northern States, with larger average family sizes, will receive higher allocations under the new formula while southern States lose out.
    7. South’s collective stake: The five southern States and Puducherry together hold 52.51 lakh of India’s 250 lakh AAY household ceiling, about one-fifth of the national total, making the region’s exposure to the formula change substantial in absolute terms.

    What is the way forward, and does it resolve the underlying tension?

    1. Process concern: A change of this scale should have been subjected to wider public scrutiny before a consensus was sought, according to food policy commentary cited in the report.
    2. Middle-path proposal: Tamil Nadu Progressive Consumer Centre president T. Sadagopan has suggested a flat allocation of 30 kg per household, irrespective of family size, as a compromise.
    3. Fiscal rationale for the middle path: A flat 30 kg allocation would still let the Union government reduce its overall subsidy bill compared to the current 35 kg ceiling.
    4. Implementation context: Current off-take and distribution data for the financial year up to May 2026 show uneven utilisation across southern States relative to their allocations, indicating that formula design alone will not resolve execution gaps in the PDS chain.
    5. Unresolved gap: Neither the Centre’s draft nor the proposed middle path addresses the separate, State-level problem of ineligible persons remaining on beneficiary lists.

    Conclusion

    The NFSA amendment corrects a genuine per-capita inequity within the AAY category, but the household ceiling built into the new formula shifts the burden onto smaller-household southern States, reviving a federal food-politics conflict rooted in each State’s distinct PDS history. The amendment leaves the parallel problem of ineligible beneficiaries at the State level untouched, meaning one inequity is corrected while another persists. A flat per-household allocation remains a proposed middle path, but the Centre has not formally responded to it.

    PYQ Relevance

    [UPSC 2013] What are the salient features of the National Food Security Act, 2013? How has the Food Security Bill helped in eliminating hunger and malnutrition in India?

    Linkage: The PYQ examines the provisions and effectiveness of the NFSA as a rights-based framework for ensuring food and nutritional security. The proposed shift from a fixed 35 kg entitlement per AAY household to 7 kg per person, capped at 35 kg, enables a critical assessment of whether rationalising foodgrain allocation may weaken existing NFSA entitlements and affect vulnerable households unevenly.

  • Lessons for India from Brazil’s ethanol pathway

    Why in the News?

    India achieved its E20 ethanol-blending target in 2025, five years ahead of the original 2030 deadline, compressing the E5-to-E20 journey into just six years. Brazil took five decades to move from E10 to E30 blending, sequencing its mandate behind vehicle readiness and consumer price incentives at every stage.

    How does the pace of India’s ethanol-blending mandate compare with Brazil’s phased trajectory?

    1. Brazil’s blending law dates to 1931: Brazil mandated a 5% anhydrous ethanol blend in petrol in 1931. This law preceded the National Alcohol Program by over four decades.
    2. 1973 oil crisis triggered Proálcool: The 1973 global oil crisis prompted Brazil to launch the National Alcohol Program in 1975. The program aimed to cut petroleum dependence through ethanol promotion.
    3. Brazil took 50 years for E10 to E30: Brazil moved from E10 to E30 blending over five decades. The 2025 blend increase to 30% followed dedicated government studies.
    4. India compressed E5 to E20 into six years: India’s blending share rose from E5 to E20 in six years. The 10% blending milestone was reached only in 2022.
    5. India’s 20% target was front-loaded: The original 20% ethanol target was set for 2030. The government advanced this to a nationwide standard years ahead of schedule.
    6. E20 target met five years early: India reached its E20 target in 2025. Blending stood at 19.2% at that point, up from 12.1% in 2023.

    What specific Brazilian policy and institutional milestones enabled its ethanol transition?

    1. 1931 blending law set the baseline: Brazil’s first ethanol law fixed a 5% anhydrous ethanol blend in petrol. This gave the fuel market an early, low-disruption entry point for ethanol.
    2. Proálcool (1975) built institutional demand: The National Alcohol Program created sustained government-backed demand for ethanol after the 1973 oil crisis. This program anchored ethanol’s role in Brazil’s energy strategy for decades.
    3. Fiat’s 147 (1979) proved single-fuel ethanol vehicles: Italian automaker Fiat launched the 147, the world’s first vehicle powered entirely by ethanol. Volkswagen, GM and Ford followed with their own ethanol models.
    4. Flex-fuel production scaled from 2003: Volkswagen introduced Brazil’s first flex-fuel vehicle on March 23, 2003. Toyota’s flex-fuel Corolla sales rose from 48,178 units in 2003 to 1.63 million units, nearly 90% of the Brazilian car fleet, within two decades.
    5. National Biofuels Policy (2017) consolidated the regulatory framework: Brazil passed this policy to formalise its biofuel targets. It followed over four decades of incremental legislative steps.
    6. ‘Fuel of the Future’ and Mover Program (2024) targeted low-carbon vehicle technology: These laws pushed low-carbon vehicle technology and further biofuel adoption. They set the stage for the 2025 E30 mandate.

    Why has India’s flex-fuel vehicle ecosystem lagged behind its blending mandate?

    1. India has only a handful of flex-fuel models: The WagonR flex-fuel model, Toyota Hycross hybrid flex-fuel prototype, Tata Punch and Hyundai Creta flex-fuel versions form India’s flex-fuel car range. Hero and TVS have introduced flex-fuel two-wheelers.
    2. Most Indian vehicles remain unequipped for high ethanol blends: Indian roads are not geared up for handling higher ethanol blends in the fuel mix. Most cars and two-wheelers use fixed-ratio fuel systems rather than flex-fuel sensors.
    3. Flex-fuel vehicles depend on a fuel composition sensor: This sensor adjusts fuel injection and ignition timing based on the ethanol-petrol blend in the tank. It allows seamless switching between petrol, ethanol, or blends of the two.
    4. India’s E85 dispensing stations are ahead of its vehicle base: E85 fuel dispensing stations are being established nationwide. Only a few flex-fuel vehicle prototypes exist to use them.
    5. Flex-fuel certification remains an incomplete category in India: Flex-fuel vehicles require an entirely separate vehicle category and a distinct set of readiness certifications. India has completed only a fraction of this process compared with Brazil’s near-complete fleet conversion.

    Why did consumer price incentives drive Brazil’s ethanol adoption while their absence undermines India’s blending push?

    1. Brazilian pumps offer motorists a fuel choice: Nearly every Brazilian petrol pump offers a choice between blended petrol, typically E27, and E100, pure hydrous ethanol. Consumers choose whichever fuel is cheaper on a given day.
    2. Price gap made ethanol the rational choice in Brazil: E100 is typically 25-35% cheaper than lower-blended petrol in Brazil. This price gap, not the blending mandate alone, drove flex-fuel vehicle adoption.
    3. Government price support cemented flex-fuel demand: Brazilian government price support made blended fuel cheaper than petrol. Nine out of every 10 new cars sold in Brazil by the late 1980s could run on ethanol alone.
    4. Ethanol carries technical performance advantages: Ethanol improves acceleration and reduces engine knocking. This is cited as a further consumer benefit in Brazil.
    5. India offered a blending mandate without a matching price incentive or choice: Indian motorists were not offered a fuel choice at the pump. They were told performance would not be affected, without addressing fuel efficiency.
    6. Mileage was excluded from India’s performance assurance: The government’s performance assurance to motorists did not include mileage. Vehicle owners have since reported a sharp dip in fuel efficiency.

    What questions does India’s rushed ethanol rollout leave unanswered?

    1. Efficiency losses are set to increase with higher blending: Vehicle owners have noticed a fuel-efficiency dip since blending began. This efficiency loss is expected to worsen as blending increases further.
    2. Vehicle damage concerns are contested but not absent: Concerns over vehicle damage appear overstated on the whole. Plastic and rubber components in older vehicles still show degradation.
    3. India’s E20-to-E25 transition is positioned as a strategic necessity: The push to raise blending from E20 to E25, ahead of a full shift to flex-fuel vehicles and E85-E100 fuels, is described as integral to reducing fossil fuel import dependence.
    4. Import dependence frames the urgency: India imports nearly 88.5% of its crude oil requirement. This dependence exposes the country’s energy security to geopolitical disruptions.
    5. The mobility strategy remains a declared combination without a sequencing plan: An official has stated that India’s future mobility ecosystem will combine EVs, biofuels, hydrogen and renewables suited to Indian conditions. No phased sequencing comparable to Brazil’s decades-long approach has been specified.
    6. The rollout proceeded without adequate disclaimers or preparation: The blending push moved forward without adequately preparing consumers or vehicle systems. This gap, more than the blending percentage itself, is the substance of the unresolved question for India.

    Conclusion

    Brazil’s ethanol success rested on sequencing blending mandates behind vehicle readiness and consumer price incentives, sustained across five decades. India has reversed this sequence, reaching its blending target years ahead of schedule without a matching flex-fuel vehicle base or price-based consumer choice. The unresolved question is not the blending percentage itself but whether India’s vehicle certifications, fuel infrastructure and consumer disclosures can catch up to a mandate already in force.

  • What is the right to be forgotten? 

    Why in the News?

    The Delhi High Court, ruling on 29 May 2026 in Laksh Vir Singh Yadav v. Union of India, laid down India’s first structured proportionality test for the right to be forgotten. The ruling forces a direct reckoning between an individual’s right to informational privacy and the constitutional commitment to open justice and free speech.

    How did the right to be forgotten emerge, and why did Indian courts arrive at it inconsistently?

    1. Origin in EU jurisprudence: The right originated in 2014 when Mario Costeja González complained to the European Court of Justice that Google continued to display an old notice about the auction of his repossessed house even after the debt was settled.
    2. Codification in General Data Protection Regulation (GDPR): The European Court ruled in his favour. This laid the groundwork for the right to erasure, later incorporated into Article 17 of the EU’s General Data Protection Regulation.
    3. Constitutional anchor in India: The Supreme Court’s judgment in K.S. Puttaswamy v. Union of India (2017) held that privacy is a Fundamental Right under Article 21 of the Constitution of India. This includes the right to informational privacy.
    4. Divergent High Court practice: High Courts adopted inconsistent approaches after Puttaswamy. Some permitted anonymisation in limited cases, such as the Delhi High Court’s masking of names in certain matrimonial and criminal matters.
    5. The unresolved gap: Other courts rejected similar requests on grounds of open justice. No coherent framework existed to balance these competing interests before the May 2026 judgment.

    What test did the Delhi High Court lay down, and what does it require?

    1. The core issue: The Delhi High Court ruled on a batch of over 30 consolidated petitions. The central question was whether informational privacy could justify de-indexing or masking judicial records in a system committed to open justice.
    2. Constitutional source of the right: The court held that the right to be forgotten flows from Article 21’s guarantee of dignity and informational privacy.
    3. The proportionality test: Any restriction must have a legitimate purpose. The harm to privacy must be balanced against the public interest.
    4. Preference for the least intrusive means: Masking names should be preferred over deleting the entire judgment.
    5. Procedural direction: The court prescribed a two-week deadline for legal databases to comply. It clarified that only the parties’ names should be redacted, not the facts of the case.

    Why does the right to be forgotten sit in tension with open justice and free speech?

    1. Not a stand-alone right: The right to be forgotten frequently conflicts with freedom of speech and press under Article 19(1)(a), the principle of open justice, and the public’s right to know.
    2. A high threshold for privacy: A right to privacy must be sacrificed when the public interest is of a high order, particularly in serious cases of crime.
    3. The limiting principle: The digital presence of a case should not destroy a person’s life long after the trial ends.
    4. Selective, not absolute, restriction: Judgments remain publicly accessible by case number or keyword search. Only name-based searches are restricted.
    5. The unresolved concern: For an acquitted person, a name-based search can still surface the original accusation, described as the “shadow of crime,” as the first result a user sees.

    Why does enforcement remain the weakest link in this framework?

    1. Search engine design defeats masking: Search results are still generated at the search-engine level. Removing a court’s own copy does not remove all traces.
    2. Persistence beyond the primary source: Mirrors, archived copies, and social media sharing keep the original content accessible even after a court orders removal.
    3. No coordination mechanism: Effective technical compliance requires coordination among multiple platforms. No such mechanism currently exists.
    4. Consequence for the right’s value: Without platform-level compliance, the right to be forgotten remains largely symbolic rather than enforceable.

    What is the statutory basis for erasure under the Digital Personal Data Protection Act, 2023 (DPDP Act), and why is it inadequate for judicial records?

    1. Limited existing statutory right: The Digital Personal Data Protection Act, 2023 offers a limited right to erasure under Section 12.
    2. Consent-based design: This statutory right is primarily based on consent. It does not explicitly address judicial records.
    3. Scope gap: The Act does not cover public archives, where the need for a right to be forgotten is most acute.
    4. Non-operational status: The Act is deficient because its rules have not been notified.
    5. Missing institution: The data protection board contemplated under the Act has not been established.

    Who should decide erasure requests, and how should that authority be structured?

    1. The efficiency-accountability trade-off: Requiring every request to be decided by a court would create significant bottlenecks. Leaving decisions entirely to technology companies raises concerns about due process and transparency.
    2. A tiered proposal: A more sensible approach would use a tiered system.
    3. First tier: platforms: Straightforward cases could be heard directly by platforms.
    4. Second tier: data protection board: Contested cases would go to the data protection board.
    5. Third tier: courts: Judicial cases, including those with constitutional questions, would be reserved for courts.

    Conclusion

    The Delhi High Court’s ruling gives the right to be forgotten its most structured judicial articulation in India, subordinating deletion to name-masking to protect dignity without eroding open justice. This framework remains judge-made and non-statutory: the DPDP Act does not cover judicial records, the data protection board does not exist, and search engines retain wide discretion over technical compliance. Until the Supreme Court settles the doctrine nationally and a statutory institution is created to adjudicate erasure requests, the right to be forgotten in India will function more as a judicial aspiration than an enforceable entitlement.

    PYQ Relevance

    [UPSC 2024] Right to privacy is intrinsic to life and personal liberty and is inherently protected under Article 21 of the constitution. Explain. In this reference, discuss the law relating to D.N.A. testing of a child in the womb to establish its paternity.

    Linkage: The article similarly examines the Right to be Forgotten as an aspect of informational privacy under Article 21 and its balance with freedom of speech, the public’s right to know and the principle of open justice.

  • The significance of Astra missiles which Indonesia will purchase

    Why in the News?

    India and Indonesia signed a deal on July 8 for the export of Astra Mk1 beyond-visual-range air-to-air missiles (BVRAAM), marking India’s first-ever export of the indigenous Astra missile system. The deal signals India’s transition from a long-standing importer of air-to-air missile technology to a credible exporter of a combat-validated strategic weapons system. The export comes months after Operation Sindoor demonstrated the missile category’s operational relevance against Pakistan.

    What does the Astra export deal reveal about the maturity of India’s indigenous BVRAAM programme?

    1. First export milestone: The deal for Astra Mk1 to Indonesia is India’s first export of an indigenous beyond-visual-range air-to-air missile. It will arm Indonesia’s Su-30 fleet.
    2. Astra Mk1 specifications: Astra Mk1 has a range of 80 to 110 km. Its altitude reach is up to 20 km. Its speed is Mach 4.5.
    3. Platform integration: Astra Mk1 is integrated with the Sukhoi-30 MKI. It is planned for future integration with the Tejas Mk1A and the Rafale.
    4. Astra Mk2 progress: Astra Mk2 has an enhanced range of 200 km, up from a previously stated 160 km. It received Acceptance of Necessity from the Defence Acquisition Council in December.
    5. Astra Mk3 development: Astra Mk3, named Gandiva, is under development. It uses a Solid Fuel Ducted Ramjet engine that sustains thrust mid-flight instead of burning out like conventional rocket motors. Its underlying SFDR technology was flight tested this year, with a potential range beyond 350 km.

    Why does the Astra export mark a shift from import dependence to strategic self-reliance in India’s air combat capability?

    1. Combat validation: Operation Sindoor, India’s operation against Pakistan last year, demonstrated the operational criticality of longer-range BVRAAM missiles.
    2. Threat benchmark: Astra is positioned as India’s answer to the PL-15, a long-range, active radar-guided BVRAAM used by both China and Pakistan.
    3. Import substitution: The Astra programme reduces India’s dependence on imported BVRAAM systems such as the Meteor and the R-77.
    4. Procurement priority: Procuring more batches of modern BVRAAM missiles is now a stated focus area for the Indian Air Force.
    5. Export as validation: Exporting Astra to Indonesia signals external confidence in an Indian-origin weapons system. Domestic deployment alone would not carry this signal.

    What do the named foreign missile systems and export destinations show about India’s position in the global BVRAAM market?

    1. China’s PL-15: An active radar-guided, long-range BVRAAM in service with both the Chinese and Pakistani air forces. It forms the primary threat benchmark for Astra.
    2. European Meteor: A BVRAAM currently operated by the IAF as an imported system. It illustrates India’s prior reliance on foreign suppliers.
    3. Russian R-77: Another imported BVRAAM in IAF service. Astra is intended to substitute this system over time.
    4. BrahMos to Southeast Asia: India is separately set to supply the BrahMos supersonic cruise missile to Indonesia, Vietnam, and the Philippines. This indicates a broader pattern of missile exports to Southeast Asian states.

    How does the Astra-BrahMos export pattern position India in the Indo-Pacific strategic order?

    1. Common export destinations: Indonesia, Vietnam, and the Philippines are recipients or prospective recipients of Indian missile systems. All three have unresolved maritime disputes with China.
    2. Countering PL-15 proliferation: Supplying Astra to a PL-15-exposed region extends India’s indigenous missile technology as a counterweight to Chinese-origin systems in the neighbourhood.
    3. Defence diplomacy tool: Missile exports function as an instrument of strategic partnership-building beyond conventional trade or diplomatic engagement.
    4. Manufacturer credibility: Sustained export interest from multiple Indo-Pacific states strengthens India’s credibility as a defence manufacturing hub. This supports the Atmanirbhar Bharat objective in the defence sector.

    Conclusion

    The Astra Mk1 export to Indonesia marks India’s transition from importing BVRAAM technology to supplying a combat-validated indigenous system abroad. Operation Sindoor supplied the operational proof. The PL-15 threat supplied the strategic rationale. What remains unresolved is whether India’s fighter fleet can secure adequate quantities of the higher-range Mk2 and Mk3 variants quickly enough to keep pace with the systems they are designed to counter.

  • On the method of caste enumeration

    Why in the News?

    The pre-test for the second phase of Census 2027 began on July 6, 2026, in 16 States and Union Territories, using an “open column” for respondents to record their caste. The outcome of this pre-test will decide the final methodology for India’s first statutory caste enumeration since 1931.

    What has changed in this pre-test, and why does its outcome carry more weight than the 2011 exercise?

    1. Pre-test scope: The rehearsal for the second phase of Census ran in 16 States and Union Territories from July 6 to July 20, 2026, and included an open column for respondents to record their caste.
    2. Statutory shift: Unlike the 2011 Socio Economic and Caste Census (SECC), which was conducted outside the purview of the Census Act, caste in 2027 will be enumerated within the second and final phase of the Census itself, giving the count statutory backing.
    3. Methodology still open: Census officials stated that the final caste enumeration methodology will be prepared based on feedback from this pre-test, not fixed in advance.
    4. Historical gap: Caste-wise population, other than Scheduled Castes and Scheduled Tribes, has not been enumerated in independent India since the 1931 Census.
    5. Limited rehearsal access: Self-enumeration was permitted, with the portal accessible only from July 1 to 5, and only in the specific area undergoing the rehearsal.

    Why did the government finally agree to caste enumeration after years of resistance?

    1. Reversal in position: The BJP-led NDA government, after repeatedly opposing caste enumeration, announced on April 30, 2025, that caste would be counted during Population Census 2027.
    2. Opposition pressure: The Congress had consistently demanded a full caste count prior to this announcement.
    3. Coalition pressure: Some NDA allies also pushed for caste enumeration, adding pressure from within the ruling coalition.
    4. State-level precedent: Bihar’s 2022-23 caste-based survey demonstrated a working alternative model and added political momentum for a national exercise.

    Does repeating the open-column method risk reproducing the same unreliable outcome the government itself rejected?

    1. Scale of past failure: The 2011 SECC’s open-column method returned over 46 lakh distinct “caste names,” compared to only 4,147 recorded in the 1931 Census.
    2. Cause of inflation: Respondents recorded surnames or sub-castes as separate categories. For example, “Gupta” and “Agarwal” were recorded separately instead of under the common Baniya caste.
    3. Government’s own admission: In a 2021 Supreme Court affidavit, the Union government stated that the caste count “cannot be exponentially high” through genuine sub-caste bifurcation alone, and that SECC data cannot be relied on for reservation in education, employment, or local body elections.
    4. Method repeated despite the admission: The 2026 pre-test uses the identical open-ended caste column. Officials describe the method as “not final.”
    5. Structured alternative already exists: Current government data lists about 2,650 OBCs on the Central List, 1,170 Scheduled Castes, and 890 Scheduled Tribes — a far smaller, curated framework similar to the list-based model Bihar used, but not yet adopted for the national pre-test.

    What concerns have been raised about the process, and how has the government responded?

    1. Demand for consultation: Opposition parties have sought wider stakeholder consultation before the caste Census is finalised.
    2. Parliamentary question: On December 2, 2025, a Member of Parliament asked in the Lok Sabha whether the government would publish the draft Census questions for public and representative input, and whether it would consider best practices from state-level caste surveys.
    3. Government’s stated process: Minister of State for Home responded that draft questionnaires are field pre-tested before finalisation, consistent with over 150 years of Census practice that incorporates past learnings and stakeholder input.
    4. Repeated deferral through 2025: The government stated multiple times through 2025 that the final caste questionnaire had not been settled.
    5. Notification timeline unresolved: Parliament was informed in February 2026 that caste-related questions would be notified only before the commencement of the second Census phase, leaving the methodology undecided even as the pre-test proceeds.

    5. Why has the Census itself not just the caste count been delayed for over a decade?

    1. Two-phase structure: The Population Census is conducted in two phases, Houselisting and Housing Operations (HLO), and Population Enumeration, spanning over 11 months.
    2. Overdue cycle: The last Census was completed in 2011; the next was constitutionally due in 2021.
    3. Pandemic disruption: The first phase, due to begin April 1, 2020, was delayed by the COVID-19 pandemic that surfaced in India around March 2020.
    4. Unexplained continued delay: Pandemic-related restrictions had ended by 2022, but the government did not specify reasons for the delay beyond that point.
    5. Announced timeline: On June 4, 2025, the government announced that the Population Census, combined with caste enumeration, would be conducted in two phases by February 28, 2027, with the reference date and time of the headcount fixed at 12 a.m., March 1, 2027.

    Conclusion

    The 2027 Census will give caste enumeration statutory backing for the first time, closing the ambiguity that surrounded the unreleased 2011 SECC. The ongoing pre-test’s use of the same open-ended, self-declared caste column risks reproducing the unreliable, exponentially inflated caste count the government itself flagged before the Supreme Court in 2021. Whether the final methodology adopts a curated caste list, as Bihar’s survey did, or persists with the open column, will determine whether the resulting data is usable for its stated purpose of informing reservation, education, and employment policy. The government’s promise to notify questions only before the second phase begins leaves this central design choice unresolved even as the exercise proceeds.

    PYQ Relevance

    [UPSC 2020] Has caste lost its relevance in understanding the multicultural Indian Society? Elaborate your answer with illustrations.

    Linkage: The PYQ directly evaluates the contemporary relevance of caste. The decision to include caste in the 2027 Census itself reflects the continued administrative, political and socio-economic significance of caste in policymaking and governance. 

  • How India’s life insurance sector funds government expenditure

    Why in the News?

    LIC’s March 2025 regulatory filings and RBI/IRDAI data confirm that life insurers collectively hold close to a quarter of India’s outstanding central government dated securities, a share that has remained stable even as total sovereign debt expanded by around 40 per cent in three years. This scale of sovereign financing has never featured in budget speeches or parliamentary debate, even as three regulatory interventions between 2023 and 2024 compressed new insurance business and, with it, the household savings pipeline that feeds this funding base.

    Why do life insurers function as a stable, counter-cyclical source of financing for government debt?

    1. Long-duration liability match: Life insurance policies carry tenures of twenty to forty years. Government securities are the only asset class that absorbs funds of this scale at matching tenures without distorting the market.
    2. Counter-cyclical behaviour: Insurers buy and hold securities. They do not exit when oil prices rise or when a geopolitical event triggers reassessment of emerging-market exposure, unlike foreign portfolio investors (FPIs).
    3. Reduced rollover risk: A steady domestic base of long-horizon holders lowers the risk that maturing government debt cannot be refinanced on favourable terms.
    4. Lower borrowing costs: Stable demand across the maturity spectrum moderates the government’s overall cost of borrowing.
    5. Structural, not discretionary: This behaviour is not a policy choice. It is the structural consequence of insurers writing long-duration promises to millions of policyholders.

    How large and entrenched is LIC’s role as a financier of the sovereign?

    1. Sector concentration: LIC carries the dominant share of the insurance sector’s sovereign exposure, a consequence of its scale, its predominantly participating product mix, and the duration of its in-force book.
    2. Regulatory filing confirmation: LIC’s Form L-26 filing with IRDAI (March 2025) shows sovereign paper accounts for nearly 63 per cent of its non-linked policyholder corpus, well above the regulatory minimum.
    3. Absolute scale: LIC’s March 2025 IRDAI filings show ₹20.2 lakh crore held in central government securities alone, and ₹32.3 lakh crore in total government and government-guaranteed securities across all funds.
    4. Single largest holder: These figures make LIC the single largest institutional holder of Indian government debt. LIC holds approximately 19 per cent of all outstanding central government dated securities (RBI Public Debt Management Quarterly Report, FY24).
    5. Official systemic recognition: IRDAI designates LIC a Domestic Systemically Important Insurer (D-SII) every year, meaning its distress would cause significant dislocation in the financial system.
    6. Private insurers’ limited but rising role: Private insurers, with a higher share of unit-linked and shorter-tenure products, contribute a smaller fraction of sovereign holdings today. Their sovereign allocation will rise as they deepen traditional, longer-duration offerings.

    Does global practice confirm that insurers hold sovereign debt because of liability structure rather than regulatory mandate?

    1. Japan: Japanese insurers are cited among the largest holders of the government’s long-dated securities. The source gives no institution-level detail.
    2. United Kingdom: UK insurers are similarly cited as large holders of long-dated government securities. No institutional specifics are given.
    3. South Korea: South Korean insurers are cited as large holders of long-dated sovereign debt. No further detail is provided.
    4. Claimed common driver: The source attributes this pattern across all three jurisdictions to liability-profile demand rather than regulatory mandate, and states India’s insurance sector is following the same path.

    Why could recent regulatory actions on the insurance sector pose a longer-term risk to the sovereign borrowing programme?

    1. Declining penetration: India’s life insurance penetration stood at 2.7 per cent of GDP in FY25, a third consecutive annual decline from a pandemic-era peak of 3.2 per cent, and below the global life insurance average of 3.0 per cent.
    2. Three simultaneous interventions: Between 2023 and 2024, regulators restructured distribution economics, imposed taxation on certain high-value policies, and mandated product repricing.
    3. Cumulative effect exceeded individual impact: Each intervention was defensible in isolation. Their simultaneous effect compressed new business across the sector.
    4. Sector currently recovering: New business has begun recovering after this compression episode.
    5. Deferred risk to sovereign funding: Compression of new business diverts household savings away from insurance-linked government debt purchases toward shorter-duration instruments elsewhere.
    6. Lagged visibility: This effect on the sovereign borrowing programme may not be visible in the short term. It would surface over a decade.

    Why has insurance’s role as a sovereign financier remained absent from public policy discourse despite its scale?

    1. Asymmetric policy attention: Banking receives policy attention in proportion to its systemic importance. Insurance, holding close to a quarter of outstanding central government dated securities, does not receive comparable attention.
    2. Discourse framed only around households: The case for deeper insurance penetration is made almost entirely in the language of household financial protection — the uninsured family, inadequate sum assured, mis-selling, or unsettled claims.
    3. Missing fiscal-stability framing: A parallel case, framed in the language of sovereign fiscal stability, has not been fully articulated in public policy discourse.
    4. Consequence for regulatory design: Regulatory interventions aimed narrowly at consumer protection did not account for their cumulative effect on the sovereign funding base.

    Conclusion

    Life insurers, led by LIC, function as India’s most stable institutional financiers of government debt, holding close to a quarter of outstanding central government securities through structurally long-duration, counter-cyclical demand. This sovereign-financing function has never entered public policy discourse, which frames insurance regulation almost exclusively around household protection. Regulatory interventions between 2023 and 2024 that compressed new insurance business exposed this gap, since their cumulative fiscal-stability cost went unweighed at the time. Insurance regulation must begin accounting for its sovereign-funding dimension alongside consumer protection, or the effect will surface only years later as higher government borrowing costs.

    PYQ Relevance

    [UPSC 2019] The public expenditure management is a challenge to the Government of India in the context of budget making during the post-liberalization period. Clarify it.

    Linkage: The PYQ examines fiscal management and financing of government expenditure. The article shows that India’s life insurance sector acts as a major domestic financier of government borrowing by channelising long-term household savings into government securities, thereby strengthening fiscal stability and reducing dependence on volatile capital flows.

  • Will El Niño Weaken India’s Economy

    Why in the News?

    India’s monsoon has opened with a 40% rainfall deficit in June, and the India Meteorological Department has forecast a second consecutive below-normal month in July. The forecast has revived concern that a potential “super” El Niño could damage agricultural output, rural income, and food prices. India enters this season on the back of record 2024-25 foodgrain output, which is what a poor monsoon now puts at risk.

    How does a weak monsoon transmit into the broader economy beyond agriculture?

    1. Three transmission channels: A weak monsoon damages the economy through lower agricultural output, reduced rural income, and rising food prices.
    2. Cropping pattern shift: Farmers are shifting toward pulses and away from maize and vegetables. Pulses need less water and cost less to cultivate.
    3. Irrigation-linked decision-making: Planting decisions also depend on irrigation access, MSP levels, procurement support, and market conditions.
    4. Rural non-farm contraction: Non-traded rural services such as construction contract when agricultural income falls.
    5. Early economic signals: Two-wheeler and tractor sales weaken first. Real estate demand in smaller towns and cities follows.
    6. Export exposure: Agriculture exports grew at a CAGR of 8.2% between fiscals 2020 and 2025, contributing 12% of India’s core exports. A weak kharif season threatens this growth.

    Why does a weak monsoon strain macro-fiscal and external balances even before the shortfall materialises?

    1. Fertiliser subsidy commitment: The Union Cabinet approved a ₹41,533 crore Nutrient-Based Subsidy (NBS: a fixed per-nutrient, rather than per-product, fertiliser subsidy) for phosphatic and potassic fertilisers for the kharif season, covering 28 grades.
    2. Import and buffer-stock risk: A shortfall in kharif output will force the government to release buffer stocks and import commodities. This widens the Current Account Deficit (CAD: the gap between a country’s foreign exchange outflows and inflows on the current account) and pressures the rupee.
    3. Compounding supply shock: Pest pressure and fertiliser input constraints linked to the Iran conflict are adding to cost pressure ahead of the monsoon outcome.
    4. Growth cost estimate: A combined El Niño-plus-drought scenario may shave 20 to 65 basis points off GDP growth, according to Kotak Mutual Fund.
    5. RBI’s growth-inflation warning: The Reserve Bank of India’s June bulletin flagged that an adverse south-west monsoon could weigh on the domestic growth-inflation outlook.

    Why did the 2009 and 2015 El Niño years produce such different economic outcomes despite similar monsoon failure?

    1. El Niño as an imperfect predictor: Six of the eleven below-normal or deficient monsoon years since 2000 were classified as El Niño years by the IMD. Five of these six saw deficient rainfall.
    2. 2009-10 outcome: Two consecutive years of rainfall stress, combined with all-India irrigation cover below 45%, contracted crop GVA (Gross Value Added: a sector’s contribution to national output, net of input costs) by 2.5% and 3.2% in fiscals 2009 and 2010. Inflation entered double digits.
    3. 2014-15 output impact: El Niño intensified from weak to strong across these two years. Crop GVA contracted again in both years.
    4. 2014-15 price impact: Food inflation stayed muted in 2015. This differed sharply from the double-digit inflation of 2009-10.
    5. Explanation for the divergence: Proactive food management, restrained MSP hikes, and a global commodity price slump kept 2015 inflation low.
    6. What the comparison establishes: Policy response, not rainfall severity alone, determines whether an El Niño year turns into an inflation crisis.

    How exposed is India’s irrigation and storage system to a second successive weak monsoon?

    1. Vulnerable districts: 315 districts have been flagged as vulnerable to a poor monsoon. Of these, 111 districts across 12 States are of primary concern for poor irrigation facilities.
    2. Reservoir storage shortfall: Storage across the 166 reservoirs monitored by the Central Water Commission stood at 47.725 BCM (Billion Cubic Metres) on July 2. This is below both the year-ago level of 78.077 BCM and the normal level of 48.402 BCM for this time of year.
    3. Current buffer, limited margin: The present storage position can meet requirements. A second consecutive weak monsoon would strain it.
    4. Structural irrigation gap: All-India average irrigation cover remains below 45%, the same constraint that contributed to the 2009-10 GVA contraction.

    Should India replace crop insurance with ex-ante risk reduction as its primary drought response?

    1. Limits of crop insurance: Crop insurance compensates farmers after a monsoon failure. It does not reduce their underlying exposure to rainfall variability.
    2. Ex-ante alternative: Ex-ante risk reduction requires sustained public investment in irrigation infrastructure rather than compensation after the event.
    3. Seed access gap: Drought-resistant, high-yielding seed varieties are necessary. Farmers who need them most lack access to them.
    4. Investment shortfall: Public investment in irrigation and seed access has been inadequate. This explains why agricultural disaster preparedness remains poor.

    Conclusion

    A weak monsoon does not automatically translate into an economic crisis. The 2009 and 2015 El Niño years show that policy response, not rainfall alone, determines the scale of the damage. India’s current toolkit remains weighted toward reactive measures, crop insurance, buffer stock release, price management, rather than ex-ante investment in irrigation and drought-resistant seed. Until public investment shifts from compensating farmers after a bad monsoon to reducing their exposure to it, each El Niño year will continue to test the same vulnerabilities: rainfed districts, thin irrigation cover, and reservoirs running below normal.

    PYQ Relevance

    [UPSC 2023] Discuss the consequences of climate change on the food security in tropical countries.

    Linkage: The PYQ assesses the relationship between climate variability, agriculture and food security. El Niño-induced rainfall deficits directly threaten crop production, food security, rural livelihoods and agricultural sustainability, making this PYQ conceptually relevant.