Content Exploring Extremes: A Landmark Year of Discoveries by India’s Ministry of Earth Sciences Intergenerational Bonds Exploring Extremes: A Landmark Year of Discoveries by India’s Ministry of Earth Sciences Why is it in News? Year-End Review 2025 of the Ministry of Earth Sciences (MoES) highlights a landmark year of scientific ‘firsts’ with direct socio-economic impact. Achievements span deep-ocean exploration, weather forecasting, disaster resilience, polar science, desalination, supercomputing, and urban climate services, aligned with India’s Vision 2047. Relevance: GS III (Science & Tech, Disaster Management, Environment): Deep-sea mining, HPC-based forecasting, tsunami warning systems. GS II (Governance): Science-to-society delivery, inter-institutional coordination (MoES–NDMA–States). Science with Measurable Human Impact Cost–Benefit Breakthrough (Third-party Audit): Investment: ~₹1,000 crore (Monsoon Mission + HPC). Economic Returns: ~₹50,000 crore (50:1 return). Beneficiaries: ~11 million BPL families—small farmers & fisherfolk using daily weather/ocean advisories. One of India’s first quantified ROI audits of scientific public spending. Breaking Records: Deep & Dark Oceans Deep-Sea Mining Trial: Successful test at 5,270 m depth—deepest such test globally. Strategic relevance: critical minerals, Atmanirbhar Bharat, UNCLOS-linked seabed exploration. Samudrayaan Mission: MATSYA human submersible cleared comfort & stability tests. Indian scientists reached 5,002 m depth in the Atlantic (international collaboration) → new benchmark for Indian oceanography. Coasts, Islands & Blue Economy Lakshadweep Water Security: 3 eco-friendly desalination plants commissioned. Flagship: 1.5 lakh litre/day LTTD plant at Chetlat (NIOT). Make in India – Ocean Research Fleet: Indigenous vessels Sagar Tara & Sagar Anveshika deployed for ocean health monitoring. Disaster Readiness: Tsunami Early Warning Centre monitored 32 major earthquakes in 2025—zero missed threats to Indian shores. Weather, Climate & Computing Power Mission Mausam & IMD Vision 2047: Launched 14 Jan 2025 to future-proof weather–climate services. Supercomputing Leap: HPC capacity enhanced to ~21 PFlops → high-resolution coupled weather–climate models among the world’s best. Forecast Infrastructure: Doppler Weather Radars inaugurated at Raipur & Mangalore (27 Nov 2025). Urban Climate Services: UES25 platform (NSM-funded) integrates weather, air quality, urban flood intelligence for municipalities & disaster managers. Polar, Ocean & Earth System Science Push NCPOR Infrastructure (22 May 2025): Polar Bhavan (11,378 sqm; ₹55 cr): advanced labs + Science on Sphere (South Asia’s first Polar & Ocean Museum—Phase I). Sagar Bhavan (1,772 sqm; ₹13 cr): ice labs & cleanrooms. Polar Science Leadership: 4th National Conference on Polar Science (Sept 2025): 265 participants; 160 young researchers. ESSO Review (Shillong | 19 Dec 2025): Roadmap alignment with Vision 2047 across weather, climate, ocean, Earth systems. Technology, Labs & Capacity Building Underwater Acoustics: Acoustic Test Facility designated as national laboratory (12 Apr 2025). Ocean Sensors: India’s first conductivity & temperature sensor calibration facility at NIOT (11 Feb 2025). Advanced Geochemistry: Q-ICP-MS Lab at NCESS (30 Oct 2025). AI in Aquaculture: 10 m submerged open-sea cage with AI/ML-based fish biomass estimation deployed in Andaman (17 Apr 2025). Governance, Safety & Global Engagement Heat Action Plans: Co-developed with NDMA + States to reduce heat mortality. SAHAV Platform: Released at UN Ocean Conference-3 as a global model for tech-enabled ocean governance. Extended Continental Shelf: Multi-channel seismic surveys via ONGC to strengthen India’s ECS submissions under UNCLOS. Atmospheric Electricity & Extremes: 9th National Lightning Conference focused on resilience to lightning–extreme weather linkages. Strategic Takeaways Science-to-Society Model: Weather & ocean science delivering quantified welfare gains. Blue Economy + Security: Deep ocean capability + tsunami vigilance enhance economic & strategic autonomy. Data-Driven Governance: HPC, AI, and integrated platforms (UES25) mainstream predictive governance. Vision 2047 Alignment: Institutions, infrastructure, and talent pipelines positioned for long-term resilience. Conclusion 2025 establishes MoES as a global-standard Earth System Science ministry—where frontier research, indigenous technology, and public welfare converge with measurable returns. Intergenerational Bonds Why is it in News?  The Department of Social Justice & Empowerment organised “Celebration of Intergenerational Bonds” on 22 December 2025 at Chhatarpur, Madhya Pradesh, reinforcing India’s policy push on active, dignified ageing and social cohesion. Relevance   GS II – Governance & Social Justice: Senior citizen welfare, inclusive policies, community participation. GS I – Society: Family structure changes, ageing population, value transmission. Policy Context: Why Intergenerational Bonds Matter ? Demographic Transition: India’s elderly (60+) population projected to rise from ~10% (2021) to ~20% by 2050 (UN estimates). Social Challenge: Urbanisation, nuclear families, and migration are weakening traditional family-based elder support. Governance Imperative: Shift from welfare-only to participatory ageing—elders as contributors, not dependents. Key Government Interventions Highlighted  1. Rashtriya Vayoshri Yojana (RVY) Objective: Assistive devices for mobility, vision, hearing to promote independent living. Coverage: 7.28 lakh+ senior citizens benefited nationwide. Governance Insight: Links health, dignity, and productivity of elders. 2. Elderline 14567 Function: 24×7 toll-free support—guidance, distress response, emergency assistance. Utilisation: 27 lakh+ calls received. Significance: First structured national-level grievance & support ecosystem for elders. 3. Community & School-Based Outreach Grandparents’ Day in Schools: Institutionalises intergenerational value transmission at early ages. Cultural & Community Programmes: Reduce loneliness, improve mental health, and strengthen social capital. Conceptual Framework Active Ageing (WHO): Optimising health, participation, and security to enhance quality of life as people age. Intergenerational Solidarity: Mutual exchange of experience (elders) and energy/innovation (youth) → balanced social development. Social Capital Theory (Putnam): Strong community bonds → higher trust, cooperation, and governance outcomes. Outcomes & Significance Social: Reduced generational divide; improved empathy and mutual respect. Psychological: Tackles elder loneliness; enhances youth social sensitivity. Institutional: Demonstrates soft-governance tools beyond cash transfers. Normative: Reinforces elders as mentors, custodians of values, and nation-builders. Critical Takeaway India’s elder-care strategy is evolving from assistance-based welfare to engagement-based governance. Programmes like Celebration of Intergenerational Bonds operationalise constitutional values of dignity, fraternity, and inclusiveness, making ageing a shared societal responsibility, not a private burden. Conclusion: Intergenerational harmony is no longer a cultural ideal alone—it is emerging as a core pillar of India’s social policy architecture under Viksit Bharat@2047.

Content AI Diffusion, Not Chip Dominance: India’s Real AI Advantage End the exploitation  AI Diffusion, Not Chip Dominance: India’s Real AI Advantage  Why is it in News? A widely cited op-ed by Samir Saran, President of the Observer Research Foundation (ORF), argues that India’s real AI opportunity lies beyond chips and data centres, at a moment when: India is scaling IndiaAI Mission (₹10,300+ crore), Global AI geopolitics is fragmenting into Compute-rich vs Compute-poor blocs, Policymakers are debating sovereignty, regulation, and economic capture in AI. Relevance GS III – Science & Technology / Economy AI as a General Purpose Technology (GPT). Industrial policy vs innovation ecosystems. Digital Public Infrastructure as growth multiplier. GS II – Governance Role of state in technology diffusion. Regulatory capacity in emerging technologies. Practice Questions “Artificial Intelligence as a General Purpose Technology rewards diffusion more than invention.”Examine this statement in the context of India’s AI strategy. (250 words) Core Thesis of the Article Compute (chips, data centres) is necessary but not sufficient. India’s comparative advantage lies in AI diffusion, applications, and governance, not in winning the chip arms race against the US or China. Three Distinct AI Phases Identified  1. Compute Era Dominated by: Advanced semiconductors (≤5 nm chips), Hyperscale data centres, Capital-intensive infrastructure. Reality Check for India: Global AI compute market dominated by US firms (NVIDIA, AMD, hyperscalers). Cutting-edge fabs require $10–20 billion per plant and long gestation. India currently imports >85% of high-end chips. Inference: Competing head-on here offers low returns, high dependency risks. 2. Diffusion Era (India’s Sweet Spot) Focus shifts from who builds models to who deploys them at scale. Involves: AI adoption across health, agriculture, logistics, MSMEs, governance. Integration with existing digital public infrastructure (DPI). India’s Structural Advantages: Population-scale platforms: Aadhaar (1.3+ bn), UPI (≈12 bn transactions/month in 2024–25), CoWIN, DigiLocker, ABHA. Cost-efficient innovation: Lowest marginal cost of digital delivery globally. Talent pool: ~1.5 million engineering graduates annually. Precedent: India led global diffusion of digital payments without owning core hardware. 3. Value Creation Era Economic value accrues not to model builders alone, but to: Domain-specific AI solutions, Workflow integration, Localised, trusted AI systems. Example logic: LLM ≠ value, LLM + sectoral data + regulation-aware deployment = value. Key Warnings in the Article Mistaking LLMs for the entire AI stack: Models are commodities over time. Differentiation lies in use-cases, data pipelines, and institutional embedding. Over-centralised AI policy: Risk of stifling innovation if regulation precedes diffusion. Copy-paste Western regulation: EU-style heavy ex-ante AI regulation may be misaligned with India’s developmental needs. Policy Prescriptions Suggested (Implicit & Explicit) 1. Strategic Compute, not Maximal Compute Secure baseline sovereign compute for: Research, Critical public services, Strategic sectors. Avoid prestige-driven chip nationalism. 2. State as Market-Maker Government to: Anchor demand via public procurement, Enable sandboxes for AI deployment in welfare, justice, climate, urban governance. Historical parallel: UPI succeeded because state created rails, private sector built innovation. 3. Regulate Outcomes, not Innovation Focus on: Accountability, Bias, Safety in high-risk use cases. Avoid regulating models in abstraction. Critical Evaluation Strengths Realistic assessment of India’s constraints. Shifts debate from hardware fetishism to developmental outcomes. Anchored in India’s proven DPI success model. Limitations Underplays long-term strategic risks of compute dependency. Requires high-quality state capacity to avoid diffusion without accountability. Conclusion India’s AI race is not about owning the fastest chips, but about deploying intelligence at population scale. If the 20th century rewarded those who controlled factories, the 21st will reward those who control platforms, workflows, and trust. The article reframes AI from a geopolitical arms race into a governance and development challenge—where India holds asymmetric advantage. End the exploitation  Why is it in News? The Supreme Court of India, in a 19 December 2025 judgment, termed child trafficking a “deeply disturbing reality” in India. The Court upheld convictions under the Immoral Traffic (Prevention) Act (ITPA) in a Bengaluru case involving sexual exploitation of a minor by organised trafficking cartels. The ruling comes amid persistently low conviction rates, despite multiple anti-trafficking laws and institutions. Relevance GS II – Polity & Governance Protection of vulnerable sections. Role of judiciary in rights enforcement. Police and criminal justice reforms. GS I – Society Child rights, social evils, organised crime. Impact of poverty, migration, and urbanisation. Practice Questions Despite a robust legal framework, conviction rates in child trafficking cases remain abysmally low in India. Analyse the structural and institutional reasons for this gap. (250 words) Key Judicial Observations (Doctrinal & Practical) Nature of Crime: Child trafficking is a grave violation of dignity, bodily integrity, and Article 21 protections. Operates through multi-layered organised networks: recruitment → transport → harbouring → exploitation. Victim-Centric Jurisprudence: A trafficked child is not an accomplice. Testimony of a minor victim must be treated as that of an “injured witness”. Evidentiary Standards: Courts must show “sensitivity and latitude”. Minor inconsistencies in testimony cannot be grounds for disbelief, given trauma and age. Bench: Justices Manoj Misra and Joymalya Bagchi. Scale of the Problem: Data Snapshot Human Trafficking Cases (2018–2022): 10,659 cases (as informed by the Ministry of Home Affairs to Parliament) Conviction Rate:~4.8% Indicates a severe enforcement and prosecution gap, not absence of law. Forms of Child Exploitation: Sexual exploitation (dominant in urban networks), Forced child labour, Domestic servitude, Begging rackets, Online grooming and trafficking via digital platforms. Legal & Institutional Framework (India) Substantive Laws: Immoral Traffic (Prevention) Act, 1956. Juvenile Justice (Care and Protection of Children) Act, 2015. Bonded Labour System (Abolition) Act, 1976. Child Labour (Prohibition and Regulation) Amendment Act, 2016. Constitutional Mandate: Article 23: Prohibition of trafficking. Article 39(e) & (f): Protection of children from abuse and exploitation. Institutional Gaps: Anti-Human Trafficking Units (AHTUs) exist on paper in many districts but suffer from: Understaffing, Poor training, Weak coordination with NGOs and prosecutors. Why Laws Are Not Enough ? Low Conviction Rates: Weak investigation, Hostile witnesses, Poor victim protection during trial. Rehabilitation Deficit: Rescue often ends with one-time compensation. Inadequate focus on: Long-term psychological care, Education continuity, Skill development. Federal & Inter-State Complexity: Trafficking networks operate across states; policing remains largely state-bound. Digital Transformation of Crime: Use of social media, messaging apps, and encrypted platforms for: Grooming, Recruitment, Sale and movement of victims. Prevention Lens: What the Editorial Emphasises Education as Prevention: Enforce Right to Education Act promise of schooling up to 14 years. School retention is among the strongest safeguards against trafficking. Community & Civil Society Role: Early warning systems, Community vigilance, NGO–police collaboration. Need for Comprehensive Anti-Trafficking Law: A standalone, modern Anti-Trafficking Act with: Victim-centric procedures, Inter-state investigation powers, Time-bound trials. Critical Takeaway India’s child trafficking challenge is no longer a legal vacuum problem, but a governance, enforcement, and rehabilitation failure. The Supreme Court has clarified the judicial approach; the remaining burden lies with: Executive capacity, Police professionalism, Prosecutorial sensitivity, Civil society participation. Conclusion: Child trafficking will not end with harsher laws alone—it demands a coordinated ecosystem of prevention, sensitive justice, and long-term rehabilitation, with the child placed firmly at the centre of the response.

Content India’s First National Anti-Terror Policy Jnanpith Award & the Demise of Vinod Kumar Shukla The Upskilling Gap: Why Women Risk Being Left Behind by AI How India’s Exports Are Concentrated in a Few States Rhino Dehorning and the Decline in Poaching Made-in-Tihar Products Go Online India’s First National Anti-Terror Policy  Why is it in News? Union Government finalising India’s first comprehensive National Counter-Terrorism Policy and Strategy. Inputs consolidated by the Ministry of Home Affairs with operational feedback from National Investigation Agency. NIA anti-terror conference (26–27 December, New Delhi) to outline policy contours. Relevance GS III – Internal Security Terrorism and counter-terrorism strategies Role of intelligence agencies (NIA, IB, NSG) Border management (India–Nepal open border) Terror financing, digital radicalisation, identity fraud Strategic Context India lacked a unified anti-terrordoctrine; counter-terror responses have been: Statute-based (UAPA, NIA Act) Agency-driven (NIA, IB, NSG) Incident-reactive rather than prevention-centric Contrast: National Policy & Action Plan on LWE (2015) → integrated security-development model. Terrorism domain lacked an equivalent pan-India template. Key Threat Vectors Driving the Policy Digital Radicalisation (High Priority) Shift from physical indoctrination to algorithm-driven online recruitment. NIA interrogation after Nov 10 car-borne suicide attack near Red Fort: Perpetrators radicalised entirely online. Identified risks: Encrypted messaging platforms Social media micro-targeting Foreign-hosted servers beyond Indian jurisdiction Institutional gap: Very limited number of trained cyber-radicalisation spotters at police-station level. Foreign-Funded Conversion & Radicalisation Networks Intelligence inputs point to: Overseas religious centres acting as ideological nodes. Suspected linkages with Pakistan’s ISI. Pattern: Funding → conversion → ideological grooming → terror facilitation. Policy likely to integrate: Financial intelligence Social media monitoring NGO & charity oversight (within constitutional limits). Open Border Exploitation (Nepal Corridor) India–Nepal border: ~1,750 km Visa-free, largely unfenced Reported modus operandi: Khalistani operatives enter Nepal on foreign passports. Discard passports → enter India illegally → move via UP–Bihar corridor to Punjab. Policy focus: Border intelligence fusion Joint surveillance with Nepal Technology-enabled profiling (without border closure). Aadhaar Spoofing & Identity Fraud Emerging threat: Synthetic identities used for SIM cards, bank accounts, logistics. Links to: Arms trafficking Drug-terror financing nexus Requires coordination between: UIDAI Financial Intelligence Units State police cyber cells. Institutional Architecture Being Integrated Core Agencies National Investigation Agency – federal investigations, terror financing, international linkages. National Security Guard – tactical response, hostage rescue, urban counter-terror. Intelligence Bureau – threat anticipation, radicalisation tracking. State ATS & Special Branches – ground-level intelligence. Technology Backbone National Intelligence Grid (NATGRID): Secure access to 20+ databases (immigration, banking, telecom, vehicle, travel). Shift from post-event investigation → pre-emptive detection. Policy Orientation: From Reaction to Prevention Old Approach Proposed Policy Shift Incident-led response Intelligence-led prevention Central agency dominance State-centric capacity building Post-attack prosecution Early detection & disruption Fragmented data Integrated data grids Elite-unit focus Police station-level vigilance Federal Dimension Policy designed as a template, not command-and-control. States consulted post-Pahalgam terror attack (April 22). Emphasis on: Training local police Standard operating procedures (SOPs) Shared best practices across States. Significance for Internal Security (GS III) First attempt at doctrinal clarity in counter-terrorism. Acknowledges non-traditional threats: digital ecosystems, identity fraud, ideological financing. Balances: National security Federal autonomy Civil liberties (critical for judicial sustainability). Likely Challenges Online radicalisation vs freedom of speech. Inter-state coordination asymmetries. Capacity gaps at thana level. Managing foreign policy sensitivities (Canada, Nepal). Conclusion The proposed policy marks India’s transition from event-driven counter-terrorism to ecosystem-based prevention. If implemented effectively, it can become the internal security equivalent of the LWE framework (2015)—but success hinges on State-level absorption, training depth, and tech-human integration. Jnanpith Award & Demise of Vinod Kumar Shukla Vinod Kumar Shukla, celebrated Hindi poet and novelist, passed away at age 88 in Raipur. He was the 2024 Jnanpith Award recipient — India’s highest literary honour. First writer from Chhattisgarh to receive the Jnanpith Award. Relevance GS I – Art & Culture Indian literature and literary institutions Regional language contributions (Hindi literature) Cultural diversity and non-metropolitan voices Prelims Jnanpith Award: year, nature, eligibility, administering body First Jnanpith awardee from Chhattisgarh Vinod Kumar Shukla Born: 1937, Rajnandgaon (present-day Chhattisgarh). Literary career began: 1971. Known for: Minimalist language Poetic treatment of everyday life Quiet subversion of power, class, and alienation. Major Works Poetry Lagbhag Jaihind (debut, 1971) Kavita Se Lambi Kavita Novels Naukar Ki Kameez Adapted into a critically acclaimed film. Deewar Mein Ek Khidki Rehti Thi Literary style often compared with: Post-Nayi Kavita humanism Everyday realism rather than ideological grand narratives. Awards & Recognitions Jnanpith Award (2024) Sahitya Akademi Award Numerous state and national recognitions for poetry and fiction. Jnanpith Award: Instituted: 1961 First awarded: 1965 Administered by: Bharatiya Jnanpith (trust founded by Sahu Jain family). Eligibility: Indian citizens Works in any 8th Schedule language. Nature: Awarded for overall literary contribution, not a single book. Award Components Cash prize: ₹11 lakh Citation Bronze replica of Saraswati, Hindu goddess of knowledge. Significance Considered the literary equivalent of the Bharat Ratna. Recognises lifetime achievement and cultural impact. Conclusion Vinod Kumar Shukla’s death is not only a personal loss but a civilisational moment for Hindi literature. His 2024 Jnanpith Award symbolised the recognition of simplicity, empathy, and everyday realism as enduring literary values. The Upskilling Gap: Why Women Risk Being Left Behind by AI Why is it in News? Recent analysis based on India’s latest Time Use Survey (2024) highlights a structural time poverty faced by women, raising concerns that the AI-driven future of work may deepen gender inequality. As India pushes AI-led growth through initiatives like the India AI Mission, evidence shows women lack time, access, and flexibility required to upskill for AI-era jobs. Aligns with broader debates on: Automation risks Right to disconnect Gender budgeting India’s Viksit Bharat @2047 vision. Relevance GS I – Society Gender roles and unpaid care work Time poverty and gender inequality GS II – Governance Gender budgeting Social infrastructure (childcare, transport, water, energy) Women’s Workload & Time Poverty Labour force participation (women): ~40% (2024). Average daily work (paid + unpaid): Women: ~9.6 hours/day Peaks at 70–80 hours/week for ages 25–39. Key driver: ~40% of women outside the labour force cite household & caregiving responsibilities. Nature of work increase: Over 80% of recent rise in women’s workforce participation comes from: Unpaid family work Low-paid self-employment Informal, low-productivity jobs. Gender Gap in Paid vs Unpaid Work Across the Life Cycle Men: Total work: 54–60 hours/week Unpaid work: minimal and stable across ages. Women: Total work exceeds men at almost all ages. 25–39 age group: Women spend 2× more time on unpaid caregiving than men. Childcare is the largest component. Even in later life: Men’s unpaid work rises marginally (elderly care), Structural inequality at home persists across income, occupation, and age. AI-Specific Risks for Women 1. Higher Automation Exposure Women overrepresented in: Routine, clerical, low-skill service jobs Informal and home-based work These roles are more automation-prone under AI adoption. 2. Algorithmic Bias AI-driven productivity metrics: Ignore caregiving interruptions Penalise time constraints Reward uninterrupted, long-hour availability Care work remains invisible to algorithms. 3. Upskilling Time Deficit Women spend ~10 hours less per week than men on: Learning Skill enhancement Self-development Gap widens to 11–12 hours/week in prime working years. Result: Limited transition from low-skill to high-value AI-linked jobs. Health & Well-being Costs Women sleep 2–2.5 hours less per week than men during peak working years. Time adjustment happens at the cost of: Rest Mental health Physical well-being Long-term impact: Lower productivity Higher burnout Reduced career longevity. Policy & Governance Solutions Highlighted 1. Time-Centric Policy Design Shift from job-counting to outcome-based employment metrics. Explicit use of time-use data in: Labour policy Skill missions AI governance. 2. Gender Budgeting as an Enabler Integrate time-use indicators into gender budgeting. Prioritise sustained spending on: Affordable childcare Elderly care services Piped water Clean cooking energy Safe public transport. 3. AI-Era Upskilling for Women Design lifelong, flexible, modular skilling: Local delivery Hybrid / online formats Low time-intensity learning Scale targeted programmes: India AI Mission AI Careers for Women Focus on: Digital literacy Applied AI tools Locally relevant vocational tech skills. Conclusion AI will not automatically empower women; without time-sensitive policy design, it may entrench inequality. Until women’s time is valued, freed, and integrated into growth strategy, India’s AI ambitions and Viksit Bharat @2047 vision will remain constrained by: Invisible labour Time poverty Underutilised human capital. How India’s Exports Are Concentrated in a Few States Why is it in News? Recent analysis using the RBI Handbook of Statistics on Indian States 2024–25 shows India’s export growth is increasingly concentrated in a handful of States, raising concerns about: Regional inequality Jobless export growth Breakdown of the traditional export–industrialisation–employment link Despite a weakening rupee and record export values, export-led development is not translating into broad-based industrial employment. Relevance GS III – Indian Economy Export-led growth model Industrialisation and jobless growth Capital deepening and labour absorption GS I – Regional Development Inter-State disparities Core–periphery model Core Export Concentration: Top 5 exporting States: Maharashtra Gujarat Tamil Nadu Karnataka Uttar Pradesh Share of national exports: ~65% (5 years ago) ~70% now Implication: National export aggregates mask severe regional divergence. Rising Geographic Concentration (HHI Evidence) Export geography measured using Herfindahl–Hirschman Index (HHI): Rising HHI → increasing concentration. Pattern emerging: Core–periphery structure Coastal western & southern States → tightly integrated into global supply chains. Northern & eastern hinterland → decoupling from export growth. Agglomeration logic: Firms prefer existing industrial clusters due to: Logistics efficiency Supplier ecosystems Skilled labour pools Global Context: Why Convergence Is Failing ? Shrinking Global Trade Window World Trade Organization data: Merchandise trade volume growth slowed to 0.5–3% band. UN Trade and Development (2023): Top 10 exporters control ~55% of global merchandise trade. Consequence: Latecomers face entry barriers. Global capital now seeks complexity, not just cheap labour. Shift from Volume to Value Economic Complexity Trap Modern exports cluster around dense product spaces: Automobiles Electronics Precision machinery These sectors: Require advanced logistics Depend on accumulated industrial capabilities Regions exporting low-complexity goods face: High barriers to upgrading Weak backward–forward linkages. Export Growth ≠ Employment Growth Capital Deepening Evidence Annual Survey of Industries (ASI) 2022–23: Fixed capital growth: +10.6% Employment growth: +7.4% Fixed capital per worker: ₹23.6 lakh Indicates: Rising capital–labour ratio Factories becoming less labour-absorptive. Manufacturing Employment Stagnation Periodic Labour Force Survey (PLFS): Manufacturing employment share: Stuck at ~11.6–12% Despite record export values. Interpretation: Employment elasticity of exports has collapsed. Exports are generating value, not mass jobs. Capital Bias & Wage Compression ASI data shows: Wage share in Net Value Added (NVA) declining. Productivity gains in: Petrochemicals Electronics Gains accrue disproportionately to capital owners. Outcome: High industrial GDP growth Limited mass prosperity Spatial Stickiness of New-Age Exports Electronics exports (PLI-driven): ~47% YoY growth Locked into: Kancheepuram (TN) Noida (UP) Reason: High supply-chain complexity Precision logistics unavailable in hinterland districts. Financial Divide: Credit-Deposit Ratios Coastal vs Hinterland RBI Credit–Deposit (CD) ratios: Tamil Nadu, Andhra Pradesh: >90% Local savings reinvested locally. Bihar, eastern Uttar Pradesh: <50% Savings mobilised but lent elsewhere. Effect: Capital flight from hinterland to coast Reinforces regional divergence. Structural Diagnosis Exports no longer act as: A bridge from agriculture → industry A mass employment generator Instead, exports are now: An outcome of prior structural capacity A mirror of accumulated industrial wealth. Policy Implications Why Old Assumptions Fail ? Export-led growth ≠ labour-intensive industrialisation. India bypassing East Asian trajectory of: Low-skill manufacturing Broad middle-class creation. Need for New Metrics Export growth ≠ inclusive development. Policy must track: Employment elasticity Wage share Regional diffusion Otherwise: Risk mistaking outcomes for instruments. Bottom Line India’s export success is real but narrow. Without correcting: Capital bias Financial asymmetry Human capital gaps Export growth will deepen regional inequality rather than resolve it, making inclusive industrialisation increasingly elusive. Rhino Dehorning & Poaching Decline  Why is it in News? A peer-reviewed study published in Science reports that rhino dehorning led to a near-elimination of poaching in African wildlife reserves. The study analysed 7 years of data (2017–2023) from 11 reserves in South Africa’s Greater Kruger ecosystem, home to the world’s largest rhino population. Findings challenge the dominance of technology-heavy anti-poaching strategies and reframe conservation economics. Relevance GS III – Environment & Conservation Wildlife protection strategies Anti-poaching models Biodiversity conservation GS II – Governance Evidence-based policymaking Institutional capacity vs incentives Global Rhino Status: Global rhino population (2024): < 28,000 (all five species combined). Major threat: Poaching for horns, driven by illicit international demand. Greater Kruger losses: 1,985 black & white rhinos killed (2017–2023). ~6.5% population loss per year, despite heavy surveillance. Anti-poaching expenditure: ~$74 million spent on: Armed patrols Tracking dogs AI cameras Aerial surveillance. Core Findings of the Study Impact of Dehorning 2,284 rhinos dehorned across 8 reserves. Poaching outcomes: 75% reduction compared to pre-dehorning levels. 78% drop where dehorning was implemented rapidly (1–2 months). 95% lower poaching risk for dehorned rhinos vs horned rhinos. Cost efficiency: Achieved using only 1.2% of total anti-poaching budgets. Methodology Data type: Quarterly poaching records (2017–2023). Analytical method: Hierarchical Bayesian regression modelling. Comparison: Dehorned vs non-dehorned reserves. Before–after intervention analysis. Outcome: Strong causal inference rather than correlation. Why Dehorning Works ? Economics of Poaching Rhino horn: Composed of keratin (same as hair & nails). No proven medicinal value. Illicit market value: $874 million – $1.13 billion (2012–2022), per Wildlife Justice Commission. Removing horns: Eliminates primary incentive, not the animal. Behavioural Reality of Poachers Killing the rhino allows: Faster removal No resistance Dehorned rhinos: Offer minimal reward Increase risk–reward imbalance for poachers. Limits of Enforcement-Only Models Arrests and patrols showed limited deterrence due to: Corruption Weak prosecution Cross-border trafficking loopholes Surveillance ≠ prevention when incentives remain intact. How Dehorning Is Done (Animal Welfare) ? Conducted by veterinarians: Sedation, blindfolding, earplugs. 90–93% of horn removed, above the germinal layer. Horn regrows naturally. Stump sealed to prevent infection. Considered non-lethal and reversible. India–Africa Contrast African Context Large landscapes. High-value illicit trade routes. Enforcement stretched thin. Indian & Nepali Model India & Nepal do not dehorn. Losses: 1–2 rhinos in last 3 years. Kaziranga National Park success drivers: Smart patrolling Community participation Local intelligence Role of Local Communities & Rangers Research involved: 1,000+ hours of workshops with rangers. Rangers: Often local residents. Hold critical ecological knowledge. Study highlights: Ranger welfare (pay, safety, training) is as vital as technology. Conservation Economics:   Dehorning shifts strategy from: Policing supply → collapsing incentive. Represents preventive conservation, not reactive enforcement. More cost-effective than high-tech surveillance alone. Conclusion Rhino dehorning is not a silver bullet, but it is: Highly effective Cost-efficient Data-validated The study redefines conservation success: Remove incentives, not just criminals. Policy lesson: Conservation outcomes improve when economics, ecology, and local capacity align. Rhinoceros   Species & Distribution Five species globally: White, Black (Africa); Greater one-horned, Javan, Sumatran (Asia). India hosts the Greater one-horned rhinoceros, mainly in Assam (Kaziranga, Pobitora). Conservation Status IUCN: Javan & Sumatran – Critically Endangered Black – Critically Endangered Greater one-horned – Vulnerable White – Near Threatened Global population (2024): < 28,000. Major Threats Poaching for horn (illegal trade worth ~$0.9–1.1 billion, 2012–22). Habitat loss, fragmentation, and human–wildlife conflict. Biology & Horn Rhino horn is made of keratin (same as hair and nails); no proven medicinal value. Used for digging, defence, and mating displays. Made-in-Tihar Products Go Online  Why is it in News? Delhi Prison Administration plans to sell Made-in-Tihar products on major e-commerce platforms such as Flipkart and Amazon. Marks a shift from offline-only sales (jail canteens, courts, exhibitions) to nationwide digital marketplaces. Aimed at improving inmate rehabilitation, skill utilisation, and post-release employability. Relevance GS II – Governance & Social Justice Prison reforms Rehabilitation of convicts and undertrials Reformative vs retributive justice What Are “Made-in-Tihar” Products? Produced by inmates inside Tihar Jail, South Asia’s largest prison complex. Product range includes: Food items: cookies, mustard oil Handicrafts: bags, footwear Household items: furniture, paper products Around 13 categories currently marketed. Scale of the Programme  Inmate workforce: ~5,000 inmates engaged daily in manufacturing and vocational work. Production units: Multiple industrial workshops across Tihar Jail complex. Revenue generation: ₹2.42 crore turnover in FY 2023–24. Average inmate earnings: ₹412 per day (credited to prison accounts). Skill coverage: About 70 different products across food processing, carpentry, tailoring, and handicrafts. Economic Dignity of Prison Labour Shifts narrative from: “Prison labour” → “Correctional industry”. Supports constitutional values: Article 21 (right to live with dignity). Reinforces Supreme Court guidance on: Fair remuneration Voluntary skill-based work. Governance & Implementation Aspects Sales via: Government-approved accounts on platforms. Branding: “Made-in-Tihar” already has recall value due to: Quality perception Ethical consumption appeal. Oversight: Delhi Prison Department ensures: No forced labour Wage crediting Product quality control. Comparative Context Similar initiatives: Open prisons in Rajasthan Prison handicraft programmes in Kerala & Maharashtra. Distinction: Tihar is among the largest prison-based industrial ecosystems in India. Challenges & Caveats Pricing competitiveness with private brands. Logistics and supply consistency. Ensuring: Non-exploitation Transparency in revenue sharing. Need for: Skill certification linkage with NSQF Post-release job placement pipelines. Conclusion Taking Made-in-Tihar products online transforms prison labour into a scalable rehabilitation model. If implemented with safeguards, it can: Humanise incarceration Generate ethical livelihoods Recast prisons as institutions of correction, not exclusion.