Current Affairs – February 28, 2026

{GS1 – Geo} El Niño Reclassification Amid Rising Global Temperatures

• Context (TH): Scientists have updated the classification criteria for El Niño and La Niña as rapid global warming alters ocean temperature baselines and energy imbalance patterns.

Change Made in the Labelling Criteria

• Oceanic Niño Index (ONI): NOAA, which tracks ENSO using the ONI based on east-central equatorial Pacific SST deviations, has modified how anomalies are calculated to filter out global warming effects.
• Baseline Method Shift: Instead of comparing SSTs to a fixed 30-year average (e.g., 1991–2020), NOAA now uses a rolling 30-year baseline updated every 5 years, making the reference period non-stationary.
• Objective of New Method: The revised rolling baseline subtracts the long-term warming trend, enabling a clearer distinction between natural ENSO variability and anthropogenic climate change signals.

Need for Changing the Labelling Criteria

• Baseline Warming Shift: Rising global sea surface temperatures have altered historical reference baselines, requiring recalibration of El Niño–La Niña thresholds.
• Energy Imbalance Link: A recent study attributes nearly 75% of the surge in Earth’s energy imbalance to the combined effect of anthropogenic warming and ENSO phase transition.
• Triple-Dip La Niña Effect: The unusual 2020–2023 triple-dip La Niña suppressed surface heat release, followed by a sharp warming spike.

Implications for Climate Policy

• Forecasting Complexity: Changing ENSO baselines complicate seasonal climate prediction models.
• Heatwave Intensification: Warmer baseline temperatures amplify El Niño-driven extreme heat events.
• Adaptation Urgency: Regions dependent on monsoon cycles must adjust climate resilience planning.

{GS2 – Governance} Proposal for a Productivity Commission in India **

• Context (BS): NITI Aayog Vice-Chairman Suman Bery suggested rebranding NITI Aayog as a “Productivity Commission” to align India’s demographic dividend with the Viksit Bharat 2047 target.

Arguments for a Productivity Commission for India

• Dividend Risk: India’s working-age population will grow for ~25 more years. Without productivity reform, this surge risks structural unemployment over income growth.
• Labour Gap: China’s labour output per hour is nearly double that of India. A Productivity Commission can design targeted sectoral reforms to close this gap.
• AI Resilience: The commission can identify “no-regret” reforms that safeguard workers regardless of how AI reshapes employment.
• MSME Deficit: Over 90% of MSMEs are informal and lack the technology benchmarking and fiscal support that a Productivity Commission can deliver.
• Planning Horizon: Viksit Bharat sets a per capita income target of ~$18,000 by 2047, up from ~$2,700 today. NITI Aayog’s medium-term mandate cannot sustain the long-term planning it requires.

Arguments Against a Productivity Commission for India

• Institutional Duplication: A new commission can create direct overlap with the existing mandates of NITI Aayog and the Economic Advisory Council to the Prime Minister.
• Enforceability Deficit: The Commission would have only advisory powers, leaving its recommendations without binding force on the executive.
• Data Blindspot: Around 90% of India’s workforce is employed in the unorganised sector. This denies the commission the firm-level data required for rigorous benchmarking.
• Automation Risk: Prioritising output-per-worker efficiency accelerates capital-intensive automation, undermining the MoLE’s objective to expand labour-intensive employment.
• Federal Friction: Since labour is included in the Concurrent List, states are likely to oppose central productivity directives that conflict with their own labour priorities.

Way Forward

• Labour Reform: Accelerate state-level implementation of India’s four Labour Codes to ease the compliance burden and promote MSME formalisation.
• MSME Clusters: Establish technology cluster centres so small firms can access modern tools and capital they cannot afford independently.
• Female Workforce: Build childcare and safe urban transport infrastructure to integrate India’s ~46 crore working-age women into formal employment.
• Productivity Accounts: Publish annual productivity accounts, disaggregated by sector and state, to identify output gaps that require targeted reform.
• Skilling Reorientation: Reorient India’s higher education system away from examinations toward vocational skilling to align graduate output with industry demand.

{GS2 – IR} India–Germany Climate Resilience Initiative *

• Context (TNIE): Germany announced a €20 million Large Grant project for India under the International Climate Initiative (IKI) to strengthen ecosystem-based climate resilience.

About International Climate Initiative (IKI)

• Launch: In 2008, the Government of Germany launched a flagship international climate finance instrument to support developing and emerging economies.
• Objective: Aims to promote climate mitigation and climate adaptation, aligning with global commitments under the Paris Agreement and the Convention on Biological Diversity.
• Funding Mechanism: Financed through Germany’s federal budget and climate-related revenue streams, supporting projects across Asia, Africa, and Latin America.

Key Features of the India–Germany Climate Resilience Initiative

• Geographical Focus: Targets high-risk ecosystems including the Himalayas, Western Ghats, North-East India, island regions, and Lower Gangetic floodplains.
• Ecosystem-Based Adaptation (EbA): Promotes Forest restoration, biodiversity corridors, flood and erosion control, groundwater recharge, and community-led resource management.
• Support to National Adaptation Plan (NAP): Strengthens monitoring, evaluation, and learning frameworks linked to India’s upcoming NAP.
• Innovative Financing: Explores blended finance, biodiversity credits, and climate risk insurance mechanisms to mobilise sustainable climate funding.

{GS3 – IE} Revision of Base Year of Merchandise Trade Indices

• Context (PIB): Directorate General of Commercial Intelligence and Statistics (DGCI&S) revised the base year of India’s Merchandise Trade Indices from FY 2012–13 to FY 2022–23.

About Merchandise Trade Indices

• Definition: Merchandise Trade Indices statistically measure changes in unit values (prices) and quantities (volumes) of India’s exports and imports across different time periods.
• Compiled By: Prepared, compiled, and officially published by the Directorate General of Commercial Intelligence and Statistics (DGCI&S) under the Ministry of Commerce & Industry.
• Core Purpose: Serve as key macroeconomic indicators tracking external sector price movements, trade competitiveness dynamics, and evolving terms of trade conditions.
• Methodological Basis: Constructed using the Laspeyres Index Formula, applying fixed weights derived from trade values of the selected base year.
• Types of Indices:
  • Unit Value Index (UVI): Captures relative price-level movements
  • Quantity Index (QI): Reflects volume-level changes independent of prices

Key Features of Revised Merchandise Trade Indices

• Revised Commodity Basket: Commodity coverage has been rationalised at the Principal Commodity level, enabling better representation of emerging trade items and declining product categories.
• Updated Weighting Structure: Weights have been recalibrated using the latest trade value shares, improving statistical reliability and representational accuracy.
• Methodological Refinements: Improvements introduced in basket selection, handling of missing unit values, and index compilation practices in line with international standards.
• Expanded Index Coverage: The revised series now includes monthly, quarterly, annual, Standard International Trade Classification (SITC), Broad Economic Categories (BEC), bilateral & region-wise indices.
• Terms of Trade Measurement: The revised framework comprehensively incorporates Gross Terms of Trade (GTT), Net Terms of Trade (NTT), and Income Terms of Trade (ITT).
  • NTT: Export Price Index ÷ Import Price Index → Reflects relative price advantage.
  • GTT: Import Quantity Index ÷ Export Quantity Index → Reflects volume exchange ratio.
  • ITT: NTT × Export Quantity Index → Reflects purchasing capacity of exports.

{GS3 – Envi} India’s Energy Shift Through the Green Ammonia Route **

• Context (TH): Green ammonia is emerging as one of the earliest scalable derivatives of green hydrogen, linking India’s clean energy transition with industrial decarbonisation

Importance of Energy Shift Towards Green Ammonia

• Hydrogen Carrier Advantage: Ammonia contains 17.6% hydrogen by weight, making it one of the most efficient chemical carriers for long-distance hydrogen storage and maritime transport.
• Industrial Decarbonisation Potential: Ammonia production currently accounts for nearly 1.8% of global CO₂ emissions, indicating large decarbonisation gains.
• Energy Security Imperative: India imports roughly 85% of its natural gas, a key input for grey ammonia, exposing fertiliser economics to global price volatility.
• Multi-Sectoral Fuel Role: The global ammonia market exceeds 180 million tonnes annually, with growing projections for use in shipping and power generation.

India’s Green Ammonia Auction-Based Approach

• Demand Aggregation: Solar Energy Corporation of India Limited (SECI) aggregated demand for 724,000 tonnes per annum of green ammonia across 13 fertiliser plants, enabling economies of scale.
• Long-Term Certainty: 10-year fixed-price contracts created predictable revenue streams, a critical enabler for capital-intensive projects.
• Competitive Pricing Outcome: Discovered prices ranged between ₹49.75 – ₹64.74/kg ($572 – $744/tonne), significantly narrowing the gap with grey ammonia (~$515/tonne).
• Broad Investor Participation: The auction attracted 15 bidders, reflecting growing private sector confidence in India’s green hydrogen ecosystem.
• Global Benchmarking: Auction outcomes reportedly achieved 40–50% lower prices than comparable international procurement mechanisms.

Challenges Ahead

• Cost Competitiveness Gap: Green ammonia continues to remain 20–40% more expensive than grey ammonia across most global markets.
• Electrolyser Economics Constraint: Electrolysis contributes nearly 60–70% of total green hydrogen production costs, making ammonia pricing heavily dependent on electrolyser efficiency.
• Infrastructure & Logistics Limitations: India handles over 300 MTPA of cargo at ports, yet specialised green ammonia storage ecosystems remain limited.
• Financing & Bankability Risks: Green ammonia projects are characterised by high upfront CAPEX, requiring long-tenor debt structures and predictable revenue models.

Way Forward

• Policy Stability: Establish predictable, long-term regulatory clarity to reduce investor uncertainty; E.g., multi-year subsidy visibility under India’s SIGHT programme within the NGHM.
• Financing Innovation: Develop blended finance mechanisms to lower capital costs and enhance project bankability; E.g., risk-sharing structures inspired by the European Union’s H2Global programme.
• Infrastructure Development: Invest in storage, transport, and port handling facilities for ammonia logistics. E.g. Coastal green fuel hubs and ammonia bunkering terminals.
• Scale Economies: Expand auction-based demand models to accelerate cost reductions through scale efficiencies; E.g., South Korea’s Clean Hydrogen Portfolio Standard (CHPS) bulk clean fuel adoption.

{GS3 – S&T} Training Large Language Models in India

• Context (TH): Indian firms are increasingly investing in domestic Large Language Model (LLM) development, reflecting strategic priorities of technological self-reliance.

• A key technological shift is the emergence of the Mixture of Experts (MoE) architecture, which significantly reduces inference costs without proportionate increases in compute intensity.

Structural Constraints in Indian LLM Development

• Data Scarcity: Limited availability of high-quality Indian-language datasets affects accuracy, contextual understanding, and domain adaptability.
• Capital Constraints: High computing and infrastructure costs pose barriers for startups lacking immediate commercial deployment pathways.
• Adoption Sensitivity: Suboptimal Indian-language performance risks weakening usability across governance, education, and rural technology ecosystems.

Government Support in Indian LLM Development

• Compute Infrastructure Expansion: The IndiaAI Mission commissioned over 36,000 GPUs, strengthening domestic capacity for AI training and inference workloads.
• Targeted Model Subsidy: Sarvam AI received access to 4,096 GPUs, with government support estimated at nearly ₹100 crore, lowering training barriers.
• Strategic Ecosystem Push: The Ministry of Electronics and Information Technology promotes domestic LLM development to strengthen AI self-reliance and language adaptability.

Mixture of Experts (MoE) Architecture

• Selective Parameter Activation: MoE models dynamically activate only specialised subsets of parameters during inference, significantly reducing computational load and latency.
• Inference Cost Reduction: By avoiding full-model activation, MoE architectures lower energy consumption, memory utilisation, and per-query compute expenses.
• Scalable Model Design: MoE frameworks enable the training of larger models while preserving operational efficiency across resource-constrained environments.
• Breakthrough Significance: MoE architecture represents a major innovation in AI system design, enabling faster responses, improved efficiency, and economically viable scaling.

{Prelims – Geo} Predicting Lethal Monsoon Moist Heatwaves for Climate Adaptation *

• Context (DTE): A recent study links lethal moist heatwaves during India’s Southwest Monsoon to predictable weather patterns, enabling forecasts up to four weeks in advance.

About Moist Heatwaves

• It is an extreme weather event that occurs when high temperatures combine with high humidity.
• They are strongly modulated by the Boreal Summer Intraseasonal Oscillation (BSISO).
• The BSISO is an atmospheric system that moves across South and Southeast Asia in 30–90-day cycles.
• It creates alternating active (heavy rainfall) and break (dry conditions) phases of the monsoon.
• Heatwave Shift: Northern and northwestern India face a higher risk of moist heatwaves during active phases, while southern and eastern India become vulnerable during break phases.
• Health Implications: It can lead to hyperthermia, heat exhaustion and fatal heatstroke as high humidity severely inhibits sweat evaporation, disrupting natural cooling.
• Measurement: Meteorologists utilise the Wet-Bulb Temperature (WBT) as the primary metric for assessing the severity of moist heat.

Read More > Wet Bulb Temperature | Heatwaves

{Prelims – S&T – Disease} About Avoidant or Restrictive Food Intake Disorder (ARFID) *

• Context (TH): Studies highlight the need to distinguish Avoidant or Restrictive Food Intake Disorder (ARFID) from general ‘picky eating’ to prevent misdiagnosis and malnutrition.

• ARFID is a recognised mental health and eating disorder characterised by a persistent failure to meet daily nutritional requirements.
• Causes: The disorder is driven by genetic predispositions, intense sensory aversions to food textures, or traumatic events like vomiting and choking.
• It differs from anorexia, caused by a distorted perception of body image and fear of weight gain.
• Affected Group: ARFID mainly affects children and adolescents, with higher prevalence in boys.
• Impact: Avoiding entire food groups causes severe physical complications like stunted growth and iron deficiency. Inability to participate in shared meals leads to profound psychosocial distress.
• Treatment Approach: Recovery requires coordinated care integrating psychiatrists, psychologists, clinical dietitians, and paediatricians.
• India’s Initiatives: Centre addresses such disorders through the Mental Healthcare Act, 2017; the National Mental Health Programme (NMHP); and Tele-MANAS counselling.

Read More > Mental Health in India

{Prelims – S&T} National Science Day 2026

• Context (TH): India celebrates National Science Day every year on February 28.

• It marks the discovery of the Raman Effect by Sir C.V. Raman (1928), for which he received the Nobel Prize in Physics (1930).
• The day propagates the daily application of science to cultivate a scientific temper and accelerate national development.
• The theme of National Science Day 2026 is “Women in Science: Catalysing Viksit Bharat“.
• It highlights the role of women-led scientific progress to make India a developed nation by 2047.
• Focus Areas: Reducing gender gaps in STEM, indigenous technologies (like IndiaAI and BharatGen), and sustainable development.