Content
- Women are disproportionately exposed to ambient air pollution in India
- Great Green Wall in Andhra Pradesh to save coastline from degradation
- Centre proposes labelling of AI-generated and deepfake content on social media
- India’s roadmap in solar and space physics: Aditya-L1 and ground-based observatories
- What EPFO numbers reveal: Premature withdrawals and retirement security in India
Women and ambient air pollution in India
Context
- Studies show that women in India are disproportionately exposed to ambient and indoor air pollution compared to men.( The Lancet Planetary Health in December 2024 titled Estimating the effect of annual PM2·5 exposure on mortality in India)
- Causes: Household chores (cooking with firewood, coal, biomass), higher dependence on non-motorized transport, and more time spent near pollution sources.
- Health impact: Long-term respiratory issues, tuberculosis, stillbirths, and mortality.
Relevance:
- GS-3 (Environment & Ecology): Air pollution, PM2.5, indoor/outdoor pollution sources.
- GS-2 (Social Justice & Governance): Gender-sensitive environmental policy, health equity, public health interventions.
Basics
| Term | Explanation |
| Ambient Air Pollution | Outdoor air pollution from vehicles, industries, power plants, dust, and other sources. |
| Indoor Air Pollution | Pollution inside homes due to burning of biomass, coal, and firewood, commonly in rural areas. |
| PM2.5 | Fine particulate matter (<2.5 μm) that penetrates lungs and bloodstream, causing cardiovascular and respiratory illnesses. |
| Vulnerable Groups | Women (due to household and transport exposure) and children (respiratory vulnerability). |
Key Data & Findings
| Indicator | Data / Fact |
| PM2.5 exposure population | 1.1 billion of 1.4 billion Indians (81.9%) live above the Indian National Ambient Air Quality Standard (annual mean ≤ 40 μg/m³). |
| Mortality impact | 10 μg/m³ increase in PM2.5 → ~9% increase in mortality. |
| Deaths due to PM2.5 (2009–2019) | ~17 million deaths attributed to PM2.5 exposure. |
| Indoor pollution impact on women | Women exposed to biomass fuels >20 years → 3x higher TB risk. |
| Pregnancy outcomes | In Ahmedabad, women exposed to biomass fuels during pregnancy → 50% higher stillbirth risk. |
| Women walking to work | 45% of women walk vs. 27% of men; majority over public/non-motorized transport → higher ambient exposure. |
| Children’s risk | Deaths from lower respiratory infections: 23–44 per 100,000 children (State of Air 2024). |
Broader Societal Implications
- Women and children as vulnerable populations require gender-sensitive environmental policies.
- Exposure has long-term economic and social costs: healthcare burden, lost productivity, reduced life expectancy.
- Need for more clean cooking fuel programs, urban planning, and public transport improvements.
Existing Initiatives:
- Pradhan Mantri Ujjwala Yojana (PMUY) – promoting LPG for clean cooking.
- National Clean Air Programme (NCAP) – reducing ambient PM2.5 and PM10.
Key Takeaways
- Women bear disproportionate air pollution exposure due to traditional household roles and transport patterns.
- Indoor air pollution remains a critical yet under-addressed contributor to mortality and morbidity among women.
- Children are highly susceptible to long-term respiratory illnesses, linking air pollution to intergenerational health risks.
- Urgent need for holistic action integrating gender, health, and environmental policy.
Great Green Wall in A.P. to save coastline from degradation
Context
- Andhra Pradesh government plans to build a “Great Green Wall” along its 1,053 km coastline by 2030.
- Objective: Protect coastal communities, biodiversity, and livelihoods from increasing coastal degradation, climate change, and extreme weather events.
- The initiative aligns with India’s climate resilience and sustainable development goals.
Relevance:
- GS-3 (Environment & Ecology): Coastal resilience, climate adaptation, biodiversity, carbon sequestration.
- GS-2 (Governance & Policy): Sustainable development initiatives, disaster management, local livelihood protection.
Basics
| Term | Explanation |
| Great Green Wall | A linear or contiguous forestation belt acting as an ecological shield against natural hazards. |
| Coastal Degradation | Loss of coastal land, soil erosion, salinization, and habitat destruction due to human activity, sea-level rise, and extreme events. |
| Ecological Shield | A natural barrier (trees, mangroves, vegetation) that reduces wind, wave, and storm surge impacts. |
| Biodiversity | Variety of plant and animal species in coastal ecosystems; crucial for ecosystem services. |
Key Project Details
| Feature | Details / Data |
| Length of coastline covered | 1,053 km |
| Width of green wall | 5 km |
| Target completion | 2030 |
| Population protected | 3 million+ people |
| Key objectives | – Protect livelihoods and property – Promote biodiversity – Enhance climate resilience – Support sustainable development |
| Vegetation type | Likely coastal forests, mangroves, and salt-tolerant species (not specified yet, inferred from coastal protection norms) |
Rationale and Significance
- Climate Resilience: Coastal Andhra Pradesh faces cyclones, storm surges, and sea-level rise. The green wall will act as a buffer reducing disaster impact.
- Livelihood Protection: Fisheries, agriculture, and coastal communities will be shielded from erosion and saline intrusion.
- Biodiversity Conservation: Coastal forests and mangroves provide habitat for birds, fish, and invertebrates, supporting ecosystem services.
- Carbon Sequestration: Trees along the coast will absorb CO₂, contributing to climate mitigation targets.
- Sustainable Development: Integration with local livelihoods, eco-tourism, and ecosystem services supports SDGs 13 (Climate Action), 14 (Life Below Water), 15 (Life on Land).
Comparative Context
- Inspired by initiatives like Africa’s Great Green Wall, which combats desertification and land degradation.
- Coastal green walls are a recognized nature-based solution in climate adaptation globally (e.g., Japan, Vietnam, Netherlands).
Implementation Challenges
- Species Selection: Salt-tolerant, cyclone-resistant species needed.
- Maintenance: Survival of saplings under storm, salinity, and human pressure.
- Land Acquisition & Community Participation: Securing 5 km wide continuous stretches along densely populated areas.
- Monitoring & Data Management: Need for satellite and GIS-based monitoring of growth, biodiversity, and coastal erosion.
Centre proposes labelling of AI-generated and deepfake content on social media
Context
- The Ministry of Electronics and Information Technology (MeitY) has proposed draft amendments to the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021.
- Objective: Tackle deepfakes and AI-generated content on social media platforms, ensuring users are aware of algorithmically generated content and misinformation is controlled.
- The amendments will also increase government accountability when issuing content notices to social media platforms.
Relevance:
- GS-2 (Governance & Technology): Regulation of social media, IT Act, administrative accountability.
- GS-3 (Science & Technology): AI governance, digital ethics, misinformation control, cyber policy.
- GS-4 (Ethics): Transparency, accountability, ethical oversight in governance.
Key Provisions of the Draft Amendments
Accountability of Government Officers
- Notices under Rule 3(1)(d) will now require reasoned intimation.
- Senior officials only:
- Central government: Joint Secretary and above
- State level: Deputy Inspector-General and above
- Notices must clarify:
- Safe harbour does not apply
- It is a warning, not an immediate takedown order
Significance: Reduces arbitrary or unconstitutional use of content takedown powers; improves transparency and legal safeguards.
AI Content Labelling
- Platforms allowing AI-generated content (e.g., X, Instagram, YouTube, ChatGPT, Sora, Google Gemini) must:
- Identify AI-generated content
- Label deepfake content
- Attach permanent metadata/unique identifiers
- Two labels proposed:
- AI-generated content
- Deepfake content
Objective: Prevent misinformation, manipulation, and user deception, especially during elections or communal tensions.
Compliance and Enforcement
- Platforms may lose legal immunity under Section 79 of the IT Act if non-compliant.
- Obligations for platforms:
- Identify and label AI/deepfake content
- Take down flagged content within 24 hours
- Publish monthly compliance reports
- Enable user complaints and voluntary labelling
Expert Oversight
- An expert committee is constituted to finalize rules.
- Consultation includes government officials, tech experts, and academics.
Significance: Brings technical expertise to governance, ensuring rules are implementable and future-ready.
Background and Challenges
- Deepfakes are digitally manipulated media that appear authentic, creating risks to:
- Personal privacy
- Political processes
- Public trust in information
- Social media firms previously challenged Rule 3(1)(d) as arbitrary and unconstitutional, but courts upheld government authority.
- Challenges in enforcement:
- Accurately detecting AI-generated content
- Fast-moving content spread
- Balancing freedom of expression with misinformation control
Key Data & Facts
| Feature | Provision / Requirement |
| Rule impacted | Rule 3(1)(d) of IT Rules 2021 |
| Seniority of officials issuing notice | Joint Secretary+ (Central), DIG+ (State) |
| Platforms in scope | X, Instagram, YouTube, ChatGPT, Sora, Google Gemini |
| AI/deepfake labelling | Mandatory with permanent metadata |
| Compliance timeline | 24 hours for flagged content |
| Reports | Monthly compliance reports by platforms |
| User participation | Option to label own content as AI-generated |
Policy Implications
- Strengthens governance: Senior officials accountable for content notices.
- Mitigates misinformation: Labels and metadata improve user awareness.
- Technological oversight: Ensures AI/deepfake detection becomes a standard responsibility of platforms.
- Democracy protection: Reduces risk of election manipulation and communal disinformation.
- Private sector collaboration: Platforms need to deploy algorithmic detection and reporting systems, boosting innovation in AI for social good.
India’s roadmap in solar and space physics: Aditya-L1 and ground-based observatories
Context
- Indian astronomers recently published an overview of current solar and space physics in the Journal of Astrophysics and Astronomy.
- The paper highlights key challenges in solar research and India’s roadmap for the next decade, including ground- and space-based initiatives.
- India’s Aditya-L1 mission and upcoming facilities like the National Large Solar Telescope are central to this effort.
- Emphasis on developing prediction models for solar flares and CMEs to protect space assets and terrestrial infrastructure.
Relevance:
- GS-3 (Science & Technology): Space research, solar physics, CME prediction, technological self-reliance.
- GS-3 (Infrastructure & National Security): Protection of satellites, communication, power grids, and defense assets.
Basics of Solar Phenomena
| Term | Definition | Key Fact |
| Solar Flare | Sudden massive explosion on sun; energy release from twisted magnetic fields | Emits across radio, X-ray, gamma rays |
| Coronal Mass Ejection (CME) | Large plasma discharge from sun’s corona | Can disrupt satellites, power grids |
| Solar Wind | Continuous outflow of charged particles from corona | Interacts with Earth’s magnetosphere |
| Coronal Loops | Plasma constrained along magnetic field lines | Visible in solar imaging, indicate magnetic activity |
Importance: Space weather affects satellites, communication, navigation, astronauts, and power grids. Understanding these phenomena is critical for technological and national security.
Indian Initiatives
Space-Based Observatories
- Aditya-L1 (ISRO, Sep 2023):
- Positioned at Lagrange Point 1 (L1), 1.5 million km from Earth.
- L1: Sun–Earth line; detects CMEs moving toward Earth.
- High-resolution imaging & spectra of solar atmosphere.
- Proposed expansion: spacecraft at L4 and L5 points for triangulated 3D tracking of solar eruptions.
- L4: 60° ahead, L5: 60° behind Earth in orbit.
- Challenge: Data transmission over 30 million km.
Significance: Dual/multiple spacecraft network allows accurate prediction of CME trajectories and improved space weather forecasting.
Ground-Based Facilities
- National Large Solar Telescope (2-meter class):
- To observe lower solar atmosphere at high resolution.
- Cannot be deployed in space due to size.
- Indian Institute of Astrophysics (IIA) and other institutions lead these efforts.
Human Resource and Community Development
- 229 early-career Indian researchers involved globally; 65 faculty/scientists in India.
- Initiatives: ISRO + ARIES workshops for students and researchers.
- Goals:
- Train young talent in data analysis & simulation.
- Develop national supercomputing facilities for computational astrophysics.
- Expand academic programs, faculty, public engagement, and industry partnerships.
Technological and Strategic Significance
- Private sector involvement encouraged in India’s space sector.
- Satellites, rockets, space weather modeling.
- Innovation in predictive models for solar storms.
- Self-reliance (Aatmanirbhar Bharat) in understanding solar-terrestrial relationships.
- National security and infrastructure protection: Accurate CME predictions can safeguard power grids, communication networks, and satellite-based defense assets.
Key Data & Figures
- Lagrange Points in Sun-Earth System: 5 points (L1 to L5).
- Aditya-L1 distance: 1.5 million km from Earth (L1).
- L4 & L5 distance from Earth: ~30 million km.
- Community involvement: 229 early-career researchers + 65 faculty.
Challenges in Solar Physics
- Incomplete understanding of CME-solar wind interaction.
- Poorly defined magnetic structures of CMEs → affects trajectory prediction.
- Emergence of magnetic fields under sunspots → complicates solar flare prediction.
- Data-heavy modeling requires supercomputing resources.
Future Vision (10–15 Years)
- Development of state-of-the-art prediction models for solar flares and CME arrival times.
- Expansion of triangulated space observatories (L1, L4, L5).
- Strengthened ground-based solar research infrastructure.
- Integration of private sector and industry partnerships.
- Focus on training next-generation solar physicists and computational astrophysicists.
What EPFO Numbers Reveal
Why in News ?
- The Employees’ Provident Fund Organisation (EPFO) has proposed easing withdrawal norms to make it simpler for workers to access funds for essential needs (like illness, education, or unemployment).
- However, high withdrawal rates during employment are depleting what should serve as post-retirement security, prompting EPFO to propose changes in 2025–26.
Relevance:
- GS-2 (Governance & Social Policy): Social security, EPFO regulations, labour welfare.
- GS-3 (Economy): Pension architecture, financial literacy, informal workforce challenges, policy reforms.
Key Issue
- Frequent premature withdrawals erode the retirement corpus meant for long-term financial security.
- EPFO data (FY 2017–2025) shows a sharp rise in withdrawals for illness, education, marriage, and unemployment.
Data Insights from EPFO (2017–2025)
1. Withdrawals for Essential Needs
Trend:
- 16-fold increase in illness-related withdrawals (2017–25).
- 3.5x increase in marriage/education-related withdrawals.
- Indicates withdrawals are becoming routine rather than emergency-based.
Overview
- Steady increase in final settlements shows rising job exits or migration.
- Nearly 95% of settlements are due to unemployment, not retirement.
Employment Profile & Structural Concerns
- 65% of EPFO members earn less than ₹15,000/month.
- Average member balance < ₹20,000; nearly 75% have < ₹50,000.
- Indicates a low-wage, informalized workforce with poor long-term savings capacity.
- Over 3 crore contributing members; 7 crore accounts but only ~2.6 crore active.
Implication:
- Large proportion of accounts remain dormant.
- Low-income earners withdraw repeatedly for short-term needs, eroding pension benefits.
Causes for Frequent Withdrawals
- Health emergencies (especially post-COVID-19).
- Marriage and education expenses (cultural and social priorities).
- Unemployment spells and job insecurity.
- Lack of financial literacy—workers view EPF as savings, not as pension.
- Ease of partial withdrawal norms post-2017 reforms.
EPFO’s 2025 Proposal
- Current rule: Minimum 2 months unemployment required before settlement.
- Proposed change: Cut to 1 month, but limit withdrawal to 25% of balance to preserve long-term savings.
- Aims to balance liquidity needs vs. pension protection.
Broader Socio-Economic Implications
- Financial insecurity in old age: Early withdrawals deplete pension corpus.
- Labour market fragility: Reflects short job tenures, retrenchments, and informal transitions.
- Policy challenge: Need to design instruments combining liquidity + longevity protection (e.g., NPS-EPFO convergence).
- Women and low-income workers particularly vulnerable due to intermittent employment.
Conclusion
- India’s retirement savings architecture is weak — less than 10% of workforce has formal social security.
- EPFO withdrawals ≈ financial stress index — spikes correspond with economic disruptions (e.g., COVID-19, layoffs, inflation).
- The 2025 reform proposal aligns with ILO’s Decent Work Agenda and SDG 8 (Decent Work and Economic Growth) by promoting financial resilience and social protection.
