Content

1. To tap India’s clean energy potential , a to-do list
2. Model conduct

To tap India’s clean energy potential , a to-do list

Why in News ?

• The editorial discusses India’s emerging clean-energy manufacturing ecosystem and the structural bottlenecks limiting its full potential — particularly in solar, transmission infrastructure, financing, and green hydrogen deployment.
• It highlights how domestic solar manufacturing capacity has expanded sharply in 2024, aided by the PLI scheme and TOPCon technology adoption, but warns that policy gaps, grid constraints, and financial stress in DISCOMs could stall momentum.

Relevance  

• GS-III | Economy, Infrastructure, Environment
  • Renewable energy transition, domestic manufacturing ecosystem, grid & DISCOM reforms.
• GS-II | Governance & Policy
  • Regulatory stability, investment climate, public–private risk management.

Practice Question

• “India’s clean-energy transition is constrained less by capacity creation and more by grid, financing and institutional bottlenecks.” Discuss with reference to transmission constraints, DISCOM stress and investment risk in the renewable energy sector.(250 Words)

From Basics — India’s Clean Energy Shift

• Past dependence on imports
  • India long relied on Chinese solar modules & cells to meet renewable expansion.
• Current transition
  • Domestic firms added ~25.3 GW of new module capacity in 2024, nearly doubling national manufacturing strength.
• Policy catalyst
  • PLI Scheme → promotes indigenous manufacturing & upstream integration.
• Technological upgradation
  • Shift toward TOPCon and higher-efficiency modules → signals move from assembly to innovation-led manufacturing.

Core Issues Highlighted in the Editorial  

1) Import Dependence Shifting — Not Eliminated

• Upstream integration weak:
  • Only ~2 GW wafer capacity vs ~40× higher module production.
• Risk → dependence shifts from modules to wafers & polysilicon (new vulnerability).

2) Policy Uncertainty & Contractual Risks

• Frequent tariff changes, duty tweaks, and retrospective renegotiations after auctions.
• Developers & financiers face uncertain revenue outlook, hurting investments.

3) Transmission Bottlenecks 

• ~60 GW renewable capacity stuck / under-utilised due to:
  • Inadequate grid connectivity
  • Curtailment by grid operators
• Result:
  • Clean power cannot flow where needed
  • Developers often get no compensation, weakening project viability.

4) DISCOM Financial Stress

• Unpaid dues & weak balance sheets affect:
  • PPA enforcement
  • Payment discipline
  • Investor confidence in RE projects

5) Renewable Finance Risk Premium

• India’s renewable financing costs are nearly 80% higher than advanced economies due to:
  • Grid risks
  • Curtailment uncertainty
  • Regulatory unpredictability

6) Green Hydrogen — Promise vs Reality

• National Mission target → 5 million metric tonnes per year by 2030.
• Challenges:
  • High costs ($4.1–$5.0/kg vs conventional hydrogen)
  • Demand creation uncertain
  • Sectors like steel, refining, transport face retrofit risks & no guaranteed offtake.
• Likely needs:
  • subsidies, price-support, contracts-for-difference, clearer transmission planning.

Strategic Significance

• Energy security → reduce import dependence in critical technologies.
• Industrial competitiveness → build full value-chain (polysilicon → wafers → modules).
• Climate commitments → enable Net Zero & RE targets.
• Geo-economic leverage → positioning India as global clean-energy manufacturing hub.

Actionable To-Do Priorities 

• Strengthen upstream manufacturing
  • Incentivise polysilicon & wafer production, not just module assembly.
• Stabilise policy & contracts
  • Reduce retrospective changes; ensure predictable tariff framework.
• Accelerate grid expansion
  • Invest heavily in transmission corridors & storage integration.
• Fix DISCOM finances
  • Enforce payment discipline, reform tariff structures.
• De-risk renewable investments
  • Curtailment compensation norms; credit enhancement mechanisms.
• Green hydrogen roadmap clarity
  • Demand-side guarantees, price support models, export strategy.

Model conduct

Why in News ?

• The editorial examines India’s evolving approach to regulating Artificial Intelligence (AI) — currently anchored in existing IT, financial, privacy and data-protection rules — and argues that India lacks a dedicated consumer-safety / duty-of-care framework for AI-driven harms, especially psychological harms.
• It contrasts India’s approach with China’s newly-proposed rules for emotionally interactive AI systems, and calls for India to improve AI capabilities, expand access to compute, upskill its workforce, and regulate high-risk AI use cases without stifling innovation.

Relevance

• GS-III | Science & Tech, Internal Security, Economy
  • AI governance, responsible innovation, digital infrastructure, financial-sector risk management.
• GS-II | Governance & Regulation
  • Duty of care, consumer protection, privacy, regulatory design, state capacity.

Practice Question

• “India currently regulates AI through adjacent legal frameworks rather than a unified duty-of-care regime.” Examine the strengths and limitations of this approach, especially in the context of psychological and behavioural harms.(250 Words)

From Basics — India’s Current AI Governance Approach

• Regulatory basis (indirect / adjacent regulation)
  • IT Act & IT Rules → platform due-diligence, deepfake and fraud response, labelling synthetic content.
  • Privacy & Data Protection rules → control over data use and consent.
  • Financial-sector governance
    • RBI — model-risk governance & FREE-AI framework for credit-risk and AI adoption.
    • SEBI — accountability norms for AI use by regulated market entities.
• Nature of regulation so far
  • Largely reactive and sector-specific, not a unified AI-product-safety regime.
  • No explicit duty of care for psychological or behavioural harms from AI systems.

Global Contrast — China’s ‘Emotional AI’ Rules

• Draft rules require platforms to:
  • Warn users about excessive reliance / addictive use
  • Intervene when signs of emotional distress appear
• Benefit → addresses psychological dependence & behavioural harms.
• Risk → could push platforms toward intrusive emotional surveillance / monitoring.

India’s stance

• Less intrusive but incomplete — avoids over-surveillance, but does not directly address AI-safety harms beyond unlawful content or fraud.

Core Policy Challenges Highlighted

• Capability gap
  • India has a large AI-adoption ecosystem, but lags the U.S. & China in frontier-model development.
• Regulate-first, build-later risk
  • Over-regulation without domestic capacity may increase technological dependency.
• Fragmented oversight
  • Controls downstream harms, but not model-safety obligations across high-risk deployments.
• Psychological & behavioural harms
  • AI systems influencing emotions, dependence, or decision-making remain weakly governed.

Strategic Way Forward — Two-Track Approach Suggested

1) Build Capability & Ecosystem Strength

• Improve access to computational resources (compute, GPUs, public cloud capacity)
• Upskill workforce — AI engineering, safety, evaluation, governance.
• Increase public procurement of AI — anchor demand, scale domestic solutions.
• Translate research → industry — support labs, startups, academia–industry bridges.
• Avoid paralysis by consensus — faster execution to prevent dependency.

2) Regulate Downstream Use — Without Choking Innovation Upstream

• Focus on high-risk AI contexts (finance, health, welfare delivery, biometric systems, employment screening).
• Add obligations to existing privacy / consumer-protection frameworks:
  • Incident-reporting duties for model failures, bias, safety breaches.
  • Monitoring & response requirements for harmful model behaviour.
  • Accountability & transparency norms for deployed systems.
• Prefer risk-based governance over emotion-monitoring mandates or intrusive surveillance.

Strategic Significance for India

• Balances innovation and safety — avoids over-broad surveillance-led regulation.
• Supports technological self-reliance — compute access + talent + public demand.
• Protects citizens in high-risk deployments — without deterring domestic model development.
• Positions India as a pragmatic regulator — use-case regulation > model-control mandates.