Content:
- The saga of regulating India’s thermal power emissions
- Should India build a sovereign, foundational AI model?
The saga of regulating India’s thermal power emissions
Background
- On December 30, 2024, the Ministry of Environment, Forest and Climate Change (MoEFCC) amended the Environment Protection Rules, extending the deadline for compliance with SO₂ emission norms for thermal power plants by three years.
- This decision affects approximately 20 GW of thermal power capacity located in densely populated areas.
- The delay is part of a long-standing trend of extending emission compliance deadlines, dating back to the first notification in 2015.
Relevance : GS 2(Governance) , GS 3(Environment)
Practice Question : Discuss the role of environmental governance in addressing frequent extensions of SO2emission deadlines for thermal power plants and also Highlight challenges and implications of these extensions. (250 Words)
Evolution of Emission Norms
- In December 2015, the MoEFCC introduced stringent norms for thermal power plants, including limits on particulate matter (PM), SO₂, and other emissions.
- Initially, compliance was required by December 2017, but multiple extensions have followed.
- The norms were designed to align with international standards (e.g., China, Australia, the US).
Challenges and Delays
Debate on Implementation Strategies:
- Indian coal has lower sulphur content, which should have made compliance easier.
- However, the debate shifted towards the high cost and logistical challenges of Flue Gas Desulphurization (FGD) systems, even though FGDs were never explicitly mandated.
Government Agencies’ Conflicting Views:
- The Central Electricity Authority (CEA) (2020, 2021) questioned uniform SO₂ norms and proposed a phased approach till 2035.
- IIT Delhi (2022) study found that while FGDs improve air quality, concerns about high costs, supply chain issues, and increased coal consumption were raised.
- CSIR-NEERI (2024) study, commissioned by NITI Aayog, argued that SO₂ norms are less critical for air quality improvement compared to PM emissions.
Multiple Deadline Extensions:
- The December 2024 amendment is the fourth extension.
- Compliance deadlines now vary by location, emission type, and plant category.
- Despite SO₂ deadlines being pushed to 2027, deadlines for PM and other emissions were set for December 2024.
Financial and Environmental Consequences
- Consumer Cost Burden:
- Many power plants have already tendered FGDs, with costs being passed to consumers via electricity tariff adjustments.
- Even if FGDs are installed, plants may not use them to avoid increasing power generation costs, leading to economic inefficiencies.
- Health and Environmental Impact:
- Prolonged SO₂ emissions contribute to acid rain, respiratory illnesses, and secondary aerosol formation.
- The extension means people living near thermal plants will continue to suffer from air pollution.
- Regulatory Gaps:
- Pollution control boards’ enforcement of existing norms remains unclear.
- There is no publicly available data to verify compliance.
Conclusion
There is growing urgency in addressing SO2 emission along with other air pollutants , extending deadlines may not address the issue sustainably . Situation to be analysed comprehensively before taking further steps .
Should India build a sovereign, foundational AI model?
Introduction
The rise of artificial intelligence (AI) has led to global discussions on the importance of foundational AI models. Foundational models, like those powering ChatGPT and DeepSeek, require significant investment in computing power, talent, and infrastructure.
India is at a crossroads in deciding whether to build its own sovereign AI model or rely on existing open-source and proprietary alternatives.
Relevance : GS 3(Technology)
Practice Question: Should India develop a sovereign foundational AI model, or should it focus on leveraging existing open-source models? Analyze in the context of technological sovereignty, economic feasibility, and strategic investment. (250 words)
Arguments for a Sovereign AI Model
Technological Sovereignty and National Security
- AI is a strategic asset, and reliance on foreign models poses risks of sanctions, access restrictions, and data sovereignty issues.
- The U.S. has imposed restrictions on semiconductor exports, impacting AI development in several nations.
- Developing indigenous AI capabilities can ensure long-term self-reliance and mitigate geopolitical vulnerabilities.
Indigenous Innovation and Economic Growth
- AI is projected to add $500 billion to India’s economy by 2025.
- A sovereign AI model can stimulate local research, foster startups, and strengthen India’s AI ecosystem.
- India has a strong IT sector that can leverage AI for economic expansion.
Cultural and Linguistic Relevance
- Existing AI models are predominantly trained on Western datasets and may not accurately understand India’s linguistic diversity and socio-cultural nuances.
- A localized AI model can enhance digital inclusion by catering to India’s 22 official languages and diverse dialects.
Challenges in Building a Foundational AI Model
High Cost and Resource Constraints
- Training a foundational AI model requires billions of dollars. For instance, DeepSeek V3 cost $5.6 million for training, and major tech firms invest over $80 billion annually in AI infrastructure.
- India lacks access to high-performance semiconductor manufacturing, as companies like Taiwan’s TSMC dominate chip production.
Infrastructure and Talent Gaps
- India needs extensive GPU clusters for AI training, but local availability is limited.
- AI talent is concentrated in top firms abroad, and India must enhance domestic AI education and research institutions.
Limited Market Size for Enterprise AI Adoption
- The primary market for AI services remains the U.S., where businesses have higher purchasing power.
- Indian enterprises may not yet be ready to invest heavily in AI solutions, limiting potential financial returns.
The Middle Path: Strategic Investments in AI
Rather than building a single large-scale foundational model, India can adopt a strategic approach:
Public-Private Collaboration
- Encouraging firms like Infosys, TCS, and startups to co-develop AI models with state support.
- Partnerships with academia to drive innovation in AI research.
Focused AI Development for Key Sectors
- Investment in AI models tailored for governance, healthcare, agriculture, and local language processing.
- Programs like AI for Bharat, which focus on Indic language processing, should receive increased support.
Government Infrastructure and Policy Support
- The IndiaAI Mission’s GPU cluster initiative should be expanded to provide affordable computing power to startups and researchers.
- Clear AI regulations should be established to balance innovation with ethical AI deployment.
Conclusion
While India should aspire to develop foundational AI capabilities, a cautious and strategic approach is necessary. Given theamay not be feasible. Instead, India should prioritize investments in applied AI, indigenous AI innovation, and collaborations to ensure a robust and sustainable AI ecosystem.
