The Hindu Editorial Analysis- 7th February 2025 | Current Affairs & Hindu Analysis: Daily, Weekly & Monthly - UPSC PDF Download

The saga of regulating India’s thermal power emissions

Why in News?

 In December 2024, India’s Ministry of Environment, Forest, and Climate Change (MoEFCC) announced a three-year extension for thermal power plants to comply with stricter sulphur dioxide (SO₂) emission norms. This decision affects around 20 gigawatts (GW) of thermal plants, particularly those located in densely populated areas. 

 Background on Emission Norms 

•  In 2015, MoEFCC introduced tougher emission standards for thermal power plants after public consultations. These standards aimed to reduce particulate matter and, for the first time, set limits on SO₂ emissions, aligning India’s regulations with those in countries like Australia, China, and the United States. 
•  Initially, all thermal plants were expected to meet these norms by December 2017. However, the implementation has faced multiple delays over the years.

 Reasons for Extension 

• The recent extension to December 31, 2027, for SO₂ emissions was made without any specific justification, continuing a trend of delays in the implementation of these norms. This marks another setback in a process that has already spanned a decade. 
•  The debate around the compliance has shifted over time. While Indian coal typically has lower sulphur content, making it easier to meet SO₂ standards, discussions have focused on the challenges of using Flue Gas Desulphurisation (FGD) technology, which is designed to remove sulphur from emissions, particularly from high-sulphur coal. 
•  FGDs were never made mandatory, but concerns about their high costs, long installation times, and supply chain issues have been central to the discussions.

 Changing Perspectives and Reports 

•  Between 2020 and 2021, the Central Electricity Authority (CEA) raised questions about the uniformity of emission norms and suggested extending deadlines to 2035. 
•  In 2022, a study by IIT Delhi acknowledged the air quality benefits of FGDs but recommended delaying their implementation due to high costs and potential increases in greenhouse gas emissions. 
•  A 2024 study by NITI Aayog and CSIR-NEERI argued that SO₂ norms might be less critical for air quality compared to particulate matter norms. 

 Financial Implications for Consumers 

 Many thermal plants have initiated contracts for FGDs but are not progressing quickly enough to meet the revised deadlines. Electricity regulators have allowed these plants to pass the costs of FGDs onto consumers, even if the plants fail to meet the emission standards. 

 As of now: 

•  22 GW of thermal plants have successfully installed FGDs. 
•  102 GW, nearly 50% of India’s thermal power capacity, is in the advanced stages of FGD installation. 

 However, with the extended deadlines, many plants might opt not to use FGDs to save costs. This could lead to: 

•  Consumers paying for unused FGD equipment. 
•  A lack of improvement in air quality for surrounding areas for at least another three years. 

 Long-Term Consequences 

 The delays in enforcing SO₂ norms have significant environmental, health, and financial implications. There is uncertainty about whether India will learn from these delays and enhance its pollution control policies in the future. 

 Conclusion 

 The repeated extensions of the SO₂ emission norms highlight regulatory inefficiencies and the financial burden on consumers. These delays not only increase environmental risks but also raise concerns about India’s commitment to pollution control and public health. 

Should India build a sovereign, foundational AI model?

Why in News?

 DeepSeek, a new low-cost foundational AI model, has sparked discussions about India's ability to create its own AI models while considering factors like sovereignty, cost, and strategic objectives. 

Why Should India Build an AI Model?

•  Developing foundational AI models is crucial for India to enhance its expertise and technological capabilities. 
•  It is essential to have skilled individuals who can create, improve, and develop applications based on AI models. 
•  Sanctions on AI-related technologies, such as chips and software, could hinder India's AI progress. 
•  Open-source AI models are available, enabling India to modify and utilize them without starting from scratch. 

Is Sovereignty a Major Concern?

•  While some argue that sovereignty should drive the development of an AI model, others believe India can effectively adapt open-source models. 
•  The advancement of AI relies on sophisticated chips, which India does not currently produce. 
•  In the event of global restrictions on AI technology, India could still leverage open-source AI models. 

Financial Challenges of Developing an AI Model

•  Building a high-quality AI model is prohibitively expensive, often requiring hundreds of millions of dollars. 
•  Even a budget-friendly AI model necessitates millions for training and ongoing expenses, including salaries and infrastructure. 
•  India’s AI market is smaller compared to other nations, making it challenging to recoup the investment. 
•  Indian businesses primarily cater to global markets, rather than focusing solely on the domestic market. 

Government’s Role in AI Development

•  The government has initiated plans to offer AI resources, such as GPU clusters, at reduced costs. 
•  This initiative can support startups and researchers by making AI training more affordable. 
•  However, major tech companies invest billions in AI, whereas India’s resources are relatively limited. 

Where Should India Focus Its AI Efforts?

•  Instead of directly competing with the largest AI models globally, India should concentrate on targeted AI solutions. 
•  AI development should prioritize crucial areas like Indian language processing and speech recognition. 
•  AI models need to be tailored for local use cases where they can have a significant impact. 
•  The primary challenge is to enhance India’s research environment and attract private investments. 

Conclusion

•  India should strategically allocate its resources and emphasize practical AI applications rather than vying with global leaders. 
•  Building a robust AI ecosystem characterized by strong research, investment, and innovation is more vital than merely creating a single AI model.