CONTENTS
- How can Traffic which causes Air Pollution be Controlled?
- Reshape the Governance Structures of AI Companies
How can Traffic which causes Air Pollution be Controlled?
Context:
A recent report highlighted that 83 of the world’s 100 most polluted cities are in India. Another study from the British Medical Journal estimated that air pollution caused 2.1 million deaths in India, the second-highest number globally after China. Alarmingly, over 99% of the Indian population breathes air that falls below the World Health Organization’s recommended standards.
Relevance:
- GS2- Health
- GS3- Environmental Pollution & Degradation
Mains Question:
What is the share of road transport with respect to India’s CO2 emissions? Analyse the level of efficacy of the vehicular scrapping policy across India in this context. (15 Marks, 250 Words).
The Severity of Air Pollution:
- The International Energy Agency (IEA) estimates that 12% of India’s CO2 emissions come from road transport, with heavy vehicles being the primary contributors to Particulate Matter (PM) 2.5 emissions.
- PM2.5 pollutants are microscopic particles that can penetrate deep into the lungs and enter the bloodstream, leading to severe respiratory and cardiovascular issues.
- Additionally, heavy vehicles emit large amounts of Nitrogen Oxide (NOx), which has similar adverse health effects and contributes to the formation of ground-level ozone, exacerbating air quality problems, especially in urban areas.
- Studies indicate that heavy vehicles are responsible for 60-70% of total vehicular PM emissions and 40-50% of total NOx emissions in cities.
Corporate Average Fuel Economy (CAFE) Norms:
- With the transport sector growing at a rate of 9.1% annually, the Bureau of Energy Efficiency (BEE) has taken a commendable step by drafting the Corporate Average Fuel Economy (CAFE) norms for cars in India.
- The proposed timelines for implementing CAFE III from 2027-2032 and CAFE IV from 2032-2037 are practical and well-timed.
- The transition from the Modified India Driving Cycle (MIDC) to the World Light Duty Vehicle Testing Procedure (WLTP) by March 31, 2027, is a significant move, as the WLTP provides a more accurate and globally standardized measure of a vehicle’s fuel consumption and CO2 emissions.
- The emission targets set for CAFE III at 91.7g CO2/km and for CAFE IV at 70g CO2/km are challenging but achievable.
- Such stringent targets are crucial for fostering innovation and encouraging the adoption of cleaner technologies.
- To protect the health of both the planet and its inhabitants, these targets must be made non-negotiable.
- However, it is important to note that the CAFE norms currently exclude emissions from heavy vehicles like trucks, lorries, and other freight vehicles.
What More can be Done?
- In 2022, the Indian government launched a vehicle scrappage policy aimed at phasing out older, polluting vehicles, including heavy-duty ones.
- According to this policy, passenger vehicles over 20 years old and commercial vehicles over 15 years old must undergo a “fitness and emissions test.”
- If vehicles fail these mandatory tests, they are designated as end-of-life vehicles, leading to the revocation of their registration certificates, and they are recommended for scrapping.
- However, this policy has been slow to take off in states like Karnataka, primarily due to limited infrastructure—there are only two scrapyards in the entire state—and because the policy remains voluntary.
- In Bangalore, the majority of older vehicles are not private cars but rather BMTC buses, private vans, and heavy vehicles.
- Maharashtra is one of 21 states that have announced incentives, such as discounts on road tax or new vehicle purchases, to encourage scrapping, but the impact on air pollution in the state has been limited so far.
- Policymakers must ensure that these guidelines are not just exemplary in writing but are effectively enforced.
Conclusion:
Existing government policies aimed at combating air pollution, such as regular vehicle emissions testing, banning open garbage burning, and regulating industrial emissions, need to be implemented with strict enforcement. While the government’s efforts to reduce air pollution are commendable, it is clear that promoting mass transit as a primary solution is essential for effectively tackling air pollution in India.
Reshape the Governance Structures of AI Companies
Context:
In modern corporate governance within capitalist and neo-capitalist economies, the focus has traditionally been on the theory of shareholder primacy. This means that generating profits and creating wealth for shareholders and investors are prioritized over other business goals, such as contributing to the public good. However, there are advocates for a stakeholder-oriented approach to corporate governance, which aims to maximize benefits for all stakeholders.
Relevance:
GS3-
- IT and Computers
- Growth and Development
- Robotics
- Industrial Policy
- Scientific Innovations and Discoveries
- Artificial Intelligence
Mains Question:
An effective governance structures of AI companies is important as social objectives are often subsumed by broader profit-driven goals. Analyse. (10 Marks, 150 Words).
Stakeholder Capitalism:
- Recently, corporations with governance models that lean towards stakeholder capitalism have become more prevalent.
- These companies are increasingly engaging in products, technologies, and services that are not solely driven by profit but also have broader social objectives.
- Generative Artificial Intelligence (AI) serves as an example of this shift, where companies are exploring alternative governance structures to balance profit generation with social responsibility.
Data Access Issues:
- The development of AI technologies requires access to data, which can potentially be used in ways that undermine privacy.
- For example, Meta was asked to pause its plans to train large language models using publicly shared content from Facebook and Instagram in Europe due to concerns raised by the Irish privacy regulator.
- Additionally, human biases can find their way into AI systems, leading to harmful algorithmic biases.
- For instance, Amazon discontinued a recruiting algorithm after discovering it was biased against women.
- Moreover, researchers at Princeton University found that AI software associated European names with more positive sentiments compared to African-American names.
- These examples highlight how AI can perpetuate existing biases and create inequalities in opportunities and access, making it crucial for AI creators to act responsibly towards all stakeholders.
- These concerns have led several companies to modify their corporate governance structures. To address the risks posed by AI advancements, companies like OpenAI and Anthropic have adopted governance models that prioritize public good and responsible AI development, resulting in the creation of public benefit corporations.
- For instance, Anthropic is governed by a structure called the Long-Term Benefit Trust, which includes five financially disinterested members who have the authority to select and remove a portion of Anthropic’s board.
- Similarly, OpenAI was initially established as a non-profit but later transitioned into a hybrid model by creating a capped-profit subsidiary to support its capital-intensive innovation efforts.
- Although these companies initially adopted alternative governance models, when a conflict arose between their purpose-driven goals and their profit-generating operations, monetary interests prevailed.
- OpenAI, the developer of ChatGPT, became entangled in a corporate governance crisis last year when its nonprofit board dismissed CEO Sam Altman due to concerns over the rapid commercialization of AI products potentially compromising user safety.
- This decision was met with strong opposition from Microsoft, OpenAI’s largest investor, and around 90% of the employees, many of whom held stock options in the company.
- As a result, Mr. Altman was reinstated, and the board was replaced. This incident has cast doubt on the viability of public benefit corporate structures in the tech industry, which often depend on significant capital from shareholders and investors to fund research and innovation.
- Recently, there have been rumors that OpenAI may be considering a shift to a for-profit governance structure.
- In 1970, Milton Friedman famously argued that businesses have a social responsibility to generate profits for their shareholders.
- These recent events suggest that even in the era of public benefit corporations, the pursuit of public good might be little more than a guise for profit-seeking.
- Attempting to prioritize social interests over financial considerations may not be achievable through creative governance structures alone.
- Instead, these structures may ultimately reinforce shareholder primacy, particularly in tech companies where employees are also incentivized through stock-based compensation.
Practical Strategy:
- The current accountability structure, which relies on appointing an independent board and adopting social benefit objectives, is insufficient to prevent the amoral drift where a corporation’s social goals are often overshadowed by its broader profit-driven objectives, as the market allows for unchecked corporate control.
- Policymakers must employ innovative approaches to regulate corporations developing AI-based products in a way that balances these conflicting interests.
- From an economic standpoint, this can be achieved by focusing on three key areas: enhancing long-term profit gains for companies that adopt a public benefit purpose, incentivizing managers to comply with such purposes, and reducing the costs associated with compliance.
- This would involve establishing ethical standards for the governance of AI product companies, backed by necessary regulatory reforms in corporate governance norms.
Conclusion:
As AI continues to play an increasingly significant role in various aspects of life, it is crucial to adopt governance models that promote the ethical development of AI while still allowing for profit generation.
