UN Secretary-General António Guterres brought into light the growing divide in climate action. Wealthy nations and corporations were advancing in artificial intelligence (AI) while developing countries risked being marginalised. This disparity was not just technological but also ethical. The Global South sought a voice in climate initiatives increasingly governed by foreign algorithms.
The Baku Climate Unity Pact
The Baku Climate Unity Pact was introduced at COP29. It promised $300 billion annually by 2035 for climate resilience in developing nations. This commitment was celebrated as breakthrough. However, its effectiveness depended on how AI technologies were applied. Would they focus on local needs or cater to corporate interests? Localised solutions were essential for success.
AI’s Role in Climate Transition
A World Economic Forum analysis emphasised the need for AI to align with local contexts. Flood prediction tools designed using data from wealthy nations often failed in developing regions. Experts argued for the importance of local model development. Without local expertise, the pact risked becoming another unfulfilled promise.
Green Digital Action Initiative
Launched at COP28, the Green Digital Action initiative aimed to improve digital accountability. It introduced the Greening Digital Companies dashboard, which tracked emissions and demanded transparency. AI-driven climate models could reduce disaster-related damages. However, many of these models struggled to adapt to diverse climatic conditions in developing countries.
Challenges in AI Implementation
Despite having scientific talent, many developing nations lacked access to essential datasets and computing power. This disparity hindered the training of effective AI systems. Capacity development was framed as a matter of survival rather than charity.
The Paris Summit’s Focus
The Paris Summit in February 2025 shifted its focus to immediate impacts rather than speculative risks associated with AI. Critics noted that this approach overlooked pressing ethical issues, such as data ownership and profit distribution. The absence of endorsements from major powers weakened the summit’s credibility.
Upcoming Bonn Climate Conference
Looking ahead, the Bonn Climate Conference in June 2025 was poised to address technology transfer and climate finance. Delays in these negotiations could have dire consequences for vulnerable nations experiencing climate crises. The UNFCCC’s push for localised solutions was commendable but required open-source AI platforms and shared patents to be effective.
Call for Tech Sovereignty
The commitment of $300 billion under the Baku Pact needed to be matched with a focus on tech sovereignty. This involved co-developing algorithms with local engineers and incorporating the lived experiences of farmers into datasets. Equity in AI governance was crucial to avoid reinforcing climate inequalities.
Questions for UPSC:
- Discuss the implications of AI in climate action for developing nations.
- Critically examine the challenges faced by the Global South in technology transfer for climate resilience.
- What are the ethical considerations surrounding data ownership in AI-driven climate solutions? Explain.
- With suitable examples, discuss how local expertise can enhance the effectiveness of climate action initiatives.
Answer Hints:
1. Discuss the implications of AI in climate action for developing nations.
- AI can exacerbate the divide between wealthy and developing nations, leading to unequal access to climate solutions.
- Algorithms trained on data from developed countries may not be applicable to the unique climatic challenges faced by developing nations.
- Dependence on foreign technologies risks perpetuating digital colonialism, undermining local knowledge and expertise.
- AI has the potential to optimize climate resilience, but it must be aligned with local needs and contexts.
- International cooperation is essential to ensure that AI tools are co-designed and relevant to the Global South.
2. Critically examine the challenges faced by the Global South in technology transfer for climate resilience.
- Many developing nations lack access to essential datasets and computing power needed for effective AI training.
- There is often a lack of trust in foreign technologies, leading to reluctance in adopting external solutions.
- Political and economic instability can hinder the implementation of technology transfer agreements.
- Capacity development is crucial but often framed as a form of charity rather than a necessity for survival.
- Without open-source platforms and shared patents, developing countries remain dependent on costly proprietary technologies.
3. What are the ethical considerations surrounding data ownership in AI-driven climate solutions? Explain.
- Data ownership raises questions about who profits from AI-driven climate solutions and how benefits are distributed.
- Ethical concerns include the potential exploitation of local data without consent or fair compensation.
- There is a risk that AI systems reinforce existing power dynamics, favoring tech giants over local communities.
- Transparency in data usage and algorithmic decision-making is essential to build trust and accountability.
- Collaboration with local stakeholders is necessary to ensure that data practices respect community rights and knowledge.
4. With suitable examples, discuss how local expertise can enhance the effectiveness of climate action initiatives.
- Local farmers in Mozambique can contribute to developing flood prediction models that are relevant to their specific conditions.
- India’s advancements in homegrown AI for crop resilience demonstrate the potential of localized solutions tailored to regional challenges.
- Indigenous knowledge can inform climate adaptation strategies that are culturally and environmentally appropriate.
- Collaboration between local engineers and international experts can result in more effective and context-aware technologies.
- Empowering local communities to build their own tools encourages ownership and ensures that solutions meet their unique needs.
