A critical and constructive reading of the UNFCCC paper on artificial intelligence and climate action, one year later
By Silvina — DelPlata Green · AI for the common good
In July 2025, the Technology Executive Committee (TEC) of the United Nations Framework Convention on Climate Change (UNFCCC), together with the United Nations Industrial Development Organization (UNIDO), published a 170-page technical paper entitled “Artificial Intelligence for Climate Action: Advancing Mitigation and Adaptation in Developing Countries.” It remains one of the most comprehensive documents to date on the intersection of artificial intelligence and climate change from the perspective of developing countries.
A year later, the AI landscape has continued to accelerate. New models, new commitments, new promises. But the fundamental questions raised by that paper remain open—and some it didn't address have become even more urgent.
At DelPlata Green, we work at the intersection of sustainability, technology, and impact measurement. We do so guided by a conviction that underpins everything we do: artificial intelligence must serve the common good. Not as a slogan, but as an evaluation criterion. And it is precisely from this perspective that I want to revisit this document: to acknowledge what it does well, point out what it lacks, and formulate the question that I believe should be at the heart of the conversation but which the paper never explicitly poses.
AI for good, yes. But… for whose good?
A much-needed map
We must start with what is appropriate: recognizing that this document fills a real void.
Before this paper, the conversation about AI and climate change was dominated by two opposing and incomplete narratives. On one hand, there was Silicon Valley's techno-optimism: AI will solve everything. On the other, there was widespread distrust: AI consumes too much energy, AI is biased, AI is part of the problem. The UNFCCC/UNIDO paper does something more useful than simply taking sides: it maps the landscape.
The central chapter of the document (chapter 4) covers ten areas of AI application for climate action in developing countries: early warning systems, Earth observation, climate simulation and prediction, natural resource management, energy management, transportation, disaster risk reduction, emerging applications of language modeling (LLMs), education and community engagement, and an overview with reference tables.
But where the paper truly distinguishes itself is in what it had the courage to include: an entire chapter dedicated to risks and challenges (Chapter 6). The document points out that the same AI that optimizes renewables is also used to make oil and gas extraction more efficient. It mentions the gender bias in climate datasets. And it mentions the risk that generative AI will amplify climate misinformation.
All of this deserves recognition. It is a serious document, honest in its limitations, and necessary.
The risks he mentions but doesn't fully address
That said, several of the risks identified in the paper are accurately described but insufficient. It names them, but doesn't address their underlying causes.
Energy and water consumption: more than just a technical data point
The paper correctly states that AI has a significant energy and water footprint and recommends using Small Language Models (SLMs) instead of large models whenever possible. However, it doesn't provide enough quantification for a decision-maker to understand the magnitude of the problem in their context. Training a large model can consume as much water as a small community needs for months.
The digital divide: it's not just "lack of internet"“
The document describes the digital divide as a combination of limited connectivity, a lack of computing power, and a shortage of skilled professionals. This is correct, but incomplete. What the paper describes as a “divide” is, in reality, something more structural: a new form of technological dependence. If the models, training data, cloud infrastructure, and talent are concentrated in the United States, China, and Europe, developing countries not only “arrive late” to climate AI—they arrive in a position where they consume solutions designed by others, with data from others, and optimized for other contexts.
The silences: what the paper doesn't say
Beyond what the document says without going into sufficient detail, there are risks that are simply not mentioned — and which, from my experience in sustainability consulting, I consider central.
Institutional technological solutionism
When an international organization or government adopts AI for climate action, there is a risk that it will replace investment in proven and cheaper solutions—basic infrastructure, local governance, ecosystem restoration—rather than complement them. The question “Is AI the best tool for this problem?” should precede any implementation. And the answer, often, will be no.
Data extractivism
Developing countries possess something that AI models desperately need: unique climate, ecological, and social data. There is a real risk that this data will be collected by external actors, used to train proprietary models, and that the benefits will not return to the communities that generated it. It is the same extractive pattern we know well in Latin America with natural resources: the resource leaves, the added value is generated elsewhere, and what returns—if it returns at all—are finished products at market price. Only this time the resource is not lithium or copper. It is data.
Accelerated obsolescence
AI is evolving at a speed unprecedented in other technologies. For a country with limited resources that has invested significantly in a climate AI solution, this obsolescence is no minor inconvenience. It represents a serious financial and operational risk. And it raises an uncomfortable question: are we building local capacity, or are we creating dependence on technological cycles we don't control?
AI greenwashing
“We implemented artificial intelligence to monitor our emissions” sounds impressive in an ESG report. But if the data is bad, if the model isn't calibrated for the local context, if the results don't translate into decisions, if no one verifies the outputs—what we have is declarative sustainability, not demonstrable sustainability.
The fundamental question: AI for the common good as a criterion, not as a slogan
All of the above converges on a question that I consider central and that the UNFCCC/UNIDO paper never explicitly formulates: for whose benefit?
I propose five questions that should accompany any AI project applied to climate action:
- Who designed it and with what data?
- Who benefits from the results?
- Who supports her when the project ends?
- Does the value generated return to the community?
- Is AI the best tool for this problem?
The last question is the most uncomfortable — and the most necessary.
One year later: the unfinished conversation
The UNFCCC/UNIDO paper of July 2025 was a valuable and necessary contribution. It mapped the territory, documented real-world applications, and had the honesty to name risks that other institutional documents prefer to ignore. It is worth reading, especially Chapter 4 as a technical reference and Chapter 6 as a starting point for a deeper discussion.
But a year later, that conversation remains unfinished. AI for climate action continues to be deployed—with more resources, with greater urgency, with more promises. And the questions that aren't being asked remain unanswered.
Because artificial intelligence for the common good is not defined by the technology used. It is defined by whom it serves, who decides, and who reaps the benefits.
At DelPlata Green We help companies move from declared sustainability to demonstrable sustainability, with the technology of Zero Carbon One.
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Main source
UNFCCC/UNIDO — “Artificial Intelligence for Climate Action in Developing Countries: Opportunities, Challenges and Risks” (July 2025). UNFCCC Technology Executive Committee (TEC) / UNIDO. to be genuinely at the service of people and the planet.
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