Can AI Save a Burning World?

by

Author: Denis Avetisyan

A critical review explores the complex relationship between artificial intelligence and the climate crisis, questioning whether technology can truly deliver ecological sustainability.

This paper examines the ethical, political, and ecological implications of deploying AI to address climate change, emphasizing the need for responsible governance and social justice.

Despite the promise of technological solutions, the escalating climate crisis demands critical assessment of even ostensibly beneficial tools. This paper, ‘Rethinking AI in the age of climate collapse: Ethics, power, and responsibility’, examines the ambivalent role of artificial intelligence, arguing that its potential for climate action is fundamentally contingent upon addressing issues of ethical governance, social justice, and ecological sustainability. Rather than assuming inherent sustainability, this analysis reveals contradictions arising from the energy demands of AI infrastructure, algorithmic biases, and concentrated corporate power. How can we ensure that the deployment of AI contributes to a truly just and ecologically responsible response to the defining challenge of our time?

The Illusion of Technological Salvation: Beyond Symptomatic Solutions

The accelerating climate crisis necessitates swift and far-reaching solutions, yet an overemphasis on technological fixes risks obscuring the deeply rooted systemic problems at play. While innovations in renewable energy, carbon capture, and geoengineering hold promise, these approaches frequently address symptoms rather than the core drivers of environmental degradation – unsustainable consumption patterns, inequitable resource distribution, and the prioritization of economic growth over ecological wellbeing. A singular focus on technology can foster a false sense of security, delaying the more challenging but essential work of dismantling the structures that perpetuate environmental harm and transitioning towards genuinely sustainable societal models. True progress demands a holistic assessment of interconnected issues, acknowledging that technological advancements alone cannot resolve a crisis fundamentally embedded within social, political, and economic systems.

Historically, Western thought, deeply influenced by the Cartesian perspective, established a problematic dualism between humanity and the natural world. This framework, prioritizing rational, mechanical understanding, positioned humans as observers – and ultimately, controllers – outside of nature, rather than integral components within it. Consequently, environmental stewardship became largely utilitarian, focused on resource management and exploitation for human benefit, rather than recognizing the inherent value and interconnectedness of all living systems. This externalized view fostered a mindset where nature was perceived as something to be conquered and manipulated, hindering the development of truly holistic and sustainable approaches to environmental challenges and perpetuating a destructive relationship with the planet.

A truly effective response to the Climate Crisis necessitates more than just innovative technologies; it demands a profound re-evaluation of humanity’s place within the natural world. Deep Ecology posits that all living beings-plants, animals, ecosystems-possess inherent worth, independent of their utility to humans. This challenges anthropocentric viewpoints that prioritize human needs above all else, and instead advocates for a biocentric equality where flourishing isn’t measured solely by economic growth but by the overall health of the planet. Shifting to this worldview encourages practices rooted in ecological sustainability and recognizes that the well-being of humans is inextricably linked to the well-being of all life, fostering a sense of responsibility that extends beyond immediate self-interest and embraces a long-term commitment to planetary health.

Artificial Intelligence: A Double-Edged Algorithm

Artificial Intelligence is increasingly utilized in environmental science through several key applications. Climate modelling benefits from AI’s ability to process complex datasets and identify patterns exceeding traditional analytical capabilities, leading to more accurate predictions of future climate scenarios. Environmental monitoring is enhanced by AI-powered analysis of satellite imagery, sensor data, and acoustic monitoring, enabling real-time detection of deforestation, pollution events, and biodiversity changes. Furthermore, AI algorithms optimize energy systems by predicting energy demand, improving grid efficiency, and managing renewable energy sources, contributing to reduced energy waste and increased sustainability. These applications demonstrate AI’s potential to accelerate understanding of environmental challenges and inform mitigation strategies.

The operational demands of Artificial Intelligence, particularly the training of large language models, necessitate substantial energy consumption. Current estimates indicate that training a single, advanced model can generate up to 280 tonnes of carbon dioxide equivalent (CO2e) emissions. This figure is comparable to the lifetime carbon footprint of approximately five gasoline-powered passenger vehicles, including manufacturing and operation. The energy intensity stems from the computational resources required – processing, memory, and cooling – housed within large data centres. These facilities consume significant electricity, often derived from fossil fuels, contributing to greenhouse gas emissions and exacerbating climate change despite AI’s potential for environmental solutions.

Green AI initiatives address the increasing energy consumption and carbon footprint associated with artificial intelligence development and deployment. These efforts prioritize strategies such as algorithmic efficiency – developing models that achieve comparable performance with fewer computational resources – and hardware optimization, including the use of energy-efficient processors and data centre infrastructure. Resource optimization encompasses minimizing data requirements through techniques like transfer learning and federated learning, reducing the need for large datasets and extensive training. Furthermore, Green AI promotes the adoption of renewable energy sources to power AI infrastructure and encourages life cycle assessments to quantify the environmental impact of AI systems from development to disposal.

Ethical Imperatives: The Algorithm and the Precautionary Principle

Algorithmic bias in Artificial Intelligence systems arises from skewed or unrepresentative training data, leading to systematically prejudiced outcomes. These biases can perpetuate and exacerbate existing social inequalities across various domains, including environmental protection. Specifically, if AI is used to allocate environmental resources, assess pollution risks, or determine enforcement priorities, biased algorithms may disproportionately burden marginalized communities with environmental hazards while simultaneously under-serving their needs. This dynamic directly relates to Environmental Justice concerns, as these communities are already historically disadvantaged and vulnerable to environmental harm. Proactive mitigation requires careful data curation, algorithm auditing, and ongoing monitoring to identify and correct for biases, ensuring equitable outcomes and preventing the reproduction of systemic inequalities through AI-driven systems.

The Precautionary Principle, originating in international environmental law, posits that in the face of potential serious or irreversible environmental damage, a lack of full scientific certainty should not be used as a reason for postponing cost-effective measures to prevent environmental degradation. Applied to Artificial Intelligence, this translates to a proactive assessment of potential harms – including resource depletion from training large models, increased energy consumption, and the exacerbation of existing environmental inequalities – before broad-scale deployment. This necessitates comprehensive impact assessments, the development of mitigation strategies, and ongoing monitoring to address unforeseen consequences, even in the absence of conclusive proof of harm. The principle does not advocate for halting innovation, but rather for responsible innovation guided by foresight and a commitment to minimizing potential environmental risks.

AI regulation, grounded in the principles of Sustainable Development and Just Transition, necessitates a proactive approach to mitigate potential harms and maximize benefits across all societal groups. Sustainable Development, as defined by the UN’s 2030 Agenda, requires that AI applications contribute to economic, social, and environmental well-being without compromising future generations. A Just Transition, specifically, emphasizes equitable distribution of the costs and benefits associated with the shift towards AI-driven systems, including workforce retraining initiatives and social safety nets to address potential job displacement. Regulatory frameworks should prioritize transparency in algorithmic design, accountability for biased outcomes, and the establishment of independent auditing mechanisms to ensure alignment with these principles and prevent the exacerbation of existing inequalities.

A New Ethical Framework: Care, Posthumanism, and the Algorithmic Future

Care ethics posits that the well-being of all entities – human and non-human alike – is fundamentally intertwined, moving beyond a solely human-centered perspective on environmental responsibility. This philosophical stance champions a relational approach to stewardship, asserting that our obligations extend not just to other people, but to the broader ecological web. It reframes environmental concern not as a matter of resource management or preserving nature for human benefit, but as a recognition of inherent value within all living systems and the delicate dependencies that sustain them. Consequently, ethical considerations must prioritize maintaining these relationships, fostering reciprocal care, and acknowledging the vulnerability and interdependence that characterize life on Earth, ultimately advocating for practices that nurture rather than exploit the natural world.

Posthumanism fundamentally disrupts traditional understandings of the natural world by questioning humanity’s unique position at its apex. This philosophy posits that the distinction between human and non-human – and increasingly, between organic and artificial – is less defined than previously assumed, recognizing artificial intelligence not merely as a tool, but as an emergent agent within complex ecological systems. Consequently, a reassessment of ethical considerations becomes crucial; no longer can environmental stewardship solely focus on preserving nature for humanity, but must acknowledge the inherent value of all entities participating in these assemblages, including AI. This shift demands a move away from anthropocentric viewpoints, prompting a broader understanding of responsibility that encompasses the well-being of all interconnected life, biological and artificial, and challenges existing power dynamics within the environment.

The convergence of care ethics and posthumanist thought offers a vital pathway toward responsible innovation in artificial intelligence. This integration moves beyond purely technical considerations, advocating for an approach where AI development is fundamentally guided by relationality and a concern for the wellbeing of all entities-human and non-human-within complex ecological systems. Rather than viewing AI as a tool for domination or simply optimization, this framework positions it as a potential collaborator in sustaining planetary health. By prioritizing interdependence and acknowledging the inherent value of diverse life forms, including increasingly sophisticated artificial intelligences, it becomes possible to cultivate AI systems that actively contribute to long-term ecological resilience and a more just future for all.

The exploration of AI’s role in climate solutions demands a rigorous foundation, mirroring the principles of mathematical elegance. The article rightly highlights the contingent nature of AI’s benefits, asserting they are not inherent but reliant on ethical frameworks and sustainable practices. This resonates with Ken Thompson’s observation: “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” Similarly, a complex AI system designed without provable ethical constraints – a ‘clever’ but poorly debugged solution – risks exacerbating existing inequalities and environmental harms. The pursuit of algorithmic governance, as discussed, requires the same precision and verifiability expected of any sound mathematical proof.

Beyond Optimization: Charting a Course for Ecological Intelligence

The preceding analysis reveals a disquieting truth: the application of artificial intelligence to the climate crisis, while often presented as a technological imperative, remains largely untethered from genuine ecological principle. The field’s preoccupation with optimization – minimizing carbon footprints, maximizing efficiency – treats symptoms, not causes. A truly sustainable algorithmic governance demands a shift in focus: from doing more with less to doing enough, and perhaps, even doing less. The core challenge lies not in creating ‘Green AI’ as an addendum to existing frameworks, but in formulating a computational ecology predicated on limits, resilience, and a provable commitment to non-domination.

Future inquiry must rigorously examine the implicit value systems embedded within these algorithms. The seductive appeal of ‘data-driven’ solutions often obscures the subjective choices – and potential biases – that define their parameters. Furthermore, the tendency to frame climate change as a problem solvable through technological innovation risks reinforcing existing power imbalances, diverting attention from fundamental systemic change. The pursuit of ‘intelligent’ systems must be tempered by a corresponding investigation into the nature of intelligence itself – is it merely predictive power, or does it encompass ethical foresight and ecological understanding?

Ultimately, the enduring question is not whether artificial intelligence can address the climate crisis, but whether it should, given its inherent limitations and the potential for unintended consequences. The elegance of a mathematical solution is irrelevant if the axioms themselves are flawed. A consistent framework for ecological intelligence – one grounded in provable sustainability and justice – remains the critical, and largely unaddressed, challenge.

Original article: https://arxiv.org/pdf/2601.18462.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-27 17:48