Beyond Electrostates and Petrostates: Europe, Latin America, and the need of a regulated transition

The emerging narrative of an “ecological cold war” between petrostates and electrostates has an undeniable analytical appeal. It captures, with a certain elegance, the potential material reorganisation of the global order around energy systems and the infrastructures that sustain them. Yet, when this framing is taken as a descriptive account of reality rather than as a heuristic device, it risks obscuring the very dynamics that will determine the success or failure of the transition. Nowhere is this tension more visible than in Latin America, and in the contrast that can be drawn with Europe.

The attraction of the argument lies in its clarity. A world divided between fossil fuel incumbency and green technological ascendancy, with China leading a new electro-industrial bloc and the United States anchoring a coalition intent on prolonging hydrocarbon dominance, offers a powerful way of making sense of current geopolitical shifts. It also provides a convenient place for so-called middle powers, which are imagined as navigating between these poles, extracting benefits where possible while preserving a degree of autonomy. The difficulty, however, is that this narrative assumes that strategic positioning is primarily a function of external alignment, when in practice it is shaped, often decisively, by internal regulatory capacity.

Latin America illustrates this point with particular force. The region is frequently described in aggregate terms as resource rich and strategically relevant, endowed as it is with hydrocarbons, critical minerals, and extraordinary renewable energy potential. This description is not inaccurate, but it is incomplete to the point of being misleading. What matters is not the mere presence of resources, but the institutional and legal frameworks through which they are governed. It is here that the region reveals a growing divergence that complicates any attempt to treat it as a coherent “middle power” actor.

In some jurisdictions, there are visible efforts to construct regulatory environments capable of managing the transition in a structured way. Chile’s approach to renewable energy and its attempts to articulate a strategy around lithium are often cited in this regard, while Brazil presents a more complex but nonetheless significant case in which hydrocarbon production coexists with a relatively clean energy matrix and emerging green industrial ambitions. These examples remain partial and contested, yet they point toward an understanding of extraction not as an end in itself but as a component within a broader developmental and environmental strategy.

In other cases, however, the absence of stable, credible, and forward-looking regulatory frameworks undermines the possibility of such a transformation. Argentina is perhaps the clearest illustration. Despite possessing both vast hydrocarbon reserves, most notably in Vaca Muerta, and a central position within the global lithium landscape, the lack of consistent regulatory direction, coupled with recurring macroeconomic instability, makes it difficult to convert these assets into a coherent transition pathway. Under such conditions, extraction tends to reinforce short term imperatives rather than enable long term change. What appears, from the outside, as strategic optionality becomes, in practice, structural vulnerability.

This divergence exposes a fundamental weakness in the binary framing of electrostates and petrostates. If the world is understood primarily in terms of competing energy blocs, then the policy question becomes one of alignment or hedging. Yet for countries that lack the institutional capacity to shape their own trajectories, neither alignment nor hedging delivers autonomy. Instead, both can lead to forms of dependency, whether through long term fossil fuel lock in or through asymmetric integration into green supply chains dominated by external actors.

Europe offers a useful, albeit incomplete, counterpoint. It would be misleading to suggest that the European Union has resolved the tensions inherent in the transition. It remains dependent on imported energy and critical minerals, and its relationship with both the United States and China is marked by structural asymmetries. Nevertheless, what distinguishes the European approach is the extent to which it has sought to construct a regulatory ecosystem that actively directs the transition. Through instruments such as carbon pricing, sustainability disclosure requirements, industrial policy linked to decarbonisation, and the potential integrated approach to digital and environmental regulation, Europe has created a framework within which market actors are compelled to adjust their behaviour.

This does not eliminate dependency, but it does transform its character. Rather than being passively exposed to external pressures, Europe exercises a degree of structured agency, shaping incentives, setting standards, and influencing global practices. Importantly, this occurs alongside continued fossil fuel production in parts of the region, demonstrating that the existence of hydrocarbons does not predetermine alignment with a petrostate logic. Instead, what matters is whether those resources are embedded within a regulatory architecture that orients them toward transition.

The contrast with parts of Latin America is therefore instructive. Where regulatory frameworks are present, even if incomplete, there is at least the possibility of converting resource endowments into instruments of transformation. Where they are absent or unstable, resources tend to reinforce existing dependencies and limit future options. In this sense, the region serves not only as an example of potential but also as a warning against the seductions of overly simplified geopolitical narratives.

The deeper issue, then, is not whether countries will align with one bloc or another, but whether they possess the institutional capacity to govern their own transition. This requires more than policy pronouncements or strategic positioning. It demands coherent legal frameworks, credible regulatory institutions, and a sustained political commitment to aligning short term economic incentives with long term environmental and developmental objectives. Without these elements, the language of nonalignment risks becoming little more than a rhetorical cover for exposure to external forces.

Seen from this perspective, the notion of an ecological cold war captures only part of the picture. It identifies the stakes, but not the mechanisms through which outcomes will be determined. Europe and Latin America, taken together, suggest that the decisive factor will not be the side on which a country places itself, but the extent to which it is able to construct and maintain the regulatory conditions for a managed transition. The future of the global energy order will be shaped as much in legislative chambers and regulatory agencies as in oil fields and lithium brines. To overlook this is to misunderstand both the nature of the transition and the possibilities that remain open within it.

AI and environmental damage

In the previous entry the issue of the environmental and climate change impact of AI use and development was presented as important, and in need of urgent treatment by policy makers (who are squarely ignoring it in most proposals of AI regulation). Those impacts are real, considerable and multifaceted, involving major energy consumption, resource depletion, and a variety of other ecological consequences.

Training large AI models requires immense computational power, leading to the use of large quantities of energy. Just as example, training a single model like GPT-3 can consume 1287 MWh of electricity, resulting in about 502 metric tons of CO2 emissions, which is comparable to the annual emissions of dozens of cars. But while the energy consumed during the training phase is significant, quite more energy is used during the inference phase, where models are deployed and used in real-world applications. There have been interesting forms to justify such a use, mainly by comparing pears not with apples but with scissors, but they seem to obviate the fact that the general human emission that are compared with the AI ones will be there regardless of the activity, so the improper use of AI adds emissions without subtracting much of them. In a world that the development and deployment of AI is bound to keep growing at bubble-like rates, this implies that the location of data centres plays a crucial role in determining the carbon footprint of AI, as they are bound to double their energy consumption by 2026 (if 170 pages is too much to read, simply go to page 8). Data centres powered by renewable energy sources, have a lower carbon footprint compared to those in regions reliant on fossil fuels, and there is an argument about making such use compulsory.

From the resource depletion and e-waste point of view, AI hardware, including GPUs and specialized chips, requires rare earth elements and other minerals. The extraction and processing of these materials can lead to environmental degradation and biodiversity loss. AI is been currently used to find ways to replace those rare earth elements, but even then, as AI technology evolves, older hardware becomes obsolete, contributing to a steep increase in the amount of electronic waste. Besides the global inequalities generated by the mountains of e-garbage currently dumped in developing countries, E-waste contains hazardous substances like lead, mercury, and cadmium, which can contaminate soil and water if not properly managed.

A less obvious but equally significant impact is water usage. Training AI models like requires abundant amounts of water for cooling data centres, with some studies claiming that the water consumed during the training of algorithmic models is equivalent to the water needed to produce hundreds of electric cars.

To add to the energy and resources consumption, the uncontrolled, improperly regulated widespread use of AI can have severe ecological impact, particularly and paradoxically, when used in activities where proper use of AI can minimize them. Not making sustainability a key aspect of algorithm design, training and AI deployment, may lead to situations that it is more profitable to carry on with environmentally harmful AI driven activities, like the overuse of pesticides and fertilizers, harming soil and water quality and reducing biodiversity, not mentioning that AI-based applications like delivery drones and autonomous vehicles can disrupt wildlife and natural ecosystems without giving much benefits (beyond increasing the already fat profits of few).

All this supports the idea that AI regulation must address sustainability issues and not leave to general environmental legislation, because it is important to know who owns what AI produces, but only if we have a planet where you can enjoy those works…