Advances in algorithm design, hardware efficiency, and renewable-powered infrastructure can significantly reduce the environmental impact of AI systems. In Spain’s case, the relatively rapid decarbonization of the electricity sector increases the likelihood that AI’s indirect emissions will decline over time.
As with a blade capable of both sustenance and harm, Artificial Intelligence (AI) embodies a dual potential, the realization of which is determined by human agency and institutional oversight. Therefore, AI, like any transformative technology, is ethically neutral in itself; its consequences depend on the intentions, governance, and indigenous and cultural knowledge of global citizens and on the contexts that shape its application.
Now, climate change’s new context poses acute environmental, economic, and social challenges for Spain, a country highly exposed to rising temperatures, water scarcity, extreme weather events, and ecosystem degradation. At the same time, Spain is undergoing a rapid digital transformation, with AI becoming a central technological pillar. Increasingly, AI is being deployed not only as a driver of economic competitiveness but also as a tool to support climate change mitigation and adaptation.
AI in Spain is not just a theoretical tool — it’s actively being integrated into climate science, risk prediction, environmental governance, disaster response, and sustainable policymaking. These efforts help governments and communities anticipate extreme events, inform resilient strategies, allocate resources efficiently, and monitor ecological changes more effectively in an era of climate disruption. Its potential has not been entirely deployed yet, and in my opinion, it should be “fed” with more cultural and ancient knowledge already existing in fields like weather forecast, for example, shaping it with “cabañuelas” methodology that comes from ancient agricultural traditions linked to seasonal cycles. The method is commonly practiced in regions of Spain and in countries such as Mexico. Cabañuelas has already predicted heavy snowfalls and water disasters that took place in Spain recently, like the “Filomena” big snowfall in Madrid and the disastrous rainfall “Barranco del pollo” in Valencia.
Development and Use of Artificial Intelligence in Spain
Spain has made AI a strategic priority within its broader digital and economic modernization agenda. Through national frameworks such as the National Artificial Intelligence Strategy and complementary digitalization plans, the Spanish government aims to integrate AI across public administration, industry, energy systems, and research. These initiatives emphasize ethical governance, transparency, and alignment with European Union regulations, while also promoting innovation through public–private partnerships and research funding.
Institutionally, Spain has reinforced its AI ecosystem by establishing supervisory and coordinating bodies and by investing in high-performance computing infrastructure. Universities, public research centers, and technology firms are increasingly involved in AI development, particularly in fields where data-intensive analysis can address complex societal challenges. Importantly, sustainability considerations are being incorporated into AI policy, with growing attention to energy-efficient algorithms and environmentally responsible digital infrastructure. As a result, AI in Spain is not developing in isolation but is closely linked to national objectives related to climate action and sustainable growth.
AI and the Monitoring of Emissions, Pollution, and Temperature
One of the most significant contributions of AI to climate policy in Spain lies in its capacity to improve environmental monitoring and analysis. Traditional methods of tracking emissions and pollution often rely on periodic reporting and fragmented datasets. AI systems, by contrast, can integrate vast volumes of real-time and historical data from satellites, sensor networks, traffic systems, industrial facilities, and meteorological stations.
In the area of GHG emissions, machine-learning models help refine national emissions inventories by identifying patterns, anomalies, and sectoral trends. AI-enhanced monitoring improves the accuracy of measurement, reporting, and verification processes, which are essential for both domestic policy evaluation and international climate transparency. This capability is particularly valuable in sectors such as transport and industry, where emissions are spatially and temporally variable.
AI also plays a growing role in air quality management. In urban areas, predictive models analyze traffic flows, weather conditions, and emissions data to forecast pollution episodes involving nitrogen dioxide, particulate matter, and ozone. These forecasts allow local authorities to implement targeted interventions, such as temporary traffic restrictions or public transport incentives, thereby reducing both local pollution and associated carbon emissions.
With respect to temperature and climate extremes, AI-driven climate and weather models enhance Spain’s capacity to anticipate heatwaves, droughts, floods, and wildfires. By combining long-term climate data with short-term forecasts, AI supports early-warning systems and adaptation planning. This is particularly relevant for Spain, where climate impacts can undermine economic activity, public health, and natural carbon sinks.
For example, universities such as the Universitat Politècnica de València are developing AI-driven platforms (e.g., PGTEC) that integrate data on weather, river flows, and environmental conditions to anticipate events such as storms, floods, and wildfires and to support coordinated emergency responses across administrations and services. Also, a collaborative method by the CSIC and the Universidad Complutense de Madrid combines climate simulations with AI-driven meteorological predictions to anticipate heat-wave intensity and assess how climate change influences them. This supports early warnings and policy planning for adaptation and public safety. Another example: optimizing ventilation and temperature control in infrastructure such as the Barcelona metro, where AI-led systems have significantly reduced energy use and improved passenger comfort — critical as cities contend with increasing heat extremes.
Energy Demand and the Environmental Cost of AI
Despite its benefits, AI poses a significant challenge to energy consumption. Training and operating advanced AI models requires substantial computational power, which in turn increases electricity demand, particularly in data centers. As AI applications expand across sectors, this energy footprint has become an important consideration for climate policy.
Spain’s approach to meeting AI’s energy needs is shaped by its broader energy transition. The country has rapidly expanded renewable energy capacity, especially solar and wind power, and is working to integrate these resources into increasingly digitalized energy systems. Efforts are underway to locate data centers in regions with high renewable penetration and to encourage the use of energy-efficient hardware and software. In addition, Spain has promoted the development of so-called “green AI,” which prioritizes model efficiency, reduced computational intensity, and lifecycle sustainability.
Nevertheless, a tension remains between the growing demand for AI-driven computation and the goal of reducing overall energy consumption and emissions. If AI deployment outpaces renewable energy expansion or relies on inefficient models, it could place upward pressure on emissions. Managing this risk requires continued policy attention and technological innovation.
Evaluating the Trade-Off Between Benefits and Constraints
Assessing whether the trade-off between AI’s climate benefits and its environmental costs is worthwhile depends largely on governance and implementation. On the benefit side, AI enhances efficiency across multiple sectors, enabling emissions reductions that would be difficult to achieve through conventional means alone. Improved energy management, optimized transport systems, reduced agricultural inputs, and better protection of natural ecosystems all contribute to lower emissions and increased climate resilience.
On the cost side, the carbon footprint of AI is real but not immutable. Advances in algorithm design, hardware efficiency, and renewable-powered infrastructure can significantly reduce the environmental impact of AI systems. In Spain’s case, the relatively rapid decarbonization of the electricity sector increases the likelihood that AI’s indirect emissions will decline over time.
Overall, the balance of evidence suggests that the trade-off is justified if AI development remains aligned with sustainability objectives. AI is most effective as a complement to, rather than a substitute for, strong climate policy and regulatory frameworks.
Implications for Spain’s Paris Agreement Commitments
Spain’s commitments under the Paris Agreement and related European climate targets require substantial reductions in GHG emissions, improvements in energy efficiency, and increased resilience to climate impacts. AI contributes to these goals in several important ways. First, it strengthens the empirical basis of climate policy by improving emissions measurement and environmental monitoring. Second, it enhances the effectiveness of mitigation strategies by optimizing energy systems, transport, agriculture, and industrial processes. Third, it supports adaptation by improving forecasts and early-warning systems for climate-related risks.
However, AI alone cannot ensure compliance with the Paris Agreement. Its impact depends on the extent to which it is embedded within broader policy measures, including renewable energy deployment, electrification, efficiency, land-use planning, and behavioral change. When integrated into a coherent climate strategy, AI can act as a force multiplier, accelerating progress toward emissions reduction and climate resilience.
Artificial intelligence is increasingly shaping Spain’s response to climate change, offering powerful tools for monitoring, mitigation, and adaptation. While AI introduces new energy demands and environmental challenges, Spain’s commitment to renewable energy and sustainable digitalization provides a strong foundation for managing these trade-offs. If governed responsibly and aligned with climate objectives, AI is likely to enhance Spain’s capacity to meet its Paris Agreement commitments rather than hinder them. In this sense, AI represents not a contradiction to climate action, but a strategic asset in Spain’s transition that, if used sharply, will foster Spain towards a low-carbon and climate-resilient future.
This Post was submitted by Climate Scorecard Spain Country Manager, Juanjo Santos.