National Green Algorithms Program: what it is, objectives, and benefits 

José Ragán

Director of ICT Operations – R&D&I Department · ACERTA CERTIFICATION

More than 5 years in software project certification and analysis of the impact of emerging technologies, such as AI, on the business world.

Artificial intelligence (AI) has become one of the main drivers of technological and economic transformation. However, its rapid deployment has also brought to light a growing challenge: the environmental impact associated with the intensive use of computational resources. This context has given rise to green algorithms, an approach that seeks to minimize the environmental footprint of AI throughout its entire life cycle. 

At the European level, this concern is part of a broader strategy aimed at reconciling technological leadership with sustainability and ecological transition objectives. The European Union has begun to integrate criteria for energy efficiency, emissions reduction, and responsible use of digital infrastructure into its digitization policies, promoting artificial intelligence aligned with theEuropean Green Deal. Initiatives such as the European AI Strategy, regulatory frameworks under development, and R&D&I funding programs encourage the design of more efficient, transparent, and sustainable algorithmic models, consolidating a common approach that seeks to reduce the environmental impact of AI without compromising its innovative capacity. 

More specifically in Spain, this vision is articulated through theNational Green Algorithms Program, a pioneering initiative that promotes the development ofsustainable artificial intelligence, integrating environmental criteria from the design phase of algorithmic systems. 

What are green algorithms and why do they matter? 

Green algorithmsare those designed toreduce their environmental impact, taking into account not only their functional performance, but also the energy consumption and resources required for their training, deployment, and maintenance. 

The rise of increasingly complex AI models has significantly increased the energy consumption associated with training and inference. This phenomenon is exacerbated when models are not optimized, are trained redundantly, or are run on energy-inefficient infrastructures. Faced with this reality, green algorithms seek to: 

• Optimize the use of computational resources. 
• Reduce energy consumption during training and inference. 
• Minimize emissions associated with the use of hardware and data centers. 

Their importance lies in the fact that they enable technological progress to be reconciled with sustainability, aligning the development of AI with ESG (environmental, social, and governance) principles. 

The need for sustainable artificial intelligence

The widespread adoption of AI in virtually all productive sectors has brought significant benefits in terms of efficiency, productivity, and innovation. However, it has also highlighted the need to incorporate environmental criteria into technological development. 

From a sustainability perspective, it is not enough to analyze the impact of AI during its use phase. It is essential to consider its entire life cycle, from algorithm design to deployment and updating. In this regard, the lack of measurement and control of energy consumption can lead to significant environmental impacts. 

Green algorithms respond precisely to this need, promoting AI that takes environmental impact into account as another design variable, on the same level as accuracy or functional efficiency. 

The magnitude of this challenge becomes clear when analyzing the energy consumption associated with training large-scale artificial intelligence models, especially in the field of deep language models. 

Widely cited estimates place the energy consumption of training models such as GPT-3 in the order of thousands of MWh, a figure comparable to the annual electricity consumption of dozens or even hundreds of households. Although these figures are approximations, they show that decisions made during the design, training, and optimization phases of algorithms have a direct and significant impact on energy consumption. In this context, the incorporation of strategies aimed at computational efficiency and the reduction of algorithmic complexity is no longer a purely technical issue, but has become a key element of sustainability and technological responsibility. 

What is the National Green Algorithms Program?

The National Green Algorithms Program is a Spanish initiative aimed at promoting the development of sustainable artificial intelligence, incorporating environmental criteria from the initial stages of design, development, and deployment of algorithmic systems.

The program seeks to promote Green AI by Design, in which energy efficiency, emissions reduction, and responsible use of digital infrastructure are integrated as key technical variables, alongside model accuracy and performance.

Within this framework, the National Green Algorithms Program acts as an instrument for aligning technological innovation and sustainability, promoting good practices, measuring the environmental impact of AI, and developing standards and tools that facilitate its implementation in companies and organizations. 

Objectives of the National Green Algorithms Program

TheNational Green Algorithms Programwas created with the aim of promoting Green AI by Design, encouraging the development of algorithms that reduce their environmental footprint from the earliest stages of the process. 

Among its main objectives are: 

• Integrate environmental criteria into the design of AI algorithms and models. 
• Promote best practices in energy efficiency and computational optimization. 
• Promote the measurement of the environmental impact associated with the development of AI. 
• Promote the adoption of standards and frameworks for digital sustainability. 

This program is part of a broader strategy of responsible innovation, aligned with public policies on sustainability and ecological transition. 

Measuring to improve: the importance of the digital carbon footprint

One of the key principles of green algorithms is that you cannot reduce what you do not measure. Therefore, measuring the energy consumption and emissions associated with AI systems is a fundamental step. 

There are already initiatives that allow the carbon footprint of models and algorithmic systems to be estimated, taking into account factors such as: 

• Hardware energy consumption. 
• Data center efficiency. 
• Indirect emissions associated with technological infrastructure

This information is essential for identifying opportunities for improvement and making informed decisions in the development and deployment of AI solutions. 

Strategies for developing green algorithms 

The development of green algorithms involves the application of various technical strategies, including: 

• Neural network optimization and complexity reduction. 
• Efficient use of hardware and selection of energy-optimized infrastructure. 
• Reuse of previously trained models and weights. 
• Reduction of unnecessary training and redundant processes. Accurate knowledge of your climate impact.  

These practices not only contribute to reducing environmental impact, but can also generate economic savings and improvements in operational efficiency. 

In other words, the development of green algorithms responds to the need to rethink traditional models for designing and training artificial intelligence systems, incorporating efficiency and sustainability criteria as top-level technical variables. Compared to approaches based exclusively on increasing computing power and data volume, this approach promotes optimized architectures, more selective training processes, and the rational use of available computational resources. In this way, sustainability ceases to be an external factor and is directly integrated into the engineering decisions of the algorithmic system itself. 

From an operational and strategic perspective, adopting these practices allows us to move toward AI solutions that are more robust, scalable, and economically viable in the long term. Reducing energy consumption, decreasing computing times, and optimizing the use of technological infrastructures contribute not only to mitigating environmental impact, but also to improving the maintainability, reproducibility, and overall efficiency of the systems developed. In this sense, green algorithms are establishing themselves as a key approach for the development of responsible artificial intelligence that is aligned with current technological challenges. 

How to reduce your carbon footprint once it has been calculated 

The adoption of green algorithms brings multiple benefits to organizations, both from an environmental and strategic point of view. Among them are: 

• Reducing the digital carbon footprint . 

• Optimization of energy consumption and associated costs. 

• Improvement of corporate reputation and alignment with criteria  

• Anticipating future regulatory requirements in technological sustainability. 

Furthermore, incorporating sustainability criteria into AI development strengthens the trust of customers, investors, and other stakeholders. 

The role of certification and reference frameworks 

The National Green Algorithms Program also promotes the development of certification tools that enable objective assessment of the degree of sustainability of AI systems. 

In this area,ACERTA participatesin the development of certification schemes aimed at ensuring that algorithms meet environmental criteria from the outset, thereby contributing to greater transparency and confidence in the use of artificial intelligence. Certification becomes a key element for: 

• Verify compliance with sustainability criteria. 
• Compare technological solutions objectively. 
• Promote best practices in the digital ecosystem. 

The incorporation of specific certification schemes for green algorithms allows sustainability principles to be translated into measurable, verifiable, and comparable indicators, facilitating their integration into technological development and evaluation processes. These certification frameworks contribute to objectifying aspects such as energy efficiency, computational optimization, and responsible management of the algorithm's life cycle, providing organizations with reliable tools to assess the environmental impact of their artificial intelligence systems in a systematic and reproducible manner. 

From a strategic perspective, certification in algorithmic sustainability acts as a driving force for continuous improvement and the adoption of advanced standards in the digital ecosystem. By providing clear and assessable criteria, it encourages the development of more efficient and responsible solutions, while strengthening the confidence of users, customers, and administrations in the use of artificial intelligence. In this sense, certification not only validates compliance with environmental requirements, but also promotes a model of technological innovation aligned with the principles of transparency, efficiency, and sustainability. 

A vision for the future of sustainable AI 

The move toward sustainable artificial intelligence requires a collaborative approach involving public administrations, companies, research centers, and technical entities. The National Green Algorithms Program represents an important step in this direction, placing sustainability at the heart of technological development. 

Integrating environmental criteria into AI is not only a responsibility, but also an opportunity to build a more efficient and transparent innovation model that is aligned with current global challenges. 

This collaborative approach allows progress toward common standards, shared methodologies, and best practices aimed at measuring, optimizing, and reducing the environmental impact of artificial intelligence systems. Coordination between the different agents facilitates knowledge transfer, the validation of sustainable technological solutions, and the progressive adoption of development models that prioritize computational efficiency from the initial stages of algorithmic design. In this way, sustainability is consolidated as a cross-cutting criterion in the evolution of AI, beyond isolated or specific initiatives. 

In this context, green algorithms are positioned as a key element in ensuring the long-term viability of artificial intelligence, especially in scenarios of scaling and mass adoption. The systematic incorporation of environmental criteria helps reduce operational and energy risks, improve technological governance, and strengthen confidence in the use of AI as a tool for economic and social transformation. Thus, sustainability not only accompanies technological development, but also becomes a structural factor in competitiveness and responsible innovation. 

Conclusion

Green algorithms areestablishingthemselvesas a key element in advancingtoward sustainable artificial intelligence, capable of generating economic value without compromising the environment. The National Green Algorithms Program paves the way for incorporating sustainability into the design of algorithmic systems, promoting responsible innovation aligned with environmental objectives. 

The future of AI necessarily involves integrating efficiency, transparency, and sustainability into every stage of its development.