Description
The convergence between open data, artificial intelligence and environmental sustainability poses one of the main challenges for the digital transformation model that is being promoted at European level. This interaction is mainly materialized in three outstanding manifestations:
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The opening of high-value data directly related to sustainability, which can help the development of artificial intelligence solutions aimed at climate change mitigation and resource efficiency.
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The promotion of the so-called green algorithms in the reduction of the environmental impact of AI, which must be materialized both in the efficient use of digital infrastructure and in sustainable decision-making.
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The commitment to environmental data spaces, generating digital ecosystems where data from different sources is shared to facilitate the development of interoperable projects and solutions with a relevant impact from an environmental perspective.
Below, we will delve into each of these points.
High-value data for sustainability
Directive (EU) 2019/1024 on open data and re-use of public sector information introduced for the first time the concept of high-value datasets, defined as those with exceptional potential to generate social, economic and environmental benefits. These sets should be published free of charge, in machine-readable formats, using application programming interfaces (APIs) and, where appropriate, be available for bulk download. A number of priority categories have been identified for this purpose, including environmental and Earth observation data.
This is a particularly relevant category, as it covers both data on climate, ecosystems or environmental quality, as well as those linked to the INSPIRE Directive, which refer to certainly diverse areas such as hydrography, protected sites, energy resources, land use, mineral resources or, among others, those related to areas of natural hazards, including orthoimages.
These data are particularly relevant when it comes to monitoring variables related to climate change, such as land use, biodiversity management taking into account the distribution of species, habitats and protected sites, monitoring of invasive species or the assessment of natural risks. Data on air quality and pollution are crucial for public and environmental health, so access to them allows exhaustive analyses to be carried out, which are undoubtedly relevant for the adoption of public policies aimed at improving them. The management of water resources can also be optimized through hydrography data and environmental monitoring, so that its massive and automated treatment is an inexcusable premise to face the challenge of the digitalization of water cycle management.
Combining it with other quality environmental data facilitates the development of AI solutions geared towards specific climate challenges. Specifically, they allow predictive models to be trained to anticipate extreme phenomena (heat waves, droughts, floods), optimize the management of natural resources or monitor critical environmental indicators in real time. It also makes it possible to promote high-impact economic projects, such as the use of AI algorithms to implement technological solutions in the field of precision agriculture, enabling the intelligent adjustment of irrigation systems, the early detection of pests or the optimization of the use of fertilizers.
Green algorithms and digital responsibility: towards sustainable AI
Training and deploying AI systems, particularly general-purpose models and large language models, involves significant energy consumption. According to estimates by the International Energy Agency, data centers accounted for around 1.5% of global electricity consumption in 2024. This represents a growth of around 12% per year since 2017, more than four times faster than the rate of total electricity consumption. Data center power consumption is expected to double to around 945 TWh by 2030.
Against this backdrop, green algorithms are an alternative that must necessarily be taken into account when it comes to minimising the environmental impact posed by the implementation of digital technology and, specifically, AI. In fact, both the European Data Strategy and the European Green Deal explicitly integrate digital sustainability as a strategic pillar. For its part, Spain has launched a National Green Algorithm Programme, framed in the 2026 Digital Agenda and with a specific measure in the National Artificial Intelligence Strategy.
One of the main objectives of the Programme is to promote the development of algorithms that minimise their environmental impact from conception ( green by design), so the requirement of exhaustive documentation of the datasets used to train AI models – including origin, processing, conditions of use and environmental footprint – is essential to fulfil this aspiration. In this regard, the Commission has published a template to help general-purpose AI providers summarise the data used for the training of their models, so that greater transparency can be demanded, which, for the purposes of the present case, would also facilitate traceability and responsible governance from an environmental perspective. as well as the performance of eco-audits.
The European Green Deal Data Space
It is one of the common European data spaces contemplated in the European Data Strategy that is at a more advanced stage, as demonstrated by the numerous initiatives and dissemination events that have been promoted around it. Traditionally, access to environmental information has been one of the areas with the most favourable regulation, so that with the promotion of high-value data and the firm commitment to the creation of a European area in this area, there has been a very remarkable qualitative advance that reinforces an already consolidated trend in this area.
Specifically, the data spaces model facilitates interoperability between public and private open data, reducing barriers to entry for startups and SMEs in sectors such as smart forest management, precision agriculture or, among many other examples, energy optimization. At the same time, it reinforces the quality of the data available for Public Administrations to carry out their public policies, since their own sources can be contrasted and compared with other data sets. Finally, shared access to data and AI tools can foster collaborative innovation initiatives and projects, accelerating the development of interoperable and scalable solutions.
However, the legal ecosystem of data spaces entails a complexity inherent in its own institutional configuration, since it brings together several subjects and, therefore, various interests and applicable legal regimes:
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On the one hand, public entities, which have a particularly reinforced leadership role in this area.
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On the other hand, private entities and citizens, who can not only contribute their own datasets, but also offer digital developments and tools that value data through innovative services.
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And, finally, the providers of the infrastructure necessary for interaction within the space.
Consequently, advanced governance models are essential to deal with this complexity, reinforced by technological innovation and especially AI, since the traditional approaches of legislation regulating access to environmental information are certainly limited for this purpose.
Towards strategic convergence
The convergence of high-value open data, responsible green algorithms and environmental data spaces is shaping a new digital paradigm that is essential to address climate and ecological challenges in Europe that requires a robust and, at the same time, flexible legal approach. This unique ecosystem not only allows innovation and efficiency to be promoted in key sectors such as precision agriculture or energy management, but also reinforces the transparency and quality of the environmental information available for the formulation of more effective public policies.
Beyond the current regulatory framework, it is essential to design governance models that help to interpret and apply diverse legal regimes in a coherent manner, that protect data sovereignty and, ultimately, guarantee transparency and responsibility in the access and reuse of environmental information. From the perspective of sustainable public procurement, it is essential to promote procurement processes by public entities that prioritise technological solutions and interoperable services based on open data and green algorithms, encouraging the choice of suppliers committed to environmental responsibility and transparency in the carbon footprints of their digital products and services.
Only on the basis of this approach can we aspire to make digital innovation technologically advanced and environmentally sustainable, thus aligning the objectives of the Green Deal, the European Data Strategy and the European approach to AI.
Content prepared by Julián Valero, professor at the University of Murcia and coordinator of the Innovation, Law and Technology Research Group (iDerTec). The content and views expressed in this publication are the sole responsibility of the author.
