EEA-Eionet horizon scan – emerging trends in production and consumption

The environmental impacts of Europe’s current consumption and production patterns  

At the global level, resource extraction and processing are major drivers of environmental harm. They cause over half of global greenhouse gas emissions, 40% of the health impacts from particulate matter and over 90% of biodiversity loss and water stress. The prevailing patterns of production and consumption in Europe provide the foundation for citizen well-being, yet also create significant environmental and climate impacts. Although the European Union (EU) represents only 5.5% of the world’s population, it consumed 6.7% of global extracted resources in 2020 and generated 8.7% of the global climate impacts linked to resource extraction and processing in 2021. EU consumption also accounts for 7.2% of global land-based biodiversity loss and 7.1% of the health impacts of emissions of fine particulate matter (PM_2.5) from these activities (EEA, 2025a). 

While the per-capita material footprint of the EU fell by 5.5% between 2010 and 2024 (see Figure 1), it remains above the global average (UNEP IRP, 2024). Resource consumption remains at a level that still exceeds the planet’s ‘safe operating space’ for resource extraction (EC, 2023). The EU’s material footprint is also projected to rise in the period to 2030 (EEA, 2025b). In this context, extending product lifespans through circular design and production practices is key to lowering demand for new products and materials (EEA, 2024). However, comprehensive data on product lifespan trends remain limited, while the supply of new products shows no sign of decreasing.

Figure 1. Raw material consumption, EU

This briefing describes seven current and emerging trends linked to digitalisation (see Figure 2) expected to increasingly shape European patterns of production and consumption — and their impacts on our environment and climate. These trends were identified in partnership with the European Environment Information and Observation Network (Eionet), with support from the European Topic Centre on Sustainability Transitions (ETC ST), using horizon-scanning methods (see Box 1). Detailed findings of the horizon scan and a description of the method are available in the ETC ST report Horizon Scanning 2025: Results of the EEA-Eionet participatory horizon scan to identify emerging issues relevant to sustainable consumption & production.

Figure 2. The seven digitalisation-linked trends explored in this briefing

Emerging trends in consumption and production

Exploiting the brain’s reward system to shape demand

Consumption is entwined with psychological reward mechanisms, whereby the acquisition of goods activates responses linked to anticipation, satisfaction, status and social belonging (Fürst et al., 2015; Dubois and Ordabayeva, 2015). When overstimulated, these same mechanisms can foster addictive patterns of repeated overconsumption (Campanella, 2024). Such patterns can have direct environmental consequences when repeated on a global scale, driving higher material throughput and increased generation of waste. The algorithms used in digital commerce leverage these mechanisms to influence consumer preferences. ‘Recommender systems’ and AI shape purchasing behaviour via ‘anchoring’ and ‘persuasive personalisation methods’ that shift consumers' willingness to pay, while ‘dynamic creative optimisation’ deploys these strategies in real time and at scale (Masłowska et al., 2022) (see Box 2 for descriptions of terms). Behavioural monitoring, including micro-level interaction cues such as keyboard dynamics and sensor data, allows systems to infer emotional states and tailor content accordingly (Kołakowska et al., 2020; Ghosh et al., 2019). Adaptive advertising further deploys scarcity signals and time pressure to heighten purchase intention, while AI-enabled localisation adapts content to local linguistic and cultural norms to build trust in cross-border e-commerce (Davoodi and Mezei, 2024).

Influencer marketing has become an important element of this ecosystem, frequently blurring the boundaries between personal recommendation and paid advertising. Algorithms amplify such content by prioritising posts with high engagement metrics, reinforcing visibility and perceived popularity (Melgarejo-Espinoza et al., 2025). Gamification similarly raises engagement, particularly among younger consumers, through point scoring, badges and challenges (Li, 2025). 

As streaming platforms, gaming ecosystems and news outlets function as direct sales channels, the boundaries between entertainment, information and retail are now increasingly porous. Digital purchasing platforms and home delivery models also facilitate online purchasing across age groups (Eurostat, 2025, 2026). How these developments can be reconciled with the EU’s goals to reduce resource use and extend product lifespans is an increasingly pressing question (EEA, 2026a).

How new consumption patterns drive suppliers to shorten product life cycles

Technological advances and economic forces have converged to shorten product life cycles in both the production and sales stages of the value chain. This includes the time from product innovation and development to market placement and use (EP, 2016).

The global reach, real-time reactivity and algorithm-based advertising of today’s digital platforms amplify competition, compelling suppliers to perpetually refresh product portfolios. These effects are compounded by China’s dominant role in driving global production and consumption patterns. Hyper-competitive domestic markets generate spillover effects that propagate around the world, speeding up product renewal rates across entire industrial sectors (JRC, 2025a). 

At the same time, digital technologies are reconfiguring product innovation (Vărzaru and Bocean, 2024). Rapidly growing AI capabilities are dramatically shortening product development cycles (Stanford HAI, 2025; Dominguez, 2025). Advanced machine learning and automated development also support the rapid development of multiple design options at significantly lower costs (Steininger et al., 2025).  

Under such conditions, new product generations materialise on a monthly rather than annual basis. This is creating rising aggregate environmental pressure, even where the efficiency of individual devices and services has improved. The strategic implications for firms are profound. As timeframes for product obsolescence shorten, suppliers must introduce successive product generations with increasing frequency while recuperating development costs within compressed timeframes. The competitive risk associated with even marginal delays in sourcing, design or launch escalates correspondingly. In the absence of mandatory sustainability guidelines, corporate strategies risk increasingly prioritising novelty and variety over sustainability.

Environmental impacts of data centres

AI itself illustrates how environmental footprints can scale rapidly. Forecasts project power demand from data centres in Europe to increase from 96 terawatt hours (TWh) in 2024, to 168TWh by 2030 and to 236TWh by 2035 — growth of almost 150% in 10 years. Data centres’ share of total power demand is anticipated to nearly double, despite the substantial overall increase in power demand anticipated over the coming decade due to electrification (ICIS, 2025; EP, 2025).  

Current projections on the water demand of AI-driven data centres for cooling and electricity generation suggest an elevenfold increase between 2024 and 2028, when demand is expected to equate to 1,068 billion litres annually (Fu, 2025). This demand will place additional pressures on regional infrastructure, even as some are already struggling under seasonal effects such as heatwaves and droughts. See the EEA briefing on artificial intelligence and sustainable consumption in Europe (EEA, 2026a) for more details on this topic.

Impact of the digital transformation on resource extraction in Europe  

The global shift to renewable energy significantly increases demand for critical raw materials such as lithium, cobalt, rare earth elements, and platinum group metals (IRENA, 2023), alongside equally substantial demand from electronic goods, batteries, and semiconductors. Forecasts indicate significant increases in demand (Carrara et al., 2023), though strategies to meet it remain uncertain (ECA, 2026). 

Mining is among the sectors aiming to further integrate AI and automation technologies, presenting opportunities and risks relating to Europe’s sustainability. AI-driven geological modelling and remote sensing may improve resource identification while reducing some environmental impacts (Davies et al., 2025). The automation and electrification of mining equipment could also cut emissions and enhance worker safety (Garbarino et al., 2021). Precision mining techniques, enabled by robotics and real-time data analytics, have been proposed as a means to reduce waste and optimise ore recovery (Hidayat, M., 2025). Digital twins may support remote monitoring and predictive maintenance (van Dinter et al., 2022). 

The extent to which these technologies translate into measurable environmental improvements in practice remains unclear. Adoption data are largely self-reported by commercial actors, large-scale deployment is still limited and benefits demonstrated in controlled or pilot settings do not always scale. Hydrometallurgical and bioleaching methods offer lower-impact alternatives for metal extraction (Saleem et al., 2023; Roberto and Schippers, 2022), though environmental advantages depend on deployment conditions and scale. 

Overall, digital and process innovations could reduce the environmental footprint of mining, though the evidence base for real-world outcomes remains thin. Progress will depend on how quickly and broadly these technologies are adopted and whether governance frameworks ensure efficiency gains translate into genuine environmental reductions, rather than expanded extraction volumes.

Deep-seabed and space mining  

Within the same context of strategic autonomy and high demand for CRMs, deep-seabed and space mining are emerging as potential sources. These emerging industries raise different but equally serious ecological and governance concerns.  

Deep‑seabed mining is at a critical crossroads, with geopolitical, environmental and regulatory dynamics pulling in opposite directions. Momentum toward exploitation is growing, driven by rising demand for critical minerals and pressure from some states and industries to begin or amplify commercial mining. However, the environmental impacts of deep-seabed mining are still under study and a source of growing concern. Impacts vary depending on the type of activities involved: direct impacts on the seabed ecosystems (most of which are not yet fully known and understood) of the selected mining sites and pollution by ships onto surface water and from the dewatering of slurry on board (JRC, 2025b). 

While relocating mining activities to space could in theory help to reduce terrestrial environmental pressures, the potential environmental impacts are still concerning. Research modelling the impacts of commercial space flight on the atmosphere shows that despite relatively small CO2 totals per launch, more frequent launches and re-entries could impact the ozone‑layer recovery achieved under the Montreal Protocol, due to the warming effect of black carbon (soot) emitted by rockets (Ryan et al., 2022; Maloney et al., 2022). Soot affects the ozone layer primarily by absorbing solar radiation in the stratosphere, heating the surrounding air and triggering chemical reactions that break down ozone molecules. Other potential impacts of space mining include the creation of space debris and the alteration of celestial bodies that are still mostly unknown.

Boosting urban mining and recycling  

Urban mining is the recovery, reuse and recycling of valuable materials from urban waste, such as electronics, construction debris and other products. Technologies for recovering lithium, cobalt and rare earths from end-of-life batteries and electronics are progressing, although current capacity remains below the EU’s 2030 targets (ECA, 2026). 

High-efficiency sorting systems and solvent extraction techniques enable secondary raw materials to re-enter production cycles, reducing pressure on virgin resources and cutting greenhouse gas emissions. However, the required development of these techniques at scale remains uncertain due to higher energy costs and a lack of mature technical expertise (Vo et al., 2026). 

Focus has also been put on enhancing the material efficiency of renewable energy technologies. In the solar sector, advances in photovoltaic (PV) cell design have reduced the quantity of silicon and silver required per watt of output, while maintaining or improving efficiency. Similarly, wind turbine manufacturers have optimised blade and generator designs to minimise the use of rare earth elements without compromising performance (García-Gusano et al., 2025). 

AI optimisation of production and agricultural processes 

More broadly, digitalisation presents opportunities to optimise production systems, which could reduce the use of materials and energy in most processes. Harmonised combinations of strategies for both upstream (anything occurring earlier in a supply chain, e.g. sourcing raw materials) and downstream stages (final stages of a supply chain, e.g. distribution) yield the best results. Therefore, different indicators are being developed to assess the effects of such strategies and how their benefits compare to the investments made to put them in place (Almelhem et al., 2025; Madanaguli et al., 2024). With optimisations, AI can support cleaner production through advanced scheduling, predictive maintenance, real-time control and other capabilities that translate into measurable reductions in energy, materials and emissions across manufacturing value chains (Agrawal et al., 2023). 

AI-enabled precision agriculture employs high-resolution sensing and predictive analytics to support farmers’ decision-making, thereby reducing the use of water, fertilisers and pesticides. Deep learning models that fuse remote sensing with temporal networks (network representation where components and their interactions evolve through time) can markedly improve irrigation demand forecasting. This can enable significant gains in water‑use efficiency and yield stability, relative to traditional scheduling (Ye et al., 2024). The use of AI and robotics in pest management enables ‘spray‑as‑needed’ techniques that reduce the use of pesticides and associated environmental impacts (Mesías-Ruiz et al., 2023).

Conclusion 

The seven trends examined in this briefing illustrate the dual and often contradictory pressures that digitalisation and AI place on Europe's sustainability agenda. 

On the consumption side, increasingly sophisticated digital marketing techniques, from algorithmic recommender systems and affect-aware targeting to influencer ecosystems and gamified retail, are accelerating demand and shortening product life cycles. These dynamics complicate EU efforts to reduce material footprints and extend product lifespans, particularly given the pace at which such techniques evolve relative to regulatory cycles. 

On the production side, digitalisation offers tools for improving resource efficiency: AI-optimised manufacturing, precision agriculture, advanced recycling and smarter mining practices all present pathways to lower environmental impacts. However, the evidence base for large-scale real-world outcomes remains limited across most of these domains. In some, such as data centres, the environmental footprint of digital infrastructure itself presents a growing and not yet offset cost. 

Across all these trends, digitalisation neither straightforwardly enables nor undermines sustainability. Its effects are mediated by governance choices, investment priorities and the speed of regulatory response. Realising the potential of digital tools while managing their risks will require policy frameworks that are both proactive and adaptive, operating across consumption, production and resource extraction in an integrated way.