A bold leap in sustainable AI infrastructure is taking shape in Sines, Portugal, where SIN01, the world’s first 100% renewable AI data centre powered by ocean water cooling, is redefining how data processing and energy use intersect. The project sits on repurposed land that once hosted a decommissioned power station, marking a dramatic shift from legacy energy infrastructure to a cutting-edge, climate-conscious data ecosystem. This phased initiative by Schneider Electric and Start Campus debuted with a 26MW facility and a clear vision for a 1.2GW campus dedicated to AI workloads. What makes SIN01 particularly noteworthy isn’t only its scale, but its pioneering cooling approach: ocean water serves as the primary heat-removal medium, drawn from the Atlantic and returned only a degree warmer. The journey from concept to execution has been transformative for everyone involved, turning skepticism into a tangible, operating reality.
SIN01: The Ocean-Cooled AI Data Centre and the 1.2GW Campus Vision
The SIN01 facility in Sines represents a radical departure from conventional data centre design, both in its cooling modality and in its intent to scale through a thoughtfully planned expansion. The site’s geography—an industrial port town with access to maritime infrastructure—was leveraged to repurpose the land from a decommissioned power plant into a modern hub for AI workloads. The initial footprint of 26 megawatts serves as the first phase of a broader ambition: a campus capable of delivering up to 1.2 gigawatts of IT capacity specifically oriented toward artificial intelligence operations. The choice to rely primarily on ocean water cooling aligns with a broader strategy to decouple AI infrastructure from conventional, often carbon-intensive cooling methods that rely heavily on energy-intensive mechanical systems. The cooling loop draws in seawater, extracts heat generated by high-density AI racks, and discharges the warmed water back into the surrounding environment at a modest delta of one degree Celsius. This approach underscores a fundamental principle of SIN01: cooling efficiency and energy use must be reimagined to keep pace with AI’s intense processing demands.
The project’s narrative also reflects a larger cultural shift within the industry. Pablo Ruiz Escribano, who serves as the Senior Vice President of Secure Power and Data Centre Business for Schneider Electric in Europe, reflects on the arc from initial concept to active operation. In his recounting, the project faced doubt and even ridicule in its early days during 2021, when the idea was nascent and perceived as impractical by some observers. By the time of his new role and the Iberia launch event, the data centre had moved from a bold proposition to a functioning facility that embodies both disruption and achievement. The emotional dimension of achieving the project’s goals is palpable for him: an idea that once seemed impossible is now a practical, operating centre. This sentiment captures the essence of SIN01 as both a technical feat and a symbol of what is possible when vision, collaboration, and innovative cooling converge.
The SIN01 building is designed to demonstrate how AI-friendly design can be achieved without sacrificing sustainability. It is not simply a large data processing box; it is a carefully engineered ecosystem where the cooling strategy informs the architecture, the energy strategy informs the financing, and the environmental strategy guides ongoing monitoring and mitigation. The plant’s operation relies on an integrated approach that brings together advanced monitoring, control systems, and a broader environmental stewardship framework. At the heart of this approach is Schneider Electric’s EcoStruxure portfolio, which provides real-time data on energy use, thermal management, and performance across the entire operation. This integration allows operators to optimize energy consumption and heat removal continuously, ensuring that the cooling system remains in sync with the data centre’s evolving load profile.
The environmental governance surrounding SIN01 is comprehensive and ongoing. Start Campus collaborates with two research institutes specializing in biology and engineering to monitor the discharge’s chemical and physical properties and its ecological impact. Biologists and engineers participate in scuba diving expeditions on a regular cadence, providing quarterly pre-reports and a robust set of data that informs the environmental impact assessment. The commitment outlined by Start Campus is substantial: a minimum of three years of study, with a full lifecycle expectation of at least 25 years. The expectation is that any environmental impact will be carefully managed through compensation measures, ongoing monitoring, and adaptive mitigation strategies as the project scales. The researchers’ assessments suggest that the one-degree temperature increase in the discharge plume is minimal and will diffuse rapidly in the bay. While there will be ecological effects, the scientists anticipate that some wildlife may experience benefits, while others may relocate to different areas, but the overall predicted impact remains low. This nuanced perspective reinforces SIN01’s stance that responsible expansion is compatible with aggressive AI growth when managed with rigorous oversight.
Beyond the immediate centre itself, SIN01 embodies a philosophy that integrates heat reuse and efficient energy flows with broader ecosystem benefits. The exhaust heat from data centre operations is envisioned as a resource for nearby applications, aligning with a circular economy mindset that extends benefits beyond the data hall. The project’s environmental strategy does not view cooling as a one-way process but as an opportunity to contribute to nearby developments in a way that can reduce overall energy intensity in the surrounding region. The design emphasizes not just the technical feasibility of ocean cooling, but its potential social and environmental dividends when paired with thoughtful heat recycling, responsible discharge, and continuous environmental monitoring.
The collaboration between Schneider Electric and Start Campus extends well beyond hardware provisioning. It encompasses a comprehensive, cross-disciplinary approach to designing, building, and operating a sustainable AI data centre. Schneider Electric’s role is not limited to supplying equipment but includes delivering an integrated system that coordinates the building management, cooling, and energy optimization through a unified platform. Start Campus provides strategic direction in terms of campus-scale development, sustainability commitments, and long-term growth planning. The relationship reflects a broader industry trend in which infrastructure incumbents are partnering with AI-focused operators to ensure that advanced cooling, energy management, and environmental stewardship are central to new data centre builds rather than retrofitted afterward.
In addition to its cooling innovations, SIN01 benefits from the repurposing of infrastructure associated with the decommissioned power plant. Instead of building from scratch, the project leverages existing maritime connections and piping systems to facilitate the sea-water cooling loop. The reuse of infrastructure reduces the logistical and environmental footprint associated with new construction while accelerating deployment timelines. The reuse philosophy also aligns with the project’s commitment to minimizing disruption to the surrounding environment and to maximizing the efficiency of water and heat management through established channels.
As the project scales toward its envisioned 1.2GW campus, the SIN01 model offers a blueprint for how to combine large-scale AI capacity with a rigorous environmental framework. The approach demonstrates that it is possible to push the boundaries of data centre density and performance while maintaining a strong emphasis on sustainability, environmental monitoring, and ecosystem stewardship. The ocean-cooled design is a bold experiment in industrial-scale cooling that, if successful, could influence future data centre developments by providing an alternative to traditional air-based cooling systems with compelling environmental advantages. The overarching narrative is one of audacious innovation paired with disciplined governance, where ambitious capacity growth is matched by equally ambitious commitments to environmental integrity and community impact.
Addressing the AI Infrastructure Challenge: Cooling, Heat, and Efficiency
The rapid growth of AI workloads has placed unprecedented demands on data centre infrastructure, turning cooling and heat management into core challenges that go beyond raw processing capacity. Traditional facilities, built for steadier workloads, often struggle to accommodate the intensive processing requirements of AI applications. The core difficulty lies not only in delivering enough computational power but in designing architectures that can sustain high-density heat production without compromising reliability or sustainability. As AI models grow larger, deploying more parameters and running increasingly complex algorithms, the intensity of the heat generated by AI accelerators has become a central bottleneck for many operators. SIN01 attempts to tackle this by rethinking how heat is removed and how power is delivered to high-density racks.
A key distinction between an AI data centre and a conventional facility lies in the cooling strategy. The primary difference resides in how heat is removed from the data hall and how that heat is extracted from the IT equipment. Pablo Ruiz Escribano explains that the difference manifests in cooling methodology rather than the appearance of the data hall. In traditional configurations, cooling infrastructure often revolves around air-based approaches or conventional liquid cooling, but SIN01’s ocean-water cooling system represents a fundamental shift in physics and engineering. The goal is to manage higher heat loads in a more efficient manner, enabling more IT capacity within the same physical footprint. This is particularly important given the high density of AI workloads, where racks can operate at power densities that previously would have required larger footprints or more aggressive energy use. The SIN01 design demonstrates that it is possible to pack more IT capacity into an existing space by redefining how heat is extracted, rather than by simply adding more cooling equipment.
The operational efficiency gains from advanced cooling translate into notable reductions in energy consumption and total cost of ownership. Cooling has historically accounted for a substantial portion of a data centre’s operating expenditure, often around 60% of total OPEX, with a significant portion of energy use dedicated directly to cooling. In high-density AI scenarios, the energy required to remove heat can escalate quickly, creating a financial and environmental imperative to optimize cooling systems aggressively. SIN01 addresses this by optimizing the cooling workflow and leveraging seawater as a sustainable heat-extraction medium, thereby reducing the energy intensity associated with conventional cooling methods. This approach aligns with a broader industry trend toward liquid cooling for AI workloads, which has demonstrated the potential for lower energy use per unit of computational throughput compared with traditional air-cooled designs.
The expansion in IT capacity that SIN01 enables within the same physical space is a central theme of its approach. The facility demonstrates that the same data hall volume can support significantly higher IT workloads when heat removal is redesigned to be more effective. The implications for design philosophy are substantial: rather than enlarging the facility or investing in more mechanical equipment, operators can achieve higher density and efficiency by reimagining the cooling architecture. This principle supports a more sustainable growth trajectory for AI data centres, allowing operators to deliver growing AI capacity while controlling energy use and emissions. The proactive utilization of liquid cooling and the integration of oceanic heat transfer systems demonstrate how cooling strategy can be a strategic driver of efficiency, cost savings, and environmental stewardship.
The broader industry implications of SIN01’s cooling approach are meaningful. As AI workloads continue to surge, the demand for scalable, efficient, and sustainable cooling solutions will intensify. SIN01’s ocean-cooled model offers a concrete example of how to meet this demand without sacrificing environmental commitments. The project illustrates how high-density computing can be aligned with renewable energy and responsible cooling, delivering performance gains while reducing the carbon footprint associated with AI processing. The operational philosophy emphasizes the importance of optimizing cooling components to maximize overall energy efficiency, recognizing that the cooling system’s design and management can have a disproportionate impact on a data centre’s environmental footprint. As such, SIN01 is not simply a technical curiosity; it is a case study in how cooling infrastructure choices shape the viability and sustainability of AI-scale computing.
In practice, the project demonstrates that leveraging liquid cooling and sea-water heat exchange requires an integrated ecosystem of hardware, software, and process governance. The EcoStruxure platform supports continuous monitoring and control, enabling real-time insights into thermal dynamics, energy usage, and equipment health. This capability is critical when attempting to balance high-performance AI workloads with the need to minimize energy consumption and environmental impact. The system’s analytics empower operators to optimize cooling configurations, identify inefficiencies, and respond rapidly to changing loads. In this sense, SIN01 embodies a forward-looking model where data centre design integrates high-density computing with intelligent, data-driven management that reduces energy waste, improves reliability, and sustains performance in line with sustainability goals.
A further dimension to SIN01’s approach is its emphasis on heat reuse and end-use application. The design contemplates channeling the extracted heat to nearby processes or facilities that can benefit from raised temperatures, thereby creating additional value streams from the data centre’s energy flows. This circular approach enhances the overall energy efficiency of the project and fosters collaboration with neighboring industries. The potential for heat recovery extends the lifespan of the cooling loop’s value, turning what could be wasted waste heat into a resource that compounds savings and efficiency. Such an approach is consistent with a broader shift toward integrated energy systems in which data centres act as nodes within a wider energy ecosystem rather than isolated energy users. By thinking beyond the data hall, SIN01 demonstrates how cooling decisions can drive downstream benefits and strengthen regional energy resilience.
In assessing the economic dimensions, it is clear that the cooling strategy is a central lever for total cost of ownership and long-term viability. While the initial capital outlay for ocean-water cooling systems can be significant, the ongoing energy savings and the ability to expand IT capacity within a fixed footprint create a compelling value proposition. The project’s design philosophy prioritizes long-term efficiency, reliability, and sustainability, recognizing that AI workloads will continue to evolve and intensify. The end result is a data centre that not only meets current AI demands but remains adaptable to future workloads, thereby delivering enduring value for investors, operators, and the broader community.
In sum, SIN01’s approach to tackling the AI infrastructure challenge—through innovative cooling, heat management, and an integrated monitoring and control framework—illustrates how a combination of physical design, digital optimization, and strategic partnerships can unlock new possibilities for sustainable AI data centres. The project positions ocean cooling as a viable, scalable alternative to traditional cooling modalities, and it demonstrates how a well-considered combination of infrastructure reuse, real-time monitoring, heat reuse, and rigorous environmental oversight can deliver a model for the next generation of AI facility deployments.
The Blueprint for Sustainable AI Infrastructure: Reuse, Integration, and Real-Time Control
A standout feature of SIN01 is not merely the cooling technology but how the project integrates with existing infrastructure to maximize efficiency and minimize disruption. Start Campus chose to harness the decommissioned power plant’s maritime connections rather than building a completely new facility from the ground up. This decision underscores a broader strategy of repurposing and adapting existing assets to support modern AI workloads. By leveraging the pre-existing piping and water transport systems that were initially designed to move seawater for cooling the older plant, SIN01 avoids the heavy environmental and logistical costs associated with constructing new intake and discharge infrastructure. This approach preserves valuable maritime infrastructure while repurposing it for a new generation of computational power.
The project’s reliance on Schneider Electric’s EcoStruxure portfolio is central to its operational efficiency. EcoStruxure provides a comprehensive set of real-time monitoring and control capabilities that optimize energy usage, water cooling, and system health across the entire operation. The system integrates sensors, analytics, and automation to provide a holistic view of performance, enabling proactive management of energy and cooling requirements. The seamless, end-to-end integration of hardware, software, and services ensures that SIN01 can respond dynamically to fluctuations in IT load, environmental conditions, and other external factors. The result is a data centre that is not only capable of delivering high-density AI compute but also of optimizing its energy performance through intelligent, data-driven management.
An important element of the integration strategy is the reuse of heat and water resources that were created by prior industrial processes. The seawater used for cooling is part of a larger water-management ecosystem that includes heat extraction and, potentially, the reuse of discharged water in other applications. Start Campus notes that the heat captured from the data centre can be reused locally, creating opportunities for synergies with nearby industrial, commercial, or municipal facilities. This aspect reinforces SIN01’s commitment to a broader environmental and economic system rather than a standalone facility. By thinking about heat and water as resources to be optimized rather than waste streams, the project demonstrates how large-scale AI infrastructure can align with regional energy and resource planning.
A comprehensive environmental monitoring program is also a crucial element of SIN01’s blueprint. Start Campus has established a robust protocol involving two independent research institutes that monitor both the chemical and physical characteristics of the water discharge and the ecological impact on the surrounding ecosystem. The research involves ongoing scuba diving activities, with quarterly pre-reports that contribute to a more rigorous environmental impact assessment. The program is designed to meet and exceed requirements, and the project commits to long-term monitoring for at least 25 years. The researchers emphasize a nuanced understanding of potential impacts, acknowledging that temperature increases in the discharge will alter the local environment in complex ways. Their assessments suggest that the specific plume dynamics will be limited and diffuse, with some wildlife experiencing potential benefits while others may migrate away. The careful monitoring framework provides a data-driven basis for adjusting environmental strategies as needed.
From a governance perspective, the SIN01 project represents a holistic integration of engineering, environmental science, and policy considerations. The team recognizes that sustainability is not a static target but a dynamic process that requires ongoing evaluation and adaptation. The inclusion of two research partners in the monitoring program ensures that the environmental assessments are credible and that findings are continuously validated. This approach aligns with the broader expectation for responsible large-scale AI infrastructure to maintain a robust, transparent, and evidence-based environmental stewardship program. By openly sharing monitoring outcomes and updating mitigation strategies in response to new data, SIN01 can remain at the forefront of sustainable data centre design.
A central question for any data centre project of this scale is how to reconcile rapid capacity expansion with environmental safeguards. SIN01 demonstrates that it is possible to balance growth with responsibility by embedding environmental considerations into every stage of design, build, and operation. The project’s strategy centers on making the most of existing infrastructure, implementing a sophisticated monitoring regime, and ensuring that any expansion aligns with the commitments to environmental protection and sustainability. The environmental impact assessment is not a one-time exercise but an ongoing process that informs future expansions, material choices, energy strategies, and regional ecological considerations. In this sense, SIN01’s blueprint offers a replicable model for other AI-focused data centres seeking to scale responsibly while maintaining a firm commitment to the surrounding environment and community.
In terms of ecological impact, the project’s early findings suggest that the one-degree Celsius increase in the discharge plume is manageable and poses minimal chemical risk to the bay’s ecosystem. The careful management of discharges—without introducing harmful chemicals—and the diffusion of warmed water are aspects that contribute to a lower environmental risk profile than might be expected for large-scale cooling operations. While there will always be some ecological consequences, the evidence supports a conclusion that with robust monitoring and mitigation, SIN01 can achieve a balance between high-density AI capacity and environmental stewardship. The ongoing collaboration with the research community will continue to illuminate the precise nature of impacts and guide adjustments to operations as needed.
An additional advantage of the SIN01 model is the potential to translate this approach into broader energy and urban planning contexts. The project’s heat reuse concept could inspire nearby developments to integrate heat recapture into their energy strategies, contributing to a more circular local energy economy. The reuse of water from the regasification plant, previously considered waste, demonstrates how resourceful thinking can turn byproducts of one industrial process into inputs for another. This cross-pollination of industrial streams helps to reduce the environmental footprint of the region’s energy ecosystem, creating a more sustainable, integrated urban-industrial landscape. The synergy between data centre operations, maritime infrastructure, energy policy, and environmental stewardship forms a compelling narrative about how future AI infrastructure can be both powerful and responsible.
In summary, SIN01’s blueprint for sustainable AI infrastructure emphasizes the value of reusing existing assets, integrating cutting-edge cooling and control technologies, and maintaining rigorous environmental oversight. The combination of repurposed infrastructure, real-time monitoring, heat and water reuse, and robust environmental governance demonstrates a holistic approach to constructing a large-scale AI facility that can grow while preserving the surrounding ecosystem and community. The project’s approach is not simply about building a bigger data centre; it is about integrating technology, sustainability, and regional development into a cohesive, scalable model for the future of AI infrastructure.
AI Integration: From Construction to Operations, and a Carbon-Modelling Revolution in BIM
From its inception, SIN01 envisions AI not merely as a workload to be supported by hardware but as an active intelligence embedded in the project’s construction, operation, and ongoing optimization. The role of AI in SIN01 extends across a broad spectrum—from planning and construction to monitoring, maintenance, and future expansion planning. The project’s leadership views AI as an industrial revolution akin to electricity or steam, a technology that is redefining every facet of data centre design and operation. The core idea is that AI is not just a set of workloads running on servers; it is a pervasive force that can transform how data centres are built, managed, and evolved to meet emerging challenges and opportunities. This philosophy has guided the project toward building an AI-enabled facility that can adapt in real time to changing conditions and demands.
AI features permeate the entire SIN01 ecosystem. During construction, AI-assisted planning and monitoring help track progress, optimize resource usage, schedule activities, and ensure safety and quality standards. In operations, AI supports predictive maintenance, efficiency optimization, and energy management. One particularly notable application is the robo-guard dog, a robotic observer that greets visitors, captures site imagery, and automatically compares the captured images with 3D models to assess site conditions. This AI-enabled on-site assistant reduces the need for unnecessary human intervention, helping to ensure that operations run smoothly while maintaining a high safety standard. The ability to compare real-world site conditions with digital representations in real time is a powerful example of how AI can enhance the accuracy and efficiency of site management.
Beyond construction and site management, SIN01 demonstrates a sophisticated approach to sustainability planning that integrates AI into predictive models and decision-support tools designed to optimize environmental performance. The project uses AI to anticipate equipment aging and potential Deviations that trigger maintenance actions. This helps to maintain reliability and performance while minimizing downtime and reducing waste. The approach aligns with the broader objective of maximizing energy efficiency and minimizing environmental impact by ensuring that maintenance and operational decisions are data-driven and timely.
One of the most compelling innovations at SIN01 is the comprehensive carbon modelling system integrated into the project’s Building Information Model (BIM). The BIM-based carbon modelling represents a fundamental shift in how environmental impact is assessed and managed throughout a facility’s lifecycle. India Oliveira, Sustainability Manager at Start Campus, describes this carbon model as a groundbreaking feature that enables deeper visibility into emissions across various scopes (one, two, and three) within the BIM layers. By overlaying carbon data with the physical infrastructure, the team can identify which components contribute the most to emissions and determine actions to reduce them. This integration of carbon modelling into BIM allows for proactive decision-making about materials, design choices, and supplier selection before construction even begins. It provides a pathway to optimize emissions from the outset, rather than attempting to rectify them post-construction.
The carbon modelling system offers a multi-dimensional view of emissions across the facility’s lifecycle. It allows the team to quantify how different design choices affect emissions in absolute terms and to rank potential alternatives according to their environmental impact and cost implications. India highlights that the BIM system enables testing emissions scenarios before any construction starts, which is a powerful capability for achieving sustainable design outcomes. The system’s data-driven approach extends to complex supply-chain analysis. For instance, the BIM model can evaluate the environmental footprint of steel structures by considering the raw material sourcing, transportation distances, energy sources used by suppliers, and the proportion of renewable energy in suppliers’ operations. This ability to analyze materials across multiple dimensions—emissions, energy use, transport, and even cost—allows the team to explore trade-offs and identify options that reduce emissions without compromising project viability.
The combination of AI-driven construction, operations, and a BIM-based carbon modelling framework positions SIN01 as a leader in sustainable data centre design. The carbon modelling system is not merely an academic exercise; it directly informs procurement strategies, material choices, and design decisions. The system enables the project to understand the environmental implications of every material and component in the supply chain, and to adjust accordingly to minimize emissions while maintaining performance and cost targets. This capability is especially valuable given the long-term lifecycle of the project (25 years or more) and the complexity of supply chains involved in large-scale data centre builds. The BIM-based approach is a powerful enabler of transparency and accountability, ensuring that the project can verify and demonstrate the environmental performance of each element.
The idea of integrating carbon modelling into BIM extends beyond the immediate project to broader industry practice. If SIN01’s BIM carbon model proves effective, it can serve as a blueprint for future AI data centres seeking to optimize emissions from the design phase onward. The model provides a structured framework for evaluating a wide range of variables—materials, transport logistics, supplier energy profiles, local sourcing, and more—within a single, coherent virtual representation of the facility. This capability is instrumental in enabling design teams to make smarter choices that balance performance, cost, and environmental considerations from the earliest stages of project development. The BIM-based carbon model thus functions as both a planning tool and a live performance monitor, continually informing decisions as the project evolves.
The real value of AI integration in SIN01 lies in the way AI supports sustainability goals through continuous optimization and predictive analytics. The project’s leadership emphasizes a practical, outcomes-focused perspective: “If we want to be efficient, to save CO2 emissions, and to increase the reliability of the data centre, we should send people onsite only when necessary.” AI assists in determining when interventions are genuinely needed, enabling a more targeted, intelligent approach to on-site operations. This philosophy minimizes disturbance, reduces energy waste, and improves overall reliability by ensuring that human resources are deployed precisely when and where they are most needed.
SIN01’s approach to AI-enabled sustainability is characterized by a combination of on-site automation, advanced predictive analytics, and lifecycle-focused carbon modelling. The goal is not to replace human expertise but to enhance it with intelligent systems that optimize performance, cost, and environmental impact. The result is a data centre plan that respects the environment, supports high-density AI workloads, and remains adaptable for ongoing changes in technology and demand. The project’s emphasis on integrative AI across construction, operation, and sustainability design demonstrates the potential for AI to deliver tangible, measurable improvements in data centre efficiency and environmental stewardship.
The broader significance of SIN01’s AI integration lies in its demonstration that AI can be embedded throughout a data centre’s lifecycle, from initial planning to ongoing operation and future expansion, in ways that enhance efficiency and reduce environmental impact. The combination of robo-guidance, predictive maintenance, and BIM-based carbon modelling creates a holistic, AI-driven system that monitors and optimizes performance across all dimensions. This integrated approach is a model for where the data centre industry is headed: a space where AI is not just a workload but a strategic enabler of sustainable growth, reliability, and accountability. The SIN01 project thus stands as a powerful example of how AI can be deployed to transform the sustainability profile of AI infrastructure itself, creating a virtuous cycle where smarter design leads to more sustainable operation and future-ready scalability.
The Carbon Modelling Revolution in BIM: How Emissions Become a Design Dimension
A centerpiece of SIN01’s sustainability strategy is the carbon modelling system embedded within its Building Information Model (BIM). This system represents a novel shift in how environmental impact is assessed and managed over the lifecycle of a facility. By layering emissions data across a spectrum of categories—scope one, scope two, and scope three—within the BIM framework, the project achieves a level of visibility and precision previously unavailable in traditional data centre design. The carbon model functions as a multi-layered lens through which teams can view the environmental footprint of every component, material, and process across the project’s lifecycle. This capability enables proactive decision-making and optimization, rather than reactive mitigation after construction and operation have begun.
India Oliveira, the Sustainability Manager at Start Campus, describes the BIM-based carbon model as a key differentiator for SIN01. She explains that the model allows the team to identify which infrastructure carries the highest emissions, a crucial insight for prioritizing mitigation strategies and reordering materials or suppliers to minimize environmental impact. The model’s utility extends to assessing potential substitutions for materials to achieve lower emissions without sacrificing performance. By simulating emissions outcomes before construction, the project can implement targeted strategies to reduce carbon intensity from the outset, creating a more sustainable foundation for the data centre’s lifecycle.
An essential dimension of the BIM carbon model is its capacity to capture supply-chain emissions. The model includes a comprehensive database of steel suppliers and other critical components, detailing raw material sourcing, transportation distances, energy profiles, and the proportion of renewable energy used by suppliers. Such data allow for a nuanced understanding of emissions contributions at the supply chain level and facilitate informed procurement decisions. The system can benchmark different material options against emissions and cost considerations, enabling a rigorous cost-benefit analysis that accounts for environmental impacts. This capability is transformative because it moves emissions analysis from a retrospective activity to a predictive, optimization-oriented process. The BIM model thus becomes a dynamic tool for guiding material choices and supplier partnerships in a way that aligns with long-term sustainability goals.
In practice, the carbon modelling system is integrated into the overall design workflow, influencing decisions such as whether to select alternative materials, where to source them, and how to balance emissions with cost. The goal is not merely to minimize carbon but to optimize the entire design’s environmental footprint while still delivering on performance, reliability, and budget constraints. By exposing the emissions implications of design decisions at the earliest stage, the BIM carbon model enables teams to iterate rapidly and evaluate trade-offs in a way that was previously impractical. The ultimate objective is to create a data centre that achieves the desired AI performance with the lowest possible environmental cost, while also building resilience to future policy changes or market shifts that could affect energy costs or carbon markets.
The BIM-based carbon modelling system also supports more effective collaboration across stakeholders. Because the model provides a shared, transparent representation of emissions across multiple dimensions, engineers, sustainability specialists, procurement teams, and operations managers can align their decisions around a common set of environmental objectives. This alignment fosters a more cohesive project approach and a stronger commitment to sustainability throughout the design, construction, and operation phases. The consequence is a data centre that is not only technically capable but also environmentally trustworthy, with a clear and auditable path toward reduced emissions and enhanced sustainability performance.
In a broader sense, SIN01’s carbon modelling approach demonstrates how BIM can be leveraged to integrate climate considerations into the core design narrative of complex infrastructure projects. Rather than treating sustainability as an afterthought or a separate compliance exercise, the BIM carbon model makes it an intrinsic part of the design decision-making process. This methodology has the potential to influence future AI data centre developments, encouraging a shift toward holistic, carbon-conscious design practices that permeate every stage of a project—from the selection of structural materials to the optimization of supply chains and the integration of energy systems.
As SIN01 continues to evolve, the carbon modelling framework is likely to become broader in scope, incorporating additional dimensions such as lifecycle energy analysis, embodied carbon in non-structural components, and ongoing performance metrics tied to real-world operation. The integration of AI into the modelling process itself could additionally enable adaptive, real-time updates to the emissions profile as operating conditions change, further enhancing the accuracy and usefulness of the model for ongoing sustainability management. This forward-looking integration of AI with carbon modelling in BIM signals a future in which environmental considerations are woven into the fabric of data centre design and operation, enabling smarter choices and more responsible growth for AI infrastructure.
The Role of Schneider Electric and Start Campus: Transparency as a Core Competitive Advantage
The collaboration between Schneider Electric and Start Campus exemplifies how traditional infrastructure companies can adapt to the demands of AI-driven, energy-conscious data centre development. Rather than limiting their role to hardware supply, the partnership embraces a broader consultancy and systems integration model. This approach reflects a philosophy of openness and collaborative problem solving that acknowledges the complexity and scale of modern AI infrastructure. The two organizations engaged in an ongoing, open dialogue from the outset, with Start Campus presenting challenges and Schneider Electric contributing solutions, including the adoption of liquid cooling through partnership with alternative suppliers as needed. The resulting solution is not a fixed blueprint but a flexible, evolving portfolio that can adapt as AI workloads grow and cooling technologies advance. The ability to shift and expand capabilities—such as incorporating liquid cooling into Schneider Electric’s portfolio—illustrates a pragmatic and responsive approach to meeting the needs of AI data centres.
Transparency and collaboration are not merely corporate values; they are operational imperatives in this context. The SIN01 project uses a multi-stakeholder model to ensure that the design and operation decisions are credible, auditable, and aligned with sustainability objectives. Such transparency is important for building trust with regulators, the local community, and the ecosystem of suppliers and partners involved in the project. By fostering open communication and sharing insights from ongoing monitoring and performance data, the collaboration helps to create a sense of shared responsibility for the project’s environmental outcomes and its contribution to the region’s economic and technological development.
The potential impact of this partnership extends beyond the immediate data centre site. The combination of Schneider Electric’s technology portfolio with Start Campus’s vision for a large-scale AI campus is poised to influence regional economic development and energy strategy. The Project of National Interest designation granted by the Portuguese government underscores the project’s strategic importance, signaling that SIN01 is more than a single facility; it is a catalyst for broader economic and industrial growth. The collaboration’s emphasis on transparency and shared learning may encourage similar partnerships in other markets, accelerating the adoption of sustainable, AI-focused data centres globally.
From a human resources perspective, the SIN01 project has the potential to create significant employment opportunities. Start Campus has projected as many as 1,200 direct jobs and approximately 9,000 indirect roles across the lifecycle of the campus project. The broader economic impact includes the project’s investment footprint, with Start Campus announcing an €8.5 billion (US$8.8 billion) investment to accelerate campus development. These figures highlight the project’s ambition and its potential to contribute to regional growth, skill development, and technology transfer. The long-term commitments to workforce development and local engagement are essential elements of SIN01’s strategic plan, reinforcing its role as a engine of economic development while also pushing the boundaries of sustainable AI infrastructure.
Portugal’s energy landscape provides a favorable backdrop for SIN01’s ambitions. The country’s competitive energy costs, combined with a substantial renewable energy capacity, create favorable conditions for large-scale AI data centres. The ability to secure more than 1GW of grid power reflects both the country’s energy resilience and the strategic importance of the SIN01 ecosystem within the broader national energy framework. In this context, the project’s cost structure, supply chain decisions, and energy strategy must harmonize with Portugal’s energy policies and market dynamics. The result is a data centre that is not only technically advanced but also well integrated into the country’s energy market and regulatory environment.
The strategic significance of SIN01 is further reinforced by its status as a national-level project. The Portuguese government’s designation of SIN01 as a Project of National Interest reflects its anticipated influence on the country’s economy, technology ecosystem, and energy strategy. This designation serves as a signal to investors and potential partners that the project aligns with national priorities for innovation, clean energy, and sustainable growth. The project’s ambition to scale to 1.2GW of AI-carrying capacity while maintaining strict environmental controls demonstrates a commitment to responsible growth and a long-term vision for the data centre industry in Portugal and beyond.
In sum, the SIN01 partnership demonstrates how transparency, collaboration, and a shared commitment to sustainability can create a powerful value proposition for AI infrastructure development. The project’s approach to technology integration, environmental monitoring, and stakeholder engagement provides a template for the next generation of AI data centres that seek to balance performance, reliability, and sustainability within a rapidly changing global energy landscape. The partnership between Schneider Electric and Start Campus stands as a leading example of how large-scale, sustainability-focused data centre projects can succeed by aligning technology leadership with environmental accountability and open knowledge-sharing.
The Future of AI Data Centre Infrastructure: Prefabrication, Standardisation, and Rapid Deployment
As the AI revolution accelerates, industry leaders are rethinking the traditional mode of data centre construction. SIN01’s blueprint points toward a future where standardization, prefabrication, and modular design become essential components of how AI infrastructure is brought online at scale. The traditional on-site construction model, characterized by intermittent activity and diverse teams working in complex, interwoven sequences, is increasingly seen as ill-suited to the speed and scale demanded by AI workloads. The SIN01 project emphasizes the need to shift toward standardized, prefabricated modules that can be replicated and deployed quickly, enabling faster startup (FST) and faster commissioning. The benefits of this approach extend beyond speed: standardized modules can improve quality, reduce errors, simplify maintenance, and produce more predictable cost and schedule outcomes. They also offer opportunities to optimize logistics, reduce on-site disruption, and lower the overall environmental impact by consolidating manufacturing and testing into controlled factory environments.
Pablo Ruiz Escribano emphasizes that the way data centres are constructed is evolving more than the equipment itself. The shift toward standardization and prefabrication is presented as a necessary evolution for deploying AI-scale facilities. In a traditional build, skilled tradespeople—plumbers, electricians, and others—work on-site in a complex, sometimes chaotic sequence. By contrast, modular construction enables equipment to be produced in controlled environments and assembled on-site with minimal disruption. The result is not only a faster deployment but also a more predictable quality outcome, with a reduced probability of on-site rework or design changes. This new approach aligns with the broader objective of reducing logistics complexity, cutting CO2 emissions associated with transportation, and delivering projects on schedule and within budget.
The prefabrication strategy also complements the ocean-water cooling concept by enabling more precise, repeatable integration of cooling modules, pumps, heat exchangers, and control systems. Prefabricated units can be designed, tested, and optimized for maximum thermal efficiency before they reach the site, ensuring that the sea-water cooling loop can be installed with minimal delays and adjustments. The modular approach permits rapid scaling by adding standardized modules to increase a data centre’s IT capacity, enabling a more agile response to evolving AI workloads and market demand. The ability to scale through repeatable modules reduces risk and supports efficient capital planning as the campus expands toward its 1.2GW goal. This is a practical application of the broader principle that scale can be achieved not by stretching a single design to its limits but by duplicating and expanding standardized building blocks.
Logistics optimization is another critical benefit of modular, prefabricated design. The more modular the solution, the more predictable the supply chain becomes, which can reduce lead times and minimize the risk of delays caused by issues such as material shortages or complex on-site installations. The modular approach also has the potential to improve safety by reducing the number of on-site tradespeople and simplifying the on-site assembly process. Additionally, standardized modules can be more easily dispatched to other locations in a future expansion scenario, enabling a faster replication of SIN01’s successful model in other markets. This replication capability aligns with the project’s broader ambition to influence the future of AI data centre infrastructure on a larger scale.
From an environmental perspective, prefabrication and standardization can yield substantial gains. Factory-based manufacturing can optimize energy use, reduce waste, and improve material efficiency. It allows for tighter control over emissions associated with manufacturing and transport and supports more precise quality control and testing. The potential to minimize on-site disruptions is also meaningful for both the local environment and the surrounding community. A smoother construction phase reduces traffic, noise, and other potential disturbances, contributing to a more favorable public perception of data centre development near industrial and urban areas. The combined effects of standardization, prefabrication, and optimized logistics contribute to a lower overall environmental footprint for large-scale AI data centres like SIN01, reinforcing the argument that rapid deployment and sustainable operation can be compatible.
The SIN01 experience also underscores the importance of regulatory and policy alignment. The project’s national significance, reflected in its Project of National Interest designation, suggests that regulatory frameworks in Portugal are prepared to accommodate and encourage such ambitious, climate-conscious data centre initiatives. This regulatory support can influence how future projects are designed, financed, and permitted, shaping the conditions under which large-scale AI infrastructure can be developed. In this context, the SIN01 model may serve as a reference point for policy discussions about the role of sustainable data centres in national innovation strategies and energy plans. The policy environment that supports SIN01 could help catalyze investment in similar projects, optimism about the role of AI in regional development, and momentum for the adoption of sustainable, scalable data centre architectures in Europe and beyond.
The long-term economic implications of SIN01 are notable. The project has the potential to create a substantial number of jobs and generate significant indirect economic activity across the region. The €8.5 billion investment and the goal of up to 1,200 direct jobs and 9,000 indirect roles signal a transformative economic impact that could help diversify the local economy and position Portugal as a hub for AI infrastructure innovation. The combination of job creation, investment, and advanced technology development aligns with broader regional development objectives and demonstrates how sustainable AI data centres can contribute to long-term economic resilience. The SIN01 model therefore represents a convergence of technological innovation, economic opportunity, and environmental stewardship that can serve as a blueprint for future AI infrastructure projects around the world.
The future of AI data centre infrastructure will likely be shaped by the lessons from SIN01 across several dimensions. First, the emphasis on sustainable cooling, heat reuse, and comprehensive environmental monitoring underscores the importance of integrating environmental considerations into the very core of data centre design. Second, the project’s BIM-based carbon modelling demonstrates the transformative potential of digitalisation for environmental management, offering a detailed, data-driven approach to emissions reduction. Third, the partnership model between corporate technology providers and campus developers suggests that collaborative ecosystems are critical for delivering large-scale, innovative projects successfully. Fourth, the focus on prefabrication and standardization points to a practical pathway for achieving rapid deployment while maintaining quality and environmental performance. Together, these dimensions paint a future in which AI data centres are not only more powerful and cost-effective but also more sustainable, resilient, and integrated with regional energy systems and ecosystems.
The SIN01 journey embodies a forward-looking vision of AI data centre infrastructure that is scalable, sustainable, and capable of supporting the rapid growth of AI workloads. The project’s emphasis on ocean cooling, heat reuse, real-time monitoring, and carbon-aware design demonstrates a holistic approach to data centre development—one that acknowledges the environmental realities of the century while delivering the performance and reliability demanded by AI. The lessons drawn from SIN01 carry weight for the broader industry: that it is possible to expand AI capacity while maintaining, and even improving, environmental stewardship when design, technology, and governance are harmonised in a unified strategy.
Conclusion
SIN01 stands as a bold, evidence-backed demonstration of what is possible when technology, sustainability, and strategic collaboration converge at scale. From its ocean-water cooling approach to its integrated AI-driven design and BIM-based carbon modelling, the project provides a comprehensive blueprint for a new generation of AI data centres. It reimagines not only how data is processed but how it is cooled, how heat and water resources are used, and how environmental stewardship is embedded into every stage of development. The collaboration between Schneider Electric and Start Campus illustrates that openness and partnership are essential to solving the most pressing challenges in AI infrastructure. The project’s status as a Project of National Interest in Portugal reflects its broader significance for the nation’s economy and energy strategy, while its ambitious job creation and investment plans signal a broader belief in the transformative potential of sustainable AI.
If SIN01 proves the model’s resilience and scalability, the implications could extend well beyond Sines. The combination of ocean cooling, heat reuse, real-time energy management, and carbon-aware design could become a template for other AI data centres seeking to balance density, reliability, and environmental responsibility. The ongoing environmental monitoring and regulatory oversight will be critical to validating and refining this model over time, ensuring that the project remains aligned with ecological constraints while continuing to push the boundaries of what is possible in AI infrastructure. In a broader sense, SIN01 is not merely a data centre; it is a living laboratory that tests how the next generation of AI facilities can be built and operated in harmony with the natural environment and the communities that host them. The vision is clear: as AI workloads grow, we can meet the demand with sustainable, scalable, and transparent data centre ecosystems that empower innovation while protecting the planet.
In the words of Pablo Ruiz Escribano, the SIN01 story is one of disruption—an illustration of how data centres can be conceived and built in a completely different way. The project demonstrates that an AI-centric campus, powered by renewable energy and cooled by the ocean, is not only feasible but also a compelling path forward for the industry. The combination of ambitious scale, forward-thinking design, and a robust environmental framework makes SIN01 a landmark achievement in sustainable AI infrastructure. As the campus expands toward its full 1.2GW capacity, the lessons of SIN01 will likely inform future developments around the world, shaping the next generation of data centres that are not only powerful and reliable but also environmentally responsible and community-conscious.
Tags: Ocean-Cooled AI, Redefining Renewable Energy, SIN01, Schneider Electric, Start Campus, BIM, Carbon Modelling, AI Applications, Data Centre Strategy