Sustainability

AI infrastructure efficiency: Ironwood TPUs deliver 3.7x carbon efficiency gains

Mon, 06 Apr 2026 16:00:00 +0000

At Google, we are committed to being transparent about the environmental impact of our AI infrastructure, publishing metrics on the lifetime emissions of our chips — from manufacturing to powering these chips in the data center. Today, we are updating these metrics for our seventh-generation TPU, Ironwood, which demonstrates an approximately 3.7x improvement in Compute Carbon Intensity (CCI) compared to TPU v5p, the previous generation of performance-optimized TPUs.¹

In other words, despite the fact that AI is driving demand for additional compute resources, our ongoing work to optimize AI hardware is helping to improve the energy consumption and emissions of AI workloads.

Measuring AI accelerator efficiency: Compute Carbon Intensity (CCI)

To help manage the environmental impact of AI workloads, we monitor the Compute Carbon Intensity (CCI) of our AI accelerator hardware. CCI is defined in An Introduction to Life-Cycle Emissions of Artificial Intelligence Hardware²as the estimated amount of CO2 equivalent emitted for every utilized floating-point operation (CO2e/FLOP). This metric provides a holistic, chip-level view by including both the embodied emissions associated with manufacturing, transportation, and data center construction (Scope 3), as well as the operational emissions associated with running these chips in data centers (Scope 1 and 2).

The Ironwood advantage: high performance, low footprint

Google’s TPU CCI continues to improve with each chip generation. Drawing from empirical data measured in January 2026, Ironwood demonstrates a remarkable 3.7x improvement in CCI relative to TPU v5p. This accelerates efficiency gains from the 1.2x CCI improvement of TPU v5p relative to TPU v4, and demonstrates continued carbon efficiency optimization of Google’s performance-optimized TPU architecture.

These efficiency gains are driven by outsized compute performance increases between TPU generations relative to growth in machine energy consumption and manufacturing emissions. In fact, fleetwide measurements demonstrate a 5x improvement in utilized FLOPs across generations, from TPU v5p to Ironwood.³ Because the performance denominator in our CCI equation (CO2e/FLOP) is scaling faster than emissions, the net carbon cost per operation drops significantly with every new chip.

^{Figure 1: Ironwood’s accelerating CCI improvement measured on Google’s performance-optimized TPU cohort, considering January 2026 workloads.4}

Operating Google’s TPU fleet more efficiently

Updated TPU CCI metrics also offer a direct comparison to the measurement we published in 2025. Specifically, from October 2024 to January 2026, Google’s versatile TPU cohort ran more efficiently than what we reported previously:

TPU v5e achieved a 43% reduction in total CCI over 15 months, dropping to 228 gCO2e/EFLOP. This was driven by a 72% increase in average utilization.
Trillium, the sixth-generation TPU, saw a 20% reduction in total CCI over the same time period, bringing its emissions intensity down to 125 gCO2e/EFLOP.

^{Figure 2: Google’s versatile TPU cohort demonstrates deployment efficiency gains for the same TPU generations between October 2024 and January 2026.5}

These results demonstrate that Google continues to improve the carbon-efficiency of our AI infrastructure. While the massive scale of AI demand requires a significant and growing amount of power, our innovations allow us to deliver substantially more compute performance for every unit of energy consumed.

Decoupling energy and emissions from performance

To what can we attribute these improvements? Beyond Ironwood’s raw hardware capabilities, these CCI gains are further enabled by deep software and system-level optimizations across our infrastructure:

Software efficiency (MoE): The widespread adoption of sparse architectures, such as Mixture of Experts (MoE), routes computation only to necessary parameters. This drastically reduces the active FLOPs required per inference or training step without sacrificing model capacity or quality.
Lower precision math (FP8): By heavily leveraging 8-bit floating-point (FP8) formats, we effectively double compute throughput and halve memory bandwidth requirements compared to 16-bit formats. This shows that we can maintain output quality while exponentially decreasing the energy cost per mathematical operation.
Workload mix and intelligent scheduling: Advanced fleet orchestration continuously balances the workload mix across our infrastructure. By intelligently scheduling tasks, we ensure high continuous utilization rates, optimize duty cycles, and minimize the carbon penalty of idle power draw.

Scale sustainably with Google Cloud

AI’s trajectory requires infrastructure that can scale exponentially without an equivalent surge in carbon emissions. The 3.7x carbon efficiency improvement from TPU v5p to Ironwood demonstrates that we can achieve greater compute density while minimizing the growth of our energy and environmental footprint through deliberate hardware and software codesign. To learn more and get started with Ironwood, register your interest with this form.

_{1. Following the methodology published in an August 2025 technical report, we quantified the full lifecycle emissions of TPU hardware as a point-in-time snapshot across Google’s generations of TPUs as of January 2026. The functional unit for this study is one AI computer deployed in the data center, which includes one or more accelerator trays (containing TPUs) connected to one host tray (i.e., a computing server). Peripheral components beyond the tray (e.g., rack, shelf, and network equipment) and auxiliary computing and storage resources are excluded from the calculation of embodied and operational emissions. We include the electricity used in data center cooling in operational emissions. To estimate operational emissions from electricity consumption of running workloads, we used a one month sample of observed machine power data from our entire TPU fleet, applying Google’s 2024 average fleetwide carbon intensity. To estimate embodied emissions from manufacturing, transportation, and retirement, we performed a life-cycle assessment of the hardware. Data center construction emissions were estimated based on Google’s disclosed 2024 carbon footprint. These findings do not represent model-level emissions, nor are they a complete quantification of Google’s AI emissions. Based on the TPU location of a specific workload, CCI results of specific workloads may vary.
2. The authors would like to thank and acknowledge the co-authors of this paper for their important contributions to enable these results: Ian Schneider, Hui Xu, Stephan Benecke, Parthasarathy Ranganathan, and Cooper Elsworth.
3. This comparison considers the utilized FLOPS (BF16) between deployed TPU v5p and Ironwood chips in Google’s fleet in January 2026. This trend is consistent with the improvement in peak FLOPS (BF16) between v5p (459 FLOPS) and Ironwood (2,307 FLOPS).
4.The GHG protocol offers two accounting standards for operational emissions. Results presented here consider market-based emissions, which includes the impact of carbon-free energy purchases. Location-based accounting, which excludes carbon-free energy purchases, would raise operational CCI to 793, 712, and 195 gCO2e/EFLOP, respectively. The ratio of CCI improvements would be at a similar level, and Ironwood’s embodied CCI would drop from 23% to 8% of its total CCI.
5. To ensure a fair comparison across varying TPU utilizations, this analysis replicates the propensity score weighting methodology from the August 2025 technical report and compares January 2026 results to the results published in 2025. This statistical technique adjusts for duty cycle variations to balance the comparison of TPUs during a given time period. This empirical methodology results in small variations in calculated CCI between temporal periods, reflecting fluctuations in real-world energy consumption and hardware utilization across the global infrastructure.}

How AI-powered tools are driving the next wave of sustainable infrastructure and reporting

Tue, 31 Mar 2026 16:00:00 +0000

As generative AI continues to scale, a shift is occurring at the intersection of sustainability and technology. For years, reporting teams were bogged down by scattered data and labor-intensive documentation, leaving little space for strategic sustainability work. But for those now adopting AI, they’re spending less time trying to answer for the impact of last year, and more time on proactive resilience, gaining efficiency and speed.

Sustainability teams often need to build reports using data from multiple disparate sources such as, financial data, real estate footprints, energy usage and materials consumption. The standards for reporting are also rapidly evolving. That presents a unique challenge, where accuracy is difficult to obtain across data sources, and the stakes for getting it incorrect are high. At Google, we’ve spent the last two years testing and integrating AI into our own environmental reporting process to solve for this.

Inside how Google uses of AI for reporting

We first turned our own internal reporting processes into a testing ground for AI solutions. In developing our Environmental Report, we tested Gemini as a first line of review for validating environmental claims, automatically cross-referencing draft claims against our internal policies and best practices. This automation doesn't replace the expert; rather, it empowers them by freeing the human reviewer to focus on validating the assessment rather than starting from scratch for every claim. We also used NotebookLM to transform our static Environmental Report into an interactive knowledge base, allowing users to query complex data and receive instant, cited answers.

Our team documented our experimentation and progress—including the prompts that worked and lessons learned from the ones that didn't—in our open-source AI playbook. In documenting our efforts we realized that AI is more than just a tool for efficiency—it’s a catalyst for impact. By streamlining the manual, complex mechanics of reporting, we can all spend less time managing files and data and more time driving the strategy that moves the world forward.

Building a sustainability data lake at Equinix

Digital infrastructure provider Equinix undertook their own reporting transformation with the help of Google Cloud. Facing a 46% year-over-year increase in customer sustainability requests, the Equinix team realized that manual spreadsheets were no longer a viable option. They needed a tool for real-time decision-making.

Equinix built a Sustainability Data Lake on BigQuery. They ingest data from 240+ global sites automatically, transforming their reporting cycle from weeks of manual data cleaning to on-demand insights.

"I’m no longer just a data analyst. I’m a strategic advisor," says Alexa Cotton, Senior Manager of Sustainability at Equinix. "Our sustainability data is now a strategic asset that impacts over 60% of our ARR [Annual Recurring Revenue]. We’ve moved from reactive mode to looking at automated actions that save both energy and money."

Sustainable by design: using the well-architected framework (WAF)

Equinix demonstrated that a solid data foundation is the prerequisite for any AI-driven breakthrough. They also demonstrated that modernizing legacy processes often come with efficiency gains when they are well-architected.

Equinix moved to a serverless architecture with BigQuery, they achieved a "triple win" of price, performance, and footprint:

Equinix leveraged serverless elasticity to ensure they only use (and pay for) the exact compute resources required. No "zombie" servers, no wasted energy.
BigQuery handles the scaling and optimization of workloads programmatically. This removes the human error in resource allocation, ensuring the data lake remains lean and high-performing as it grows.
By leveraging Google's carbon-intelligent data infrastructure, Equinix fundamentally improved their "performance per watt," turning a reporting requirement into a showcase of operational efficiency.

These are great examples of what we refer to as the "4Ms" of our well-architected framework: Machine, Model, Mechanization, and Map.

The ambition loop

The work being done at Equinix creates what we call an "ambition loop." When you intervene at the architectural level, you aren't just checking a box for a sustainability report; you’re improving your economics. Which in turn improves your sustainability outcomes and ultimately the success you report.

To learn more, dive into Google’s AI Playbook for Sustainability Reporting and explore the new WAF sustainability pillar to start your own data-to-AI journey. Together, we can build the future of reporting—and a more resilient world.

Build software sustainably in the AI era

Wed, 05 Nov 2025 17:00:00 +0000

Artificial intelligence is reshaping our world – accelerating discovery, optimising systems, and unlocking new possibilities across every sector. But with its vast potential comes a shared responsibility.

AI can be a powerful ally for transforming businesses and reducing cost. It can help organizations minimize carbon emissions, industries manage energy use, and scientists model complex climate systems in real time. Yet the way we design, deploy, and run AI also matters. Building software sustainably means making every stage of the digital journey – from architecture to inference – more efficient, transparent, and resilient.

Innovation that serves sustainability

At Google, we believe innovation and sustainability go hand in hand. The same intelligence that powers breakthroughs can also help us use resources more wisely.

Projects like Green Light, which uses AI to optimise traffic signals and reduce emissions, and Project Contrails, which helps airlines cut the warming effects of condensation trails, show what happens when technology serves both performance and planet.

Each example reveals a helpful truth – that sustainability doesn’t slow innovation but instead fuels it, enabling efficiency to become an engine of progress.

From footprint to framework

Every software system, including AI, has an environmental footprint – from the hardware and energy that powers data centers to the water used to cool them. Water is one of the planet’s most precious and increasingly scarce resources and protecting it must be part of any technology strategy. That’s why Google is investing in advanced cooling systems and water stewardship projects with the goal to replenish more than we consume, helping preserve local ecosystems and community supplies.

Understanding this footprint helps engineers and organisations make smarter choices, like selecting efficient accelerators, rightsizing workloads, and scheduling operations when the grid is cleanest.

Across Google Cloud, we’re continually improving efficiency. Our Ironwood Tensor Processing Units (TPUs) are nearly 30 times more energy-efficient than our first Cloud TPU from 2018, and our data centres operate at a fleet-wide Power Usage Effectiveness (PUE) of 1.09, which is amongst the best in the world.

By designing systems that consume less energy and run on more carbon-free power, we help close the gap between ambition and action – turning digital progress into tangible emissions reductions.

But this isn’t achieved through infrastructure alone. It’s the result of decisions made at every layer of the software lifecycle. That’s why we encourage teams to think Sustainable by Design, bringing efficiency, measurement, and responsibility into every stage of building software.

Sustainable by Design: a mindset for the AI era

Today’s sustainability questions aren't coming just from sustainability teams; they are coming directly from executives, financial operations teams, technology leads and developers. And they are often asking sustainability questions using infrastructure language: "Are we building the most price-performant AND efficient way to run AI?" This is not a niche environmental question; it's relevant across -industries, across-geo’s and it requires that leaders consider sustainability criteria when they are designing infrastructure. A Sustainable by Design infrastructure strategy makes AI training and operation dramatically more cost- and energy-efficient. It’s built around a set of principles known as the 4Ms which lay out powerful ways to embed efficiency into software:

Machine - choose efficient computing resources that deliver more performance per watt.
Model - use or adapt existing models rather than starting from scratch — smaller, fine-tuned models can be faster and more resource efficient.
Mechanisation - automate data and AI operations through serverless and managed services to minimise idle compute.
Map - run workloads where and when the energy supply is cleanest.

The 4Ms help turn sustainability into a design principle, and a shared responsibility across every role in tech.

A collective journey toward resilience

As we host the AI Days in the Nordics, the conversation about AI’s environmental impact is accelerating, and so is the opportunity to act. Every software team, cloud architect, and product manager has a role to play in designing a digital ecosystem that enables and fuels innovation without compromising environmental impact.

Building software sustainably is essential for business resilience –AI applications that use fewer resources are not only more energy efficient; they're scalable, and cost-effective for the organisations that depend on them.

To learn more about how we can make the future sustainable by design, download our Build Software Sustainably ebook.

Our approach to carbon-aware data centers: Central data center fleet management

Wed, 10 Sep 2025 16:00:00 +0000

Data centers are the engines of the cloud, processing and storing the information that powers our daily lives. As digital services grow, so do our data centers and we are working to responsibly manage them. Google thinks of infrastructure at the full stack level, not just as hardware but as hardware abstracted through software, allowing us to innovate.

We have previously shared how we’re working to reduce the embodied carbon impact at our data centers by optimizing our technical infrastructure hardware. In this post, we shine a spotlight on our “central fleet” program, which has helped us shift our internal resource management system from a machine economy to a more sustainable resource and performance economy.

What is Central Fleet?

At its core, our central fleet program is a resource distribution approach that allows us to manage and allocate computing resources, like processing power, memory, and storage in a more efficient and sustainable way. Instead of individual teams or product teams within Google ordering and managing their own physical machines, our central fleet acts as a centralized pool of resources that can be dynamically distributed to where they are needed most.

Think of it like a shared car service. Rather than each person owning a car they might only use for a couple of hours a day, a shared fleet allows for fewer cars to be used more efficiently by many people. Similarly, our central fleet program ensures our computing resources are constantly in use, minimizing waste and reducing the need to procure new machines.

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How it works: A shift to a resource economy

The central fleet approach fundamentally changes how we provision and manage resources. When a team needs more computing power, instead of ordering specific hardware, they place an order for "quota" from the central fleet. This makes the computing resources fungible, that is, interchangeable and flexible. For instance, a team will ask for a certain amount of processing power or storage capacity, not a particular server model.

This "intent-based" ordering system provides flexibility in how demand is fulfilled. Our central fleet can intelligently fulfill requests using either existing inventory or procure at scale, which can lower cost and environmental impact. It also facilitates the return of unneeded resources that can then be reallocated to other teams, further reducing waste.

All of this is possible with our full-stack infrastructure and built on the Borg cluster management system to abstract away the physical hardware into a single, fungible resource pool. This software-level intelligence allows us to treat our infrastructure as a fluid, optimizable system rather than a collection of static machines, unlocking massive efficiency gains.

The sustainability benefits of central fleet

The central fleet approach aligns with Google's broader dedication to sustainability and a circular economy. By optimizing the use of our existing hardware, we can achieve carbon savings. For example, in 2024, our central fleet program helped avoid procurement of new components and machines with an embodied impact equivalent to approximately 260,000 metric tons of CO2e. This roughly equates to avoiding 660 million miles driven by an average gasoline-powered passenger vehicle.¹

This fulfillment flexibility leads to greater resource efficiency and a reduced carbon footprint in several ways:

Reduced electronic waste: By extending the life of our machines through reallocation and reuse, we minimize the need to manufacture new hardware and reduce the amount of electronic waste.
Lower embodied carbon: The manufacturing of new servers carries an embodied carbon footprint. By avoiding the creation of new machines, we avoid these associated CO2e emissions.
Increased energy efficiency: Central fleet allows for the strategic placement of workloads on the most power-efficient hardware available, optimizing energy consumption across our data centers.
Promote a circular economy: This model is a prime example of circular economy principles in action, shifting from a linear "take-make-dispose" model to one that emphasizes reuse and longevity.

The central fleet initiative is more than an internal efficiency project; it's a tangible demonstration of embedding sustainability into our core business decisions. By rethinking how we manage our infrastructure, we can meet growing AI and cloud demand while simultaneously paving the way for a more sustainable future. Learn more at sustainability.google.

^{1. Estimated avoided emissions were calculated by applying internal LCA emissions factors to machines and component resources saved through our central fleet initiative in 2024. We input the estimated avoided emissions into the EPA’s Greenhouse Gas Equivalencies Calculator to calculate the equivalent number of miles driven by an average gasoline-powered passenger vehicle (accessed August 2025). The data and claims have not been verified by an independent third-party.}

How much energy does Google’s AI use? We did the math

Thu, 21 Aug 2025 12:00:00 +0000

AI is unlocking scientific breakthroughs, improving healthcare and education, and could add trillions to the global economy. Understanding AI’s footprint is crucial, yet thorough data on the energy and environmental impact of AI inference — the use of a trained AI model to make predictions or generate text or images — has been limited. As more users use AI systems, the importance of inference efficiency rises.

That’s why we’re releasing a technical paper detailing our comprehensive methodology for measuring the energy, emissions, and water impact of Gemini prompts. Using this methodology, we estimate the median Gemini Apps text prompt uses 0.24 watt-hours (Wh) of energy, emits 0.03 grams of carbon dioxide equivalent (gCO₂e), and consumes 0.26 milliliters (or about five drops) of water¹ — figures that are substantially lower than many public estimates. The per-prompt energy impact is equivalent to watching TV for less than nine seconds.

At the same time, our AI systems are becoming more efficient through research innovations and software and hardware efficiency improvements. For example, over a recent 12 month period, the energy and total carbon footprint of the median Gemini Apps text prompt dropped by 33x and 44x, respectively,² all while delivering higher quality responses. These results are built on our latest data center energy emissions reductions and our work to advance carbon-free energy and water replenishment. While we’re proud of the innovation behind our efficiency gains so far, we’re committed to continuing substantial improvements. Here’s a closer look at these ongoing efforts.

Calculating the environmental footprint of AI at Google

Detailed measurement lets us compare across different AI models, and the hardware and energy they run on, while enabling system-wide efficiency optimizations — from hardware and data centers to the models themselves. By sharing our methodology, we hope to increase industry-wide consistency in calculating AI’s resource consumption and efficiency.

Measuring the footprint of AI serving workloads isn’t simple. We developed a comprehensive approach that considers the realities of serving AI at Google’s scale, which include:

Full system dynamic power: This includes not just the energy and water used by the primary AI model during active computation, but also the actual achieved chip utilization at production scale, which can be much lower than theoretical maximums.
Idle machines: To ensure high availability and reliability, production systems require a degree of provisioned capacity that is idle but ready to handle traffic spikes or failover at any given moment. The energy consumed by these idle chips must be factored into the total energy footprint.
CPU and RAM: AI model execution doesn't happen solely in ML accelerators like TPUs and GPUs. The host CPU and RAM also play a crucial role in serving AI, and use energy.
Data center overhead: The energy consumed by the IT equipment running AI workloads is only part of the story. The infrastructure supporting these computations — cooling systems, power distribution, and other data center overhead — also consumes energy. Overhead energy efficiency is measured by a metric called Power Usage Effectiveness (PUE).
Data center water consumption: To reduce energy consumption and associated emissions, data centers often consume water for cooling. As we optimize our AI systems to be more energy-efficient, this naturally decreases their overall water consumption as well.

Many current AI energy consumption calculations only include active machine consumption, overlooking several of the critical factors discussed above. As a result, they represent theoretical efficiency instead of true operating efficiency at scale. When we apply this non-comprehensive methodology that only considers active TPU and GPU consumption, we estimate the median Gemini text prompt uses 0.10 Wh of energy, emits 0.02 gCO2e, and consumes 0.12 mL of water. This is an optimistic scenario at best and substantially underestimates the real operational footprint of AI.

Our comprehensive methodology’s estimates (0.24 Wh of energy, 0.03 gCO2e, 0.26 mL of water) account for all critical elements of serving AI globally. We believe this is the most complete view of AI’s overall footprint.

Our full-stack approach to AI — and AI efficiency

Gemini’s dramatic efficiency gains stem from Google’s full-stack approach to AI development — from custom hardware and highly efficient models, to the robust serving systems that make these models possible. We’ve built efficiency into every layer of AI, including:

More efficient model architectures: Gemini models are built on the Transformer model architecture developed by Google researchers, which provide a 10-100x efficiency boost over the previous state-of-the-art architectures for language modeling. We design models with inherently efficient structures like Mixture-of-Experts (MoE) and hybrid reasoning. MoE models, for example, allow us to activate a small subset of a large model specifically required to respond to a query, reducing computations and data transfer by a factor of 10-100x.
Efficient algorithms and quantization: We continuously refine the algorithms that power our models with methods like Accurate Quantized Training (AQT) to maximize efficiency and reduce energy consumption for serving, without compromising response quality.
Optimized inference and serving: We constantly improve AI model delivery for responsiveness and efficiency. Technologies like speculative decoding serve more responses with fewer chips by allowing a smaller model to make predictions that are then quickly verified by a larger model, which is more efficient than having the larger model make many sequential predictions on its own. Techniques like distillation create smaller, more efficient models (Gemini Flash and Flash-Lite) for serving that use our larger, more capable models as teachers. Faster machine learning hardware and models enable us to use more efficient larger batch sizes when handling requests, while still meeting our latency targets.
Custom-built hardware: We’ve been designing our TPUs from the ground up for over a decade to maximize performance per watt. We also co-design our AI models and TPUs, ensuring our software takes full advantage of our hardware — and that our hardware is able to efficiently run our future AI software when both are ready. Our latest-generation TPU, Ironwood, is 30x more energy-efficient than our first publicly-available TPU and far more power-efficient than general-purpose CPUs for inference.
Optimized idling: Our serving stack makes highly efficient use of CPUs and minimizes TPU idling by dynamically moving models based on demand in near-real-time, rather than using a “set it and forget” approach.
ML software stack: Our XLA ML compiler, Pallas kernels, and Pathways systems enable model computations expressed in higher-level systems like JAX to run efficiently on our TPU serving hardware.
Ultra-efficient data centers: Google’s data centers are among the industry’s most efficient, operating at a fleet-wide average PUE of 1.09.
Responsible data center operations: We continue to add clean energy generation in pursuit of our 24/7 carbon-free ambition, while advancing our aim to replenish 120% of the freshwater we consume on average across our offices and data centers. We also optimize our cooling systems, balancing the local trade-off between energy, water, and emissions, by conducting science-backed watershed health assessments, to guide cooling type selection and limit water use in high-stress locations.

Our commitment to efficient AI

Gemini’s efficiency gains are the result of years of work, but this is just the beginning. Recognizing that AI demand is growing, we're heavily investing in reducing the power provisioning costs and water required per prompt. By sharing our findings and methodology, we aim to drive industry-wide progress toward more efficient AI. This is essential for responsible AI development.

^{1. A point-in-time analysis quantified the energy consumed per median Gemini App text-generation prompt, considering data from May 2025. Emissions per prompt was estimated based on energy per prompt, and applying Google’s 2024 average fleetwide grid carbon intensity. Water consumption per prompt was estimated based on energy per prompt, and applying Google’s 2024 average fleetwide water usage effectiveness. These findings do not represent the specific environmental impact for all Gemini App text-generation prompts nor are they indicative of future performance.
2. The results of the above analysis from May 2025 were compared to baseline data from the median Gemini App text-generation prompt in May 2024. Energy per median prompt is subject to change as new models are added, AI model architecture evolves, and AI chatbot user behavior develops. The data and claims have not been verified by an independent third-party.}

Expanding BigQuery geospatial capabilities with Earth Engine raster analytics

Fri, 09 May 2025 01:00:00 +0000

At Google Cloud Next 25, we announced a major step forward in geospatial analytics: Earth Engine in BigQuery. This new capability unlocks Earth Engine raster analytics directly in BigQuery, making advanced analysis of geospatial datasets derived from satellite imagery accessible to the SQL community.

Before we get into the details of this new capability and how it can power your use cases, it's helpful to distinguish between two types of geospatial data and where Earth Engine and BigQuery have historically excelled:

Raster data: This type of data represents geographic information as a grid of cells, or pixels, where each pixel stores a value that represents a specific attribute such as elevation, temperature, or land cover. Satellite imagery is a prime example of raster data. Earth Engine excels at storing and processing raster data, enabling complex image analysis and manipulation.
Vector data: This type of data represents geographic features such as points, lines, or polygons. Vector data is ideal for representing discrete objects like buildings, roads, or administrative boundaries. BigQuery is highly efficient at storing and querying vector data, making it well-suited for large-scale geographic analysis.

Earth Engine and BigQuery are both powerful platforms in their own right. By combining their geospatial capabilities, we are bringing the best of both raster and vector analytics to one place. That’s why we created Earth Engine in BigQuery, an extension to BigQuery's current geospatial capabilities that will broaden access to raster analytics and make it easier than ever before to answer a wide range of real-world enterprise problems.

Earth Engine in BigQuery: Key features

You can use the two key features of Earth Engine in BigQuery to perform raster analytics in BigQuery:

A new function in BigQuery: Run ST_RegionStats(), a new BigQuery geography function that lets you efficiently extract statistics from raster data within specified geographic boundaries.
New Earth Engine datasets in BigQuery Sharing (formerly Analytics Hub): Access a growing collection of Earth Engine datasets in BigQuery Sharing (formerly Analytics Hub), simplifying data discovery and access. Many of these datasets are analysis-ready, immediately usable for deriving statistics for an area of interest, and providing valuable information such as elevation, emissions, or risk prediction.

Five easy steps to raster analytics

The new ST_RegionStats() function is similar to Earth Engine’s reduceRegion function, which allows you to compute statistics for one or more regions of an image. The ST_RegionStats() function is a new addition to BigQuery’s set of geography functions invoked as part of any BigQuery SQL expression. It takes an area of interest (e.g., a county, parcel of land, or zip code) indicated by a geography and an Earth Engine-accessible raster image and computes a set of aggregate values for the pixels that intersect with the specified geography. Examples of aggregate statistics for an area of interest would be maximum flood depth or average methane emissions for a certain county.

These are the five steps to developing meaningful insights for an area of interest:

Identify a BigQuery table with vector data: This could be data representing administrative boundaries (e.g., counties, states), customer locations, or any other geographic areas of interest. You can pull a dataset from BigQuery public datasets or use your own based on your needs.
Identify a raster dataset: You can discover Earth Engine raster datasets in BigQuery Sharing, or you can use raster data stored as a Cloud GeoTiff or Earth Engine image asset. This can be any raster dataset that contains the information you want to analyze within the vector boundaries.
Use ST_RegionStats() to bring raster data into BigQuery: The ST_RegionStats() geography function takes the raster data (raster_id), vector geometries (geography), and optional band (band_name) as inputs and calculates aggregate values (e.g., mean, min, max, sum, count) on the intersecting raster data and vector feature.
Analyze the results: You can use the output of running ST_RegionStats() to analyze the relationship between the raster data and the vector features, generating valuable insights about an area of interest.
Visualize the results: Geospatial analysis is usually most impactful when visualized on a map. Tools like BigQuery Geo Viz allow you to easily create interactive maps that display your analysis results, making it easier to understand spatial patterns and communicate findings.

Toward data-driven decision making

The availability of Earth Engine in BigQuery opens up new possibilities for scaled data-driven decision-making across various geospatial and sustainability use cases, by enabling raster analytics on datasets that were previously unavailable in BigQuery. These datasets can be used with the new ST_RegionStats() geography function for a variety of use cases, such as calculating different land cover types within specific administrative boundaries or analyzing the average elevation suitability within proposed development areas. You can also find sample queries for these datasets in BigQuery Sharing’s individual dataset pages. For example, if you navigate to the GRIDMET CONUS Drought Indices dataset page, you can find a sample query for calculating mean Palmer Drought Severity Index (PDSI) for each county in California, used to monitor drought conditions across the United States.

Let’s take a deeper look at some of the use cases that this new capability unlocks:

1. Climate, physical risk, and disaster response

Raster data can provide critical insights on weather patterns and natural disaster monitoring. Many of the raster datasets available in BigQuery Sharing provide derived data on flood mapping, wildfire risk assessment, drought conditions, and more. These insights can be used for disaster risk and response, urban planning, infrastructure development, transportation management, and more. For example, you could use the Wildfire Risk to Communities dataset for predictive analytics, allowing you to assess wildfire hazard risk, exposure of communities, and vulnerability factors, so you can develop effective resilience strategies. For flood mapping, you could use the Global River Flood Hazard dataset to understand regions in the US that have the highest predicted inundation depth, or water height above ground surface.

2. Sustainable sourcing and agriculture

Raster data also provides insights on land cover and land use over time. Several of the new Earth Engine datasets in BigQuery include derived data on terrain, elevation, and land-cover classification, which are critical inputs for supply chain management and assessing agriculture and food security. For businesses that operate in global markets, sustainable sourcing requires bringing transparency and visibility to supply chains, particularly as regulatory requirements are shifting commitments to deforestation-free commodity production from being voluntary to mandatory. With the new Forest Data Partnership maps for cocoa, palm and rubber, you can analyze where commodities are grown over time, and add in the Forest Persistence or the JRC Global Forest Cover datasets to understand if those commodities are being grown in areas that had not been deforested or degraded before 2020. With a simple SQL query, you could, for instance, determine the estimated fraction of Indonesia's land area that had undisturbed forest in 2020.

3. Methane emissions monitoring

Reducing methane emissions from the oil and gas industry is crucial to slow the rate of climate change. The MethaneSAT L4 Area Sources dataset, which can be used as an Earth Engine Image asset with the ST_RegionStats() function, provides insights into small, dispersed area emissions of methane from various sources. This type of diffuse but widespread emissions can make up the majority of methane emissions in an oil and gas basin. You can analyze the location, magnitude, and trends of these emissions to identify hotspots, inform mitigation efforts, and understand how emissions are characterized across large areas, such as basins.

4. Custom use cases

In addition to these datasets, you can bring your own raster datasets via Cloud Storage GeoTiffs or Earth Engine image assets, to support other use cases, while still benefiting from BigQuery's scalability and analytical tools.

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Bringing it all together with an example

Let’s take a look at a more advanced example based on modeled wildfire risk and AI-driven weather forecasting technology. The SQL below uses the Wildfire Risk to Communities dataset listed in BigQuery Sharing, which is designed to help communities understand and mitigate their exposure to wildfire. The data contains bands that index the likelihood and consequence of wildfire across the landscape. Using geometries from a public dataset of census-designated places, you can compute values from this dataset using ST_RegionStats() to compare communities’ relative risk exposures. You can find an example query of how to identify census tracts in Oregon that are at the highest wildfire risk on the Wildfire Risk to Communities dataset page.

You can also combine weather data from WeatherNext Graph forecasts to see how imminent fire weather is predicted to affect those communities (note: you may need to re quest access to the WeatherNext dataset first). To do this, start by heading to the BigQuery Sharing console, click “Search listings”, filter to “Climate and environment,” select the “Wildfire Risk to Community” dataset (or search for the dataset in the search bar), and click “Subscribe” to add the Wildfire Risk dataset to your BigQuery project. Then search for “WeatherNext Graph” and subscribe to the WeatherNext Graph dataset.

With these subscriptions in place, run a query to combine these datasets across many communities with a single query. You can break this task into subqueries using the SQL WITH statement for clarity:

First, select the input tables that you subscribed to in the previous step.

Second, compute the weather forecast using WeatherNext Graph forecast data for a specific date and for the places of interest. The result is the average and maximum wind speeds within each community.

Third, use the ST_RegionStats() function to sample the Wildfire Risk to Community raster data for each community. Since we are only concerned with computing mean values within regions, you can set the scale to 1 kilometer in the function options in order to use lower-resolution overviews and thus reduce compute time. To compute at the full resolution of the raster (in this case, 30 meters), you can leave this option out.

code_block: <ListValue: [StructValue([('code', 'WITH\r\n -- Step 1: Select inputs from datasets that we\'ve subscribed to\r\n wildfire_raster AS (\r\n SELECT\r\n assets.image.href\r\n FROM\r\n `wildfire_risk_to_community_v0_mosaic.fire`\r\n ),\r\n places AS (\r\n SELECT\r\n place_id,\r\n place_name,\r\n place_geom AS geo,\r\n FROM\r\n `bigquery-public-data.geo_us_census_places.places_colorado`\r\n ),\r\n\r\n\r\n -- Step 2: Compute the weather forecast using WeatherNext Graph forecast data\r\n weather_forecast AS (\r\n SELECT\r\n ANY_VALUE(place_name) AS place_name,\r\n ANY_VALUE(geo) AS geo,\r\n AVG(SQRT(POW(t2.`10m_u_component_of_wind`, 2)\r\n + POW(t2.`10m_v_component_of_wind`, 2))) AS average_wind_speed,\r\n MAX(SQRT(POW(t2.`10m_u_component_of_wind`, 2)\r\n + POW(t2.`10m_v_component_of_wind`, 2))) AS maximum_wind_speed\r\n FROM\r\n `weathernext_graph_forecasts.59572747_4_0` AS t1,\r\n t1.forecast AS t2\r\n JOIN\r\n places\r\n ON\r\n ST_INTERSECTS(t1.geography_polygon, geo)\r\n WHERE\r\n t1.init_time = TIMESTAMP(\'2025-04-28 00:00:00 UTC\')\r\n AND t2.hours < 24\r\n GROUP BY\r\n place_id\r\n ),\r\n\r\n\r\n -- Step 3: Combine with wildfire risk for each community\r\n wildfire_risk AS (\r\n SELECT\r\n geo,\r\n place_name,\r\n ST_REGIONSTATS( -- Wildfire likelihood\r\n geo, -- Place geometry\r\n (SELECT href FROM wildfire_raster), -- Raster ID\r\n \'BP\', -- Band name (Burn Potential)\r\n OPTIONS => JSON \'{"scale": 1000}\' -- Computation resolution in meters\r\n ).mean AS wildfire_likelihood,\r\n ST_REGIONSTATS( -- Wildfire consequence\r\n geo, -- Place geometry\r\n (SELECT href FROM wildfire_raster), -- Raster ID\r\n \'CRPS\', -- Band name (Conditional Risk to Potential Structures)\r\n OPTIONS => JSON \'{"scale": 1000}\' -- Computation resolution in meters\r\n ).mean AS wildfire_consequence,\r\n weather_forecast.* EXCEPT (geo, place_name)\r\n FROM\r\n weather_forecast\r\n )\r\n\r\n\r\n -- Step 4: Compute a simple composite index of relative wildfire risk.\r\nSELECT\r\n *,\r\n 100 * (PERCENT_RANK() OVER (ORDER BY wildfire_likelihood)\r\n +PERCENT_RANK() OVER (ORDER BY wildfire_consequence)\r\n +PERCENT_RANK() OVER (ORDER BY average_wind_speed)) / 3\r\n AS relative_risk\r\nFROM\r\n wildfire_risk'), ('language', 'lang-sql'), ('caption', <wagtail.rich_text.RichText object at 0x7fb86828d400>)])]>

The result is a table containing the mean values of wildfire risk for both bands within each community and wind speeds projected over the course of a day. In addition, you can combine the computed values for wildfire risk, wildfire consequence, and average wind speed to create a simple composite index to show relative wildfire exposure for a selected day in Colorado.

Mean values of wildfire risk and wind speeds for each community

You can save this output in Google Sheets to visualize how wildfire risk and consequences are related among communities statewide.

Google sheet visualizing wildfire risk (x-axis) and wildfire consequence (y-axis) colored by wind speed

Alternatively, you can visualize relative wildfire risk exposure in BigQuery GeoViz with the single composite index to show relative wildfire exposure for a selected day in Colorado.

GeoViz map showing composite index for wildfire risk, wildfire consequence, and max wind speed

What’s next for Earth Engine in BigQuery?

Earth Engine in BigQuery marks a significant advancement in geospatial analytics, and we’re excited to further expand raster analytics in BigQuery, making sustainability decision-making easier than ever before. Learn more about this new capability in the BigQuery documentation for working with raster data, and stay tuned for new Earth Engine capabilities in BigQuery in the near future!

Pushing the limits of electric mobility: Formula E's Mountain Recharge

Sat, 03 May 2025 14:00:00 +0000

When’s the last time you watched a race for the braking?

It’s the heart-pounding acceleration and death-defying maneuvers that keep most motorsport fans on the edge of their seats. Especially when it comes to Formula E — and really all EVs — the explosive, near-instantaneous acceleration of an electric motor is part of the appeal.

A less considered, yet no less important feature, is how EVs can regeneratively brake, turning friction into fuel. Part of Formula E’s mission is to make EVs a compelling automotive choice for consumers, not just world-class racers; highlighting this powerful aspect of the vehicles has become a priority. The question remained: How do you get others to feel the same exhilaration from deceleration?

The answer came from the mountains above Monaco, as well as some prompts in Gemini 2.5.

In the lead up to the Monaco E-Prix, Formula E and Google undertook a project dubbed Mountain Recharge. The challenge: Whether a Formula E GENBETA race car, starting with only 1% battery, could regenerate enough energy from braking during a descent through France’s coastal Alps to then complete a full lap of the iconic Monaco circuit.

More than just a stunt, this experiment is testing the boundaries of technology — and not just in EVs, but on the cloud, too. Without the live analytics and plenty of AI-powered planning, the Mountain Recharge might not have come to pass. In fact, AI even helped determine which mountain pass would be best suited for this effort. (Read on to find out which one, and see if we made it to the bottom.)

Mountain Recharge is exciting not only for thrills on the course but also the potential it shows for AI across industries. In addition to its role in helping to execute tasks, AI proved valuable to the brainstorming, experimentation, and rapidfire simulations that helped get Mountain Recharge to the finish line.

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Planning the charge up the mountain

Before even setting foot or wheel to the course, the team at Formula E and Google Cloud turned to Gemini to try and figure out if such an endeavor was possible.

To answer the fundamental question of feasibility, the team entered a straightforward prompt into Google’s AI Studio: “Starting with just 1% battery, could the GENBETA car potentially generate enough recharge by descending a high mountain pass to do a lap of the Circuit of Monaco?”

The AI Studio validator, running Gemini 2.5 Pro with its deep reasoning functionality, analyzed first-party data that had been uploaded by Formula E on the GENBETA’s capabilities; we then grounded the model with Google Search to further improve accuracy and reliability by connecting to the universe of information available online.

AI Studio shared its “thinking” in a detailed eight-step process, which included identifying the key information needed; consulting the provided documents; gathering external information through a simulated search; performing calculations and analysis; and finally synthesizing the answer based on the core question.

The final output: “theoretically feasible.” In other words, the perfect challenge.

Navigating the steep turns above Monaco helped generate plenty of power for Mountain Recharge.

Still working in AI Studio, we then used a new feature, the ability to build custom apps such as the Maps Explorer, to determine the best route, which turned out to be the Col de Braus. AI Studio then mapped out a route for the challenge. This rigorous, data-backed validation, facilitated by AI Studio and Gemini's ability to incorporate technical specifications and estimations, transformed the project from a speculative what-if into something Formula E felt confident attempting.

AI played an important role away from the course, as well. To aid in coordination and planning, teams at Formula E and Google Cloud used NotebookLM to digest the technical regulations and battery specifications and locate relevant information within them, which, given the complexity of the challenge and the number of parties involved, helped ensure cross-functional teams were kept up to date and grounded with sourced data to help make informed decisions.

Smart cars, smart drivers, and a smartphone

During the mountain descent, real-time monitoring of the car's progress and energy regeneration would be crucial. Firebase and BigQuery were instrumental in visualizing this real-time telemetry. Data from both multiple sensors and Google Maps was streamed to BigQuery, Google Cloud's data warehouse, from a high-performance mobile phone connected to the car (a Pixel 9 was well suited to the task).

This data stream proved to be yet another challenge to overcome, because of the patchy mobile signal in the mountainous terrain of the Maritime Alps. When data couldn’t be sent, it was cached locally on the phone until the signal was available again.

BigQuery's capacity for real-time data ingestion and in-platform AI model creation enabled speedy analysis and the calculation of essential metrics. A web-based dashboard was developed using Firebase that connected to BigQuery to display both data and insights. AI Studio greatly facilitated the development of the application by translating a picture of a dashboard mockup into fully functional code.

“From figuring out if our crazy Mountain Recharge idea was even possible, to giving us live insights during the descent, AI was our guide,” said Alex Aidan, Formula E’s VP of Marketing. “It’s what turned an ambitious ‘what if' into a reality we could track moment by moment.”

After completing its descent, the car stored up enough energy that it is expected to complete its lap of the Monaco circuit on Saturday, as part of the E-Prix’s pre-race festivities.

A different kind of push start.

Benefits beyond the finish line

Both the success and the development of the Mountain Recharge campaign offer valuable lessons to others pursuing ambitious projects. It shows that AI doesn’t have to be central to a project — it can be just as powerful at facilitating and optimizing something we’ve been doing for years, like racing cars. Our results in the Mountain Recharge only underscores the potential benefits of AI for a wide range of industries:

Enhanced planning and exploration: Just as Gemini helped Formula E explore unconventional ideas and identify the optimal route, businesses can leverage large language models for innovative problem-solving, market analysis, and strategic planning, uncovering unexpected angles and accelerating the journey from "what if" to "we can do that".
Streamlined project management: NotebookLM's ability to centralize and organize vast amounts of information demonstrates how AI can significantly improve efficiency in complex projects, from logistics and resource allocation to research and compliance. This reduces the risk of errors and ensures smoother coordination across teams.
Data-driven decision making: The real-time data analysis capabilities showcased in the Mountain Recharge underscore the power of cloud-based data platforms like BigQuery. Organizations can leverage these tools to gain immediate insights from their data, enabling them to make agile adjustments and optimize performance on the fly. This is invaluable in dynamic environments where rapid responses are critical.
Deeper understanding of complex systems: By applying AI to analyze intricate data streams, teams can gain a more profound understanding of the factors influencing performance.

Such capabilities certainly impressed James Rossiter, a former Formula E Team Principal, current test driver, and broadcaster for the series. "I was really surprised at the detail of the advice and things to consider,” Rossiter said. “We always talk about these things as a team, but as this is so different to racing, I had to totally rethink the drive."

The Formula E Mountain Recharge campaign is more than just an exciting piece of content; it's a testament to the power of human ingenuity amplified by intelligent technology. It’s also the latest collaboration between Formula E and Google Cloud and our shared commitment to use AI to push the boundaries of what’s possible in the sport in the sport and in the world.

We’ve already developed an AI-powered digital driving coach to help level the field for EV racing. Now, with the Mountain Recharge, we can inspire everyday drivers well beyond the track with the capabilities of electric vehicles.

It’s thinking big, even if it all starts with a simple prompt on a screen. You just have to ask the right questions, starting with the most important ones: Is this possible, and how can we make it so?

Power up your BigQuery analysis with Google's new geospatial datasets

Wed, 09 Apr 2025 12:00:00 +0000

Today at Google Cloud Next 25, we’re unveiling new geospatial analytics datasets and capabilities from Earth Engine and Google Maps Platform, integrated directly into BigQuery, Google’s unified data to AI platform. As BigQuery users, you know the power of data-driven insights, and these new capabilities will give you new dimensions of analysis, leveraging comprehensive geospatial data, to make better and faster decisions.

Geospatial analytics trends and challenges

The geospatial analytics market is rapidly evolving, driven by the emergence of powerful analytical tools, hyper-localization, and generative AI. Despite these advancements, organizations across industries face significant challenges in harnessing the full potential of geospatial analytics. For one, finding fresh, accurate, and comprehensive data in an analysis-ready format can be time-consuming and resource-intensive. Second, organizations face integration and analysis challenges, with disparate data sources introducing variability, requiring extensive transformation and preparation. Finally, scaling geospatial analytics programs can be difficult without specialized expertise and consistent approaches.

How our new geospatial capabilities address these challenges

Google Maps Platform is a trusted geospatial platform for over 10 million websites and apps, enriching the experiences for over 2 billion users. And for the last 15 years, Earth Engine has empowered data scientists with over 90 petabytes of satellite imagery and geospatial data.

Customers want to access more insights from our up-to-date, comprehensive geospatial data, so they can make more informed business and sustainability decisions. That’s why for the first time, we're bringing select Google Maps Platform datasets, along with Earth Engine's datasets and analysis capabilities, directly into BigQuery. This means data analysts and decision-makers can now easily access and analyze fresh, comprehensive, and global geospatial data within the familiar BigQuery platform.

Here's what these new datasets and capabilities unlock:

New insights, familiar tools: Tap into Google’s fresh, global geospatial data without needing advanced remote sensing or GIS expertise.
Geospatial data integration: Integrate rich geospatial datasets with your existing data to unlock new insights that were previously difficult to obtain.
Simplified data discovery and access: Say goodbye to time-consuming data wrangling. Access and analyze geospatial data as easily as your other BigQuery datasets.

For the first time, customers can now integrate analysis-ready imagery and datasets from Earth Engine, Places, and Street View into their existing BigQuery workflows using data clean rooms, extracting insights without sharing raw data. To learn more about our new geospatial analytics datasets and capabilities, visit our new website.

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Imagery Insights

Our first Imagery Insights dataset, available in Experimental for US, Canada, UK and Japan, helps you accelerate your infrastructure asset management by uniquely combining the global scale of Street View data, Vertex AI-powered analysis, and the scale of BigQuery.

With this combination you can quickly identify and automatically assess the current conditions of your infrastructure assets like utility poles and road signs from Street View imagery, with the potential for many more attribute types to come. For example, if you are a city planner needing to determine your annual budget for road sign repairs, Imagery Insights can help you identify the exact number and locations of signs requiring attention using Street View imagery. This integration streamlines operations, optimizes workflows, and enables smarter, data-driven decisions for improved planning and operational efficiency. Express interest in testing Imagery Insights.

Street View image of a stop sign that is analyzed and categorized using Vertex AI

Places Insights

Places Insights provides access to aggregate insights from Google Maps data for more than 250 million businesses and places– refreshed monthly–to make more informed business decisions. With rich insights from this Places dataset, you can go beyond basic POI data like wheelchair accessibility and price level. You’ll access a more granular understanding of millions of businesses and points of interest like where most of the coffee shops are located in a zip code.

Using BigQuery’s data clean room environment you can combine proprietary data with these insights from our Places data, to uncover deeper insights about locations. Common use cases include identifying optimal store locations based on locations of complementary businesses, and a deeper understanding of local market dynamics. Express interest in testing Places Insights.

Density of restaurants in Manhattan visualized using a heatmap

Roads Management Insights

Roads Management Insights helps Public Sector and Road Authorities improve road network efficiency and safety through data-driven traffic management. These insights stem from analysis of historical data to identify congestion patterns within your road networks, pinpoint potential causes of slowdowns, and take informed action. With access to real time monitoring, authorities can detect and respond to sudden speed drops, pinpoint the cause, and potentially reroute traffic within seconds of changes happening on the roads. Express interest in testing Roads Management Insights.

Earth Engine in BigQuery

Earth Engine in BigQuery brings the best of Earth Engine's geospatial raster data analytics directly into BigQuery. This feature makes advanced geospatial analysis of datasets derived from satellite imagery accessible to the SQL community–even if you don’t have remote sensing expertise. The ST_REGIONSTATS() function is a new BigQuery geography function that invokes Earth Engine to efficiently read and analyze geospatial raster data within a geographic area of interest. In addition, Earth Engine datasets in Analytics Hub now give you access to a growing collection of Earth Engine datasets directly within BigQuery, simplifying data discovery and access. Learn more here.

By using Google's geospatial analytics datasets within BigQuery, you can enable key business and environment decisions, such as how to optimize operations and maintenance of infrastructure, enable sustainable sourcing with global supply chain transparency, improve road safety and reduce congestion, and much more.

Visit our new website to discover how Google can help you unlock the power of geospatial data to drive better, faster business and sustainability decisions.

^{Note: in addition to new geospatial analytics datasets and capabilities from Google Maps Platform and Earth Engine, read here to learn more about Geospatial Reasoning, a new research effort announced at Cloud Next 25 by Google Research. Geospatial Reasoning will include new geospatial foundational models and a framework for building agentic workflows.}

Harvesting hardware: Our approach to carbon-aware fleet deployment

Thu, 20 Mar 2025 16:00:00 +0000

When it comes to managing the infrastructure and AI that powers Google’s products and platforms – from Search to YouTube to Google Cloud – every decision we make has an impact. Traditionally, meeting growing demands for machine capacity means deploying new machines and that has an associated embodied carbon impact. That’s why we’re working to reduce the embodied carbon impact at our data centers by optimizing machine placement and promoting the reuse of technical infrastructure hardware.

In this post, we shine a spotlight on our hardware harvesting program, an approach to fleet deployment that prioritizes the reuse of existing hardware.

The hardware harvesting program

The concept is simple: As we deploy new machines or components in our fleet, we repurpose older equipment for alternative and/or additional use cases. The harvesting program prioritizes the reuse of existing hardware, which reduces our carbon emissions compared to exclusively buying brand new machines from the market. This program also helps conserve valuable resources and minimize waste, which contributes to a more circular economy. By scrutinizing the carbon impact of deployment decisions, we’re not just reducing emissions — we’re embedding carbon considerations into the very core of our data center machine operations and business decisions.

Hardware harvesting is not without its challenges. For the program to be successful, we need to ensure the harvested machines meet the specific demands of our workloads and our customers’ requirements, which vary depending on the type of machine and its configuration. However, our heterogeneous fleet, with a wide variety of computational, storage, and accelerator machines, gives us the flexibility to find creative solutions that support both our services and our sustainability goals.

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Hardware harvesting in action

Google's harvesting program has already yielded strong benefits. By prioritizing the reuse of existing hardware, we've been able to optimize the use of new equipment, reduce our carbon footprint, minimize waste and lower costs.

For example, in 2024, we needed more specific models and configurations of certain components (PCBs, CPUs, motherboards, and HDDs). We harvested them from existing machines by migrating configuration-agnostic jobs from existing machines to more efficient ones, then reclaimed the components from these specific machines. In 2024, the harvesting program helped us reuse over 293,000 components to fulfill new demand, save carbon emissions, and reduce costs. Scaling this hardware harvesting approach across Google's data center infrastructure presents an opportunity for cost, resource, and carbon reduction.

Looking ahead: Leading by example

Harvesting is just one example of how we’re embedding carbon considerations into our data center practices. We believe that these initiatives will play a role in helping us achieve our company-wide net-zero goal and build a more sustainable future for cloud computing and AI. Read our 2024 Environmental Report to learn more about our sustainability practices.

As we continue to refine our strategies, we aim to lead by example and encourage other companies, especially those in the cloud computing industry, to consider similar approaches.

How Google Cloud measures its climate impact through Life Cycle Assessment (LCA)

Tue, 11 Mar 2025 19:00:00 +0000

As AI creates opportunities for business growth and societal benefits, we’re working to reduce their carbon intensity through efforts like optimizing software, improving hardware efficiency, and supporting our operations with carbon-free energy.

At Google, we’re committed to understanding the entirety of our environmental impact so we can apply the best, boldest, and most holistic solutions. In this post, we’ll talk through an assessment technique called Life Cycle Assessment (LCA) to understand the complete picture of carbon emissions.

Measuring environmental impact with Life Cycle Assessment

LCA is a process-analysis method for evaluating the environmental impact of a product-system or service throughout its entire life cycle. This includes everything from raw material extraction and processing, manufacturing, transportation, use, and end-of-life treatment (recycling, disposal, etc.). LCA enables us to measure emissions along every step of our hardware manufacturing, find the sources of those emissions, identify ways to reduce them, and track our progress towards global net-zero emissions.

The Google Cloud Carbon Footprinting team has developed a best-in-class LCA approach to evaluate the embodied carbon emissions associated with the supply chain of our data center hardware¹, including AI/ML accelerators, compute machines, storage platforms, and networking equipment.

Figure 1. LCA stages and system boundary

The approach is consistent with global LCA standards, ISO 14040/14044, and is specifically tailored to Google Cloud’s data center technology portfolio and underlying manufacturing production processes. In addition, Google Cloud’s LCA methodology has been critically reviewed by Fraunhofer IZM, ensuring completeness, accuracy, and adherence to industry standards. This enables Google to accurately account for emissions that come from the manufacturing of various types of data center hardware, all the way down to the smallest components that compose the fleet.

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Driving the industry forward

By collaborating closely with our supply chain partners, academic leaders, and industry peers, we're pioneering the development of highly configurable Life Cycle Inventory (LCI) models. This innovative approach empowers us to move beyond generic assessments, unlocking the potential for detailed, customized environmental insights for vital components like semiconductors, hard disk drives, PCBAs, and thermal management solutions.

To achieve unparalleled accuracy, Google Cloud is transforming LCA data collection by partnering directly with suppliers to gather primary data. This means capturing the direct flows (i.e., material and energy transactions with the natural environment) that occur throughout manufacturing. These custom LCIs are powerful tools, enabling us to precisely measure our environmental impact and accelerate our journey towards net-zero.

Figure 2. Process-level environmental transactions

In addition to driving accuracy, Google is driving standardization in the hardware industry by participating in a collaborative effort to develop consistent LCA guidelines. This initiative aims to create Product Category Rules (PCRs) that facilitate primary data collection and improve comparability across product assessments. By building on established ISO standards and aligning with GHG protocol and Product Environmental Footprint (PEF), this collaboration seeks to enhance the accuracy and transparency of environmental accounting efforts.

In a recent LCA study, we evaluated the environmental impact of our Tensor Processing Units (TPUs) throughout their entire lifespan. The introduction of a new metric, Compute Carbon Intensity (CCI), helped uncover findings showing that over two generations, more efficient TPU hardware design has led to a 3x improvement in the carbon-efficiency of AI workload. LCA studies like this are crucial for understanding and reducing the carbon footprint of hardware across the ecosystem.

Advancements in LCA

At Google, we believe that informed action is essential, and that requires a foundation of accurate measurement. Through our advancements in LCA and by fostering collaboration within the global community, we’re driving meaningful, measurable progress towards a more resilient future.

To learn more, visit these resources:

^{1. Upstream supply chain activities are also defined as cradle-to-gate or Scope 3}

Designing sustainable AI: A deep dive into TPU efficiency and lifecycle emissions

Wed, 05 Feb 2025 17:00:00 +0000

As AI continues to unlock new opportunities for business growth and societal benefits, we’re working to reduce the carbon intensity of AI systems — including by optimizing software, improving hardware efficiency, and powering AI models with carbon-free energy.

Today we’re releasing a first-of-its-kind study¹ on the lifetime emissions of our Tensor Processing Unit (TPU) hardware. Over two generations — from TPU v4 to Trillium — more efficient TPU hardware design has led to a 3x improvement in the carbon-efficiency of AI workloads.²

Our life-cycle assessment (LCA) provides the first detailed estimate of emissions from an AI accelerator, using observational data from raw material extraction and manufacturing, to energy consumption during operation. These measurements provide a snapshot of the average, chip-level carbon intensity of Google’s TPU hardware, and enable us to compare efficiency across generations.

Introducing Compute Carbon Intensity (CCI)

Our study examined five models of TPUs to estimate their full life-cycle emissions and understand how hardware design decisions have impacted their carbon-efficiency. To measure emissions relative to computational performance and enable apples-to-apples comparisons between chips, we developed a new metric — Compute Carbon Intensity (CCI) — that we believe can enable greater transparency and innovation across the industry.

CCI quantifies an AI accelerator chip’s carbon emissions per unit of computation (measured in grams of CO2e per Exa-FLOP).³ Lower CCI scores mean lower emissions from the AI hardware platform for a given AI workload — for example training an AI model. We've used CCI to track the progress we've made in increasing the carbon-efficiency of our TPUs, and we’re excited to share the results.

Key takeaways

Google’s TPUs have become significantly more carbon-efficient. Our study found a 3x improvement in the CCI of our TPU chips over 4 years, from TPU v4 to Trillium. By choosing newer generations of TPUs — like our 6th-generation TPU, Trillium — our customers not only get cutting-edge performance, but also generate fewer carbon emissions for the same AI workload.
Operational electricity emissions are key. Today, operational electricity emissions comprise the vast majority (70%+) of a Google TPU’s lifetime emissions. This underscores the importance of improving the energy efficiency of AI chips and reducing the carbon intensity of the electricity that powers them. Google’s efforts to run on 24/7 carbon-free energy (CFE) on every grid where we operate by 2030 aims directly at reducing the largest contributor to TPU emissions — operational electricity consumption.
Manufacturing matters. While operational emissions dominate an AI chip's lifetime emissions, emissions associated with chip manufacturing are still notable — and their share of total emissions will increase as we reduce operational emissions with carbon-free energy. The study’s detailed manufacturing LCA helps us target our manufacturing decarbonization efforts towards the highest-impact initiatives. We're actively working with our supply chain partners to reduce these emissions through more sustainable manufacturing processes and materials.

Our significant improvements in AI hardware carbon-efficiency in this paper complement rapid advancements in AI model and algorithm design. Outside of this study, continued optimization of AI models is reducing the number of computations required for a given model performance. Some models that once required a supercomputer to run can now be run on a laptop, and at Google we’re using techniques like Accurate Quantized Training and speculative decoding to further increase model efficiency. We expect model advancements to continue unlocking carbon-efficiency gains, and are working to quantify the impact of software design on carbon-efficiency in future studies.

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Partnering for a sustainable AI future

The detailed approach we’ve taken here allows us to target our efforts to continue increasing the carbon-efficiency of our TPUs.

This life-cycle analysis of AI hardware is an important first step in quantifying and sharing the carbon-efficiency of our AI systems, but it's just the beginning. We will continue to analyze other aspects of AI’s emissions footprint — for example AI model emissions and software efficiency gains — and share our insights with customers and the broader industry.

Together, we can harness the transformative power of AI while minimizing its impact on the planet.

Explore our latest TPU offerings and learn more about how customers can unlock sustainable growth with Google Cloud.

^{1. The authors would like to thank and acknowledge the co-authors for their important contributions: Ian Schneider, Hui Xu, Stephan Benecke, Tim Huang, and Cooper Elsworth.
2. A February 2025 Google case study quantified the full lifecycle emissions of TPU hardware as a point-in-time snapshot across Google’s generations of TPUs. To estimate operational emissions from electricity consumption of running workloads, we used a one month sample of observed machine power data from our entire TPU fleet, applying Google’s 2023 average fleetwide carbon intensity. To estimate embodied emissions from manufacturing, transportation, and retirement, we performed a life-cycle assessment of the hardware. Data center construction emissions were estimated based on Google’s disclosed 2023 carbon footprint. These findings do not represent model-level emissions, nor are they a complete quantification of Google’s AI emissions. Based on the TPU location of a specific workload, CCI results of specific workloads may vary.
3. CCI includes both estimates of lifetime embodied and operational emissions in order to understand the impact of improved chip design on our TPUs. In this study, we hold the impact of carbon-free energy on carbon intensity constant across generations, by using Google's 2023 average fleetwide carbon intensity. We did this purposefully to remove the impact of deployment location on the results.}

Looking back on a year of deeper connectivity across Earth Engine and Cloud

Tue, 10 Dec 2024 18:00:00 +0000

2024 has been a landmark year for Google Earth Engine, marked by significant advancements in platform management, cloud integration, and core functionality. With increased interoperability between Google Cloud tools and services, and Earth Engine, we’ve unlocked powerful new workflows and use cases for our users. Here’s a round up of this year’s top Earth Engine launches, many of which were highlighted in our Geo for Good 2024 summit.

Management: Streamlining Workflows

Earlier this year, we launched the new Earth Engine Overview page in the Cloud Console, serving as a centralized hub for Earth Engine resources, allowing you to manage Earth Engine from the same console used to manage and monitor other Cloud services.

In this console, we also introduced a new Tasks page, allowing you to view and monitor Earth Engine export and import tasks alongside usage management and billing. The Tasks page provides a useful set of fields for each task, including state, runtime, and priority. Task cancellation is also easier than ever with single or bulk task cancellation in this new interface.

As we deepen Earth Engine's interoperability across Google Cloud, we'll be adding more information and controls to the Cloud Console so that you can further centralize the management of Earth Engine alongside other services.

Integrations: deepening cloud interoperability

Earth Engine users can integrate with a number of cloud services and tools to enable advanced solutions requiring custom machine learning and robust data analytics. This year, we launched a set of features that improved existing interoperability, making it easier to both enable and deploy these solutions.

Vertex AI integration
Using Earth Engine with Vertex AI enables use cases that require deep learning, such as crop classification. You can host a model in Vertex AI and get predictions from within the Earth Engine Code Editor. This year, we announced a major performance improvement to our Vertex Preview connector, which will give you more reliability and more throughput than the current Vertex connector.

Earth Engine access
To ensure all Earth Engine users can take advantage of these new integration improvements and management features, we’ve also transitioned all Earth Engine users to Cloud projects. With this change, all Earth Engine users can now leverage the power and flexibility of Google Cloud’s infrastructure, security, and growing ecosystem of tools to drive forward the science, research, and operational decision making required to make the world a better place.

Security: enhancing control

This year we launched Earth Engine support for VPC Service Controls - a key security feature that allows organizations to define a security perimeter around their Google Cloud resources. This new integration, available to customers with professional and premium plans, provides enhanced control over data, and helps prevent unauthorized access and data exfiltration. With VPC-SC, customers can now set granular access policies, restrict data access to authorized networks and users, and monitor and audit data flows, ensuring compliance with internal security policies and external regulations.

Platform: improving performance

Zonal Statistics
Computing statistics about regions of an image is a core Earth Engine capability. We recently launched a significant performance improvement to batch zonal statistics exports in Earth Engine. We've optimized the way we parallelize zonal statistics exports, such as exports that generate statistics for all regions in a large collection. This means that you will get substantially more concurrent compute power per batch task when you use ReduceRegions().

With this launch, large-scale zonal statistics exports are running several times faster than this time last year, meaning you get your results faster, and that Earth Engine can complete even larger analyses than ever. For example, you can now calculate the average tree canopy coverage of every census tract in the continental United States at 1 meter scale in 7 hours. Learn more about how we sped up large-scale zonal statistics computations in our technical blog post.

Python v1
Over the last year, we’ve focused on ease-of-use, reliability, and transparency for Earth Engine Python. The client library has moved into an open-source repository at Google which means we can sync changes to GitHub immediately, keeping you up-to-date on changes between releases. We are also sharing pre-releases, so you can see and work with Python library candidate releases before they come out. We have a static loaded client library, which makes it easier to build on our Python library and better testing and error messaging. We’ve also continued making progress on improving geemap and integrations like xee.

With all of these changes, we’re excited to announce that the Python Client library is now ‘v1’, representing the maturity of Earth Engine Python. Check out this blog post to read more about these improvements and see how you can take full advantage of Python and integrate it into Google’s Cloud tooling.

COG-backed asset improvements
If you have data stored in Google Cloud Storage (GCS), in Cloud-Optimized GeoTIFF (COG) format, you can easily use it in Earth Engine via Cloud Geotiff Backed Earth Engine Assets, improving the previous experience requiring a single file GeoTIFF, where all bands have the same projection and type.

Now you can create an Earth Engine asset backed by multiple GeoTiffs, which may have different projections, different resolutions, and different band types–and Earth Engine will take care of these complexities for you. There are also major performance improvements to the previous feature: Cloud GeoTiff backed assets now have similar performance to native Earth Engine assets. In addition, If you want to use your GCS COGs elsewhere, like open source pipelines or other tools, the data is stored once and you can use it seamlessly across products.

Looking forward to 2025

We’re excited to see Earth Engine users leverage more advanced tools, stronger security, and seamless integrations to improve sustainability and climate resilience. In the coming year, we’re looking forward to further deepening cloud interoperability, making it easier to develop actionable insights and inform sustainability decision-making through geospatial data.

How cloud and AI are bringing scale to corporate climate mitigation and adaptation

Fri, 15 Nov 2024 17:00:00 +0000

Climate change is the biggest challenge our society faces. As scientists, governments, and industry leaders gather in Baku, Azerbaijan for the 2024 United Nations Climate Change Conference, a.k.a. COP29, it’s incumbent upon all of us to find innovative solutions that can drive impact at a global scale.

The gravity of climate change requires solutions that go beyond incremental change. To find those solutions, we need the ability to make better decisions about how to approach climate mitigation and adaptation across every human activity — from transport, industry, and agriculture to communications, finance, and housing. This requires processing vast volumes of data generated by these industries. The combination of AI and cloud technologies offer the potential to unlock climate change solutions that can be both transformational and global in scale.

We already have a lot of examples that we can draw from.

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Today, for example, Google Earth Engine is being used by the Forest Data Partnership, a collaboration for global monitoring of commodity-driven deforestation, to monitor every oil palm plantation around the globe, providing participating companies live early-warning signals for deforestation risks, and dramatically reducing the costs involved in forest monitoring. Similarly, NGIS is using Google Earth Engine to power TraceMark, helping businesses deliver traceability and transparency across global supply chains.

Another example is Global Fishing Watch, an international nonprofit co-founded by Google that is using geospatial analytics and AI to understand how human activity impacts the seas, global industries, climate, biodiversity and more. The datasets map global ocean infrastructure and vessels that don’t publicly broadcast their positions. This helps to advance policy conversations about offshore renewables development, provides insight into carbon dioxide emissions from maritime vessels, and enables marine protection.

It’s clear that AI can process large volumes of data, optimize complex systems, and drive the development of new business models. We see businesses harnessing the technology in the fight against climate change in four ways:

1. Measuring business performance

Businesses are using AI-powered insights to help monitor their advance towards sustainability targets, which ultimately contributes to building business resilience.

In today's business landscape, this is of paramount importance as companies face growing demands for transparency and accountability regarding their environmental and social impact.

We are seeing cloud and AI being used to de-risk investments, improve transparency, and increase profitability through the use of large-scale datasets, machine learning, and generative AI. These technologies allow companies to analyze their ESG performance, gain insights into climate risks, and monitor supplier behaviors.

For example, Palo Alto Networks partnered with Watershed, a Google Cloud Ready - Sustainability Partner, to measure and track their carbon emissions across their entire business using Google Cloud. This partnership enabled them to gain a comprehensive understanding of their environmental impact and set actionable targets for reducing emissions.

Another example is HSBC, which developed a new credit ranking tool on Google Cloud that allows them to run multiple climate risk scenarios simultaneously. This tool empowers HSBC to make more informed investment decisions while considering the potential impact of climate change on their portfolio.

Or Swiss Re, which is using Google Earth Engine and AI for flood modeling for better risk calculation in insurance.

2. Optimizing operations and supply chains

Secondly, businesses are using AI to optimize their operations and supply chains for energy and resource efficiency, as well as to cut costs.

This is crucial for companies seeking to enhance their sustainability performance while simultaneously improving their bottom line. Through the use of AI and machine learning, cloud technologies empower organizations to optimize their existing operations, improve cost efficiency, and minimize waste.

For example, Geotab, another Google Cloud Ready - Sustainability partner, is managing 75 billion data records in BigQuery for 4 million commercial fleet vehicles every day to optimize vehicle routes, increase driver safety behaviors and accelerate the path to fleet electrification.

3. Identifying cleaner business models

As the world shifts towards more sustainable practices, businesses must adapt and identify new avenues for growth. Cloud and AI is helping businesses do just that. Cloud and AI allow organizations to reimagine their business models, explore new markets, and create innovative products and services that align with their sustainability goals.

Recykal, for instance, has partnered with Google Cloud to build Asia's largest circular economy marketplace. By leveraging Google Cloud's AI and machine learning capabilities, Recykal is revolutionizing waste management and promoting sustainable practices in Asia.

Another example is Einride, a company that is reimagining freight transport by using electric, self-driving vehicles and an AI-powered platform. Their innovative approach to logistics is disrupting the transportation industry and contributing to a more sustainable future.

More recently, Climate Engine and Robeco have started using AI and geospatial technologies with their scientific expertise and investment knowledge to inform how publicly traded companies’ actions impact biodiversity. You can read their joint thought leadership paper here.

4. Building more sustainably

Finally, and very importantly, businesses want to ensure that the actual use of cloud and AI technologies doesn’t lead to increased climate impacts. From the get-go, developers need to take concrete steps towards reducing the carbon footprint and cost of their applications in the cloud.

This is why, through our Carbon Sense suite, we provide developers with the tools and resources they need to build and deploy applications in a way that minimizes their environmental impact, all while maintaining cost efficiency.

L'Oréal, for example, leverages Google Cloud's Carbon Footprint tool to track the gross carbon emissions associated with their cloud usage. This allows L'Oréal to understand the environmental impact of their technology decisions and implement strategies to reduce their footprint.

Finally, Google takes its own carbon footprint very seriously, and is pursuing an ambitious goal to achieve net-zero emissions across all of its operations and value chain, supported by a goal to run on 24/7 carbon-free energy on every grid where it operates by 2030.

Google Cloud is committed to helping organizations of all sizes achieve their sustainability goals. With cloud, data analytics, and AI, we’re delivering new ways to build resilience, reduce costs, and unlock sustainable growth, while also accelerating the impact of organizations’ sustainability initiatives through the smarter use of data. This is an opportunity to drive tangible business results and create a more sustainable future for all.

Meet the second Google for Startups Accelerator: Climate Change Cohort in Europe

Tue, 17 Sep 2024 16:00:00 +0000

The urgency of addressing climate change cannot be overstated, and Google recognizes the pivotal role that digital technologies play in this global effort. Our commitment encompasses ambitious goals such as helping individuals, cities, and partners to collectively reduce 1 gigaton of their carbon equivalent emissions annually by 2030, and our aim to achieve net-zero emissions across Google-wide operations and value chains by 2030.

Building on these efforts, we are pleased to introduce the latest cohort for the Google for Startups Accelerator in Europe: Climate Change. This 10-week, equity-free hybrid program is designed to empower high-potential Seed to Series A startups that are leveraging technology to combat climate change. This year's cohort represents a diverse and promising group of innovators. Comprising 15 startups from different countries across Europe, these companies bring a wealth of perspectives and approaches to climate change mitigation and adaptation. Their solutions leverage cutting-edge cloud technologies, including AI, geospatial data analysis, and advanced analytics, to develop groundbreaking approaches to environmental challenges.

Artificial Intelligence (AI) stands at the forefront of climate action, offering unprecedented potential to tackle some of the most pressing environmental challenges. Google is dedicated to scaling AI solutions to accelerate climate action while simultaneously addressing the technology's own environmental impact. This dual approach ensures that the benefits of AI in climate mitigation are maximized while its carbon footprint is minimized.

As we welcome this new cohort, we are filled with optimism about the potential impact of these startups. Their innovative approaches, coupled with Google's resources and expertise, represent a powerful force in the fight against climate change. We look forward to witnessing the growth and success of these companies as they work towards creating a more sustainable future for us all.

“To fuel our company’s growth during this Accelerator program, we’re keen to hear expertise on two key areas we are currently prioritising - market expansion to new regions and cloud infrastructure optimization." - Imeshi Weerasinghe, CEO and Co-founder, WEO

Meet the cohort:

Agrow Analytics (Spain)
Agrow Analytics uses AI-driven precision irrigation to optimize water use, boost crop yields, and enhance sustainability.
ALPHA Augmented (Switzerland)
ALPHA Augmented uses AI to solve packing inefficiencies in logistics, easily integrating with existing processes to simultaneously cut costs and CO2 emissions, making logistics smarter, greener, and potentially reducing the need for every third container ship and every second truck.
AWorld (Italy)
AWorld is an AI-driven, transformative sustainability engagement platform that uses gamification to engage and educate individuals on living sustainably.
Axle Energy (United Kingdom)
Axle Energy allows EVs, home batteries and heat pumps to help balance the grid and cut emissions.
Clevergy (Spain)
Clevergy helps companies to empower their customers with intuitive tools on web and app platforms, enabling them to not only understand their electricity bill but also take control of and optimize their overall energy usage, including solar, batteries, EVs and heat pumps.
Companion.energy
(Belgium)Companion.energy helps industrial companies unlock their energy flexibility potential with an advanced data analytics and optimization software product.
Gentian (United Kingdom)
Gentian provides nature insight using AI and Satellite imagery in order to empower decision makers.
HarvestAi (Germany)
HarvestAi is a SaaS platform that uses machine learning and computer vision to standardize crop growth and yield predictions, enabling indoor farming operators of all experience levels to make data-driven decisions and optimize their operations.
Kanop (France)
Kanop streamlines environmental monitoring and compliance for the voluntary carbon market needs, nature and climate-related disclosures and deforestation-free regulations by leveraging satellite imagery and AI at scale.
Nature Robots (Germany)
Nature Robots offers a Design-In AI and robotics software platform that enables fully autonomous solutions for regenerative and complex farming environments, enhancing efficiency, sustainability, and productivity through precise navigation, plant monitoring, and optimized resource management.
Perse (United Kingdom)
Perse provides data services to optimise the financial and carbon position of any asset connected to the grid
Qflow (United Kingdom)
Qflow empowers the built environment with real-time data on cost, carbon, and quality, collected at the source, to drive informed decision-making and support the mission of decarbonising construction through AI and data-driven insights.
WASTETIDE (France)
Wastetide is an AI software cutting the costs and carbon footprint of industrial waste.
WEO (Luxembourg)
WEO harnesses the power of space data and AI to generate actionable insights for making cities more resilient to climate impacts.
Zerofy (Estonia)
Zerofy is a cloud-based home energy management system. Zerofy optimizes home energy consumption, solar utilization, and EV charging with smart algorithms. It's a single app that works across vendors and without any additional hardware.

“We are looking forward to understanding how we can accelerate our AI development for scale, and connecting with the Google team and other companies for sharing lessons learned in this process.” - Jade Cohen, Co-founder and CPO, Qflow

In partnership with Google Cloud and Google Sustainability teams, this program offers a unique opportunity for selected startups from Europe to harness the full power of Google's resources. Participants will benefit from a tailored mix of in-person and virtual activities, personalized 1:1 mentoring sessions, and collaborative group learning experiences. The program delves deep into crucial areas such as product design, business growth strategies, and leadership development, providing startups with the tools they need to effectively scale their climate solutions. Participating startups will also receive dedicated Google Cloud technical expertise and credits via the Google for Startups Cloud Program. Congratulations to this latest cohort! To learn more about applying for an upcoming cohort, visit the program page here.

Our clean energy progress in Japan

Thu, 23 May 2024 16:00:00 +0000

Today, we're announcing two new solar power purchase agreements in Japan that bring us closer to our goal to run on 24/7 carbon-free energy on every grid where we operate by 2030. These Power Purchase Agreements (PPAs) with Itochu’s partner Clean Energy Connect and Shizen Energy are our first in the country, and together they will add a combined 60 megawatts (MW) of new solar energy capacity to the Japanese grid. This will not only support our data centers in the region, but also align with Japan's clean energy ambitions.

We are expanding our clean energy portfolio in the Asia-Pacific region

Our PPA with Clean Energy Connect, a partner of Itochu Corporation, involves constructing a network of roughly 800 small-scale solar plants across multiple grid regions in Japan. This novel, distributed approach is a creative solution to the challenge of limited land availability for large-scale solar projects in the country. It will generate a significant 40 MW of clean energy to support our operations in Japan.

The PPA with Shizen Energy, a leading renewable energy company in Japan, focuses on the development of a 20 MW utility-scale solar project situated in the same power grid as our recently opened data center in Inzai City, Chiba prefecture.

Through these agreements, we will procure the renewable energy generated from these solar farms across Japan, along with the associated energy attribute certificates. This will significantly reduce our carbon footprint in the region.

A collaborative effort for a sustainable future

These projects are expected to be fully operational within four years and underscore our commitment to invest nearly $690 million (nearly 100 billion yen) into sustainable infrastructure in Japan.

Signing these PPAs is just the beginning of our decarbonization journey in Japan. We aim to continue our efforts in the region by collaborating with local partners and exploring even more innovative solutions to accelerate the country's clean energy transition. To learn more about Google data centers, visit google.com/datacenters. To learn more about our sustainability work, visit sustainability.google.

How Prewave is helping to secure deep supply chains worldwide with AI on Google Cloud

Thu, 25 Apr 2024 08:00:00 +0000

Most businesses know that taking responsibility for their environmental and social impact is key for long-term success. But how can they make fully-informed decisions when most companies only have visibility into their immediate suppliers? At Prewave, we’re driven by the mission to help companies make their entire supply chains more resilient, transparent, and sustainable.

Our end-to-end platform monitors and predicts a wide range of supply chain risks, and AI is the driving-force behind its success. Without AI, handling vast volumes of data and extracting meaningful insights from publicly-available information would be almost unfathomable at the scale that we do to help our clients. Because of that, Prewave needs a rock-solid technology foundation that is reliable, secure, and highly scalable to continually handle this demand. That’s why we built the Prewave supply chain risk intelligence platform on Google Cloud from inception in 2019.

Back then, as a small team, we didn’t want to have to maintain hardware or infrastructure, and Google Cloud managed services stood out for providing reliability, availability, and security while freeing us up to develop our product and focus on Prewave’s mission. A shared concern for sustainability also influenced our decision, and we’re proud to be working with data centers with such a low carbon footprint.

Tracking hundreds of thousands of suppliers

Prewave’s end-to-end platform solves two key challenges for customers: First, it makes supply chains more resilient by identifying description risks and developing the appropriate mitigation plans. And second, it makes supply chains more sustainable by detecting and solving ESG risks, such as forced labor or environmental issues.

It all starts with our Risk Monitoring capability, which uses AI that was developed by our co-founder Lisa in 2012 during her PhD research. With it, we’re scanning publicly available information in 120+ languages, looking for insights that can indicate early signals of Risk Events for our clients, such as labor unrest, an accident, fire, or 140 other different risk types that can disrupt their supply chain. Based on the resulting insights, clients can take actions on our platform to mitigate the risk, from filing an incident review to arranging an on-site audit.

With this information, Prewave also maps our clients’ supply chains from immediate and sub-tier suppliers down to the raw materials’ providers. Having this level of granularity and transparency is now a requirement of new regulations such as the European corporate sustainability due diligence directive (CSDDD), but it can be challenging for our clients to do without help. They usually have hundreds or thousands of suppliers and our platform helps them to know each one, but also to focus attention, when needed, on those with the highest risk.

The Prewave platform keeps effort on the supplier’s side as light as possible. They only have to act if potential risk is flagged by our Tier-N Monitoring capability, in which case, we support them to fix issues and raise their standards. Additionally, this level of visibility frees them up from having to manually answer hundreds of questionnaires in order to qualify to do business with more partners.

To make all this possible, our engineering teams rely heavily on scalable technology such as Google Kubernetes Engine (GKE) to support our SaaS. We recently switched from Standard to Autopilot and noticed great results in time efficiency now that we don’t need to ensure that node pools are in place or that all CPU power available is being used appropriately, helping save up to 30% of resources. This also has helped us to reduce costs because we only pay for the deployments we run.

We also believe that having the best tools in place is key to delivering the best experience not only to customers but also to our internal teams. So we use Cloud Build and Artifact Registry to experiment, build, and deploy artifacts and manage docker containers that we also use for GKE. Meanwhile, Cloud Armor acts as a firewall protecting us against denial of service and web attacks.

Because scalability is key for our purposes, the application development and data science teams use Cloud SQL as a database. This is a fully managed service that helps us focus on developing our product, since we don’t have to worry about managing the servers according to demand. Data science teams also use Compute Engine to host our AI implementations as we develop and maintain our own models, and these systems are at the core of Prewave’s daily work.

Helping more businesses improve their deep supply chains

Since 2020, Prewave has grown from three clients to more than 170, our team of 10 grew to more than 160, and the company’s revenue growth multiplied by 100, achieving a significant milestone. We’ve also since then released many new features to our platform that required us to scale the product alongside scaling the company. With Google Cloud, this wasn’t an issue. We simply extended the resources that the new implementations needed, helping us to gain more visibility at the right time and win new customers. Because our foundation is highly stable and scalable, growing our business has been a smooth ride.

Next, Prewave is continuing its expansion plans into Europe that began in 2023, before moving to new markets, such as the US. This is going well and our association with Google Cloud is helping us win the trust of early-stage clients who clearly also trust in its reliability and security. We’re confident that our collaboration with Google Cloud will continue to bring us huge benefits as we help more companies internationally to achieve transparency, resilience, sustainability, and legal compliance along their deep supply chains.

1.5 gigawatts later, our smarter way of buying clean energy is here to stay

Wed, 27 Mar 2024 16:00:00 +0000

The climate-change clock is ticking, and the carbon-free energy transition needs to happen fast, and at global scale. To help accelerate grid decarbonization worldwide, last year we debuted an efficient, smarter way of doing business in the industry. Co-developed with LevelTen, our ambition was to more efficiently execute the way clean energy is bought and sold.

Today, this ambition is already becoming reality. Within one year of introducing our new approach to the market, we’ve signed power purchase agreements (PPAs) for more than 1.5 gigawatts (GW) of clean energy capacity in North America and Europe — bringing us closer to our 2030 goal of running on 24/7 carbon-free energy (CFE) on every grid where we operate.

Here’s a look at two recent PPAs that contributed to the 1.5 GW milestone, and how the new approach enabled easy, transparent, reliable, and efficient contracting in today’s market.

Clean energy to keep pace with digital demand in the U.S. Midwest

Our first transaction was a PPA for 100 megawatts (MW) of solar energy from Cubico Sustainable Investments, contributing to our CFE goals at our data center in Council Bluffs, Iowa. Leaning on our risk-balanced PPA template, developed with feedback from LevelTen and the market, we quickly agreed on common contract provisions with Cubico. LevelTen’s click-through UI helped us finalize project-specific terms, and we landed a final agreement within weeks, instead of months.

Our Iowa operations had achieved at least 90% CFE when measured on an hourly basis in 2020, 2021, and 2022. The ability to quickly add more carbon-free energy to the electricity grid — like with the Cubico solar project, which has a complementary generation profile to our existing wind portfolio — ultimately helps us keep CFE high in Iowa, even as we grow to meet demand for our products and services.

Procurement at the speed of light (well, almost) in the Netherlands

We inked long-term PPAs with Kronos Solar EDPR to offtake part of the clean energy production from its four photovoltaic plants under development in the Netherlands, partnering for the annual production of more than 49 GWh of clean energy. The solar energy will complement our Dutch investments in near-shore and offshore wind, as well as our existing partnership with EDPR NA Distributed Generation (EDPR NA DG) in the U.S.

Even though these PPAs involved a familiar partner and geographic market, traditional procurement practices still would have created time-consuming obstacles, which our accelerated contracting approach helped us sidestep. Within days of closing our request for proposals (RFP), we swiftly moved to project selection and, just two weeks later, worked with Kronos Solar EDPR to reach a signature-ready PPA.

Only 82 days passed between RFP launch and contract execution. In the world of clean energy procurement, that’s fast — warp-factor-ten fast.

In addition to helping Google advance our own 24/7 CFE goal, the efficient end-to-end transaction process helped provide Kronos Solar EDPR with the certainties needed to deploy capital for developing the photovoltaic plants, enabling them to lock in costs early. And by spending less time negotiating the PPA itself, both companies freed up resources to focus on harder problems, like finding solutions to address interconnection queue uncertainty.

Leaping toward a new standard

Reaching 1.5 GW with our accelerated approach is a significant milestone, but we’re just getting started. This new way of doing business has become our standard for wind and solar PPAs, and we’re exploring ways to contract for additional types of carbon-free energy generation and storage.

Now that LevelTen has made the approach — known as LevelTen Energy’s Accelerated Process™ (LEAP) — available to clean energy buyers of all sizes across North America and Europe, we hope other organizations will join us. A carbon-free future is within reach if we work smarter and with more urgency, together.

To learn more about how the approach works, see our video:

8 new ways to bridge the gap to geospatial analysis with Earth Engine

Thu, 08 Feb 2024 17:00:00 +0000

Over 10 years ago we launched Earth Engine, Google’s cloud-based service for geospatial processing, to address the greatest sustainability challenges of our time. Since then, it has continually evolved alongside the growing urgency of environmental issues to include 90+ petabytes of analysis-ready geospatial data. With a wider range of options available for analyzing satellite data, we’ve focused our recent efforts on building smart connections between Earth Engine and other critical tools, datasets, and systems.

Here are eight improvements and integrations released in the past few months to make it easier for you to use Earth Engine:

1. BigQuery

BigQuery is Google's petabyte scale analytical database. Earth Engine focuses on image (raster) processing, whereas BigQuery is optimized for processing large tabular datasets. By using BigQuery and Earth Engine together, users get the best of both worlds. Find out more about the Earth Engine to BigQuery export connector here: Improving sustainability with our Earth Engine and BigQuery connector.

2. Python

Many Earth Engine users want to analyze data in Python because it’s the most commonly used language for machine learning and data analysis. The Python community has developed useful tools for ML and analysis, and these have expanded recently to geospatial workloads. For example, Cloud Optimized GeoTiffs are a file format optimized for remote sensing data, and GeoPandas extends Pandas dataframes with common geospatial functions and plotting.

Earth observation science is an inherently visual experience: panning, zooming, clicking to inspect image band values at a point, and drawing map polygons for zonal statistics are all important parts of science workflows. Until recently, Earth Engine only supported this experience in its JavaScript-based code editor. Today, we're excited to announce official support for geemap, a Python library that delivers many code editor experiences in a Colab or Jupyter notebook environment created in April 2020 by Dr. Qiusheng Wu, a Google Developer Expert. For more information, see Python Powers Up: The Rise of the Python API for Earth Engine.

3. Data extraction

If you're training a TensorFlow model or if you want to run hydrology simulations outside Earth Engine, you might want to get data out of Earth Engine into another system. You've always been able to use the Earth Engine export API to let Earth Engine do the heavy lifting. But if you've ever run into scaling issues or you're already familiar with a framework like Apache Beam, Spark, or Dask, check out our new data extraction methods. Our Python client library now comes bundled with client-side logic to convert between Earth Engine objects and NumPy, Pandas, and GeoPandas types. For more information, see Pixels to the people!

4. Xarray

Xarray is a popular open-source Python package for working with multidimensional arrays. We believe that Xarray offers the most convenient way to work with pixels from Earth Engine: it allows you to operate on Earth Engine ImageCollection as Xarray Datasets. We recently announced an Earth Engine integration with Xarray called Xee, which integrates closely with Dask to distribute work across multiple processors. Xarray does "lazy evaluation," which means it only pulls down the data that's necessary for a calculation, but it connects to many other systems. So for instance, you can use Xee to export Earth Engine data into a Zarr file, a relatively newer data format that is becoming more popular for weather and climate data.

5. Vertex AI

Once you have data outside Earth Engine, you may want to train a deep learning model on it and then get predictions from it. Earth Engine now integrates with Vertex AI (currently in Public Preview). This replaces a prior integration with Google Cloud AI Platform. You can host your model in Vertex AI and get predictions from within the Earth Engine code editor. Vertex supports much larger images for prediction than AI Platform. Vertex also allows for a lot more extensibility. For more information, please see Earth Engine brings Vertex AI to the geospatial party.

6. Erdas LiveLink for Earth Engine

ERDAS IMAGINE is a very popular remote-sensing software package. We were excited to partner with Hexagon to launch LiveLink for Earth Engine this year. LiveLink allows you to combine Earth Engine catalog data with private data on your local computer. You can get the best of both worlds: leverage Earth Engine's expansive data catalog and backend processing capabilities while developing in a familiar interactive environment.

7. New datasets to streamline analysis

Finally, we routinely add new, broadly useful datasets to Earth Engine's data catalog so you can join them to the rest of Earth Engine's data or your own private data. In the last year, we have added over 100 new datasets, including JRC’s Global Map of Forest Cover for 2020, and NASA’s Harmonized Landsat and Sentinel-2 dataset, which creates a seamless surface reflectance record for the globe.

8. Cloud Score+

Anyone who has worked with Sentinel-2 data or remote sensing data in general will tell you that cloud cover is a major problem. The world just doesn’t look like the beautiful imagery in Google Earth all the time – it’s covered in clouds, making analysis challenging. This is why our team built Cloud Score+, which is the first comprehensive QA score for Sentinel-2, powered by a state-of-the-art deep learning approach. Cloud Score+ is now available for the entire Sentinel-2 collection. For more information, please see All Clear with Cloud Score+.

We know that your sustainability efforts make the most impact when you can connect remote sensing data to other parts of the Google Cloud ecosystem. Contact your Google Cloud sales representative for more information on these latest Earth Engine enhancements, and stay tuned for more in 2024.

Reflections from COP28: To drive meaningful climate solutions, it’s all tech on deck

Fri, 15 Dec 2023 08:00:00 +0000

The UAE Consensus agreement that was signed earlier this week at the conclusion of COP28 was not what many participants had hoped for, but it is more than many of us expected. While it failed to definitively call for the phase-out of fossil fuels, it did land agreement on a range of critical issues — from scaling renewables, funding for loss and damage, and more. Still, the risk of catastrophic climate change is high, and the pressure to effect meaningful change continues to fall on individuals and the private sector.

The direction of travel remains clear: all industries need to transform to deliver the decarbonization we need. There will be winners and losers and technology — especially AI — will be at the heart of driving new value and accelerating action.

Thankfully, as we wrote over the past two weeks, there’s a lot that businesses and individuals can do. Over the course of 14 blogs, we detailed the numerous ways that Google, Google Cloud and our partners are curtailing our own carbon footprints, and helping customers harness AI to drive impact. The rise of generative AI in particular is enabling new ways of surfacing climate-relevant information and driving climate-friendly actions.

To recap, here’s a summary of what we wrote about over the course of the conference:

On Day 1, Adaire Fox-Martin and I, Justin Keeble, laid out our vision for improving access to climate data (e.g., participating in the Net Zero Data Public Utility), building a climate tech ecosystem, and unlocking the power of geospatial analysis.
On Day 2, the first-ever COP28 Health Day, Googlers Phil Davis and Daniel Elkabetz highlighted how environmental data can raise awareness, help citizens make better decisions, and spur adoption of solutions for better climate health.
On Day 3, as the COP28 community pondered finance and trade, Kevin Ichhpurani and Denise Pearl discussed how partners can adopt programs in Google Cloud Ready - Sustainability catalog to help their customers identify and deploy better solutions.
Likewise, EMEA Googlers Tara Brady and Jackie Pynadath highlighted how Google Cloud financial services customers are using generative AI to fund climate transition initiatives.
For Day 5, Energy and Industry Day, Google Cloud Consulting leader Lee Moore and Poki Chui showed how businesses are decarbonizing their supply chains and helping consumers make enlightened choices.
On Day 6, Urbanization and Transport Day, Umesh Vemuri and Jennifer Werthwein showed off examples of Google Cloud automotive customers using AI to, um, drive innovation in automobile design, supply chain, power and mobility.
Denise Pearl then took the pen with our partner mCloud to discuss how we, in addition to shifting away from carbon-based energy, are actually using less energy to begin with.
We then heard from the Google Cloud Office of the CTO, where Will Grannis and Jen Bennett relayed the lessons they’ve learned from talking to CTOs about sustainability.
In honor of Day 9, Nature Day, Chris Lindsay and Charlotte Hutchinson talked about how geospatial analytics and solutions built on top of Google Earth Engine are helping partners preserve the natural world.
Then, Yael Maguire and Eugene Yeh of our Geo Sustainability team looked at the new European Union Deforestation Regulation (EUDR), and how to comply with it using geospatial data and tools.
In honor of Day 10, Food, Agriculture and Water Day, Karan Bajwa and Leah Kaplan provided a view from Google Cloud Asia at how Google Earth Engine and AI can support sustainable agriculture; and
Carrie Tharp and Prue Mackenzie from the Industries team examined how generative AI can encourage healthier food systems.
Product management leads Gabe Monroy and Cynthia Wu described how Google Cloud tools like Carbon Footprint are helping companies decarbonize their cloud footprints; and
Finally, Michael Clark and Diane Chaleff shared techniques that software builders can use to bolster sustainable software design, and encourage end users to make better choices from their apps.

This is just a small sample of the many things that we are doing to address the climate crisis, both in our own operations, and through technologies that our customers can use to drive their own efforts. We hope that you take the time to read about what Google Cloud customers and partners are doing to accelerate action on climate, and that you will take inspiration in them for your own business transformation. You can also learn more about Google’s sustainability efforts, and keep up-to-date on Google Cloud’s sustainability news. See you next year for COP29.

Responsible water use: Assessing watershed health in data center communities

Thu, 14 Dec 2023 13:00:00 +0000

There’s more demand than ever for the digital products and services that people and businesses rely on every day. Greater digital demand in turn requires greater data center capacity, and here at Google we’re committed to finding sustainable ways to deliver that capacity.

Today, we’re sharing our new framework to more precisely evaluate the health of a local community’s watershed and establish a data-driven approach to advancing responsible water use in our data centers. Building on our climate-conscious approach to data center cooling, the framework is an important element of our commitment to water stewardship in the communities where we operate.

When we build a data center, we consider a variety of factors, including proximity to customers or users, the presence of a community that’s excited to work with us, and the availability of natural resources that align with our sustainability and climate goals. Water cooling is generally more energy-efficient than air cooling, but with every campus, we ask an important question: Is it environmentally responsible to use water to cool our data center?

To find the answer in the past, we used publicly available tools to gain high-level insights, or provide an “Earth View” of the water challenges facing large aquifers or river basins, such as the Columbia River in the Pacific Northwest, or the Rhine River in northern Europe. But when we wanted to get more of a local “street view'' and dive deeper into the state of a specific water source — like the Dog River in Oregon, or the Eems Canal in Groningen, Netherlands — we struggled to find a tool that sufficiently captured the local water challenges to inform us about how to cool the data center in a climate-conscious way.

We recognize that addressing global water challenges requires local solutions, so we developed a data-driven water risk framework in collaboration with a team of industry-leading environmental scientists, hydrologists, and water sustainability experts.

Behind the scenes of our water risk framework

Our framework provides an actionable and repeatable decision-making process for new data centers and helps us evaluate evolving water risks at existing sites, with the specificity we need to understand watershed health at a hyperlocal level. The evaluation results tell us if a watershed’s risk level is high enough that we should consider alternative solutions like reclaimed water or air-cooling technology, which uses minimal water but consumes more energy.

The framework has two main steps to assess the water risk level for a data center location:

Evaluate responsible use. We compare the current and future demand for water — from both the community and our potential data center — to the available supply, using data from the local utility and water district management plans. In this evaluation, we also consider the recent water-level history compared to levels expected of a healthy watershed using flowstream and groundwater monitoring data, as well as whether the local water authority has rationed water use. Based on these indicators, our watershed health experts determine if a water source is considered at high risk of scarcity or depletion. If it’s high risk, we will pursue alternative sources or cooling solutions at the data center campus.
Measure composite risk. We look at the feasibility of treating and delivering water to and from the data center, whether with existing infrastructure or by collaborating with a utility partner to build new solutions such as reclaimed wastewater or an industrial water solution. In addition, we assess community access to water, regulatory risk, local sentiment, and any climate trends that could affect the future water supply, such as reduced precipitation or increased drought.

We designed this framework to take a comprehensive look at the water-related risks for each potential data center location. The results provide context for locally relevant watershed challenges and how our own investments in improved or expanded infrastructure or replenishment projects can help support local watershed health. Given the dynamic nature of water resources, we will repeat these assessments across our portfolio every three to five years to identify new and ongoing risks at existing sites that may require mitigation.

Responsible use in action

We have integrated the water risk framework into our planning and development processes for all new data center locations.

Notably, the responsible-use evaluation completed during the planning stage for our recently announced data center in Mesa, Arizona, found the local water source was at high risk of depletion and scarcity. Therefore, we opted to air-cool the data center, minimizing our impact on the local watershed. To further support water security in the area, we also donated to Salt River Project’s (SRP) effort focused on watershed restoration and wildfire risk. This collaboration with SRP was a follow-up to our 2021 investment in the Colorado River Indian Tribes system conservation and canal-lining projects to improve water conservation in the Southwest.

Moving forward

Collective effort and transparency are necessary to keep global watersheds thriving and healthy, as we work to deliver products and services that people and businesses use every day. We want to share what we’ve learned through our work with climate-conscious cooling and provide an example for how other industrial water users can make responsible water decisions for their own operations.

Our water risk framework is one of many pieces of responsible water use in action. Implementing this framework is another step on our water stewardship journey and complements our ambitious 24/7 carbon-free energy goal. Watershed health is both complex and dynamic, and as we make progress on our framework, we will continue to refine it, sharing lessons learned with others who aspire to practice responsible water use in their own organizations and communities. Check out our water risk framework white paper for more detail.