How data ecosystems support climate targets

🛑BREAKING: Brazil Calls for a Global Public Digital Infrastructure to Speed Up Climate Action🛑 💠At the request of COP30 President, Ambassador André Corrêa do Lago, the Instituto de Tecnologia e Sociedade (ITS Rio) and Ronaldo Lemos formulated Brazil's call for a Global Public Digital Infrastructure for Climate (Climate DPI), a proposal that positions data, finance, and intelligence as the missing layer of the Paris Agreement’s implementation. 💠The green transition lacks a shared digital backbone. Climate action today is fragmented across nations, funds, and data silos. Climate DPI aims to correct this by functioning as an “operating system for climate action”, where digital identity, interoperable payments, and open environmental data converge into a single ecosystem. 💠Its architecture, ClimateStack, links five layers: 🟢 Identity - unique digital records for individuals, organizations, and climate assets. 🟢 Finance - smart contracts enabling transparent flows, compensation, and carbon credits. 🟢 Open data - integration of satellite and sensor networks (GEOSS, Copernicus, INPE/PRODES). 🟢 Applications - public digital services for deforestation alerts, risk forecasting, and climate markets. 🟢 Access - multi-interface delivery (web, SMS, radio) to ensure inclusion. 💠By connecting existing but isolated technologies, the project envisions real-time emissions tracking, faster disaster response (up to 40%), and universal climate alerts by 2035. 💠Strategically, Brazil frames Climate DPI as COP30’s digital legacy, a move that links digital public goods and climate governance. A project that can redefine how the world measures, finances, and enforces its climate commitments. 🔗 Read the full proposal on the official COP30 website. https://lnkd.in/dTUXJFw6

How can data help us tackle environmental challenges like climate change and deforestation? The Amazon Sustainability Data Initiative (ASDI) is making waves by connecting researchers, businesses, and governments with powerful environmental data resources, so they can take meaningful action on critical issues. Imagine trying to assess the impact of deforestation across a supply chain: The data needed is vast and complex. ASDI steps in to simplify that, offering global datasets for studying climate, natural resources, and biodiversity—all on a shared platform. Take Apple, for example. With ASDI data, they were able to pinpoint specific areas where deforestation was affecting their supply chain and then invest in reforestation. It’s not just corporations benefitting either; in California, government agencies used ASDI’s satellite imagery to identify parts of the coastline most at risk of erosion from rising sea levels, allowing for focused conservation efforts. These stories show that ASDI is more than just a data platform; it’s a tool for turning complex environmental data into clear insights. Having easy access to such data can make all the difference. Researchers, environmental groups, and businesses alike can now collaborate more easily, using shared knowledge to address sustainability challenges more effectively. For those interested in exploring ASDI’s offerings, the AWS Registry of Open Data provides tutorials and hands-on resources that make this data accessible to anyone. Tackling environmental issues takes a collective effort, and initiatives like ASDI are opening doors for more people to get involved in finding solutions that benefit us all. #Biodiversity #DataAnalysis #Sustainability #ClimateChange #EnvironmentalSustainability

🌍 Methane matters more than ever — and we finally have the tools to act globally. Methane is over 80 times more potent than CO₂ in the short term, and reducing emissions is one of the fastest ways to slow global warming. That’s why a new era of transparency, accountability, and international collaboration is emerging — powered by science and data. 🔬 MethaneSAT, launched by the Environmental Defense Fund, is a game-changer. It delivers high-resolution, independent measurements of methane emissions across the globe, especially in the oil and gas sector. With this satellite, we can pinpoint emissions intensity in producing regions and measure progress toward reduction goals. Carbon Mapper is another satellite platform that allows tracking super emitters. 📊 This data feeds into the International Methane Emissions Observatory - IMEO a UN Environment Programme initiative that brings together verified emissions data to drive policy change. It’s a critical tool for standardizing how methane is reported and acted upon. 🇪🇺 And the European Union is raising the bar. With its 2024 Methane Regulation — including requirements for imported fossil fuels to meet EU-equivalent standards — the EU is signaling that methane transparency is not optional. This creates real incentives for exporters worldwide to reduce emissions or risk losing access to key markets. 🎙️ Here Daniel Zavala-Araiza, Senior Scientist at EDF and leading voice in methane science, discussed how this ecosystem of tools — MethaneSAT, IMEO, and EU regulation — can drive climate progress fast. His story about how Mexico City also used data to reduce pollution points to how data long driven mitigation action. Bottom line: Accurate data + strong policy = real climate action. MethaneSAT , Carbon Mapper and IMEO don’t just measure emissions — they make it impossible to ignore them. Congratulations Daniel Zavala-Araiza on an excellent way to show how mitigation methane is the fastest way to reduce temperature in the short term. https://lnkd.in/e_dzPZU5

As #datacenters scale rapidly to support #AI, cloud computing, and other digital services, much has been written about whether existing grids can meet the surge in electricity demand—and whether emissions will spike, since most grids remain carbon intensive. This outlook - focusing on the risks and challenges of data center growth - misses the transformative role that data centers and technology companies could play in accelerating national and regional energy, climate, and sustainable development goals. In our new blog, Perrine Toledano, Bradford M. Willis and I explain how #hyperscalers can help resolve the very constraints that are slowing the energy transition and undermining broader climate and development goals. Specifically, hyperscalers can uniquely: 🔹 reduce investment risks and marginal costs for new clean grid infrastructure, as large, predictable off-takers 🔹 create financeable demand for large-scale energy storage. Storage integration - which strengthens grid reliability and resilience - has been hard to finance because of uncertain revenue streams. 🔹 expand access to water and thermal systems through shared-use infrastructure platforms, learning from successful models in other sectors like mining 🔹 deploy rapidly-evolving AI and digital tools to increase energy efficiency, manage energy demand, streamline interconnection, lower system costs, and optimize maintenance, among other evolving functions, and 🔹 expand access to broadband and digital services, closing the persistent digital divide, and bringing transformative benefits in health, education, agriculture, and financial inclusion to underserved communities. These benefits are happening already in ad hoc ways - but could be massively scaled when embedded in strategic policy frameworks and coordinated with public and private partners. 🔗 https://lnkd.in/ea6vMMaG Side note: our current focus on carbon footprinting has distracted from—rather than supported—tech firms’ transformative potential. While footprinting can provide a useful snapshot of emissions/exposure/influence, our over-emphasis on emissions reporting has crowded out any discussion of strategic systemic integration and even creates perverse incentives. The over-reliance on footprinting as the key metric has also very predictably led to myriad illegitimate practices, including 'offsetting' emissions with unbundled RECs or dubious carbon credits, and other accounting loopholes (see yet another timely, insightful article from Simon Mundy on big tech's climate claims: https://lnkd.in/eNvtGxGu). Let's shift the focus to encouraging tech firms to engage in strategic public/private cooperation in grid design and expansion, financing solutions, and expanded digital inclusion -- optimizing transformative digital innovations for societal and planetary benefit.

Earth Observation is no longer just about capturing images from orbit. It’s rapidly becoming one of the most important tools we have to understand what’s happening on the ground—and act on it. Recent developments in EO are showing a clear trend: using satellite data to support real-time, local decisions in areas that impact lives, environments, and economies. Here are four examples that stand out: 1. Detecting Wildfires Before They Spread Google and Muon Space are building Fire Sat, a constellation of over 50 satellites that will scan fire-prone areas every 15 minutes. With real-time thermal imaging and cloud-based AI, it’s designed to catch wildfires early—before they become disasters. 2. Mapping Carbon Storage from Orbit The European Space Agency’s Biomass satellite uses a powerful radar system to measure how much carbon Earth’s forests are actually storing—by looking through the canopy itself. This gives scientists a more accurate understanding of climate-related forest change and carbon sinks. 3. Monitoring Land Use with Consistent Imaging EarthDaily Analytics launched the first satellite in a new constellation purpose-built for high-frequency, high-accuracy landscape monitoring. It’s especially relevant in agriculture, forestry, and environmental policy—where visibility over time matters more than snapshots. 4. Enabling Localized Impact Forecasting Xoople has developed a cloud-native Earth Observation platform that blends EO data with local models to forecast regional environmental risks—like floods, soil degradation, or vegetation stress. It’s EO made practical for governments and agencies on the front lines of climate and resource planning. These aren’t just satellites in orbit. They’re part of a growing EO ecosystem that’s focused on enabling faster, more confident action—where and when it’s needed most. From archive to alert. From static to streaming. From observation to intervention.

If you’re a developer and wondering how your work can help tackle climate change, here’s something that should be on your radar: GitHub’s Climate Action Plan for Developers (https://lnkd.in/df2wKqau / Paull Young). It’s not just another open source initiative—it’s a call to action, a structured launchpad designed to help developers like us take tangible steps toward a greener, more sustainable future. The plan brings together over 60,000 climate-focused and green software repositories, connecting us to real tools and real projects that matter in the race to net-zero emissions. The idea is simple: as developers, our code consumes energy. From cloud functions to machine learning training pipelines, our digital footprint has a real-world carbon cost. And that means we’re in a unique position—not only to reduce the impact of our own work, but to contribute meaningfully to tools and platforms that help the world decarbonize. GitHub has curated an entire ecosystem of open-source climate solutions so we don’t have to start from scratch. It includes everything from emissions trackers and energy-efficient algorithms to real-time grid monitoring tools and climate data visualizers. One standout tool is CodeCarbon (Dr. Sasha Luccioni), an open-source Python package that integrates directly into your code and tracks its carbon emissions based on real-time electricity grid data. It considers your hardware usage, estimates energy consumption, and converts that into CO2 equivalents. Combine that with the API from Electricity Maps, and you can actually align your compute tasks with cleaner energy on the grid, thanks to its flow-tracing technology and ML-based forecasting. Together, they make emissions visibility a default feature of your development workflow. If you’re looking to contribute to climate tech projects but aren’t sure where to begin, ClimateTriage (https://climatetriage.com/ / Richard Littauer) is another brilliant resource. It lists active open source climate projects from OpenSustain.tech that are looking for contributors, especially those labeled with help wanted and good first issue. It’s perfect for getting involved, whether you’re a beginner, a scientist, or just someone who wants to apply their skills to the climate crisis. This is about more than making your code efficient. It’s about joining a growing movement of people who are using tech to solve the planet’s biggest problems. It’s time we start treating green software not as an afterthought, but as the new standard. Explore the plan, pick a project, and measure your impact. This is where real change starts.

We can vault forward our progress in #sustainability and #climate by solving issues around the lack of data and complexity of processing these data... things #AI happens to excel at. For example, one of PagerDuty's SBTi-validated climate targets is a 25% absolute reduction in our Scope 3 emissions (https://lnkd.in/em4uVh_5). We don't disclose the per-category emissions breakdown, but like most SaaS companies (where the "manufacturing process" is turning caffeine ☕ + electricity ⚡ into business-critical code 💻 😊), most of our supply chain emissions fall into Category 1, Purchased Goods and Services, with cloud data and compute as a substantial component. The problem with such a reduction target is that, again like most tech companies, our Scope 3, Category 1 is calculated using a spend-based methodology -- our spend in each supplier industry, quantified to carbon using standard industry-level emission factors (EIOLCA EFs) of kg CO2e/$. If we never advanced our data beyond this dollar denominator, the only way to reduce Scope 3 emissions would be to spend less -- not feasible for growing companies. So the first challenge of achieving our Scope 3 target is more accurate and specific supplier emissions data. In decreasing data quality, we want: 1. Actual calculation of the emissions of our activities from the supplier 2. Spend-based using the supplier-specific EF, i.e. our share of the supplier's total carbon footprint 3. Spend-based using industry-level EFs Some of our top suppliers already provide #1 (thanks AWS!). To obtain a company-specific EF (#2), there are several nuances: is it their full Scope 1-3 footprint? Is it location-based or market-based for Scope 2? For Scope 3, what categories does it include, and is that all the relevant ones? And that, of course, presupposes that both revenue and footprint are publicly disclosed, which often isn't the case for private and/or small suppliers. Only as a fallback would we then use #3 where no data exists, or where the data quality is below an acceptable threshold. Pre-AI, I'd have spent weeks -- or hired an intern to spend weeks -- walking down as far down the long tail of our supply chain as I had the patience for, looking up each supplier's revenue and footprint total to cross-reference with our supplier spend and calculate our share of each supplier's emissions. But now, on a wintry Sunday afternoon in New England with my puppy asleep at my feet, I spent an hour prompting Claude to write a script to do this, conduct the searches, and output the results (with references). Once Claude's analysis is complete, I'll write a post discussing the results and sharing what prompts I used... if interested, follow this space! My dream is that the degree to which AI accelerates sustainability data acquisition, analysis, and disclosure to allow us to focus our efforts on decarbonization outweighs the environmental cost of its use. #AI4ESG

It can be easy to give in to a certain cynicism about UN #climate summits. The Paris Agreement itself is consensus-driven and largely voluntary, and both national commitments and progress seem to perennially lag far behind what is required. And yet, at #COP30, there are promising developments on display, especially those involving new #data and #AI capabilities, which act as a force-multiplier for #climate action. This isn’t Silicon Valley hyperbole: the development of these tools is one of the few areas of human endeavor that is actually advancing *faster* than the climate crisis itself. Here are four examples of people using Planet #data and #AI to drive practical #climate action highlighted at the COP: 1. The California Air Resources Board and Carbon Mapper announced that they have already used our Tanager-1 hyperspectral satellite to identify and close 10 large #methane leaks in California. That’s equivalent to taking about 18,000 cars off the roads for a year — and they’re just getting started. More here: https://urli.info/1ezRO 2. Brazil's vast Amazonian territory is difficult to monitor for environmental crimes. With the Rede MAIS platform, built by our partner SCCON, and powered by @planet imagery, the Brazilian Polícia Federal can now detect and investigate fire scars, illegal crop planting, deforestation, and other environmental crimes as never before. Using these tools, the Federal police have led 400+ enforcement operations and collected R$4.39 billion in fines for environmental crimes. (And counting!) Visit the platform here: https://lnkd.in/eepwYZie 3. The Paris Agreement, in Article 6, envisions markets where countries can trade high-quality carbon credits. To work those markets need independent, digital measurement, reporting, and verification (“dMRV”) capabilities to ensure trust and transparency. Fortunately, @Planet has developed products that measure and monitor aboveground carbon for every hectare of forest on Earth. The states of Mato Grosso and Espírito Santo signed agreements to assess @Planet’s forest carbon products to improve the integrity of their carbon markets. 4. Our close partners at Climate TRACE use Planet satellite data to model millions of sources of emissions around the world. At #COP30, they released new tools built on top of this open dataset that identify specific solutions to reduce emissions at every major source of greenhouse gases in the world. Excitingly, ClimateTRACE, @Planet, and others will also be partnering with @UN Climate to help countries use these kinds of capabilities to plan and deliver on more ambitious climate plans (called NDCs) — starting with Uganda. More here: https://urli.info/1jnLc Each example above is action-oriented, practical, and generating results, and each represents the kind of multi-stakeholder collaboration that is the only sure path to effective climate action. And these are just the ones I know best, out of many others I’ve seen on display at #COP30.

Leveraging GHG data and analytics to accelerate business transformation 🌎 As regulations tighten and the demand for transparency grows, businesses face increasing pressure to adopt robust greenhouse gas (GHG) data and analytics systems. Establishing a structured framework for emissions measurement and analysis is critical for compliance, but its benefits extend far beyond regulatory requirements. A comprehensive GHG data architecture enables businesses to measure, manage, and act on emissions across the full value chain, paving the way for meaningful transformation. To meet both current and future expectations, organizations must focus on measuring emissions across Scopes 1, 2, and 3. Addressing direct emissions (Scope 1), energy-related emissions (Scope 2), and value chain emissions (Scope 3) ensures a complete understanding of an organization’s carbon footprint. Scope 3, in particular, represents the largest and most complex challenge, but it also holds the greatest opportunity for reducing environmental impact and driving systemic change across supply chains. With precise data on emissions across all scopes, businesses can move beyond compliance to actionable insights. By identifying carbon hotspots and setting reduction targets, organizations can optimize processes such as energy efficiency, supply chain sourcing, and logistics management. These actions help integrate sustainability into business operations while delivering cost efficiencies and improving resilience. A robust GHG data and analytics system also facilitates full-value chain transformation. Leveraging technologies like machine learning, scenario modeling, and ecosystem data exchanges enables businesses to plan for long-term carbon reduction strategies and innovate low-carbon products. Addressing emissions holistically across Scopes 1, 2, and 3 ensures alignment with global climate goals while creating competitive advantages in sustainable markets. Measuring and acting on emissions across the entire value chain is no longer optional. Businesses equipped with accurate data and advanced analytics capabilities can meet regulatory demands, reduce emissions at scale, and drive meaningful progress toward a low-carbon economy. Source: Gartner #sustainability #sustainable #business #esg #climatechange #GHG

Connecting nature data to global supply chains just got easier. For years, companies have struggled to link sustainability targets with the actual places their materials come from. That is now getting much easier. This image shows the World Resources Institute SBTN Natural Lands dataset running on Cecil This global dataset classifies every 30 m pixel on Earth into natural vs. non-natural land cover for 2020 across 21 detailed classes. It’s designed to help companies and institutions set and track science based targets for nature, ensuring no conversion of natural ecosystems. Why this matters: Businesses are under increasing pressure to measure and reduce their impact on nature but until recently, data at this scale and quality was hard to use. With datasets like this, teams can: 🔎 Identify where operations or supply chains overlap with natural ecosystems 📈 Build risk and opportunity models for sustainability and compliance 🌱 Report and act against deforestation and ecosystem conversion with confidence Big thanks to Alex Logan and the team at Cecil for building out the platform that makes this data discoverable and actionable. 🌎 I'm Matt and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 9k+ others learning from my newsletter → forrest.nyc