How AI can Support Net Zero Goals

AI's growing energy footprint cannot be ignored. But AI driven solutions are a key unlock to a cleaner, more resilient grid. The real challenge is solving both at once: cleaning up the footprint while scaling the solution I am incredibly proud that Google contributed evidence and insights to the newly launched Climate Action Coalition's Net Benefit AI: Scaling Solutions, Opening Opportunities report, focused on the role of AI in the energy transition. A huge thank you to the co-chairs, Patricia Espinosa Cantellano and Chris Skidmore OBE for bringing the industry together to navigate this critical digital-energy nexus. The report highlights that while the infrastructure footprint requires deep responsibility, applying AI to physical systems allows us to shift from a paradigm of "building more" to "building smarter". Highlighting a few case studies from the report: ⚡ Smarter Power Grids: In Chile, a project combining Google DeepMind’s GraphCast with grid modelling tools delivered wind speed forecasting up to 15% more accurate than the industry gold standard, drastically reducing clean energy waste. 🚘 Flexible EV Infrastructure: A large-scale UK trial with over 13,000 consumers proved that AI-managed smart charging tariffs can shift 100% of EV demand to off-peak hours, reducing peak household electricity use by 42%. 🏭 Industrial Decarbonisation: By leveraging industrial AI for process simulation and predictive analytics, manufacturer Covestro achieved a 30% reduction in energy consumption and a 39% decrease in CO2 emissions per tonne of product. Check out the report: https://lnkd.in/eAp_M2zW #Sustainability #ArtificialIntelligence #EnergyTransition #NetZero #CleanEnergy #Google

How Artificial Intelligence (AI) can assist Carbon Capture/ Management? Carbon management is essential for achieving the goal of a net-zero carbon economy by 2050. This requires rapid deployment of technologies for carbon capture, transport, storage, and emissions mitigation. Artificial Intelligence (AI) is positioned as a transformative tool to accelerate innovation, optimize infrastructure, and reduce risks in this domain. 🟦 Main Challenges: 1) “DISCO₂VER” – Digital Planet Twin - A comprehensive AI-enabled "digital twin" of Earth to simulate and forecast energy systems, environmental dynamics, and societal impacts. - Integrates disparate models and "datasets" (e.g., energy sources, emissions, infrastructure) to support planning, resiliency, and mitigation strategies. 2) Virtual Subsurface Earth Model - AI-driven modelling of the "subsurface" to enable safe resource extraction and storage (e.g., CO₂, hydrogen). - Uses multi-modal data and advanced inference to overcome limitations in current geophysical techniques. 3) Accelerating Materials for Carbon Capture - AI helps identify and optimize materials for scalable carbon capture and removal. - Supports DOE’s Carbon Negative Earthshot goal: removing gigatons of CO₂ at less than $100/ton. 4) Emissions Prediction, Measurement, and Mitigation - Targets hard-to-electrify sectors (e.g., aviation, heavy industry) and legacy infrastructure (e.g., orphan wells). - AI enables detection of unknown emission sources, forecasts degradation, and supports remediation strategies. 🟦 Advances in the Next Decade 1) AI Integration: Combining physics-based models with AI for better forecasting and scenario analysis. 2) Sensor Networks: Real-time data collection and analysis for emissions monitoring. 3) Surrogate Modelling: Accelerating simulations for materials and subsurface systems. 4) Foundational Models: Training large AI models on diverse datasets to support decision-making and discovery. 🟦 Accelerating Development 1) Data Aggregation & Veracity: Centralized, curated datasets from DOE platforms (e.g., NETL’s EDX). 2) Advanced AI Methods: Transformer models, causal reasoning, uncertainty quantification, and scalable architectures. 3) Testbeds & Infrastructure: DOE labs provide platforms for validation (e.g., DOME, LOTUS, ARIES). 4) Partnerships: Collaboration with industry, academia, and agencies like USGS, NASA, and EPA. 🟦 Expected Outcomes 1) Digital Planet Twin: Enables strategic planning and emissions mitigation. 2) Subsurface Transparency: Improves resource utilization and risk reduction. 3) Materials Innovation: Accelerates deployment of carbon capture technologies. 4) Emissions Reduction: Enhances efficiency by 30–40% across sectors. Source: see post image This post is for educational purposes only. 👇 How does an AI-enabled digital twin of Earth enhance our understanding of global carbon emissions and identify key areas for reduction? #AI #renewables #sustainability

Today at #AdoptAI, I thought back to the moment when artificial intelligence truly clicked for me. It didn’t come from a strategic report or a board discussion. It came from my teenage daughter. One evening, I caught her using Le Chat (France’s homegrown ChatGPT) while doing her homework. My first reaction was the one you would expect: a bit of parental panic of course. But then, I looked closer. She was not using it to cheat. She had uploaded her notes and was asking the AI to quiz her, acting as a study partner with infinite patience. That moment changed my perspective completely. I realized #AI really is about putting us back in control of our own progress, rather than merely replacing human intelligence. It is not a magic wand, but rather a very powerful catalyst to accelerate innovation and human expertise. At the same time, this experience reminded me of the importance of developing AI responsibly and ethically, and of carefully choosing when and how to use it. So, since we are on a quest to massively accelerate #EcologicalTransformation that delivers for our clients, I see AI as the means to multiply Veolia’s impact tenfold. How? We are already partnering with the world’s largest data center operators over 100 sites worldwide to transform these energy-intensive giants into agents of territorial circularity. ➡️ Instead of wasting the massive heat generated by computing power, we can capture it to warm nearby schools, hospitals, and homes, leading to +20% of energy reuse. ➡️ Instead of draining local water supplies, AI-enabled treatment systems can recycle cooling water, reducing the water footprint by up to 75%. ➡️ And instead of letting strategic metals go to waste, we can massively recycle them, getting up to 95% circularity. So yes, the AI boom will undeniably put tremendous stress on our natural resources. But yes, we have the tools to use AI itself to massively optimize the resource intensiveness, not only of data centers, but of all industrial activities. This is how we reconcile the digital and environmental transitions. By 2030, our obsession is zero waste, tracking every drop of water and every kilowatt in real time. At the end of the day, we will know that AI can succeed if we achieve a transition where its environmental benefits exceed the costs. I am fully confident that we can make it happen at Veolia, because we already are for many projects. Thank you to Adopt AI and Samantha Simmonds of the BBC for the opportunity to discuss this all-important topic. The future starts now!  

As AI reshapes the global economy, its environmental implications remain significant. Data centre workloads are set to more than triple by 2027, driven by surging compute demand. Without intervention, this trajectory risks trading digital progress for ecological strain. Sustainable AI embeds efficiency across the tech lifecycle—anchoring Green IT through optimised software, cleaner infrastructure, smart hardware management, and circular design. There are strategic signals: 1. Adoption Metrics: KPMG reports that 68% of organisations have Green IT goals, but formal strategies nearly double as AI scales—47% among early adopters compared to 93% among more advanced users. However, only 4% have fully optimised their AI impact with specific targets, strong partner networks, sustainability-focused SLAs, and Scope 3 reporting. 2. Cost Efficiency: Efficient AI models reduce cloud and compute expenses. When scaled properly, AI helps identify operational waste, streamline logistics, and enhance asset use. 3. ROI Impact: Global confidence in AI returns has increased, with 86% expecting ROI within three years, up from 21% last year. Sustainable AI ensures each dollar spent delivers more value through resource efficiency, reduced operating costs, and improved performance. Continuous feedback loops powered by AI measure results and track ROI over time. At ReNew, we witness this shift firsthand. As India’s leading clean energy company, we see AI as essential. Embedding efficiency into AI operations goes beyond lowering emissions—it aligns digital growth with climate ambition, ensures resilience, and strengthens stakeholder trust. An impact in action: a 26% reduction in our asset downtime, a 150% surge in predictive analytics adoption over the past two years, and AI-powered market price forecasting driving a 66% increase in trading team productivity. AI can improve operations and accelerate corporate decarbonisation, but if it relies on fossil fuel infrastructure, it undermines the sustainability goals it is meant to support. #SustainableAI #AIForGood #ReNewTheFuture

𝗔𝗜 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘀𝗺𝗮𝗿𝘁𝗲𝗿. 𝗕𝘂𝘁 𝗰𝗮𝗻 𝗶𝘁 𝗴𝗲𝘁 𝗴𝗿𝗲𝗲𝗻𝗲𝗿 𝘁𝗼𝗼? Here’s the hard reality- Every model trained leaves an energy footprint. Every deployment shapes emissions at scale. By the end of this year, AI could use 49% of all data center power. AI won’t be called “transformative” if it drains the planet to power itself. The next big advantage? Intelligence that’s green by design. The report by Economist Impact and Delta Electronics shows how AI’s future can be sustainable as well as scalable. Here are the insights that leaders shouldn't ignore ⚡AI’s Environmental Footprint The energy costs of training large models are staggering but innovation is shifting focus from raw compute to efficient compute. ⚡Greening the AI Lifecycle From chip design to model deployment, sustainability needs to be embedded across the full value chain. ⚡Policy & Regulation Governments are setting carbon-neutral targets. AI adoption without sustainability guardrails risks regulatory pushback and reputational harm. ⚡Corporate Strategies Pioneers are tying AI investments to net-zero roadmaps using renewable-powered data centers, efficient architectures, and green procurement practices. ⚡Innovation Opportunities Sustainable AI isn’t a burden. It’s an edge. From carbon tracking to smart grids, AI can accelerate climate solutions while reducing its own footprint. ⚡Collaboration Imperative Tech providers, policymakers and industry must co-create standards for measuring, reporting and reducing AI’s environmental impact. 𝗖𝗮𝗹𝗹 𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻  ✅ Balance AI progress with planet care. ✅ Create global strategies, collaborate worldwide. ✅ Use diverse tactics for AI sustainability. ✅ Connect users and suppliers on climate goals. ✅ Prioritize lasting sustainability over quick savings. ✅ Forge strong partnerships for shared expertise. 𝗧𝗵𝗲 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗶𝘀 𝗨𝗻𝗺𝗶𝘀𝘁𝗮𝗸𝗮𝗯𝗹𝗲 AI will only deliver true progress if its trajectory is shaped by environmental intelligence as well as technical acumen. Leaders who are able to innovation, set strong governance and align business goals with climate priorities will define AI’s legacy not by speed alone, but by responsible impact. 💡According to you what’s the single biggest lever for making AI truly sustainable - tech breakthroughs, policy action or business leadership? Prof. Dr. Ingrid Vasiliu-Feltes|Helen Yu|JOY CASE|Hr Dr. Takahisa Karita|Antonio Grasso|Nicolas Babin |Alberto Espinosa Machado|Dr. Ram Kumar|Phillip J Mostert| Sara Simmonds |Anthony Rochand|Prasanna Lohar|Shalini Rao #AI #GreenAI #GreenTech #SustainableAI #ResponsibleAI #NetZero #Leadership #Innovation #TechforGood

🌎 AI is not a climate killer. Let me prove it: Here are sample companies and statistics... We’ve all read the scary predictions: AI’s massive power demand will erase corporate net zero goals faster than a teenager’s browsing history before mom borrows the laptop. 😂 But here are seven ways AI might be a welcomed collaborator instead. ✅ 1. Precision Energy Management AI can slash commercial building energy use by > 15%. Examples: - AutoGrid balances supply/demand, turning waste into revenue. - Google DeepMind cut data center cooling costs by 40% via machine learning. — ✅ 2. Smart Agriculture & Crop Optimization AI can boost crop yields by up to 20%, reducing water and fertilizer. Examples: - Taranis uses AI and high-res imagery to monitor crop health.  - FarmWise uses AI-driven robots to weed without herbicides. — ✅ 3. AI-Enhanced Carbon Capture & Storage Machine learning can improve carbon capture efficiency by 10-30%. Examples: - Climeworks refines direct air capture with data analytics. - Carbon Clean optimizes solvents using AI, thereby cutting costs. — ✅ 4. AI-Driven Supply Chain Optimization Waste can be reduced when supply chains see 15-65% improved efficiency. Examples: - Flexport streamlines freight, minimizing empty cargo space. - project44 tracks shipments in real-time, slashing delays and fuel use. — ✅ 5. Automated Waste Sorting & Recycling AI-powered systems boost recycling recovery rates by 2-3x. Examples: - AMP Robotics uses computer vision to sort recyclables quickly. - ZenRobotics - A Terex Brand tackles construction and demolition waste via AI. — ✅ 6. Climate Risk Analysis & Insurance 73% of carriers say AI models can help to manage climate-related losses better. Examples: - Jupiter Intelligence models flood, fire, and sea-level threats. - Climate X quantifies climate-related financial risks. — ✅ 7. AI-Verified Carbon Offsets & Reforestation Satellite-based AI can reduce carbon removal accounting costs by > 40%. Examples: - Pachama tracks forest growth to confirm carbon absorption. - Sylvera rates carbon offset projects, ensuring transparency. 🎯 So what? Instead of trying to put the genie back in the bottle — about as likely as me doing a beautiful ballet dance in a sparkly pink tutu — we should make friends with AI. Consider its power below. Surely, it’s a “team member” that’s hard to ignore. - Knowledge equal in scale to 200 million books - IQ of 120-140 (top 1% of human brain power) - Analytical speed of [1 to 15] seconds ------ 🎯 For more climate tech news and to see the sources for the stats above, check out my newsletter via EFI (Entrepreneurs for Impact): https://lnkd.in/eNBKKmNG

What steps is your organization taking to align AI growth with energy sustainability? By 2035, data centers could consume 1,200 TWh; nearly triple today's levels. AI's energy appetite is outpacing the grid. But here's the flip side: AI deployed right can cut data center cooling by 40%, slash building energy use by 15-40%, and optimize everything from grids to logistics. The World Economic Forum just dropped a blueprint to make this happen. Three actions. Three enablers. One goal: Net-positive AI energy. ✅ Design for Efficiency – Build sustainability into models and infrastructure from day one ✅ Deploy for Impact – Drive measurable energy savings across operations ✅ Shape Demand Wisely – Incentivize smart, selective AI use Backed by: ✅ Education and upskilling ✅ Cross-sector collaboration ✅ Transparent measurement The proof is already here: ✅ Google: 33x reduction in energy per prompt ✅ Industrial parks: 100,000 MWh saved ✅ Smart grids: Losses cut, reliability up Energy is now a bottleneck for AI growth. Without action, capacity concentrates in energy-rich regions. Digital divides widen. Infrastructure buckles. Companies embedding efficiency into AI see gains in performance, resilience, and bottom line. Net-positive AI is competitive advantage. AI will scale. The question is whether it scales responsibly. Read the full WEF report below. #AI #Sustainability #EnergyTransition #ResponsibleAI #NetZero #ClimateAction To stay current with the latest trends in #Technology and #Innovation, Subscribe to 👉 #CXOSpiceNewsletter here https://lnkd.in/gy2RJ9xg or 👉 #CXOSpiceYouTube here https://lnkd.in/gnMc-Vpj

Energy is the key. Over the weekend I got a lot of messages about articles and stories talking about the links between the energy hungry AI models and the path to net zero. On one hand, the computational power required to train and run AI models is soaring, placing increasing demands on our energy grids. On the other, AI itself holds remarkable potential to drive innovations in climate technology, potentially aiding our quest for net zero emissions. This dichotomy presents both a formidable challenge and a beacon of hope in our journey toward a sustainable future. Advanced AI models, particularly those involved in machine learning and deep learning, require substantial computational resources. Training a single AI model can consume as much electricity as several hundred homes use in a month. As AI becomes more integrated into our daily lives, from autonomous vehicles to personalized medicine, the demand on energy grids will inevitably rise. This surge complicates our path to achieving net zero emissions, as increased energy demand generally translates to higher carbon footprints unless met entirely by renewable sources. However, the same technology that poses such a challenge also harbors solutions to some of the most pressing environmental issues. AI can optimize energy consumption in industries and homes, create more efficient renewable energy systems, and improve waste management practices. For example, AI algorithms can predict energy demand more accurately, enabling smarter grid management and reducing reliance on fossil fuel-powered peaker plants. In renewable energy, AI can enhance the efficiency of solar panels and wind turbines by optimizing their placement and operation based on weather predictions. Moreover, AI-driven innovations in materials science are paving the way for more efficient batteries and renewable energy storage solutions, addressing one of the significant hurdles in the transition to green energy. AI also plays a crucial role in monitoring and combating climate change. Through the analysis of satellite imagery and environmental data, AI can track deforestation, ocean health, and the melting of polar ice caps with unprecedented precision and speed. This capability not only informs better policy and conservation efforts but also helps in quantifying the impact of climate action, making it a potent tool in the global effort to mitigate climate change. The dual role of AI as both a contributor to and a solver of the energy and climate crises underscores the need for a balanced approach in its development and deployment. By prioritizing energy-efficient AI models and leveraging AI to accelerate the transition to renewable energy, we can harness the power of AI to move closer to our net zero goals, turning a formidable challenge into a formidable ally in the fight against climate change. At Nadia Partners we are building companies on both sides to help achieve both goals. Anybody who can help us please tag or share!

⌛𝗔𝗜 𝘄𝗼𝗻’𝘁 𝘀𝗼𝗹𝘃𝗲 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗮𝗹𝗼𝗻𝗲 — 𝗯𝘂𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝘁, 𝘄𝗲 𝘄𝗼𝗻’𝘁 𝘀𝗼𝗹𝘃𝗲 𝗶𝘁 𝗳𝗮𝘀𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 To reach net zero by 2050, we need rapid transformation across energy systems, infrastructure, and materials. 𝗔𝗜 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝘁𝗼 𝗯𝗲 𝗮 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿.   Electricity generation must nearly double by 2050 and renewable capacity must triple this decade — to meet rising demand from electrified transport, heating, and industry without increasing emissions.   🔌 𝗚𝗿𝗶𝗱 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: AI-enabled dynamic line ratings use real-time weather data to safely boost transmission capacity by up to 40%. That means more renewable energy can flow through existing infrastructure, without waiting years for upgrades.   Steel, cement, chemical and other industries account for nearly one-third of global CO2 emissions. Getting them to near-zero will require affordable, scalable alternatives.   🧱 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: Researchers at MIT used AI to scan 88,000 papers and analyze one million rock samples, identifying 19 promising substitutes for clinker, the key ingredient in cement. This process would have taken lifetimes using traditional methods.   These breakthroughs show how AI can unlock speed and scale we need in the energy transition. But discovery is just the beginning. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗮𝗸𝗲𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻.   👉 Learn more about five actions to harness AI for deep, sustained decarbonization in Amy Luers, PhD essay in Nature Magazine: https://rdcu.be/eBRw3  

Artificial intelligence has a far-reaching potential to help humanity in achieving the climate action goals by transforming the power sector. By analyzing vast amounts of data from grids and other sources, AI can make power generation and distribution more efficient and sustainable. A noteworthy example is the recent decision by Uttarakhand’s state-owned hydropower generation body to integrate AI into its operations. It will better address the future energy demands and strengthen the capacity to meet renewable energy goals. The future of AI and renewable energy is intertwined. Renewable energy will power the data centers that drive AI, while AI, in turn, will enhance the effectiveness of the energy systems.