Key Strategies for Sustainable Technology

What if the key to achieving our global sustainability goals isn’t just more renewable energy or circular economy practices but the criticality of deploying AI, too? A new 2025 study published in Nature reveals that AI investment is a powerful accelerator for UN Sustainable Development Goals in the US. Here’s what every supply chain and sustainability leader needs to know: 1) AI drives measurable sustainability progress: Every 1% increase in AI investment correlates with a 0.26% improvement in SDG performance, proving technology can be a force multiplier for environmental and social impact. 2) Green electricity amplifies results: The study confirms that renewable energy and AI create a powerful synergy effect, with both factors independently boosting sustainability outcomes. 3) Economic growth paradox: Traditional GDP growth actually negatively impacts SDG scores, highlighting why we need smarter, not just bigger, economic models. 4) Innovation over expansion: The research validates that strategic technology investments outperform pure economic expansion for sustainable development. Supply Chain Implications: From my perspective leading supply chain transformation, this research validates what we’re seeing in practice: - Precision agriculture powered by AI is revolutionizing food system sustainability - Smart energy grids are optimizing renewable resource allocation - Predictive analytics in healthcare is improving access and outcomes - Supply chain optimization is reducing waste and emissions at scale The Critical Caveat: The study emphasizes that AI’s sustainability impact depends ENTIRELY on responsible deployment. What does that mean? -Robust data infrastructure -Ethical oversight frameworks -Equitable access to benefits -Strong governance structures Bottom Line for Leaders: This isn’t about choosing between profit and planet. It’s about leveraging intelligent technology to achieve both. Companies investing in AI for sustainability aren’t just future proofing their operations. They’re actively contributing to global development goals. How is your organization balancing AI innovation with sustainability objectives? What barriers are you encountering? I hope you find this research and perspective useful.

𝗕𝘆 𝟮𝟬𝟮𝟳, 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿𝘀 𝗪𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗕𝗲 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗣𝗼𝘄𝗲𝗿—𝗧𝗵𝗲𝘆’𝗹𝗹 𝗕𝗲 𝗙𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗪𝗮𝘁𝗲𝗿. The AI revolution is fueling unprecedented growth, but beneath the surface lies a critical vulnerability: 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘀𝗰𝗮𝗿𝗰𝗶𝘁𝘆. Gartner predicts that by 2027, 𝟰𝟬% 𝗼𝗳 𝗔𝗜 𝗱𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗳𝗮𝗰𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 𝗱𝘂𝗲 𝘁𝗼 𝗽𝗼𝘄𝗲𝗿 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲𝘀. And while power gets the headlines, 𝘄𝗮𝘁𝗲𝗿 𝘀𝗰𝗮𝗿𝗰𝗶𝘁𝘆 is emerging as an equally pressing challenge. Cooling systems—critical for managing the immense heat from AI workloads—rely heavily on water. As demand for power rises, so does the strain on this finite resource. Regions like 𝗖𝗮𝗹𝗶𝗳𝗼𝗿𝗻𝗶𝗮 and parts of 𝗘𝘂𝗿𝗼𝗽𝗲 are already grappling with power shortages, forcing data centers to rethink their strategies. The stakes couldn’t be higher: Without urgent action, these constraints could slow AI innovation and 𝗿𝗮𝗶𝘀𝗲 𝗰𝗼𝘀𝘁𝘀 for businesses and end-users alike. But this isn’t just a crisis—it’s a call to innovate. 𝗛𝗼𝘄 𝗗𝗼 𝗪𝗲 𝗦𝗼𝗹𝘃𝗲 𝗧𝗵𝗶𝘀? The key lies in tackling inefficiency at its source. Start with 𝗣𝗨𝗘 (𝗣𝗼𝘄𝗲𝗿 𝗨𝘀𝗮𝗴𝗲 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀): • A lower PUE (closer to 1.0) means less wasted energy, which directly reduces heat generation—and by extension, cooling demands and water use. • Smarter energy and workload management can shrink the power and water footprint of AI operations. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝘃𝗲 𝗣𝗮𝘁𝗵𝘀 𝗙𝗼𝗿𝘄𝗮𝗿𝗱: 1. 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Use AI itself to dynamically manage energy and cooling systems. 2. 𝗪𝗮𝘁𝗲𝗿𝗹𝗲𝘀𝘀 𝗖𝗼𝗼𝗹𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Embrace liquid immersion and advanced cooling technologies to reduce reliance on water. 3. 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲𝘀 𝗮𝗻𝗱 𝗖𝗶𝗿𝗰𝘂𝗹𝗮𝗿 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Pair renewable energy with closed-loop cooling to build long-term resilience. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Sustainability isn’t just about compliance—it’s a 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲 in a world demanding responsible innovation. Organizations that act now will not only future-proof their operations but also enhance their brand and bottom line. 𝗪𝗵𝗮𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗗𝗼 𝗧𝗼𝗱𝗮𝘆? Start by assessing your data center’s 𝗣𝗨𝗘 𝗮𝗻𝗱 𝗰𝗼𝗼𝗹𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. Small improvements now can lead to significant cost and resource savings as demand grows. 𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝗿 𝗣𝗶𝗰𝘁𝘂𝗿𝗲: AI isn’t just a test of innovation—it’s a test of our ability to 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆. The future of AI depends not just on its potential—but on how sustainably we can scale it. The time to rethink and innovate is now. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗹𝗮𝗻? #AIInnovation #SustainableTech #DataCenterEfficiency #LeadershipInAI

𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗖𝗶𝗿𝗰𝘂𝗹𝗮𝗿 𝗪𝗮𝘁𝗲𝗿 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗮 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜 𝗙𝘂𝘁𝘂𝗿𝗲 Renewable energy is poised to play a significant role in meeting the energy demands of the AI boom. For a number of reasons, renewables may not scale quickly enough to meet the immediate surge in demand, as Heather Clancy writes in this GreenBiz article. To ensure reliability in the short term, we must also consider reinstating fossil fuel and nuclear power plant resources. The growth of AI presents challenges for both our existing electrical grid and water infrastructure. As we navigate the boom and the need for more energy to fuel it, we must also focus on water. Water is essential for generating the energy that data centers need and the water required to cool them. While renewable energy is key, we must also leverage existing technologies to implement circular water management practices. This approach not only conserves water but also enhances operational efficiency and sustainability. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗮𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽𝘀 𝘄𝗲 𝗺𝘂𝘀𝘁 𝘁𝗮𝗸𝗲 𝘁𝗼𝗱𝗮𝘆: 𝟭. 𝗔𝗱𝗼𝗽𝘁 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗪𝗮𝘁𝗲𝗿 𝗥𝗲𝘂𝘀𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀: Implement existing smart water technology to facilitate water conservation and reuse within data centers, industrial processes and power plants. 𝟮. 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Deploy IoT sensors and AI-driven analytics to monitor water usage and quality, enabling proactive management and optimization. 𝟯. 𝗘𝗻𝗴𝗮𝗴𝗲 𝗶𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽𝘀 𝗶𝗻 𝗪𝗮𝘁𝗲𝗿-𝗦𝘁𝗿𝗲𝘀𝘀𝗲𝗱 𝗕𝗮𝘀𝗶𝗻𝘀: Collaborate with technology providers, sustainability experts and local governments to develop and implement circular water management solutions. One example of public private partnerships is the California Water Resilience Initiative (CWRI). 𝟰. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲 𝗮𝗻𝗱 𝗧𝗿𝗮𝗶𝗻 𝗧𝗲𝗮𝗺𝘀: Invest in training programs to equip power plants and data centers with the knowledge and skills needed to manage water resources efficiently.   By embracing these strategies, we can ensure a resilient and sustainable future for AI and beyond. Let's lead the way in circular water management and make a lasting impact. #Sustainability #AI #CircularWater #WaterManagement #Innovation https://lnkd.in/gQST8FY4

Interesting read on ESG Dive from KPMG's U.S. ESG Lead, Maura Hodge, highlight how Artificial Intelligence (AI) is empowering Chief Sustainability Officers (CSOs) to drive real impact & efficiencies. Maura notes how AI boosts efficiency by automating the collection and analysis of data for areas like energy, water, and Scope 3 emissions, providing real-time insights that enable operational optimization. AI improves visibility within complex supply chains, which is critical for enhancing transparency and conducting due diligence. And by automating data management, AI streamlines sustainability reporting processes, allowing teams to focus more on strategic analysis and taking action. *** This one is super important, as reporting can be such a complex and time intensive endeavor! Of course it's essential to acknowledge the "elephant in the room": the energy consumption of AI itself. The increasing power demands, particularly from data centers, present a significant sustainability challenge. However, this isn't a reason to shy away from AI in sustainability. Instead, it underscores the need for parallel innovation in and acceleration of green energy for AI infrastructure and the development of more energy-efficient AI models - a dynamic challenge that's central to the responsible deployment of this technology. I'm glad that we're seeing a rise in use cases where AI helps companies leverage sustainability data for better decision-making, risk mitigation, and ultimately, a more sustainable and resilient business. Read more on ESG dive - https://lnkd.in/eRM42iPP #sustainability #AI #ESG #corporatesustainability #circulareconomy #technology

AI is here to stay. The question is: How do we make it work for sustainability, not against it? Engaging with clients and colleagues, I’ve heard a wide range of concerns—energy consumption, ethical usage, and even fears of AI displacing human labor. But beyond the headlines, we’re at a crossroads where sustainability & AI intersect—bringing both challenges and transformative opportunities. Like past general-purpose technologies—from the steam engine to the internet—the potential is enormous, but its impact is still unfolding. So what should our goal be? → Minimize the harm. Maximize the benefit. I’ve been working on a graduate-level curriculum & workshop exploring the concepts below, some obvious and some with immense nuance. I’d love to hear where the sustainability community stands on these key issues: Key Challenges: •  High Energy & Resource Use – AI infrastructure (data centers) requires massive electricity & water, raising sustainability concerns. •  Data Gaps – Many sustainability applications rely on high-quality data, but AI models often face bias, inaccessibility, or limitations in key areas like biodiversity & climate science. •  Policy & Governance – The lack of clear regulations can lead to environmental inefficiencies, ethical risks, and unintended consequences. •  Unequal Access – AI-driven solutions are concentrated in high-income countries, leaving underserved regions without critical technology. •  Community Impacts – AI data centers can strain local resources and face challenges related to land use, energy consumption, and social acceptance. Key Opportunities: • Optimizing Complex Systems – AI helps us measure, predict & optimize sustainability efforts by leveraging massive datasets. • Accelerating Innovation – AI fast-tracks discoveries in materials science, e-waste recycling, precision agriculture & more. • Workforce Empowerment – AI closes knowledge gaps, automates routine tasks & improves decision-making across industries. • Enhancing Risk & Resilience – AI-powered models predict extreme weather, optimize disaster preparedness & manage resources. • Driving the Energy Transition – AI improves grid management, boosts energy efficiency & accelerates renewables adoption. A Few Thought-Starters: 💧 Google used 5.2B gallons of water globally for data centers in 2022—a staggering number, but also less than 0.5% of the water used for California’s almond farming. 🔋 AI's energy demands have caused Google & Microsoft emissions to rise—but tech is one of the largest drivers of renewable energy demand. Can we ultimately decarbonize the electrons that are in demand? 🛠️ McKinsey predicted that 30% of work hours across the economy could be automated by 2030. The number is debatable but the question is whether this will create job losses or more productivity—or something else entirely. I think AI & sustainability don’t have to be at odds—together they can be transformational. Intentional strategy & action are key.

Artificial intelligence is transforming everything. Including the way we tackle environmental challenges, creating smarter, more efficient solutions for a sustainable future. At the same time AI will demand much more energy. How can AI work for Climate? ⚡ Smarter Energy Management – AI holds promise to revolutionize renewable energy grids by predicting demand and adjusting power distribution in real-time. This helps reduce energy waste, making solar and wind power more reliable and efficient. In countries like Germany and the U.S., AI-powered smart grids are already helping balance electricity loads and prevent outages. 🌪️ Disaster Prediction & Climate Monitoring – AI can be leveraged to predict climate patterns to predict hurricanes, wildfires, and floods with greater accuracy. By using machine learning to assess satellite data and weather trends, AI provides early warnings, giving communities time to prepare and minimize destruction. Google’s Flood Hub is already using AI to forecast floods days in advance, helping protect vulnerable areas. 🌾 Sustainable Agriculture & Water Conservation – AI-driven precision agriculture is helping farmers use resources more efficiently. Smart irrigation systems powered by AI can reduce water waste by up to 30%, while AI-driven pest detection minimizes pesticide use. In India, AI technologies are surfacing to predict droughts and advise farmers on optimal planting times, increasing food security. 🔄 Revolutionizing Waste Management & Recycling – AI-powered sorting systems can now recognize and separate materials with 95% accuracy, making recycling more effective. Companies like AMP Robotics use AI-powered robots to sort plastics, metals, and paper, reducing contamination in recycling streams and keeping more waste out of landfills. 🌍 Lowering Carbon Emissions & Tracking Pollution – AI is helping industries monitor their carbon footprints and optimize energy use. Businesses are now using AI to track emissions in real-time, find ways to cut energy waste, and develop more effective carbon capture technologies. AI-powered satellites can even detect methane leaks from oil and gas facilities, providing critical insights to prevent harmful greenhouse gas emissions. 🚀 The Future is Green & AI-Powered – From optimizing renewable energy to fighting climate change, AI is playing a critical role in building a more sustainable world. As technology advances, we have the power to create smarter, eco-friendly solutions that protect our planet for future generations. But the benefits must outweigh the impact of increased energy demand. ♻️ What do you think about AI’s role in environmental sustainability? Drop your thoughts below! 👇

International Energy Agency (IEA) just released their special report on 'Energy and AI'. It's a 300 page deep-dive and compiles existing research with new findings and cross-industry analysis. My main conclusion - and the answer to one of the most common questions I'm getting these days - is that the 'Sustainability of AI' isn't determined yet. Sustainability of AI = [demand for energy, water, land, critical minerals, etc] - [its supply of groundbreaking (or just significantly more efficient) tools and solutions that help us address climate change and biodiversity loss] Simplified: SustAI = [energy demand] - [emissions reduction/removal supply] Here are some takeaways from the report drawing on a longer analysis by Boris Gamazaychikov - head of AI Sustainability at Salesforce: -> Societal perception of AI, 'AI's success' or approval rates will depend on its sustainability. ↳ “Electricity grids are already under strain…20% of planned data centre projects could be delayed” (p.14) ↳ “Affordable, reliable, and sustainable electricity supply will be a crucial determinant of AI development, and countries that can deliver the energy needed at speed and scale will be best placed” (p.13) ↳ “Lack of transparency…This lack of data makes it hard for…companies to make informed choices when it comes to energy efficiency.” (p.44) -> Yes, energy use is growing but lots of effort is going towards minimizing this growth. ↳ “Incentivising the efficient use of models (i.e. the right model for the right task) will have a large impact on the energy pathway of AI” p.44 ↳ “Model design and choice have large impacts on electricity intensity” (p.46) ↳ “Key options to mitigate these risks include locating new data centres in areas of high power and grid availability, and operating…more flexibly.” (p.15) ↳ Policy (p.238) and collaboration (p.18) play crucial roles ↳ We don't need to use AI for everything just because we could! .. and the most important one for us at Climate Collective: -> AI could supercharge sustainability - but “𝐭𝐡𝐞𝐫𝐞 𝐢𝐬 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐧𝐨 𝐦𝐨𝐦𝐞𝐧𝐭𝐮𝐦 𝐭𝐡𝐚𝐭 𝐜𝐨𝐮𝐥𝐝 𝐞𝐧𝐬𝐮𝐫𝐞 𝐭𝐡𝐞 𝐰𝐢𝐝𝐞𝐬𝐩𝐫𝐞𝐚𝐝 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧” 𝐨𝐟 𝐀𝐈 𝐟𝐨𝐫 𝐞𝐦𝐢𝐬𝐬𝐢𝐨𝐧𝐬 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 (p.250) ↳ Key blockers include “unfavourable regulation, lack of access to data, , interoperability concerns, critical gaps in skills, and, in some cases, a resistance to change.” (p. 109) ↳ Rebound effects are a massive uncertainty Unless we make AI useful and available to conservation orgs and decarbonization entrepreneurs the AI-Sustainability-Equation will come out negative. That means supporting AI entrepreneurs to build for Sustainability AND building AI literacy among environmental orgs. We're here for all of those. Brigitte Hoyer Gosselink Amy Rosenthal Amy Luers Juan M. Lavista Ferres Andrew Means, M.P.P. Jake Porway Ali Swanson Hui Wen Chan Maureen McDonagh Naomi Morenzoni Tariq Khokhar Matthew Gee Blair Swedeen

🌍 New Article: Data Stewardship as Environmental Stewardship 🌱 ✍️Co-authored with Sara Marcucci ➡️ As the world becomes increasingly reliant on data and artificial intelligence (AI), the environmental impact of data-related activities is growing—raising urgent questions about sustainability in the digital age. The rise of generative AI, fueled by massive datasets and computational power, risks exacerbating these challenges. 🤔 In our latest article, we propose that responsible data stewardship is the most common-sense pathway to mitigate the environmental footprint of data-related activities. By promoting practices such as: 🌐 Data minimization, reuse and circular economies: maximizing value while minimizing environmental costs. ♻️ Reducing digital waste and energy consumption: streamlining storage and minimizing resource use. 🔍 Transparent and shared data: enabling better decision-making for sustainability. ➡️ We argue that positioning data stewardship as environmental stewardship offers a dual benefit—advancing technological innovation while safeguarding our planet. 📊 The stakes are high: ✅Data centers alone consumed 460 TWh of electricity in 2022 (2% of global usage) and are projected to double by 2026 due to the rise of AI. And water resources are getting depleted as a result... ✅Rare earth mining for data-related infrastructure leads to biodiversity loss, habitat destruction, and water scarcity. ✅ Increased space activities, satellites, and poorly managed data processes add to the growing environmental strain. 💡 What’s the way forward? We call for: 1️⃣ Practical guidelines for sustainable data stewardship. 2️⃣ Recognizing data stewards as strategic sustainability leaders. 3️⃣ Adoption of circular data economies. 4️⃣ Integration of environmental metrics into data governance. 5️⃣ Cross-sector collaboration to align sustainability goals. 👉 Read the full article:https://lnkd.in/g2zbF_c5 #Sustainability #DataStewardship #EnvironmentalResponsibility #AI #CircularEconomy #DataGovernance

#AI technologies can be powerful #sustainability accelerators, which I believe are necessary for the world to achieve our climate and nature goals. But they are not a guarantee. Good governance is essential. This is the motivation behind our new comment out in 𝘕𝘢𝘵𝘶𝘳𝘦 𝘚𝘶𝘴𝘵𝘪𝘯𝘢𝘣𝘪𝘭𝘪𝘵𝘺 where we outline what we call the 𝗘𝗮𝗿𝘁𝗵 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗳𝗼𝗿 𝗔𝗜. "𝘈𝘵 𝘢 𝘵𝘪𝘮𝘦 𝘸𝘩𝘦𝘯 𝘵𝘩𝘦 𝘸𝘰𝘳𝘭𝘥 𝘮𝘶𝘴𝘵 𝘤𝘶𝘵 𝘨𝘳𝘦𝘦𝘯𝘩𝘰𝘶𝘴𝘦 𝘨𝘢𝘴 𝘦𝘮𝘪𝘴𝘴𝘪𝘰𝘯𝘴 𝘱𝘳𝘦𝘤𝘪𝘱𝘪𝘵𝘰𝘶𝘴𝘭𝘺, 𝘢𝘳𝘵𝘪𝘧𝘪𝘤𝘪𝘢𝘭 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 (𝘈𝘐) 𝘣𝘳𝘪𝘯𝘨𝘴 𝘭𝘢𝘳𝘨𝘦 𝘰𝘱𝘱𝘰𝘳𝘵𝘶𝘯𝘪𝘵𝘪𝘦𝘴 𝘢𝘯𝘥 𝘭𝘢𝘳𝘨𝘦 𝘳𝘪𝘴𝘬𝘴. 𝘛𝘰 𝘢𝘥𝘥𝘳𝘦𝘴𝘴 𝘪𝘵𝘴 𝘶𝘯𝘤𝘦𝘳𝘵𝘢𝘪𝘯 𝘦𝘯𝘷𝘪𝘳𝘰𝘯𝘮𝘦𝘯𝘵𝘢𝘭 𝘪𝘮𝘱𝘢𝘤𝘵, 𝘸𝘦 𝘱𝘳𝘰𝘱𝘰𝘴𝘦 𝘵𝘩𝘦 ‘𝘌𝘢𝘳𝘵𝘩 𝘢𝘭𝘪𝘨𝘯𝘮𝘦𝘯𝘵’ 𝘱𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦 𝘵𝘰 𝘨𝘶𝘪𝘥𝘦 𝘈𝘐 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵 𝘢𝘯𝘥 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 𝘵𝘰𝘸𝘢𝘳𝘥𝘴 𝘱𝘭𝘢𝘯𝘦𝘵𝘢𝘳𝘺 𝘴𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺." 𝗧𝗵𝗶𝘀 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗶𝘀 𝗻𝗼𝗻-𝗯𝗶𝗻𝗮𝗿𝘆. 𝗪𝗲 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝙩𝙝𝙧𝙚𝙚 𝙘𝙧𝙞𝙩𝙚𝙧𝙞𝙖 𝗳𝗼𝗿 𝘀𝘁𝗿𝗼𝗻𝗴 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: 1️⃣ AI systems should help to accelerate the transition to sustainable production and consumption in ways that respect planetary boundaries, or at least do not obstruct these objectives. 2️⃣AI systems should be developed, deployed, and used in ways that ensure equitable access to AI tools for global sustainability and avoid concentrations of power 3️⃣AI systems should be developed, deployed and used to support greater societal cohesion, build trust and provide access to reliable information for planetary stewardship 𝐘𝐨𝐮 𝐜𝐚𝐧 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐡𝐞𝐫𝐞: 👉https://lnkd.in/gw3CKkQn Thank you to all our co-authors for many discussions, debates, and explorations that started all off during a workshop that National Academy of Sciences, The Nobel Prize Outreach and Microsoft co-hosted on ‘Global Sustainability and Science Integrity in the Age of Generative AI’ Optimist Gaffney (aka Owen) Franklin Carrero-Martínez, Felix Creutzig, Victor Galaz, Francesca Larosa, PhD, Berna Oztekin-Gunaydin Virginia Dignum, Naoko Ishii Maria Leptin, Ken Takahashi

Could the intersection of data centers, #AI, and sustainability offer real opportunities for our industry?   The short answer? Yes.   While AI’s energy needs are growing rapidly, the same technologies can be used to drive substantial energy savings. At CBRE, we’re focusing on three approaches:   1. Using AI to optimize building energy use 2. Integrating smart power grids 3. Strategically locating data centers where they can utilize renewable energy (like waste heat!)   The potential return is remarkable. Our analysis shows that these strategies can deliver massive energy and carbon savings that far outweigh the resources invested. This isn’t just good for our planet—it’s smart business. When we design these data centers thoughtfully and intentionally, these facilities can become active participants in the green economy.    If we can make one thing clear from these findings, it’s that #sustainability and technology can and should advance together. https://lnkd.in/edZ6CTWy