How Technology Influences Environmental Goals

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.

AI models are increasingly handling coding tasks. Like many, I assumed this would naturally lead to more energy-efficient code, with AI optimizing and avoiding anti-patterns. But new research reveals a paradox: AI-generated code often consumes significantly more energy than human-written code. A study on LeetCode problems found AI solutions consistently used more energy, with the gap widening for harder challenges – sometimes up to 8.2x the energy of human code. Why is this a major climate problem, especially as we rely on AI for sustainability? The Paradox of AI efficiency: We expect AI to optimize, but its current focus seems to be on functional correctness or generation speed, not deep energy efficiency. This means AI code can be functionally sound but computationally heavy. A scaled problem: Every line of code, whether on a local machine or a vast data center, requires electricity. If AI is generating code that's dramatically less efficient, the cumulative energy demand skyrockets as AI coding becomes ubiquitous. The bottom line: Inefficient code demands more processing power, longer run times, and higher energy consumption in data centers. These centers already consume around 1.5% of the world's electricity (415 TWh) in 2024, projected to grow four times faster than total electricity consumption. Inefficient AI code directly exacerbates this growth, potentially undermining any 'climate gains' from AI tooling. I genuinely believe AI can advance our sustainability targets faster, more cost-efficiently, and with better precision. However, if its outputs are inherently energy-intensive, it creates a self-defeating loop. We're increasing our carbon footprint through the very tools meant to accelerate efficiency. Going forward, we must integrate energy efficiency as a core metric in training and evaluating AI coding models, prioritizing lean, optimized code. Kudos to pioneers like Hugging Face and Salesforce, with their energy-index for AI models, and Orange for championing Frugal AI. And big thanks to the research team for looking beyond the hype: Md Arman Islam, Devi Varaprasad J., Ritika Rekhi, Pratik Pokharel, Sai Siddharth Cilamkoti, Asif Imran, Tevfik Kosar, Bekir Oguzhan Turkkan. [Post 1/2 on a reality check for AI's effectiveness and efficiency]

Have you ever stopped to think about the energy cost of a Google search or generating content with AI? I hadn’t, until it was part of my job to understand the environmental impact of the data centers that make these everyday actions possible. Data centers—hidden behind the digital services we rely on—consume vast amounts of energy to store, process, and transmit data. They’re essential infrastructure, but their carbon footprint poses a serious challenge. That’s where sustainable data centers come in, and they’re about much more than just switching to renewable energy. Here’s what many people *don’t* know: - Some data centers now recycle the heat they generate, using it to warm communities or power other businesses. - AI is being deployed to optimize energy usage, predicting demand and automating cooling systems. - Waterless cooling systems are reducing the environmental toll of traditional water-intensive processes. (confusingly, this is also known as liquid cooling, because it uses specialized liquid in a closed loop to cool the servers, like how a radiator works in cars) The best part? These sustainable solutions don’t just benefit the planet—they’re saving companies millions in operational costs and setting new benchmarks for innovation in technology. #Sustainability #TechInnovation #DataCenters

🌍 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

Are we doing enough to make energy affordable and sustainable? As we tackle the demand for energy in a growing world, there’s a pressing question we can’t ignore: How do we ensure that everyone has access to clean, affordable energy without compromising the environment? Sustainable Development Goal #7 is all about addressing this need—ensuring reliable, sustainable, and modern energy for everyone. Take a closer look at how smart technology is transforming the energy landscape. The rise of IoT in renewable energy, for example, has been nothing short of remarkable. Through IoT sensors, we’re not just generating solar or wind power—we’re monitoring, optimizing, and even predicting energy use in real-time. These sensors allow businesses to adjust based on demand, helping to make renewable energy sources more resilient and cost-effective. Consider a business using solar panels or wind turbines to generate its own electricity. With smart grid tech, they can manage power locally, rather than depending solely on a centralized grid. The result? Reduced costs and improved energy efficiency. And it’s not just about generating power; AI and machine learning models help organizations identify peak hours to tap into energy sources efficiently, saving both money and resources. Measuring impact is essential. For many companies, tracking the real-time effects of their energy choices is critical. IoT sensors can monitor energy usage continuously, allowing organizations to prove their progress toward sustainability. By using data instead of manual reports, they can also show customers and employees that they’re taking meaningful action. And then there’s the financial side: How to allocate resources effectively. Data from these smart systems enables leaders to make thoughtful decisions about where to focus their budget. If a particular renewable project shows a greater impact, they can prioritize that effort, optimizing both sustainability and cost efficiency. It’s easy to talk about sustainability, but taking measurable steps—and having the data to back it up—makes a difference. As more organizations embrace these tools, we’re seeing a shift in how companies approach energy, balancing their environmental responsibilities with practical, business-focused strategies. Where do you see your organization on this journey?

How Saudi Arabia is using AI to combat land degradation and drive environmental restoration: Land degradation poses one of the most pressing environmental challenges for Gulf nations, threatening biodiversity, food security, and sustainable development. However, Saudi Arabia’s innovative use of artificial intelligence is offering hope and solutions through its ambitious Saudi Green Initiative (SGI). 1️⃣ Detecting land degradation early -By harnessing satellite imagery and remote sensing technologies, AI analyzes vast datasets to detect areas at risk of land degradation. -AI-powered insights allow for early intervention and informed decisions to protect natural habitats and ensure sustainable land use. 2️⃣ Rehabilitating degraded lands -AI is guiding the SGI’s ambitious target to rehabilitate 40 million hectares of degraded land by 2030. -Using climate and soil data, AI identifies optimal tree-planting locations, ensuring the efficient use of resources and maximum environmental impact. 3️⃣ Planting 10 billion trees -AI technology is helping Saudi Arabia realize its 10 billion tree-planting goal, part of the broader effort to combat desertification, improve air quality, and restore ecosystems. With arable land making up less than 2% of the total area, Gulf nations face increasing pressure from urbanization, overgrazing, and unsustainable agricultural practices and AI models, combined with tools like digital twins and climate data analytics, predict land degradation trends and recommend effective restoration strategies. Saudi Arabia’s AI efforts align with global Sustainable Development Goals (SDGs), particularly those focused on combating climate change, protecting biodiversity, and ensuring food security. These initiatives also support the Kingdom’s Vision 2030 by promoting sustainable land and resource management. Saudi Arabia’s use of AI in land management not only addresses local challenges but also serves as a global model for tackling environmental degradation through innovation. As we face the realities of climate change, initiatives like the Saudi Green Initiative remind us that technology and nature can work hand-in-hand to build a more sustainable future. Let’s celebrate these pioneering efforts and work together toward a greener planet! 🌳 #SaudiGreenInitiative #AIForGood #LandRestoration

To leverage AI for sustainability, it is critical that this technology itself continues to improve (reduce!) its environmental impact. Today, I am happy to share that Google published a first-of-its-kind study on the lifetime emissions of Tensor Processing Units (TPUs), and outlined how they have become 3x more carbon-efficient over the last 4 years! (Blogpost here https://lnkd.in/dVnuzaaf). But what are TPUs? They're specialized hardware accelerators that help advance artificial intelligence (AI). Their efficiency impacts AI's environmental sustainability. This progress is due to more efficient hardware design, which means fewer carbon emissions for the same AI workload. Here are some of the highlights: 🟢 Operational electricity emissions make up more than 70% of a Google TPU's lifetime emissions. So, this 3x operational efficiency gain is extra important!! 🟢 While manufacturing emissions are still notable and will increase as operational emissions decrease with the use of carbon-free energy. 🟢 We've also significantly improved our AI model efficiency (i.e. the software not just hardware), reducing the number of computations required for a given performance.   🟢 This is key for our strategy to run on 24/7 carbon-free energy (CFE) on every grid where we operate by 2030. These findings highlight the importance of optimizing both hardware AND software for a sustainable AI future. It's important to remember where AI has important implications for reducing emissions and fostering sustainability - ex. AI can optimize energy consumption in buildings, improve traffic flow, and develop new materials for renewable energy technologies. On a personal level, as someone who pursued a masters in environmental management with a focus on industrial ecology, I'm particularly proud to see this kind of full lifecycle / LCA review of AI :) By taking a holistic view, we can identify and address the biggest contributors to AI's carbon footprint. #Sustainability #AI #GoogleCloud #TPU #CarbonFootprint #TechForGood #Innovation #IndustrialEcology #LifecycleAssessment

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! 👇

Demand for U.S. power is projected to grow rapidly for the first time in decades, driven by the power needs of Artificial Intelligence. AI is having a huge impact on many sectors of the economy, and already we are seeing significant growth in data center development based on expected power needs associated with AI. This power crisis likely will cause major challenges for utilities and those focused on eliminating the use of fossil fuels. Tech firms at the forefront of the AI revolution are starting to think about their power needs and making investments into non-fossil fuel sources, including nuclear. Alphabet Inc., Amazon, Meta, and Microsoft recently made announcements about bringing back decommissioned and/or building new nuclear facilities. Despite concerns about safety and waste disposal, supporters of nuclear power will be happy by the technology sector’s backing of this power source, including small modular reactors. However, a bigger concern was the recent announcement by FirstEnergy, an Akron, Ohio-based utility, that it plans to continue operating its Fort Martin and Harrison coal-fired plants in West Virginia, having previously announced it would close these facilities by 2030. It is likely that FirstEnergy will not be the only utility to break its pledge to decommission coal power plant in order to ensure it has enough power for future AI needs. This decision to extend the use of coal power plants is deeply troubling, particularly after witnessing the environmental impact from Germany’s decision to close its nuclear reactors following the Fukushima nuclear disaster. Short on power, Germany turned to lignite, the dirtiest type of coal, and in less than 5 years, Germany reversed its decades long trend of lower emissions, as CO2 emissions started rising again. The German experience is something that we don’t want to emulate in the U.S. We should not stand in the way of progress, and AI has the ability to have a positive impact on many industries and our daily lives. Nevertheless, we must be careful to ensure we are not moving backward on our environmental commitments. Increasing power needs from data centers and electric vehicles can, and should, be met by rapid growth in renewable energy. With renewable energy and energy storage prices continuing to decline, the U.S. should be able to meet its power needs without hurting the environment. https://lnkd.in/gHNaKYsr EcoTech Capital Cy Obert #cleantech; #climatetech; #energytransition; #sustainability

Technology is revolutionising how businesses tackle carbon emissions, offering innovative solutions that are both measurable and impactful. IoT sensors and AI-driven platforms allow organisations to monitor energy use and emissions in real time, identifying once-invisible inefficiencies. These insights enable precise strategies for reducing operational carbon footprints. Businesses can leverage technology to offset emissions they can’t yet eliminate. Exchanges such as Carbon Trade eXchange (CTX) are increasing the transparency of carbon offset programs by offering verified carbon credits and ensuring investments in projects like reforestation, renewable energy, and reuse deliver tangible results. Globechain the ESG Reuse Marketplace exemplifies how technology and reuse go hand in hand, providing companies with ESG and carbon-deferred data to quantify the impact of rehoming items that might otherwise be discarded. This integration reduces waste and lowers emissions tied to production and disposal, showcasing the power of circular economy solutions. As we navigate the path to net zero, adopting advanced technology for carbon management is no longer optional—it’s essential. These tools empower organisations to take measurable, accountable, and scalable climate action, making sustainability a core driver of success. How is your business embracing technology to reduce its environmental impact and build resilience for the future? #CarbonManagement #SustainabilityInBusiness #NetZeroGoals #TechForGood #CircularEconomy #CarbonTracking #SustainableInnovation #CarbonOffsetting #ClimateAction #GreenTechnology