Net climate effect calculation methods

🔍 What’s the net climate impact of artificial intelligence? I just spent the last three hours reading a compelling new thesis from Jennifer Turliuk, introducing the Net Climate Impact Score (NCIS)—a novel framework for assessing whether AI reduces or increases greenhouse gas emissions when viewed holistically. The research explores the dual role of AI as both a climate challenge and a potential climate solution. I am a big fan of Jennifer - for those unaware she is practice leader of climate and energy AI at the Martin Trust Center for MIT Entrepreneurship, developing new AI tools in collaboration with the MIT Climate Policy Center to support Climate and Energy Ventures and she recently led a panel at Davos 2025 on how AI can accelerate the energy transition. Her thesis researches how AI is rapidly expanding across infrastructure, industry, and digital services. But its environmental footprint—through energy-intensive data centres, chip manufacturing, water use, and end-user applications—is growing faster than most realise. Some projections suggest that AI and its supporting infrastructure could consume up to 21% of U.S. electricity by 2030, placing AI in direct tension with corporate net-zero targets and increasing humanity's need for energy at a time when we should be reducing. The paper is built around a system dynamics model. It highlights the potential rebound effect of AI - that as AI becomes more energy efficient (e.g., via better chips or software), it becomes cheaper and more accessible. This often leads to increased overall use, offsetting the efficiency gains. These effects can be : - Direct rebound effects: Lower costs per AI task drive higher usage across sectors. - Indirect rebound effects: Efficiency frees up resources that are reinvested in other high-emission activities, compounding total climate impact. The NCIS framework balances: 🔴 AI’s climate harms (emissions across AI infrastructure, plus enabling fossil fuel exploration), and 🟢 AI-enabled emissions reductions (e.g., optimised grid operations, predictive maintenance, smart EV charging). 🧠 Key insights: = AI’s potential to cut emissions is 1.5–4% by 2030 (PwC, IEA, BCG estimates). = However, the actual emissions from AI are growing and may outweigh benefits without targeted deployment and regulation. = The time value of carbon means that emissions today are more damaging than potential savings tomorrow. 📌 Bottom line: AI 'can' support decarbonisation only if strategically aligned. Industry leaders need to: 1. Prioritise AI for climate-beneficial use cases (e.g., energy, transport, buildings). 2. Monitor NCIS-style metrics when evaluating AI investments. 3. Advocate for policies that internalise carbon costs and drive clean computing. We’re at a fork in the road. Will AI accelerate the climate transition—or become a high-emitting enabler of business-as-usual? You can get early access to the report here: https://lnkd.in/gwJQ8MjW

What is the correct #GHGemissions metric to use in my study? The first thing to say is that according to the IPCC, the emissions metric used to calculate aggregated emissions and removals of various GHGs influences the expected time the aggregated GHGs are calculated to be net zero. There are different #IPCC metrics (impact methods) to measure GHG emissions. When to use one or the other depends on the study's goal. I was honored to participate in an IPCC workshop that covered this topic a few months ago. When the IPCC deals with Global Warming Potential (GWP) metrics, they focus on two objectives: cost-benefit and cost-effectiveness. The GWP is the calculation method for dealing with a #cost-benefit approach (weighting emissions based on the damage they might cause). GWP100 and GWP20 represent that damage in different time horizons, with discount rates of 3% and >10%, respectively. When to use one or the other discount rate depends on how we value the damage from those GHGs. On the other hand, we have the GTP metric, a cost-effective focused one. It means weighting the emissions based on the contribution of those emissions to the temperature on the year a specific target is reached. It implies that this metric can approximate the least-cost abatement choice to achieve a given temperature target. A dynamic GTP means the time horizon changes over time depending on the target year and the assessment year. If the goal is to reach (keep us under) 1.5C in 2100, an assessment made in 2030 will use a GTP70, in 2050 a GTP50, and so on. But there is a third metric alternative. One more focused on sectoral perspectives as it recognizes the differences between those GHGs that will remain in the atmosphere for centuries and those that might lead to a decline in the warming rate if emissions are stopped (i.e. methane). GWP* introduces the quantitative evaluation of "additional warming" rather than marginal warming as the GWP and GTP do. When to use these metrics, it is up to the expert performing the assessment based on the study's goal. Is the goal of a company to understand the benefits of avoiding emissions of different gases or to compare the marginal damage from not abating those emissions? I hope we soon start seeing sensitivity analysis on net-zero timings using different metrics. I will drop the links to the WGI and WGIII full presentations in the comments. #climatechange #carbonfootprint #ghgcalculation #climatemitigation

📍Climate change is a complex phenomenon that involves various physical and chemical processes in the Earth system. 📍To quantify and compare the effects of different greenhouse gases (GHGs) on the climate, a common metric is needed. 📍One such metric is CO2 equivalent (CO2e), which is defined as the amount of CO2 that would have the same radiative forcing as a given amount of another GHG over a specified time horizon. 📍Radiative forcing is the change in the net energy balance of the Earth due to the presence of GHGs in the atmosphere. The global warming potential (GWP) is a measure of the radiative forcing per unit mass of a GHG relative to that of CO2. 📍For example, the GWP of methane (CH4) over 100 years is 28, which means that one tonne of CH4 has the same radiative forcing as 28tonnes of CO2 over a century. 📍By multiplying the mass of each GHG by its GWP, the total CO2e emissions from various sources and sectors can be calculated. 📍This allows for a consistent assessment of the contribution of different GHGs to climate change and the development of mitigation strategies. 📍By using CO2e, we can estimate the total greenhouse gas emissions from various sources and activities, such as agriculture, industry, transportation, etc. 📍This helps us to identify the major contributors to climate change and to set emission reduction targets accordingly. #climatechange #carbonreduction #energy #carbondioxide