Emissions

Subterms

    More stories

    • 63 Shares159 Views

      in Energy

      Explained: Generative AI’s environmental impact

      by

      In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate.The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed.Beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems. The increasing number of generative AI applications has also spurred demand for high-performance computing hardware, adding indirect environmental impacts from its manufacture and transport.“When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in. There are much broader consequences that go out to a system level and persist based on actions that we take,” says Elsa A. Olivetti, professor in the Department of Materials Science and Engineering and the lead of the Decarbonization Mission of MIT’s new Climate Project.Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society.Demanding data centersThe electricity demands of data centers are one major factor contributing to the environmental impacts of generative AI, since data centers are used to train and run the deep learning models behind popular tools like ChatGPT and DALL-E.A data center is a temperature-controlled building that houses computing infrastructure, such as servers, data storage drives, and network equipment. For instance, Amazon has more than 100 data centers worldwide, each of which has about 50,000 servers that the company uses to support cloud computing services.While data centers have been around since the 1940s (the first was built at the University of Pennsylvania in 1945 to support the first general-purpose digital computer, the ENIAC), the rise of generative AI has dramatically increased the pace of data center construction.“What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” says Noman Bashir, lead author of the impact paper, who is a Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium (MCSC) and a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL).Scientists have estimated that the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI. Globally, the electricity consumption of data centers rose to 460 terawatts in 2022. This would have made data centers the 11th largest electricity consumer in the world, between the nations of Saudi Arabia (371 terawatts) and France (463 terawatts), according to the Organization for Economic Co-operation and Development.By 2026, the electricity consumption of data centers is expected to approach 1,050 terawatts (which would bump data centers up to fifth place on the global list, between Japan and Russia).While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands.“The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants,” says Bashir.The power needed to train and deploy a model like OpenAI’s GPT-3 is difficult to ascertain. In a 2021 research paper, scientists from Google and the University of California at Berkeley estimated the training process alone consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide.While all machine-learning models must be trained, one issue unique to generative AI is the rapid fluctuations in energy use that occur over different phases of the training process, Bashir explains.Power grid operators must have a way to absorb those fluctuations to protect the grid, and they usually employ diesel-based generators for that task.Increasing impacts from inferenceOnce a generative AI model is trained, the energy demands don’t disappear.Each time a model is used, perhaps by an individual asking ChatGPT to summarize an email, the computing hardware that performs those operations consumes energy. Researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search.“But an everyday user doesn’t think too much about that,” says Bashir. “The ease-of-use of generative AI interfaces and the lack of information about the environmental impacts of my actions means that, as a user, I don’t have much incentive to cut back on my use of generative AI.”With traditional AI, the energy usage is split fairly evenly between data processing, model training, and inference, which is the process of using a trained model to make predictions on new data. However, Bashir expects the electricity demands of generative AI inference to eventually dominate since these models are becoming ubiquitous in so many applications, and the electricity needed for inference will increase as future versions of the models become larger and more complex.Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applications. Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds. New models often consume more energy for training, since they usually have more parameters than their predecessors.While electricity demands of data centers may be getting the most attention in research literature, the amount of water consumed by these facilities has environmental impacts, as well.Chilled water is used to cool a data center by absorbing heat from computing equipment. It has been estimated that, for each kilowatt hour of energy a data center consumes, it would need two liters of water for cooling, says Bashir.“Just because this is called ‘cloud computing’ doesn’t mean the hardware lives in the cloud. Data centers are present in our physical world, and because of their water usage they have direct and indirect implications for biodiversity,” he says.The computing hardware inside data centers brings its own, less direct environmental impacts.While it is difficult to estimate how much power is needed to manufacture a GPU, a type of powerful processor that can handle intensive generative AI workloads, it would be more than what is needed to produce a simpler CPU because the fabrication process is more complex. A GPU’s carbon footprint is compounded by the emissions related to material and product transport.There are also environmental implications of obtaining the raw materials used to fabricate GPUs, which can involve dirty mining procedures and the use of toxic chemicals for processing.Market research firm TechInsights estimates that the three major producers (NVIDIA, AMD, and Intel) shipped 3.85 million GPUs to data centers in 2023, up from about 2.67 million in 2022. That number is expected to have increased by an even greater percentage in 2024.The industry is on an unsustainable path, but there are ways to encourage responsible development of generative AI that supports environmental objectives, Bashir says.He, Olivetti, and their MIT colleagues argue that this will require a comprehensive consideration of all the environmental and societal costs of generative AI, as well as a detailed assessment of the value in its perceived benefits.“We need a more contextual way of systematically and comprehensively understanding the implications of new developments in this space. Due to the speed at which there have been improvements, we haven’t had a chance to catch up with our abilities to measure and understand the tradeoffs,” Olivetti says. More

    • 88 Shares119 Views

      in Energy

      Q&A: The climate impact of generative AI

      by

      Vijay Gadepally, a senior staff member at MIT Lincoln Laboratory, leads a number of projects at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient. Here, Gadepally discusses the increasing use of generative AI in everyday tools, its hidden environmental impact, and some of the ways that Lincoln Laboratory and the greater AI community can reduce emissions for a greener future.Q: What trends are you seeing in terms of how generative AI is being used in computing?A: Generative AI uses machine learning (ML) to create new content, like images and text, based on data that is inputted into the ML system. At the LLSC we design and build some of the largest academic computing platforms in the world, and over the past few years we’ve seen an explosion in the number of projects that need access to high-performance computing for generative AI. We’re also seeing how generative AI is changing all sorts of fields and domains — for example, ChatGPT is already influencing the classroom and the workplace faster than regulations can seem to keep up.We can imagine all sorts of uses for generative AI within the next decade or so, like powering highly capable virtual assistants, developing new drugs and materials, and even improving our understanding of basic science. We can’t predict everything that generative AI will be used for, but I can certainly say that with more and more complex algorithms, their compute, energy, and climate impact will continue to grow very quickly.Q: What strategies is the LLSC using to mitigate this climate impact?A: We’re always looking for ways to make computing more efficient, as doing so helps our data center make the most of its resources and allows our scientific colleagues to push their fields forward in as efficient a manner as possible.As one example, we’ve been reducing the amount of power our hardware consumes by making simple changes, similar to dimming or turning off lights when you leave a room. In one experiment, we reduced the energy consumption of a group of graphics processing units by 20 percent to 30 percent, with minimal impact on their performance, by enforcing a power cap. This technique also lowered the hardware operating temperatures, making the GPUs easier to cool and longer lasting.Another strategy is changing our behavior to be more climate-aware. At home, some of us might choose to use renewable energy sources or intelligent scheduling. We are using similar techniques at the LLSC — such as training AI models when temperatures are cooler, or when local grid energy demand is low.We also realized that a lot of the energy spent on computing is often wasted, like how a water leak increases your bill but without any benefits to your home. We developed some new techniques that allow us to monitor computing workloads as they are running and then terminate those that are unlikely to yield good results. Surprisingly, in a number of cases we found that the majority of computations could be terminated early without compromising the end result.Q: What’s an example of a project you’ve done that reduces the energy output of a generative AI program?A: We recently built a climate-aware computer vision tool. Computer vision is a domain that’s focused on applying AI to images; so, differentiating between cats and dogs in an image, correctly labeling objects within an image, or looking for components of interest within an image.In our tool, we included real-time carbon telemetry, which produces information about how much carbon is being emitted by our local grid as a model is running. Depending on this information, our system will automatically switch to a more energy-efficient version of the model, which typically has fewer parameters, in times of high carbon intensity, or a much higher-fidelity version of the model in times of low carbon intensity.By doing this, we saw a nearly 80 percent reduction in carbon emissions over a one- to two-day period. We recently extended this idea to other generative AI tasks such as text summarization and found the same results. Interestingly, the performance sometimes improved after using our technique!Q: What can we do as consumers of generative AI to help mitigate its climate impact?A: As consumers, we can ask our AI providers to offer greater transparency. For example, on Google Flights, I can see a variety of options that indicate a specific flight’s carbon footprint. We should be getting similar kinds of measurements from generative AI tools so that we can make a conscious decision on which product or platform to use based on our priorities.We can also make an effort to be more educated on generative AI emissions in general. Many of us are familiar with vehicle emissions, and it can help to talk about generative AI emissions in comparative terms. People may be surprised to know, for example, that one image-generation task is roughly equivalent to driving four miles in a gas car, or that it takes the same amount of energy to charge an electric car as it does to generate about 1,500 text summarizations.There are many cases where customers would be happy to make a trade-off if they knew the trade-off’s impact.Q: What do you see for the future?A: Mitigating the climate impact of generative AI is one of those problems that people all over the world are working on, and with a similar goal. We’re doing a lot of work here at Lincoln Laboratory, but its only scratching at the surface. In the long term, data centers, AI developers, and energy grids will need to work together to provide “energy audits” to uncover other unique ways that we can improve computing efficiencies. We need more partnerships and more collaboration in order to forge ahead.If you’re interested in learning more, or collaborating with Lincoln Laboratory on these efforts, please contact Vijay Gadepally.

      Play video

      Video: MIT Lincoln Laboratory More

    • 113 Shares149 Views

      in Environment

      A new catalyst can turn methane into something useful

      by

      Although it is less abundant than carbon dioxide, methane gas contributes disproportionately to global warming because it traps more heat in the atmosphere than carbon dioxide, due to its molecular structure.MIT chemical engineers have now designed a new catalyst that can convert methane into useful polymers, which could help reduce greenhouse gas emissions.“What to do with methane has been a longstanding problem,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT and the senior author of the study. “It’s a source of carbon, and we want to keep it out of the atmosphere but also turn it into something useful.”The new catalyst works at room temperature and atmospheric pressure, which could make it easier and more economical to deploy at sites of methane production, such as power plants and cattle barns.Daniel Lundberg PhD ’24 and MIT postdoc Jimin Kim are the lead authors of the study, which appears today in Nature Catalysis. Former postdoc Yu-Ming Tu and postdoc Cody Ritt also authors of the paper.Capturing methaneMethane is produced by bacteria known as methanogens, which are often highly concentrated in landfills, swamps, and other sites of decaying biomass. Agriculture is a major source of methane, and methane gas is also generated as a byproduct of transporting, storing, and burning natural gas. Overall, it is believed to account for about 15 percent of global temperature increases.At the molecular level, methane is made of a single carbon atom bound to four hydrogen atoms. In theory, this molecule should be a good building block for making useful products such as polymers. However, converting methane to other compounds has proven difficult because getting it to react with other molecules usually requires high temperature and high pressures.To achieve methane conversion without that input of energy, the MIT team designed a hybrid catalyst with two components: a zeolite and a naturally occurring enzyme. Zeolites are abundant, inexpensive clay-like minerals, and previous work has found that they can be used to catalyze the conversion of methane to carbon dioxide.In this study, the researchers used a zeolite called iron-modified aluminum silicate, paired with an enzyme called alcohol oxidase. Bacteria, fungi, and plants use this enzyme to oxidize alcohols.This hybrid catalyst performs a two-step reaction in which zeolite converts methane to methanol, and then the enzyme converts methanol to formaldehyde. That reaction also generates hydrogen peroxide, which is fed back into the zeolite to provide a source of oxygen for the conversion of methane to methanol.This series of reactions can occur at room temperature and doesn’t require high pressure. The catalyst particles are suspended in water, which can absorb methane from the surrounding air. For future applications, the researchers envision that it could be painted onto surfaces.“Other systems operate at high temperature and high pressure, and they use hydrogen peroxide, which is an expensive chemical, to drive the methane oxidation. But our enzyme produces hydrogen peroxide from oxygen, so I think our system could be very cost-effective and scalable,” Kim says.Creating a system that incorporates both enzymes and artificial catalysts is a “smart strategy,” says Damien Debecker, a professor at the Institute of Condensed Matter and Nanosciences at the University of Louvain, Belgium.“Combining these two families of catalysts is challenging, as they tend to operate in rather distinct operation conditions. By unlocking this constraint and mastering the art of chemo-enzymatic cooperation, hybrid catalysis becomes key-enabling: It opens new perspectives to run complex reaction systems in an intensified way,” says Debecker, who was not involved in the research.Building polymersOnce formaldehyde is produced, the researchers showed they could use that molecule to generate polymers by adding urea, a nitrogen-containing molecule found in urine. This resin-like polymer, known as urea-formaldehyde, is now used in particle board, textiles and other products.The researchers envision that this catalyst could be incorporated into pipes used to transport natural gas. Within those pipes, the catalyst could generate a polymer that could act as a sealant to heal cracks in the pipes, which are a common source of methane leakage. The catalyst could also be applied as a film to coat surfaces that are exposed to methane gas, producing polymers that could be collected for use in manufacturing, the researchers say.Strano’s lab is now working on catalysts that could be used to remove carbon dioxide from the atmosphere and combine it with nitrate to produce urea. That urea could then be mixed with the formaldehyde produced by the zeolite-enzyme catalyst to produce urea-formaldehyde.The research was funded by the U.S. Department of Energy. More

    • 75 Shares179 Views

      in Environment

      Q&A: Transforming research through global collaborations

      by

      The MIT Global Seed Funds (GSF) program fosters global research collaborations with MIT faculty and their peers abroad — creating partnerships that tackle complex global issues, from climate change to health-care challenges and beyond. Administered by the MIT Center for International Studies (CIS), the GSF program has awarded more than $26 million to over 1,200 faculty research projects since its inception in 2008. Through its unique funding structure — comprising a general fund for unrestricted geographical use and several specific funds within individual countries, regions, and universities — GSF supports a wide range of projects. The current call for proposals from MIT faculty and researchers with principal investigator status is open until Dec. 10. CIS recently sat down with faculty recipients Josephine Carstensen and David McGee to discuss the value and impact GSF added to their research. Carstensen, the Gilbert W. Winslow Career Development Associate Professor of Civil and Environmental Engineering, generates computational designs for large-scale structures with the intent of designing novel low-carbon solutions. McGee, the William R. Kenan, Jr. Professor in the Department of Earth, Atmospheric and Planetary Sciences (EAPS), reconstructs the patterns, pace, and magnitudes of past hydro-climate changes.Q: How did the Global Seed Funds program connect you with global partnerships related to your research?Carstensen: One of the projects my lab is working on is to unlock the potential of complex cast-glass structures. Through our GSF partnership with researchers at TUDelft (Netherlands), my group was able to leverage our expertise in generative design algorithms alongside the TUDelft team, who are experts in the physical casting and fabrication of glass structures. Our initial connection to TUDelft was actually through one of my graduate students who was at a conference and met TUDelft researchers. He was inspired by their work and felt there could be synergy between our labs. The question then became: How do we connect with TUDelft? And that was what led us to the Global Seed Funds program. McGee: Our research is based in fieldwork conducted in partnership with experts who have a rich understanding of local environments. These locations range from lake basins in Chile and Argentina to caves in northern Mexico, Vietnam, and Madagascar. GSF has been invaluable for helping foster partnerships with collaborators and universities in these different locations, enabling the pilot work and relationship-building necessary to establish longer-term, externally funded projects.Q: Tell us more about your GSF-funded work.Carstensen: In my research group at MIT, we live mainly in a computational regime, and we do very little proof-of-concept testing. To that point, we do not even have the facilities nor experience to physically build large-scale structures, or even specialized structures. GSF has enabled us to connect with the researchers at TUDelft who do much more experimental testing than we do. Being able to work with the experts at TUDelft within their physical realm provided valuable insights into their way of approaching problems. And, likewise, the researchers at TUDelft benefited from our expertise. It has been fruitful in ways we couldn’t have imagined within our lab at MIT.McGee: The collaborative work supported by the GSF has focused on reconstructing how past climate changes impacted rainfall patterns around the world, using natural archives like lake sediments and cave formations. One particularly successful project has been our work in caves in northeastern Mexico, which has been conducted in partnership with researchers from the National Autonomous University of Mexico (UNAM) and a local caving group. This project has involved several MIT undergraduate and graduate students, sponsored a research symposium in Mexico City, and helped us obtain funding from the National Science Foundation for a longer-term project.Q: You both mentioned the involvement of your graduate students. How exactly has the GSF augmented the research experience of your students?Carstensen: The collaboration has especially benefited the graduate students from both the MIT and TUDelft teams. The opportunity presented through this project to engage in research at an international peer institution has been extremely beneficial for their academic growth and maturity. It has facilitated training in new and complementary technical areas that they would not have had otherwise and allowed them to engage with leading world experts. An example of this aspect of the project’s success is that the collaboration has inspired one of my graduate students to actively pursue postdoc opportunities in Europe (including at TU Delft) after his graduation.McGee: MIT students have traveled to caves in northeastern Mexico and to lake basins in northern Chile to conduct fieldwork and build connections with local collaborators. Samples enabled by GSF-supported projects became the focus of two graduate students’ PhD theses, two EAPS undergraduate senior theses, and multiple UROP [Undergraduate Research Opportunity Program] projects.Q: Were there any unexpected benefits to the work funded by GSF?Carstensen: The success of this project would not have been possible without this specific international collaboration. Both the Delft and MIT teams bring highly different essential expertise that has been necessary for the successful project outcome. It allowed both the Delft and MIT teams to gain an in-depth understanding of the expertise areas and resources of the other collaborators. Both teams have been deeply inspired. This partnership has fueled conversations about potential future projects and provided multiple outcomes, including a plan to publish two journal papers on the project outcome. The first invited publication is being finalized now.McGee: GSF’s focus on reciprocal exchange has enabled external collaborators to spend time at MIT, sharing their work and exchanging ideas. Other funding is often focused on sending MIT researchers and students out, but GSF has helped us bring collaborators here, making the relationship more equal. A GSF-supported visit by Argentinian researchers last year made it possible for them to interact not just with my group, but with students and faculty across EAPS. More

    • 175 Shares129 Views

      in Energy

      Is there enough land on Earth to fight climate change and feed the world?

      by

      Capping global warming at 1.5 degrees Celsius is a tall order. Achieving that goal will not only require a massive reduction in greenhouse gas emissions from human activities, but also a substantial reallocation of land to support that effort and sustain the biosphere, including humans. More land will be needed to accommodate a growing demand for bioenergy and nature-based carbon sequestration while ensuring sufficient acreage for food production and ecological sustainability.The expanding role of land in a 1.5 C world will be twofold — to remove carbon dioxide from the atmosphere and to produce clean energy. Land-based carbon dioxide removal strategies include bioenergy with carbon capture and storage; direct air capture; and afforestation/reforestation and other nature-based solutions. Land-based clean energy production includes wind and solar farms and sustainable bioenergy cropland. Any decision to allocate more land for climate mitigation must also address competing needs for long-term food security and ecosystem health.Land-based climate mitigation choices vary in terms of costs — amount of land required, implications for food security, impact on biodiversity and other ecosystem services — and benefits — potential for sequestering greenhouse gases and producing clean energy.Now a study in the journal Frontiers in Environmental Science provides the most comprehensive analysis to date of competing land-use and technology options to limit global warming to 1.5 C. Led by researchers at the MIT Center for Sustainability Science and Strategy (CS3), the study applies the MIT Integrated Global System Modeling (IGSM) framework to evaluate costs and benefits of different land-based climate mitigation options in Sky2050, a 1.5 C climate-stabilization scenario developed by Shell.Under this scenario, demand for bioenergy and natural carbon sinks increase along with the need for sustainable farming and food production. To determine if there’s enough land to meet all these growing demands, the research team uses the global hectare (gha) — an area of 10,000 square meters, or 2.471 acres — as the standard unit of measurement, and current estimates of the Earth’s total habitable land area (about 10 gha) and land area used for food production and bioenergy (5 gha).The team finds that with transformative changes in policy, land management practices, and consumption patterns, global land is sufficient to provide a sustainable supply of food and ecosystem services throughout this century while also reducing greenhouse gas emissions in alignment with the 1.5 C goal. These transformative changes include policies to protect natural ecosystems; stop deforestation and accelerate reforestation and afforestation; promote advances in sustainable agriculture technology and practice; reduce agricultural and food waste; and incentivize consumers to purchase sustainably produced goods.If such changes are implemented, 2.5–3.5 gha of land would be used for NBS practices to sequester 3–6 gigatonnes (Gt) of CO2 per year, and 0.4–0.6 gha of land would be allocated for energy production — 0.2–0.3 gha for bioenergy and 0.2–0.35 gha for wind and solar power generation.“Our scenario shows that there is enough land to support a 1.5 degree C future as long as effective policies at national and global levels are in place,” says CS3 Principal Research Scientist Angelo Gurgel, the study’s lead author. “These policies must not only promote efficient use of land for food, energy, and nature, but also be supported by long-term commitments from government and industry decision-makers.” More

    • 225 Shares149 Views

      in Energy

      Consortium led by MIT, Harvard University, and Mass General Brigham spurs development of 408 MW of renewable energy

      by

      MIT is co-leading an effort to enable the development of two new large-scale renewable energy projects in regions with carbon-intensive electrical grids: Big Elm Solar in Bell County, Texas, came online this year, and the Bowman Wind Project in Bowman County, North Dakota, is expected to be operational in 2026. Together, they will add a combined 408 megawatts (MW) of new renewable energy capacity to the power grid. This work is a critical part of MIT’s strategy to achieve its goal of net-zero carbon emissions by 2026.The Consortium for Climate Solutions, which includes MIT and 10 other Massachusetts organizations, seeks to eliminate close to 1 million metric tons of greenhouse gases each year — more than five times the annual direct emissions from MIT’s campus — by committing to purchase an estimated 1.3-million-megawatt hours of new solar and wind electricity generation annually.“MIT has mobilized on multiple fronts to expedite solutions to climate change,” says Glen Shor, executive vice president and treasurer. “Catalyzing these large-scale renewable projects is an important part of our comprehensive efforts to reduce carbon emissions from generating energy. We are pleased to work in partnership with other local enterprises and organizations to amplify the impact we could achieve individually.”The two new projects complement MIT’s existing 25-year power purchase agreement established with Summit Farms in 2016, which enabled the construction of a roughly 650-acre, 60 MW solar farm on farmland in North Carolina, leading to the early retirement of a coal-fired plant nearby. Its success has inspired other institutions to implement similar aggregation models.A collective approach to enable global impactMIT, Harvard University, and Mass General Brigham formed the consortium in 2020 to provide a structure to accelerate global emissions reductions through the development of large-scale renewable energy projects — accelerating and expanding the impact of each institution’s greenhouse gas reduction initiatives. As the project’s anchors, they collectively procured the largest volume of energy through the aggregation.  The consortium engaged with PowerOptions, a nonprofit energy-buying consortium, which offered its members the opportunity to participate in the projects. The City of Cambridge, Beth Israel Lahey, Boston Children’s Hospital, Dana-Farber Cancer Institute, Tufts University, the Mass Convention Center Authority, the Museum of Fine Arts, and GBH later joined the consortium through PowerOptions.  The consortium vetted over 125 potential projects against its rigorous project evaluation criteria. With faculty and MIT stakeholder input on a short list of the highest-ranking projects, it ultimately chose Bowman Wind and Big Elm Solar. Collectively, these two projects will achieve large greenhouse gas emissions reductions in two of the most carbon-intensive electrical grid regions in the United States and create clean energy generation sources to reduce negative health impacts.“Enabling these projects in regions where the grids are most carbon-intensive allows them to have the greatest impact. We anticipate these projects will prevent two times more emissions per unit of generated electricity than would a similar-scale project in New England,” explains Vice President for Campus Services and Stewardship Joe Higgins.By all consortium institutions making significant 15-to-20-year financial commitments to buy electricity, the developer was able to obtain critical external project financing to build the projects. Owned and operated by Apex Clean Energy, the projects will add new renewable electricity to the grid equivalent to powering 130,000 households annually, displacing over 950,000 metric tons of greenhouse gas emissions each year from highly carbon-intensive power plants in the region. Complementary decarbonization work underway In addition to investing in offsite renewable energy projects, many consortium members have developed strategies to reduce and eliminate their own direct emissions. At MIT, accomplishing this requires transformative change in how energy is generated, distributed, and used on campus. Efforts underway include the installation of solar panels on campus rooftops that will increase renewable energy generation four-fold by 2026; continuing to transition our heat distribution infrastructure from steam-based to hot water-based; utilizing design and construction that minimizes emissions and increases energy efficiency; employing AI-enabled sensors to optimize temperature set points and reduce energy use in buildings; and converting MIT’s vehicle fleet to all-electric vehicles while adding more electric car charging stations.The Institute has also upgraded the Central Utilities Plant, which uses advanced co-generation technology to produce power that is up to 20 percent less carbon-intensive than that from the regional power grid. MIT is charting the course toward a next-generation district energy system, with a comprehensive planning initiative to revolutionize its campus energy infrastructure. The effort is exploring leading-edge technology, including industrial-scale heat pumps, geothermal exchange, micro-reactors, bio-based fuels, and green hydrogen derived from renewable sources as solutions to achieve full decarbonization of campus operations by 2050.“At MIT, we are focused on decarbonizing our own campus as well as the role we can play in solving climate at the largest of scales, including supporting a cleaner grid in line with the call to triple renewables globally by 2030. By enabling these large-scale renewable projects, we can have an immediate and significant impact of reducing emissions through the urgently needed decarbonization of regional power grids,” says Julie Newman, MIT’s director of sustainability.   More

    • 138 Shares119 Views

      in Energy

      Reality check on technologies to remove carbon dioxide from the air

      by

      In 2015, 195 nations plus the European Union signed the Paris Agreement and pledged to undertake plans designed to limit the global temperature increase to 1.5 degrees Celsius. Yet in 2023, the world exceeded that target for most, if not all of, the year — calling into question the long-term feasibility of achieving that target.To do so, the world must reduce the levels of greenhouse gases in the atmosphere, and strategies for achieving levels that will “stabilize the climate” have been both proposed and adopted. Many of those strategies combine dramatic cuts in carbon dioxide (CO2) emissions with the use of direct air capture (DAC), a technology that removes CO2 from the ambient air. As a reality check, a team of researchers in the MIT Energy Initiative (MITEI) examined those strategies, and what they found was alarming: The strategies rely on overly optimistic — indeed, unrealistic — assumptions about how much CO2 could be removed by DAC. As a result, the strategies won’t perform as predicted. Nevertheless, the MITEI team recommends that work to develop the DAC technology continue so that it’s ready to help with the energy transition — even if it’s not the silver bullet that solves the world’s decarbonization challenge.DAC: The promise and the realityIncluding DAC in plans to stabilize the climate makes sense. Much work is now under way to develop DAC systems, and the technology looks promising. While companies may never run their own DAC systems, they can already buy “carbon credits” based on DAC. Today, a multibillion-dollar market exists on which entities or individuals that face high costs or excessive disruptions to reduce their own carbon emissions can pay others to take emissions-reducing actions on their behalf. Those actions can involve undertaking new renewable energy projects or “carbon-removal” initiatives such as DAC or afforestation/reforestation (planting trees in areas that have never been forested or that were forested in the past). DAC-based credits are especially appealing for several reasons, explains Howard Herzog, a senior research engineer at MITEI. With DAC, measuring and verifying the amount of carbon removed is straightforward; the removal is immediate, unlike with planting forests, which may take decades to have an impact; and when DAC is coupled with CO2 storage in geologic formations, the CO2 is kept out of the atmosphere essentially permanently — in contrast to, for example, sequestering it in trees, which may one day burn and release the stored CO2.Will current plans that rely on DAC be effective in stabilizing the climate in the coming years? To find out, Herzog and his colleagues Jennifer Morris and Angelo Gurgel, both MITEI principal research scientists, and Sergey Paltsev, a MITEI senior research scientist — all affiliated with the MIT Center for Sustainability Science and Strategy (CS3) — took a close look at the modeling studies on which those plans are based.Their investigation identified three unavoidable engineering challenges that together lead to a fourth challenge — high costs for removing a single ton of CO2 from the atmosphere. The details of their findings are reported in a paper published in the journal One Earth on Sept. 20.Challenge 1: Scaling upWhen it comes to removing CO2 from the air, nature presents “a major, non-negotiable challenge,” notes the MITEI team: The concentration of CO2 in the air is extremely low — just 420 parts per million, or roughly 0.04 percent. In contrast, the CO2 concentration in flue gases emitted by power plants and industrial processes ranges from 3 percent to 20 percent. Companies now use various carbon capture and sequestration (CCS) technologies to capture CO2 from their flue gases, but capturing CO2 from the air is much more difficult. To explain, the researchers offer the following analogy: “The difference is akin to needing to find 10 red marbles in a jar of 25,000 marbles of which 24,990 are blue [the task representing DAC] versus needing to find about 10 red marbles in a jar of 100 marbles of which 90 are blue [the task for CCS].”Given that low concentration, removing a single metric ton (tonne) of CO2 from air requires processing about 1.8 million cubic meters of air, which is roughly equivalent to the volume of 720 Olympic-sized swimming pools. And all that air must be moved across a CO2-capturing sorbent — a feat requiring large equipment. For example, one recently proposed design for capturing 1 million tonnes of CO2 per year would require an “air contactor” equivalent in size to a structure about three stories high and three miles long.Recent modeling studies project DAC deployment on the scale of 5 to 40 gigatonnes of CO2 removed per year. (A gigatonne equals 1 billion metric tonnes.) But in their paper, the researchers conclude that the likelihood of deploying DAC at the gigatonne scale is “highly uncertain.”Challenge 2: Energy requirementGiven the low concentration of CO2 in the air and the need to move large quantities of air to capture it, it’s no surprise that even the best DAC processes proposed today would consume large amounts of energy — energy that’s generally supplied by a combination of electricity and heat. Including the energy needed to compress the captured CO2 for transportation and storage, most proposed processes require an equivalent of at least 1.2 megawatt-hours of electricity for each tonne of CO2 removed.The source of that electricity is critical. For example, using coal-based electricity to drive an all-electric DAC process would generate 1.2 tonnes of CO2 for each tonne of CO2 captured. The result would be a net increase in emissions, defeating the whole purpose of the DAC. So clearly, the energy requirement must be satisfied using either low-carbon electricity or electricity generated using fossil fuels with CCS. All-electric DAC deployed at large scale — say, 10 gigatonnes of CO2 removed annually — would require 12,000 terawatt-hours of electricity, which is more than 40 percent of total global electricity generation today.Electricity consumption is expected to grow due to increasing overall electrification of the world economy, so low-carbon electricity will be in high demand for many competing uses — for example, in power generation, transportation, industry, and building operations. Using clean electricity for DAC instead of for reducing CO2 emissions in other critical areas raises concerns about the best uses of clean electricity.Many studies assume that a DAC unit could also get energy from “waste heat” generated by some industrial process or facility nearby. In the MITEI researchers’ opinion, “that may be more wishful thinking than reality.” The heat source would need to be within a few miles of the DAC plant for transporting the heat to be economical; given its high capital cost, the DAC plant would need to run nonstop, requiring constant heat delivery; and heat at the temperature required by the DAC plant would have competing uses, for example, for heating buildings. Finally, if DAC is deployed at the gigatonne per year scale, waste heat will likely be able to provide only a small fraction of the needed energy.Challenge 3: SitingSome analysts have asserted that, because air is everywhere, DAC units can be located anywhere. But in reality, siting a DAC plant involves many complex issues. As noted above, DAC plants require significant amounts of energy, so having access to enough low-carbon energy is critical. Likewise, having nearby options for storing the removed CO2 is also critical. If storage sites or pipelines to such sites don’t exist, major new infrastructure will need to be built, and building new infrastructure of any kind is expensive and complicated, involving issues related to permitting, environmental justice, and public acceptability — issues that are, in the words of the researchers, “commonly underestimated in the real world and neglected in models.”Two more siting needs must be considered. First, meteorological conditions must be acceptable. By definition, any DAC unit will be exposed to the elements, and factors like temperature and humidity will affect process performance and process availability. And second, a DAC plant will require some dedicated land — though how much is unclear, as the optimal spacing of units is as yet unresolved. Like wind turbines, DAC units need to be properly spaced to ensure maximum performance such that one unit is not sucking in CO2-depleted air from another unit.Challenge 4: CostConsidering the first three challenges, the final challenge is clear: the cost per tonne of CO2 removed is inevitably high. Recent modeling studies assume DAC costs as low as $100 to $200 per ton of CO2 removed. But the researchers found evidence suggesting far higher costs.To start, they cite typical costs for power plants and industrial sites that now use CCS to remove CO2 from their flue gases. The cost of CCS in such applications is estimated to be in the range of $50 to $150 per ton of CO2 removed. As explained above, the far lower concentration of CO2 in the air will lead to substantially higher costs.As explained under Challenge 1, the DAC units needed to capture the required amount of air are massive. The capital cost of building them will be high, given labor, materials, permitting costs, and so on. Some estimates in the literature exceed $5,000 per tonne captured per year.Then there are the ongoing costs of energy. As noted under Challenge 2, removing 1 tonne of CO2 requires the equivalent of 1.2 megawatt-hours of electricity. If that electricity costs $0.10 per kilowatt-hour, the cost of just the electricity needed to remove 1 tonne of CO2 is $120. The researchers point out that assuming such a low price is “questionable,” given the expected increase in electricity demand, future competition for clean energy, and higher costs on a system dominated by renewable — but intermittent — energy sources.Then there’s the cost of storage, which is ignored in many DAC cost estimates.Clearly, many considerations show that prices of $100 to $200 per tonne are unrealistic, and assuming such low prices will distort assessments of strategies, leading them to underperform going forward.The bottom lineIn their paper, the MITEI team calls DAC a “very seductive concept.” Using DAC to suck CO2 out of the air and generate high-quality carbon-removal credits can offset reduction requirements for industries that have hard-to-abate emissions. By doing so, DAC would minimize disruptions to key parts of the world’s economy, including air travel, certain carbon-intensive industries, and agriculture. However, the world would need to generate billions of tonnes of CO2 credits at an affordable price. That prospect doesn’t look likely. The largest DAC plant in operation today removes just 4,000 tonnes of CO2 per year, and the price to buy the company’s carbon-removal credits on the market today is $1,500 per tonne.The researchers recognize that there is room for energy efficiency improvements in the future, but DAC units will always be subject to higher work requirements than CCS applied to power plant or industrial flue gases, and there is not a clear pathway to reducing work requirements much below the levels of current DAC technologies.Nevertheless, the researchers recommend that work to develop DAC continue “because it may be needed for meeting net-zero emissions goals, especially given the current pace of emissions.” But their paper concludes with this warning: “Given the high stakes of climate change, it is foolhardy to rely on DAC to be the hero that comes to our rescue.” More

    • 138 Shares169 Views

      in Energy

      Turning automotive engines into modular chemical plants to make green fuels

      by

      Reducing methane emissions is a top priority in the fight against climate change because of its propensity to trap heat in the atmosphere: Methane’s warming effects are 84 times more potent than CO2 over a 20-year timescale.And yet, as the main component of natural gas, methane is also a valuable fuel and a precursor to several important chemicals. The main barrier to using methane emissions to create carbon-negative materials is that human sources of methane gas — landfills, farms, and oil and gas wells — are relatively small and spread out across large areas, while traditional chemical processing facilities are huge and centralized. That makes it prohibitively expensive to capture, transport, and convert methane gas into anything useful. As a result, most companies burn or “flare” their methane at the site where it’s emitted, seeing it as a sunk cost and an environmental liability.The MIT spinout Emvolon is taking a new approach to processing methane by repurposing automotive engines to serve as modular, cost-effective chemical plants. The company’s systems can take methane gas and produce liquid fuels like methanol and ammonia on-site; these fuels can then be used or transported in standard truck containers.”We see this as a new way of chemical manufacturing,” Emvolon co-founder and CEO Emmanuel Kasseris SM ’07, PhD ’11 says. “We’re starting with methane because methane is an abundant emission that we can use as a resource. With methane, we can solve two problems at the same time: About 15 percent of global greenhouse gas emissions come from hard-to-abate sectors that need green fuel, like shipping, aviation, heavy heavy-duty trucks, and rail. Then another 15 percent of emissions come from distributed methane emissions like landfills and oil wells.”By using mass-produced engines and eliminating the need to invest in infrastructure like pipelines, the company says it’s making methane conversion economically attractive enough to be adopted at scale. The system can also take green hydrogen produced by intermittent renewables and turn it into ammonia, another fuel that can also be used to decarbonize fertilizers.“In the future, we’re going to need green fuels because you can’t electrify a large ship or plane — you have to use a high-energy-density, low-carbon-footprint, low-cost liquid fuel,” Kasseris says. “The energy resources to produce those green fuels are either distributed, as is the case with methane, or variable, like wind. So, you cannot have a massive plant [producing green fuels] that has its own zip code. You either have to be distributed or variable, and both of those approaches lend themselves to this modular design.”From a “crazy idea” to a companyKasseris first came to MIT to study mechanical engineering as a graduate student in 2004, when he worked in the Sloan Automotive Lab on a report on the future of transportation. For his PhD, he developed a novel technology for improving internal combustion engine fuel efficiency for a consortium of automotive and energy companies, which he then went to work for after graduation.Around 2014, he was approached by Leslie Bromberg ’73, PhD ’77, a serial inventor with more than 100 patents, who has been a principal research engineer in MIT’s Plasma Science and Fusion Center for nearly 50 years.“Leslie had this crazy idea of repurposing an internal combustion engine as a reactor,” Kasseris recalls. “I had looked at that while working in industry, and I liked it, but my company at the time thought the work needed more validation.”Bromberg had done that validation through a U.S. Department of Energy-funded project in which he used a diesel engine to “reform” methane — a high-pressure chemical reaction in which methane is combined with steam and oxygen to produce hydrogen. The work impressed Kasseris enough to bring him back to MIT as a research scientist in 2016.“We worked on that idea in addition to some other projects, and eventually it had reached the point where we decided to license the work from MIT and go full throttle,” Kasseris recalls. “It’s very easy to work with MIT’s Technology Licensing Office when you are an MIT inventor. You can get a low-cost licensing option, and you can do a lot with that, which is important for a new company. Then, once you are ready, you can finalize the license, so MIT was instrumental.”Emvolon continued working with MIT’s research community, sponsoring projects with Professor Emeritus John Heywood and participating in the MIT Venture Mentoring Service and the MIT Industrial Liaison Program.An engine-powered chemical plantAt the core of Emvolon’s system is an off-the-shelf automotive engine that runs “fuel rich” — with a higher ratio of fuel to air than what is needed for complete combustion.“That’s easy to say, but it takes a lot of [intellectual property], and that’s what was developed at MIT,” Kasseris says. “Instead of burning the methane in the gas to carbon dioxide and water, you partially burn it, or partially oxidize it, to carbon monoxide and hydrogen, which are the building blocks to synthesize a variety of chemicals.”The hydrogen and carbon monoxide are intermediate products used to synthesize different chemicals through further reactions. Those processing steps take place right next to the engine, which makes its own power. Each of Emvolon’s standalone systems fits within a 40-foot shipping container and can produce about 8 tons of methanol per day from 300,000 standard cubic feet of methane gas.The company is starting with green methanol because it’s an ideal fuel for hard-to-abate sectors such as shipping and heavy-duty transport, as well as an excellent feedstock for other high-value chemicals, such as sustainable aviation fuel. Many shipping vessels have already converted to run on green methanol in an effort to meet decarbonization goals.This summer, the company also received a grant from the Department of Energy to adapt its process to produce clean liquid fuels from power sources like solar and wind.“We’d like to expand to other chemicals like ammonia, but also other feedstocks, such as biomass and hydrogen from renewable electricity, and we already have promising results in that direction” Kasseris says. “We think we have a good solution for the energy transition and, in the later stages of the transition, for e-manufacturing.”A scalable approachEmvolon has already built a system capable of producing up to six barrels of green methanol a day in its 5,000 square-foot headquarters in Woburn, Massachusetts.“For chemical technologies, people talk about scale up risk, but with an engine, if it works in a single cylinder, we know it will work in a multicylinder engine,” Kasseris says. “It’s just engineering.”Last month, Emvolon announced an agreement with Montauk Renewables to build a commercial-scale demonstration unit next to a Texas landfill that will initially produce up to 15,000 gallons of green methanol a year and later scale up to 2.5 million gallons. That project could be expanded tenfold by scaling across Montauk’s other sites.“Our whole process was designed to be a very realistic approach to the energy transition,” Kasseris says. “Our solution is designed to produce green fuels and chemicals at prices that the markets are willing to pay today, without the need for subsidies. Using the engines as chemical plants, we can get the capital expenditure per unit output close to that of a large plant, but at a modular scale that enables us to be next to low-cost feedstock. Furthermore, our modular systems require small investments — of $1 to 10 million — that are quickly deployed, one at a time, within weeks, as opposed to massive chemical plants that require multiyear capital construction projects and cost hundreds of millions.” More