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      in Environment

      MIT entrepreneurs think globally, act locally

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      Born and raised amid the natural beauty of the Dominican Republic, Andrés Bisonó León feels a deep motivation to help solve a problem that has been threatening the Caribbean island nation’s tourism industry, its economy, and its people.

      As Bisonó León discussed with his long-time friend and mentor, the Walter M. May and A. Hazel May Professor of Mechanical Engineering (MechE) Alexander Slocum Sr., ugly mats of toxic sargassum seaweed have been encroaching on the Dominican Republic’s pristine beaches and other beaches in the Caribbean region, and public and private organizations have fought a losing battle using expensive, environmentally damaging methods to clean it up. Slocum, who was on the U.S. Department of Energy’s Deepwater Horizon team, has extensive experience with systems that operate in the ocean.

      “In the last 10 years,” says Bisonó León, now an MBA candidate in the MIT Sloan School of Management, “sargassum, a toxic seaweed invasion, has cost the Caribbean as much as $120 million a year in cleanup and has meant a 30 to 35 percent tourism reduction, affecting not only the tourism industry, but also the environment, marine life, local economies, and human health.”

      One of Bisonó León’s discussions with Slocum took place within earshot of MechE alumnus Luke Gray ’18, SM ’20, who had worked with Slocum on other projects and was at the time was about to begin his master’s program.

      “Professor Slocum and Andrés happened to be discussing the sargassum problem in Andrés’ home country,” Gray says. “A week later I was on a plane to the DR to collect sargassum samples and survey the problem in Punta Cana. When I returned, my master’s program was underway, and I already had my thesis project!”

      Gray also had started a working partnership with Bisonó León, which both say proceeded seamlessly right from the first moment.

      “I feel that Luke right away understood the magnitude of the problem and the value we could create in the Dominican Republic and across the Caribbean by teaming up,” Bisonó León says.

      Both Bisonó León and Gray also say they felt a responsibility to work toward helping the global environment.

      “All of my major projects up until now have involved machines for climate restoration and/or adaptation,” says Gray.

      The technologies Bisonó León and Gray arrived at after 18 months of R&D were designed to provide solutions both locally and globally.

      Their Littoral Collection Module (LCM) skims sargassum seaweed off the surface of the water with nets that can be mounted on any boat. The device sits across the boat, with two large hoops holding the nets open, one on each side. As the boat travels forward, it cuts through the seaweed, which flows to the sides of the vessel and through the hoops into the nets. Effective at sweeping the seaweed from the water, the device can be employed by anyone with a boat, including local fishermen whose livelihoods have been disrupted by the seaweed’s damaging effect on tourism and the local economy.

      The sargassum can then be towed out to sea, where Bisonó León’s and Gray’s second technology can come into play. By pumping the seaweed into very deep water, where it then sinks to the bottom of the ocean, the carbon in the seaweed can be sequestered. Other methods for disposing of the seaweed generally involve putting it into landfills, where it emits greenhouse gases such as methane and carbon dioxide as it breaks down. Although some seaweed can be put to other uses, including as fertilizer, sargassum has been found to contain hard-to-remove toxic substances such as arsenic and heavy metals.

      In spring 2020, Bisonó León and Gray formed a company, SOS (Sargassum Ocean Sequestration) Carbon.

      Bisonó León says he comes from a long line of entrepreneurs who often expressed much commitment to social impact. His family has been involved in several different industries, his grandfather and great uncles having opened the first cigar factory in the Dominican Republic in 1903.

      Gray says internships with startup companies and the undergraduate projects he did with Slocum developed his interest in entrepreneurship, and his involvement with the sargassum problem only reinforced that inclination. During his master’s program, he says he became “obsessed” with finding a solution.

      “Professor Slocum let me think extremely big, and so it was almost inevitable that the distillation of our two years of work would continue in some form, and starting a company happened to be the right path. My master’s experience of taking an essentially untouched problem like sargassum and then one year later designing, building, and sending 15,000 pounds of custom equipment to test for three months on a Dominican Navy ship made me realize I had discovered a recipe I could repeat — and machine design had become my core competency,” Gray says.

      During the initial research and development of their technologies, Bisonó León and Gray raised $258,000 from 20 different organizations. Between June and December 2021, they succeeded in removing 3.5 million pounds of sargassum and secured contracts with Grupo Puntacana, which operates several tourist resorts, and with other hotels such as Club Med in Punta Cana. The company subcontracts with the association of fishermen in Punta Cana, employing 15 fishermen who operate LCMs and training 35 others to join as the operation expands.

      Their success so far demonstrates “’mens et manus’ at work,” says Slocum, referring to MIT’s motto, which is Latin for “mind and hand.” “Geeks hear about a very real problem that affects very real people who have no other option for their livelihoods, and they respond by inventing a solution so elegant that it can be readily deployed by those most hurt by the problem to address the problem.

      “The team was always focused on the numbers, from physics to finance, and did not let hype or doubts deter their determination to rationally solve this huge problem.”

      Slocum says he could predict Bisonó León and Gray would work well together “because they started out as good, smart people with complementary skills whose hearts and minds were in the right place.”

      “We are working on having a global impact to reduce millions of tons of CO2 per year,” says Bisonó León. “With training from Sloan and cross-disciplinary collaborative spirit, we will be able to further expand environmental and social impact platforms much needed in the Caribbean to be able to drive real change regionally and globally.”

      “I hope SOS Carbon can serve as a model and inspire similar entrepreneurial efforts,” Gray says. More

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      in Environment

      Students dive into research with the MIT Climate and Sustainability Consortium

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      Throughout the fall 2021 semester, the MIT Climate and Sustainability Consortium (MCSC) supported several research projects with a climate-and-sustainability topic related to the consortium, through the MIT Undergraduate Research Opportunities Program (UROP). These students, who represent a range of disciplines, had the opportunity to work with MCSC Impact Fellows on topics related directly to the ongoing work and collaborations with MCSC member companies and the broader MIT community, from carbon capture to value-chain resilience to biodegradables. Many of these students are continuing their work this spring semester.

      Hannah Spilman, who is studying chemical engineering, worked with postdoc Glen Junor, an MCSC Impact Fellow, to investigate carbon capture, utilization, and storage (CCUS), with the goal of facilitating CCUS on a gigaton scale, a much larger capacity than what currently exists. “Scientists agree CCUS will be an important tool in combating climate change, but the largest CCUS facility only captures CO2 on a megaton scale, and very few facilities are actually operating,” explains Spilman. 

      Throughout her UROP, she worked on analyzing the currently deployed technology in the CCUS field, using National Carbon Capture Center post-combustion project reports to synthesize the results and outline those technologies. Examining projects like the RTI-NAS experiment, which showcased innovation with carbon capture technology, was especially helpful. “We must first understand where we are, and as we continue to conduct analyses, we will be able to understand the field’s current state and path forward,” she concludes.

      Fellow chemical engineering students Claire Kim and Alfonso Restrepo are working with postdoc and MCSC Impact Fellow Xiangkun (Elvis) Cao, also on investigating CCUS technology. Kim’s focus is on life cycle assessment (LCA), while Restrepo’s focus is on techno-economic assessment (TEA). They have been working together to use the two tools to evaluate multiple CCUS technologies. While LCA and TEA are not new tools themselves, their application in CCUS has not been comprehensively defined and described. “CCUS can play an important role in the flexible, low-carbon energy systems,” says Kim, which was part of the motivation behind her project choice.

      Through TEA, Restrepo has been investigating how various startups and larger companies are incorporating CCUS technology in their processes. “In order to reduce CO2 emissions before it’s too late to act, there is a strong need for resources that effectively evaluate CCUS technology, to understand the effectiveness and viability of emerging technology for future implementation,” he explains. For their next steps, Kim and Restrepo will apply LCA and TEA to the analysis of a specific capture (for example, direct ocean capture) or conversion (for example, CO2-to-fuel conversion) process​ in CCUS.

      Cameron Dougal, a first-year student, and James Santoro, studying management, both worked with postdoc and MCSC Impact Fellow Paloma Gonzalez-Rojas on biodegradable materials. Dougal explored biodegradable packaging film in urban systems. “I have had a longstanding interest in sustainability, with a newer interest in urban planning and design, which motivated me to work on this project,” Dougal says. “Bio-based plastics are a promising step for the future.”

      Dougal spent time conducting internet and print research, as well as speaking with faculty on their relevant work. From these efforts, Dougal has identified important historical context for the current recycling landscape — as well as key case studies and cities around the world to explore further. In addition to conducting more research, Dougal plans to create a summary and statistic sheet.

      Santoro dove into the production angle, working on evaluating the economic viability of the startups that are creating biodegradable materials. “Non-renewable plastics (created with fossil fuels) continue to pollute and irreparably damage our environment,” he says. “As we look for innovative solutions, a key question to answer is how can we determine a more effective way to evaluate the economic viability and probability of success for new startups and technologies creating biodegradable plastics?” The project aims to develop an effective framework to begin to answer this.

      At this point, Santoro has been understanding the overall ecosystem, understanding how these biodegradable materials are developed, and analyzing the economics side of things. He plans to have conversations with company founders, investors, and experts, and identify major challenges for biodegradable technology startups in creating high performance products with attractive unit economics. There is also still a lot to research about new technologies and trends in the industry, the profitability of different products, as well as specific individual companies doing this type of work.

      Tess Buchanan, who is studying materials science and engineering, is working with Katharina Fransen and Sarah Av-Ron, MIT graduate students in the Department of Chemical Engineering, and principal investigator Professor Bradley Olsen, to also explore biodegradables by looking into their development from biomass “This is critical work, given the current plastics sustainability crisis, and the potential of bio-based polymers,” Buchanan says.

      The objective of the project is to explore new sustainable polymers through a biodegradation assay using clear zone growth analysis to yield degradation rates. For next steps, Buchanan is diving into synthesis expansion and using machine learning to understand the relationship between biodegradation and polymer chemistry.

      Kezia Hector, studying chemical engineering, and Tamsin Nottage, a first-year student, working with postdoc and MCSC Impact Fellow Sydney Sroka, explored advancing and establishing sustainable solutions for value chain resilience. Hector’s focus was understanding how wildfires can affect supply chains, specifically identifying sources of economic loss. She reviewed academic literature and news articles, and looked at the Amazon, California, Siberia, and Washington, finding that wildfires cause millions of dollars in damage every year and impact supply chains by cutting off or slowing down freight activity. She will continue to identify ways to make supply chains more resilient and sustainable.

      Nottage focused on the economic impact of typhoons, closely studying Typhoon Mangkhut, a powerful and catastrophic tropical cyclone that caused extensive damages of $593 million in Guam, the Philippines, and South China in September 2018. “As a Bahamian, I’ve witnessed the ferocity of hurricanes and challenges of rebuilding after them,” says Nottage. “I used this project to identify the tropical cyclones that caused the most extensive damage for further investigation.”She compiled the causes of damage and their costs to inform targets of supply chain resiliency reform (shipping, building materials, power supply, etc.). As a next step, Nottage will focus on modeling extreme events like Mangkunt to develop frameworks that companies can learn from and utilize to build more sustainable supply chains in the future.

      Ellie Vaserman, a first-year student working with postdoc and MCSC Impact Fellow Poushali Maji, also explored a topic related to value chains: unlocking circularity across the entire value chain through quality improvement, inclusive policy, and behavior to improve materials recovery. Specifically, her objectives have been to learn more about methods of chemolysis and the viability of their products, to compare methods of chemical recycling of polyethylene terephthalate (PET) using quantitative metrics, and to design qualitative visuals to make the steps in PET chemical recycling processes more understandable.

      To do so, she conducted a literature review to identify main methods of chemolysis that are utilized in the field (and collect data about these methods) and created graphics for some of the more common processes. Moving forward, she hopes to compare the processes using other metrics and research the energy intensity of the monomer purification processes.

      The work of these students, as well as many others, continued over MIT’s Independent Activities Period in January. More

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      in Environment

      Q&A: More-sustainable concrete with machine learning

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      As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

      Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

      Q: What applications does concrete have, and what properties make it a preferred building material?

      Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

      Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

      Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

      While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

      Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

      Q: Why is this problem important to address now, at this point in history?

      Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

      Q: How are you planning to address this problem to produce better concrete?

      Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

      Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

      Q: What kinds of AI and computational techniques are you employing for this?

      Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

      Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

      We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

      Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

      Q: How do you balance cost-benefit trade-offs?

      Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

      Q: What kinds of challenges do you face in this work?

      Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

      Q: What do you hope to achieve through this work?

      Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

      Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

      Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

      This research is supported by the MIT-IBM Watson AI Lab. More

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      in Environment

      Global warming begets more warming, new paleoclimate study finds

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      It is increasingly clear that the prolonged drought conditions, record-breaking heat, sustained wildfires, and frequent, more extreme storms experienced in recent years are a direct result of rising global temperatures brought on by humans’ addition of carbon dioxide to the atmosphere. And a new MIT study on extreme climate events in Earth’s ancient history suggests that today’s planet may become more volatile as it continues to warm.

      The study, appearing today in Science Advances, examines the paleoclimate record of the last 66 million years, during the Cenozoic era, which began shortly after the extinction of the dinosaurs. The scientists found that during this period, fluctuations in the Earth’s climate experienced a surprising “warming bias.” In other words, there were far more warming events — periods of prolonged global warming, lasting thousands to tens of thousands of years — than cooling events. What’s more, warming events tended to be more extreme, with greater shifts in temperature, than cooling events.

      The researchers say a possible explanation for this warming bias may lie in a “multiplier effect,” whereby a modest degree of warming — for instance from volcanoes releasing carbon dioxide into the atmosphere — naturally speeds up certain biological and chemical processes that enhance these fluctuations, leading, on average, to still more warming.

      Interestingly, the team observed that this warming bias disappeared about 5 million years ago, around the time when ice sheets started forming in the Northern Hemisphere. It’s unclear what effect the ice has had on the Earth’s response to climate shifts. But as today’s Arctic ice recedes, the new study suggests that a multiplier effect may kick back in, and the result may be a further amplification of human-induced global warming.

      “The Northern Hemisphere’s ice sheets are shrinking, and could potentially disappear as a long-term consequence of human actions” says the study’s lead author Constantin Arnscheidt, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences. “Our research suggests that this may make the Earth’s climate fundamentally more susceptible to extreme, long-term global warming events such as those seen in the geologic past.”

      Arnscheidt’s study co-author is Daniel Rothman, professor of geophysics at MIT, and  co-founder and co-director of MIT’s Lorenz Center.

      A volatile push

      For their analysis, the team consulted large databases of sediments containing deep-sea benthic foraminifera — single-celled organisms that have been around for hundreds of millions of years and whose hard shells are preserved in sediments. The composition of these shells is affected by the ocean temperatures as organisms are growing; the shells are therefore considered a reliable proxy for the Earth’s ancient temperatures.

      For decades, scientists have analyzed the composition of these shells, collected from all over the world and dated to various time periods, to track how the Earth’s temperature has fluctuated over millions of years. 

      “When using these data to study extreme climate events, most studies have focused on individual large spikes in temperature, typically of a few degrees Celsius warming,” Arnscheidt says. “Instead, we tried to look at the overall statistics and consider all the fluctuations involved, rather than picking out the big ones.”

      The team first carried out a statistical analysis of the data and observed that, over the last 66 million years, the distribution of global temperature fluctuations didn’t resemble a standard bell curve, with symmetric tails representing an equal probability of extreme warm and extreme cool fluctuations. Instead, the curve was noticeably lopsided, skewed toward more warm than cool events. The curve also exhibited a noticeably longer tail, representing warm events that were more extreme, or of higher temperature, than the most extreme cold events.

      “This indicates there’s some sort of amplification relative to what you would otherwise have expected,” Arnscheidt says. “Everything’s pointing to something fundamental that’s causing this push, or bias toward warming events.”

      “It’s fair to say that the Earth system becomes more volatile, in a warming sense,” Rothman adds.

      A warming multiplier

      The team wondered whether this warming bias might have been a result of “multiplicative noise” in the climate-carbon cycle. Scientists have long understood that higher temperatures, up to a point, tend to speed up biological and chemical processes. Because the carbon cycle, which is a key driver of long-term climate fluctuations, is itself composed of such processes, increases in temperature may lead to larger fluctuations, biasing the system towards extreme warming events.

      In mathematics, there exists a set of equations that describes such general amplifying, or multiplicative effects. The researchers applied this multiplicative theory to their analysis to see whether the equations could predict the asymmetrical distribution, including the degree of its skew and the length of its tails.

      In the end, they found that the data, and the observed bias toward warming, could be explained by the multiplicative theory. In other words, it’s very likely that, over the last 66 million years, periods of modest warming were on average further enhanced by multiplier effects, such as the response of biological and chemical processes that further warmed the planet.

      As part of the study, the researchers also looked at the correlation between past warming events and changes in Earth’s orbit. Over hundreds of thousands of years, Earth’s orbit around the sun regularly becomes more or less elliptical. But scientists have wondered why many past warming events appeared to coincide with these changes, and why these events feature outsized warming compared with what the change in Earth’s orbit could have wrought on its own.

      So, Arnscheidt and Rothman incorporated the Earth’s orbital changes into the multiplicative model and their analysis of Earth’s temperature changes, and found that multiplier effects could predictably amplify, on average, the modest temperature rises due to changes in Earth’s orbit.

      “Climate warms and cools in synchrony with orbital changes, but the orbital cycles themselves would predict only modest changes in climate,” Rothman says. “But if we consider a multiplicative model, then modest warming, paired with this multiplier effect, can result in extreme events that tend to occur at the same time as these orbital changes.”

      “Humans are forcing the system in a new way,” Arnscheidt adds. “And this study is showing that, when we increase temperature, we’re likely going to interact with these natural, amplifying effects.”

      This research was supported, in part, by MIT’s School of Science. More

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      in Energy

      Electrifying cars and light trucks to meet Paris climate goals

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      On Aug. 5, the White House announced that it seeks to ensure that 50 percent of all new passenger vehicles sold in the United States by 2030 are powered by electricity. The purpose of this target is to enable the U.S to remain competitive with China in the growing electric vehicle (EV) market and meet its international climate commitments. Setting ambitious EV sales targets and transitioning to zero-carbon power sources in the United States and other nations could lead to significant reductions in carbon dioxide and other greenhouse gas emissions in the transportation sector and move the world closer to achieving the Paris Agreement’s long-term goal of keeping global warming well below 2 degrees Celsius relative to preindustrial levels.

      At this time, electrification of the transportation sector is occurring primarily in private light-duty vehicles (LDVs). In 2020, the global EV fleet exceeded 10 million, but that’s a tiny fraction of the cars and light trucks on the road. How much of the LDV fleet will need to go electric to keep the Paris climate goal in play? 

      To help answer that question, researchers at the MIT Joint Program on the Science and Policy of Global Change and MIT Energy Initiative have assessed the potential impacts of global efforts to reduce carbon dioxide emissions on the evolution of LDV fleets over the next three decades.

      Using an enhanced version of the multi-region, multi-sector MIT Economic Projection and Policy Analysis (EPPA) model that includes a representation of the household transportation sector, they projected changes for the 2020-50 period in LDV fleet composition, carbon dioxide emissions, and related impacts for 18 different regions. Projections were generated under four increasingly ambitious climate mitigation scenarios: a “Reference” scenario based on current market trends and fuel efficiency policies, a “Paris Forever” scenario in which current Paris Agreement commitments (Nationally Determined Contributions, or NDCs) are maintained but not strengthened after 2030, a “Paris to 2 C” scenario in which decarbonization actions are enhanced to be consistent with capping global warming at 2 C, and an “Accelerated Actions” scenario the caps global warming at 1.5 C through much more aggressive emissions targets than the current NDCs.

      Based on projections spanning the first three scenarios, the researchers found that the global EV fleet will likely grow to about 95-105 million EVs by 2030, and 585-823 million EVs by 2050. In the Accelerated Actions scenario, global EV stock reaches more than 200 million vehicles in 2030, and more than 1 billion in 2050, accounting for two-thirds of the global LDV fleet. The research team also determined that EV uptake will likely grow but vary across regions over the 30-year study time frame, with China, the United States, and Europe remaining the largest markets. Finally, the researchers found that while EVs play a role in reducing oil use, a more substantial reduction in oil consumption comes from economy-wide carbon pricing. The results appear in a study in the journal Economics of Energy & Environmental Policy.

      “Our study shows that EVs can contribute significantly to reducing global carbon emissions at a manageable cost,” says MIT Joint Program Deputy Director and MIT Energy Initiative Senior Research Scientist Sergey Paltsev, the lead author. “We hope that our findings will help decision-makers to design efficient pathways to reduce emissions.”  

      To boost the EV share of the global LDV fleet, the study’s co-authors recommend more ambitious policies to mitigate climate change and decarbonize the electric grid. They also envision an “integrated system approach” to transportation that emphasizes making internal combustion engine vehicles more efficient, a long-term shift to low- and net-zero carbon fuels, and systemic efficiency improvements through digitalization, smart pricing, and multi-modal integration. While the study focuses on EV deployment, the authors also stress for the need for investment in all possible decarbonization options related to transportation, including enhancing public transportation, avoiding urban sprawl through strategic land-use planning, and reducing the use of private motorized transport by mode switching to walking, biking, and mass transit.

      This research is an extension of the authors’ contribution to the MIT Mobility of the Future study. More

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      in Energy

      Reducing emissions by decarbonizing industry

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      A critical challenge in meeting the Paris Agreement’s long-term goal of keeping global warming well below 2 degrees Celsius is to vastly reduce carbon dioxide (CO2) and other greenhouse gas emissions generated by the most energy-intensive industries. According to a recent report by the International Energy Agency, these industries — cement, iron and steel, chemicals — account for about 20 percent of global CO2 emissions. Emissions from these industries are notoriously difficult to abate because, in addition to emissions associated with energy use, a significant portion of industrial emissions come from the process itself.

      For example, in the cement industry, about half the emissions come from the decomposition of limestone into lime and CO2. While a shift to zero-carbon energy sources such as solar or wind-powered electricity could lower CO2 emissions in the power sector, there are no easy substitutes for emissions-intensive industrial processes.

      Enter industrial carbon capture and storage (CCS). This technology, which extracts point-source carbon emissions and sequesters them underground, has the potential to remove up to 90-99 percent of CO2 emissions from an industrial facility, including both energy-related and process emissions. And that begs the question: Might CCS alone enable hard-to-abate industries to continue to grow while eliminating nearly all of the CO2 emissions they generate from the atmosphere?

      The answer is an unequivocal yes in a new study in the journal Applied Energy co-authored by researchers at the MIT Joint Program on the Science and Policy of Global Change, MIT Energy Initiative, and ExxonMobil.

      Using an enhanced version of the MIT Economic Projection and Policy Analysis (EPPA) model that represents different industrial CCS technology choices — and assuming that CCS is the only greenhouse gas emissions mitigation option available to hard-to-abate industries — the study assesses the long-term economic and environmental impacts of CCS deployment under a climate policy aimed at capping the rise in average global surface temperature at 2 C above preindustrial levels.

      The researchers find that absent industrial CCS deployment, the global costs of implementing the 2 C policy are higher by 12 percent in 2075 and 71 percent in 2100, relative to policy costs with CCS. They conclude that industrial CCS enables continued growth in the production and consumption of energy-intensive goods from hard-to-abate industries, along with dramatic reductions in the CO2 emissions they generate. Their projections show that as industrial CCS gains traction mid-century, this growth occurs globally as well as within geographical regions (primarily in China, Europe, and the United States) and the cement, iron and steel, and chemical sectors.

      “Because it can enable deep reductions in industrial emissions, industrial CCS is an essential mitigation option in the successful implementation of policies aligned with the Paris Agreement’s long-term climate targets,” says Sergey Paltsev, the study’s lead author and a deputy director of the MIT Joint Program and senior research scientist at the MIT Energy Initiative. “As the technology advances, our modeling approach offers decision-makers a pathway for projecting the deployment of industrial CCS across industries and regions.”

      But such advances will not take place without substantial, ongoing funding.

      “Sustained government policy support across decades will be needed if CCS is to realize its potential to promote the growth of energy-intensive industries and a stable climate,” says Howard Herzog, a co-author of the study and senior research engineer at the MIT Energy Initiative.

      The researchers also find that advanced CCS options such as cryogenic carbon capture (CCC), in which extracted CO2 is cooled to solid form using far less power than conventional coal- and gas-fired CCS technologies, could help expand the use of CCS in industrial settings through further production cost and emissions reductions.

      The study was supported by sponsors of the MIT Joint Program and by ExxonMobil through its membership in the MIT Energy Initiative. More