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    Jackson Jewett wants to design buildings that use less concrete

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    After three years leading biking tours through U.S. National Parks, Jackson Jewett decided it was time for a change.

    “It was a lot of fun, but I realized I missed buildings,” says Jewett. “I really wanted to be a part of that industry, learn more about it, and reconnect with my roots in the built environment.”

    Jewett grew up in California in what he describes as a “very creative household.”

    “I remember making very elaborate Halloween costumes with my parents, making fun dioramas for school projects, and building forts in the backyard, that kind of thing,” Jewett explains.

    Both of his parents have backgrounds in design; his mother studied art in college and his father is a practicing architect. From a young age, Jewett was interested in following in his father’s footsteps. But when he arrived at the University of California at Berkeley in the midst of the 2009 housing crash, it didn’t seem like the right time. Jewett graduated with a degree in cognitive science and a minor in history of architecture. And even as he led tours through Yellowstone, the Grand Canyon, and other parks, buildings were in the back of his mind.

    It wasn’t just the built environment that Jewett was missing. He also longed for the rigor and structure of an academic environment.

    Jewett arrived at MIT in 2017, initially only planning on completing the master’s program in civil and environmental engineering. It was then that he first met Josephine Carstensen, a newly hired lecturer in the department. Jewett was interested in Carstensen’s work on “topology optimization,” which uses algorithms to design structures that can achieve their performance requirements while using only a limited amount of material. He was particularly interested in applying this approach to concrete design, and he collaborated with Carstensen to help demonstrate its viability.

    After earning his master’s, Jewett spent a year and a half as a structural engineer in New York City. But when Carstensen was hired as a professor, she reached out to Jewett about joining her lab as a PhD student. He was ready for another change.

    Now in the third year of his PhD program, Jewett’s dissertation work builds upon his master’s thesis to further refine algorithms that can design building-scale concrete structures that use less material, which would help lower carbon emissions from the construction industry. It is estimated that the concrete industry alone is responsible for 8 percent of global carbon emissions, so any efforts to reduce that number could help in the fight against climate change.

    Implementing new ideas

    Topology optimization is a small field, with the bulk of the prior work being computational without any experimental verification. The work Jewett completed for his master’s thesis was just the start of a long learning process.

    “I do feel like I’m just getting to the part where I can start implementing my own ideas without as much support as I’ve needed in the past,” says Jewett. “In the last couple of months, I’ve been working on a reinforced concrete optimization algorithm that I hope will be the cornerstone of my thesis.”

    The process of fine-tuning a generative algorithm is slow going, particularly when tackling a multifaceted problem.

    “It can take days or usually weeks to take a step toward making it work as an entire integrated system,” says Jewett. “The days when that breakthrough happens and I can see the algorithm converging on a solution that makes sense — those are really exciting moments.”

    By harnessing computational power, Jewett is searching for materially efficient components that can be used to make up structures such as bridges or buildings. These are other constraints to consider as well, particularly ensuring that the cost of manufacturing isn’t too high. Having worked in the industry before starting the PhD program, Jewett has an eye toward doing work that can be feasibly implemented.

    Inspiring others

    When Jewett first visited MIT campus, he was drawn in by the collaborative environment of the institute and the students’ drive to learn. Now, he’s a part of that process as a teaching assistant and a supervisor in the Undergraduate Research Opportunities Program.  

    Working as a teaching assistant isn’t a requirement for Jewett’s program, but it’s been one of his favorite parts of his time at MIT.

    “The MIT undergrads are so gifted and just constantly impress me,” says Jewett. “Being able to teach, especially in the context of what MIT values is a lot of fun. And I learn, too. My coding practices have gotten so much better since working with undergrads here.”

    Jewett’s experiences have inspired him to pursue a career in academia after the completion of his program, which he expects to complete in the spring of 2025. But he’s making sure to take care of himself along the way. He still finds time to plan cycling trips with his friends and has gotten into running ever since moving to Boston. So far, he’s completed two marathons.

    “It’s so inspiring to be in a place where so many good ideas are just bouncing back and forth all over campus,” says Jewett. “And on most days, I remember that and it inspires me. But it’s also the case that academics is hard, PhD programs are hard, and MIT — there’s pressure being here, and sometimes that pressure can feel like it’s working against you.”

    Jewett is grateful for the mental health resources that MIT provides students. While he says it can be imperfect, it’s been a crucial part of his journey.

    “My PhD thesis will be done in 2025, but the work won’t be done. The time horizon of when these things need to be implemented is relatively short if we want to make an impact before global temperatures have already risen too high. My PhD research will be developing a framework for how that could be done with concrete construction, but I’d like to keep thinking about other materials and construction methods even after this project is finished.” More

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      Integrating humans with AI in structural design

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      Modern fabrication tools such as 3D printers can make structural materials in shapes that would have been difficult or impossible using conventional tools. Meanwhile, new generative design systems can take great advantage of this flexibility to create innovative designs for parts of a new building, car, or virtually any other device.

      But such “black box” automated systems often fall short of producing designs that are fully optimized for their purpose, such as providing the greatest strength in proportion to weight or minimizing the amount of material needed to support a given load. Fully manual design, on the other hand, is time-consuming and labor-intensive.

      Now, researchers at MIT have found a way to achieve some of the best of both of these approaches. They used an automated design system but stopped the process periodically to allow human engineers to evaluate the work in progress and make tweaks or adjustments before letting the computer resume its design process. Introducing a few of these iterations produced results that performed better than those designed by the automated system alone, and the process was completed more quickly compared to the fully manual approach.

      The results are reported this week in the journal Structural and Multidisciplinary Optimization, in a paper by MIT doctoral student Dat Ha and assistant professor of civil and environmental engineering Josephine Carstensen.

      The basic approach can be applied to a broad range of scales and applications, Carstensen explains, for the design of everything from biomedical devices to nanoscale materials to structural support members of a skyscraper. Already, automated design systems have found many applications. “If we can make things in a better way, if we can make whatever we want, why not make it better?” she asks.

      “It’s a way to take advantage of how we can make things in much more complex ways than we could in the past,” says Ha, adding that automated design systems have already begun to be widely used over the last decade in automotive and aerospace industries, where reducing weight while maintaining structural strength is a key need.

      “You can take a lot of weight out of components, and in these two industries, everything is driven by weight,” he says. In some cases, such as internal components that aren’t visible, appearance is irrelevant, but for other structures aesthetics may be important as well. The new system makes it possible to optimize designs for visual as well as mechanical properties, and in such decisions the human touch is essential.

      As a demonstration of their process in action, the researchers designed a number of structural load-bearing beams, such as might be used in a building or a bridge. In their iterations, they saw that the design has an area that could fail prematurely, so they selected that feature and required the program to address it. The computer system then revised the design accordingly, removing the highlighted strut and strengthening some other struts to compensate, and leading to an improved final design.

      The process, which they call Human-Informed Topology Optimization, begins by setting out the needed specifications — for example, a beam needs to be this length, supported on two points at its ends, and must support this much of a load. “As we’re seeing the structure evolve on the computer screen in response to initial specification,” Carstensen says, “we interrupt the design and ask the user to judge it. The user can select, say, ‘I’m not a fan of this region, I’d like you to beef up or beef down this feature size requirement.’ And then the algorithm takes into account the user input.”

      While the result is not as ideal as what might be produced by a fully rigorous yet significantly slower design algorithm that considers the underlying physics, she says it can be much better than a result generated by a rapid automated design system alone. “You don’t get something that’s quite as good, but that was not necessarily the goal. What we can show is that instead of using several hours to get something, we can use 10 minutes and get something much better than where we started off.”

      The system can be used to optimize a design based on any desired properties, not just strength and weight. For example, it can be used to minimize fracture or buckling, or to reduce stresses in the material by softening corners.

      Carstensen says, “We’re not looking to replace the seven-hour solution. If you have all the time and all the resources in the world, obviously you can run these and it’s going to give you the best solution.” But for many situations, such as designing replacement parts for equipment in a war zone or a disaster-relief area with limited computational power available, “then this kind of solution that catered directly to your needs would prevail.”

      Similarly, for smaller companies manufacturing equipment in essentially “mom and pop” businesses, such a simplified system might be just the ticket. The new system they developed is not only simple and efficient to run on smaller computers, but it also requires far less training to produce useful results, Carstensen says. A basic two-dimensional version of the software, suitable for designing basic beams and structural parts, is freely available now online, she says, as the team continues to develop a full 3D version.

      “The potential applications of Prof Carstensen’s research and tools are quite extraordinary,” says Christian Málaga-Chuquitaype, a professor of civil and environmental engineering at Imperial College London, who was not associated with this work. “With this work, her group is paving the way toward a truly synergistic human-machine design interaction.”

      “By integrating engineering ‘intuition’ (or engineering ‘judgement’) into a rigorous yet computationally efficient topology optimization process, the human engineer is offered the possibility of guiding the creation of optimal structural configurations in a way that was not available to us before,” he adds. “Her findings have the potential to change the way engineers tackle ‘day-to-day’ design tasks.” 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 Energy

      Timber or steel? Study helps builders reduce carbon footprint of truss structures

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      Buildings are a big contributor to global warming, not just in their ongoing operations but in the materials used in their construction. Truss structures — those crisscross arrays of diagonal struts used throughout modern construction, in everything from antenna towers to support beams for large buildings — are typically made of steel or wood or a combination of both. But little quantitative research has been done on how to pick the right materials to minimize these structures’ contribution global warming.

      The “embodied carbon” in a construction material includes the fuel used in the material’s production (for mining and smelting steel, for example, or for felling and processing trees) and in transporting the materials to a site. It also includes the equipment used for the construction itself.

      Now, researchers at MIT have done a detailed analysis and created a set of computational tools to enable architects and engineers to design truss structures in a way that can minimize their embodied carbon while maintaining all needed properties for a given building application. While in general wood produces a much lower carbon footprint, using steel in places where its properties can provide maximum benefit can provide an optimized result, they say.

      The analysis is described in a paper published today in the journal Engineering Structures, by graduate student Ernest Ching and MIT assistant professor of civil and environmental engineering Josephine Carstensen.

      “Construction is a huge greenhouse gas emitter that has kind of been flying under the radar for the past decades,” says Carstensen. But in recent years building designers “are starting to be more focused on how to not just reduce the operating energy associated with building use, but also the important carbon associated with the structure itself.” And that’s where this new analysis comes in.

      The two main options in reducing the carbon emissions associated with truss structures, she says, are substituting materials or changing the structure. However, there has been “surprisingly little work” on tools to help designers figure out emissions-minimizing strategies for a given situation, she says.

      The new system makes use of a technique called topology optimization, which allows for the input of basic parameters, such as the amount of load to be supported and the dimensions of the structure, and can be used to produce designs optimized for different characteristics, such as weight, cost, or, in this case, global warming impact.

      Wood performs very well under forces of compression, but not as well as steel when it comes to tension — that is, a tendency to pull the structure apart. Carstensen says that in general, wood is far better than steel in terms of embedded carbon, so “especially if you have a structure that doesn’t have any tension, then you should definitely only use timber” in order to minimize emissions. One tradeoff is that “the weight of the structure is going to be bigger than it would be with steel,” she says.

      The tools they developed, which were the basis for Ching’s master’s thesis, can be applied at different stages, either in the early planning phase of a structure, or later on in the final stages of a design.

      As an exercise, the team developed a proposal for reengineering several trusses using these optimization tools, and demonstrated that a significant savings in embodied greenhouse gas emissions could be achieved with no loss of performance. While they have shown improvements of at least 10 percent can be achieved, she says those estimates are “not exactly apples to apples” and likely savings could actually be two to three times that.

      “It’s about choosing materials more smartly,” she says, for the specifics of a given application. Often in existing buildings “you will have timber where there’s compression, and where that makes sense, and then it will have really skinny steel members, in tension, where that makes sense. And that’s also what we see in our design solutions that are suggested, but perhaps we can see it even more clearly.” The tools are not ready for commercial use though, she says, because they haven’t yet added a user interface.

      Carstensen sees a trend to increasing use of timber in large construction, which represents an important potential for reducing the world’s overall carbon emissions. “There’s a big interest in the construction industry in mass timber structures, and this speaks right into that area. So, the hope is that this would make inroads into the construction business and actually make a dent in that very large contribution to greenhouse gas emissions.” More

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      in Environment

      Concrete’s role in reducing building and pavement emissions

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      Encountering concrete is a common, even routine, occurrence. And that’s exactly what makes concrete exceptional.

      As the most consumed material after water, concrete is indispensable to the many essential systems — from roads to buildings — in which it is used.

      But due to its extensive use, concrete production also contributes to around 1 percent of emissions in the United States and remains one of several carbon-intensive industries globally. Tackling climate change, then, will mean reducing the environmental impacts of concrete, even as its use continues to increase.

      In a new paper in the Proceedings of the National Academy of Sciences, a team of current and former researchers at the MIT Concrete Sustainability Hub (CSHub) outlines how this can be achieved.

      They present an extensive life-cycle assessment of the building and pavements sectors that estimates how greenhouse gas (GHG) reduction strategies — including those for concrete and cement — could minimize the cumulative emissions of each sector and how those reductions would compare to national GHG reduction targets. 

      The team found that, if reduction strategies were implemented, the emissions for pavements and buildings between 2016 and 2050 could fall by up to 65 percent and 57 percent, respectively, even if concrete use accelerated greatly over that period. These are close to U.S. reduction targets set as part of the Paris Climate Accords. The solutions considered would also enable concrete production for both sectors to attain carbon neutrality by 2050.

      Despite continued grid decarbonization and increases in fuel efficiency, they found that the vast majority of the GHG emissions from new buildings and pavements during this period would derive from operational energy consumption rather than so-called embodied emissions — emissions from materials production and construction.

      Sources and solutions

      The consumption of concrete, due to its versatility, durability, constructability, and role in economic development, has been projected to increase around the world.

      While it is essential to consider the embodied impacts of ongoing concrete production, it is equally essential to place these initial impacts in the context of the material’s life cycle.

      Due to concrete’s unique attributes, it can influence the long-term sustainability performance of the systems in which it is used. Concrete pavements, for instance, can reduce vehicle fuel consumption, while concrete structures can endure hazards without needing energy- and materials-intensive repairs.

      Concrete’s impacts, then, are as complex as the material itself — a carefully proportioned mixture of cement powder, water, sand, and aggregates. Untangling concrete’s contribution to the operational and embodied impacts of buildings and pavements is essential for planning GHG reductions in both sectors.

      Set of scenarios

      In their paper, CSHub researchers forecast the potential greenhouse gas emissions from the building and pavements sectors as numerous emissions reduction strategies were introduced between 2016 and 2050.

      Since both of these sectors are immense and rapidly evolving, modeling them required an intricate framework.

      “We don’t have details on every building and pavement in the United States,” explains Randolph Kirchain, a research scientist at the Materials Research Laboratory and co-director of CSHub.

      “As such, we began by developing reference designs, which are intended to be representative of current and future buildings and pavements. These were adapted to be appropriate for 14 different climate zones in the United States and then distributed across the U.S. based on data from the U.S. Census and the Federal Highway Administration”

      To reflect the complexity of these systems, their models had to have the highest resolutions possible.

      “In the pavements sector, we collected the current stock of the U.S. network based on high-precision 10-mile segments, along with the surface conditions, traffic, thickness, lane width, and number of lanes for each segment,” says Hessam AzariJafari, a postdoc at CSHub and a co-author on the paper.

      “To model future paving actions over the analysis period, we assumed four climate conditions; four road types; asphalt, concrete, and composite pavement structures; as well as major, minor, and reconstruction paving actions specified for each climate condition.”

      Using this framework, they analyzed a “projected” and an “ambitious” scenario of reduction strategies and system attributes for buildings and pavements over the 34-year analysis period. The scenarios were defined by the timing and intensity of GHG reduction strategies.

      As its name might suggest, the projected scenario reflected current trends. For the building sector, solutions encompassed expected grid decarbonization and improvements to building codes and energy efficiency that are currently being implemented across the country. For pavements, the sole projected solution was improvements to vehicle fuel economy. That’s because as vehicle efficiency continues to increase, excess vehicle emissions due to poor road quality will also decrease.

      Both the projected scenarios for buildings and pavements featured the gradual introduction of low-carbon concrete strategies, such as recycled content, carbon capture in cement production, and the use of captured carbon to produce aggregates and cure concrete.

      “In the ambitious scenario,” explains Kirchain, “we went beyond projected trends and explored reasonable changes that exceed current policies and [industry] commitments.”

      Here, the building sector strategies were the same, but implemented more aggressively. The pavements sector also abided by more aggressive targets and incorporated several novel strategies, including investing more to yield smoother roads, selectively applying concrete overlays to produce stiffer pavements, and introducing more reflective pavements — which can change the Earth’s energy balance by sending more energy out of the atmosphere.

      Results

      As the grid becomes greener and new homes and buildings become more efficient, many experts have predicted the operational impacts of new construction projects to shrink in comparison to their embodied emissions.

      “What our life-cycle assessment found,” says Jeremy Gregory, the executive director of the MIT Climate Consortium and the lead author on the paper, “is that [this prediction] isn’t necessarily the case.”

      “Instead, we found that more than 80 percent of the total emissions from new buildings and pavements between 2016 and 2050 would derive from their operation.”

      In fact, the study found that operations will create the majority of emissions through 2050 unless all energy sources — electrical and thermal — are carbon-neutral by 2040. This suggests that ambitious interventions to the electricity grid and other sources of operational emissions can have the greatest impact.

      Their predictions for emissions reductions generated additional insights.  

      For the building sector, they found that the projected scenario would lead to a reduction of 49 percent compared to 2016 levels, and that the ambitious scenario provided a 57 percent reduction.

      As most buildings during the analysis period were existing rather than new, energy consumption dominated emissions in both scenarios. Consequently, decarbonizing the electricity grid and improving the efficiency of appliances and lighting led to the greatest improvements for buildings, they found.

      In contrast to the building sector, the pavements scenarios had a sizeable gulf between outcomes: the projected scenario led to only a 14 percent reduction while the ambitious scenario had a 65 percent reduction — enough to meet U.S. Paris Accord targets for that sector. This gulf derives from the lack of GHG reduction strategies being pursued under current projections.

      “The gap between the pavement scenarios shows that we need to be more proactive in managing the GHG impacts from pavements,” explains Kirchain. “There is tremendous potential, but seeing those gains requires action now.”

      These gains from both ambitious scenarios could occur even as concrete use tripled over the analysis period in comparison to the projected scenarios — a reflection of not only concrete’s growing demand but its potential role in decarbonizing both sectors.

      Though only one of their reduction scenarios (the ambitious pavement scenario) met the Paris Accord targets, that doesn’t preclude the achievement of those targets: many other opportunities exist.

      “In this study, we focused on mainly embodied reductions for concrete,” explains Gregory. “But other construction materials could receive similar treatment.

      “Further reductions could also come from retrofitting existing buildings and by designing structures with durability, hazard resilience, and adaptability in mind in order to minimize the need for reconstruction.”

      This study answers a paradox in the field of sustainability. For the world to become more equitable, more development is necessary. And yet, that very same development may portend greater emissions.

      The MIT team found that isn’t necessarily the case. Even as America continues to use more concrete, the benefits of the material itself and the interventions made to it can make climate targets more achievable.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More