Data-Informed Modeling to Accelerate the Improvement of Sustainable Tech 

Video Summary: As reliance on solar and wind energy resources grows, additional technologies will be needed to ensure that energy demand is met reliably. Options include energy storage, backup generation, demand-side management, and transmission expansion. Micah Ziegler describes research that identifies strategies to improve these approaches, including efforts to evaluate energy storage technologies and understand the improvement of lithium-ion batteries.

Speaker: Micah S. Ziegler, Assistant Professor, School of Chemical and Biomolecular Engineering and School of Public Policy, Georgia Tech.

Bio: Micah S. Ziegler evaluates sustainable energy and chemical technologies, their impact, and their potential. His research helps shape strategies to accelerate the improvement and deployment of technologies that can enable a global transition to sustainable and equitable energy systems.

Micah Ziegler: Hi, my name is Micah Ziegler. I am an assistant professor in the School of Chemical and Biomolecular Engineering and in the School of Public Policy. I arrived at Georgia Tech in January, and today I wanted to introduce some of my research objectives and provide examples from my previous work.

Micah Ziegler: In this talk, I plan to discuss how we can accelerate the improvement of technologies that could help mitigate climate change. I will describe quantitative insights that can improve decision-making when people design technologies, decide what to research, craft public policies, and allocate investments.

Audience Participant: What was your trajectory, given your joint appointment in chemical and biomolecular engineering and public policy?

Micah Ziegler: As an undergraduate, I wanted to have an environmental impact. I was originally interested in environmental studies and law school, but I became increasingly interested in science and the technologies that underlie solutions to energy and environmental problems.

Micah Ziegler: I studied chemistry, worked on environmental issues in China and at the World Resources Institute, and later pursued a Ph.D. in chemistry. During my Ph.D., I studied artificial photosynthesis and chemical catalysis, then became interested in why some technologies succeed while others do not.

Micah Ziegler: At MIT, I studied energy systems and technological change, including how technologies improve and how we can understand and accelerate that improvement.

Audience Participant: Why did you choose Georgia Tech?

Micah Ziegler: One of the major reasons was the acceptance of who I was and the type of research I wanted to do. Georgia Tech was supportive of joint appointments and interdisciplinary research, which was important for my work.

Micah Ziegler: My research follows a pragmatic, data-informed approach. We have limited time and resources to reach environmental goals, so we need to improve how we spend that time and those resources to avoid the worst impacts of climate change and other environmental issues.

Micah Ziegler: The first step is understanding what we want from technologies in context. We ask how technologies need to operate to help society achieve energy, environmental, economic, and social goals. We use models and simulations to identify challenges, opportunities, and quantitative performance targets.

Micah Ziegler: The second step is identifying features of technology that could reach those targets. We aim to be technology agnostic, focusing on features such as energy efficiency, low greenhouse gas emissions, or low cost, rather than choosing specific technologies too early.

Micah Ziegler: The third step is investigating strategies to improve and deploy technologies, such as whether more research and development, manufacturing scale-up, or other approaches are needed. These strategies can then inform decision-makers across research, policy, and investment.

Micah Ziegler: One example of this work involves modeling renewable energy systems with storage. As solar and wind resources increase, we need technologies that can help match intermittent renewable supply with electricity demand. These include storage, backup power, transmission expansion, and demand-side management.

Audience Participant: Is this based on existing renewable generation capacity, or is it hypothetical?

Micah Ziegler: It is hypothetical. We ask what happens if installing more solar and wind helps achieve our goals, without constraining the model to existing capacity.

Micah Ziegler: We modeled systems that combine solar, wind, and storage to provide reliable electricity at the lowest possible cost. We examined different locations, storage costs, energy capacities, and output profiles.

Audience Participant: Are you trying to meet demand only with renewables?

Micah Ziegler: We allow other technologies to play a role, but because we are focusing on renewables and storage, we summarize the role of other technologies in a simplified way.

Micah Ziegler: We found that the cost of shaped renewable electricity is more sensitive to the cost of storage energy capacity than to the cost of storage power capacity. This suggests that reducing storage energy capacity cost is especially important.

Micah Ziegler: If renewables and storage are expected to provide cost-competitive electricity 100 percent of the time, storage energy capacity costs may need to fall below approximately $20 per kilowatt hour. If other technologies can help just 5 percent of the time, that target rises dramatically.

Audience Participant: What is the storage capacity cost today?

Micah Ziegler: It depends heavily on the technology. Lithium-ion stationary storage costs are a moving target, while pumped hydroelectric facilities can have low energy capacity costs but high power capacity costs. Battery costs continue to decline rapidly.

Micah Ziegler: We also found that rare but severe renewable shortage events can drive the size and cost of storage needed when systems rely heavily on renewables and storage.

Micah Ziegler: A second research example investigates why technologies change and improve. We focus on lithium-ion batteries because they are important for transportation electrification and increasingly for stationary storage.

Micah Ziegler: Lithium-ion battery cell prices fell by about 97 percent between 1991 and 2018, while energy density increased substantially. When both cost decline and energy density improvement are considered, lithium-ion batteries improved at rates comparable to solar photovoltaics.

Micah Ziegler: To understand why costs declined, we developed a bottom-up cost model for lithium-ion batteries. The model connects overall cell cost to engineering variables related to the cathode, anode, electrolyte, separator, current collectors, and manufacturing.

Micah Ziegler: We collected data from articles, corporate reports, legal filings, industry studies, government reports, and specification sheets. This allowed us to quantify the mechanisms that contributed to cost change over time.

Audience Participant: Are you looking at trends in order to project into the future?

Micah Ziegler: Not in this study. Here, the goal is to understand why the technology improved so rapidly, learn from that, and use those insights to inform future decisions.

Micah Ziegler: We found that the largest contribution to cost reduction came from increased cell charge density. Other major contributors included decreases in cathode material prices and increases in manufacturing plant size.

Micah Ziegler: At a higher level, public and private research and development contributed the most to cost reduction, followed by economies of scale. Learning by doing contributed comparatively little in this analysis.

Micah Ziegler: These findings suggest that research and development can remain important even after a technology has been commercialized. They also suggest that future battery chemistries should not focus only on input material prices, but should also preserve pathways to increase charge density and energy density.

Audience Participant: Is there a theoretical limit on charge density?

Micah Ziegler: There are thermodynamic, physical, and chemical limits, but they depend heavily on the materials and cell design. It is difficult to say whether we are close to a limit because different chemistries have different limits.

Audience Participant: How do you compare the benefits and costs of public and private R&D versus economies of scale?

Micah Ziegler: There may be ways to estimate that prospectively, but this study did not do that. We focused on identifying contributions to historical cost reduction.

Micah Ziegler: Another key conclusion is that access to diverse chemistries and materials may have enabled rapid improvement. Researchers were able to improve cathode, anode, and other components somewhat independently while still using the same basic battery architecture.

Micah Ziegler: I would like to acknowledge my collaborators, including Professor Jessika Trancik at MIT and Joe Song, as well as support from the Sloan Foundation, the MIT Office of Sustainability, and MIT Portugal.

Audience Participant: Does your research reveal where there might be benefits to exploring different manufacturing technologies for stationary and transportation batteries?

Micah Ziegler: We have not looked directly at that question, but stationary storage is less constrained by energy density than transportation. That may allow faster improvement in low-cost storage designed specifically for stationary applications.

Online Audience Participant: Why do batteries have low learning-by-doing rates compared to other renewable technologies, and how will battery recycling affect cost?

Micah Ziegler: When we considered improvement along multiple dimensions, lithium-ion batteries had learning rates comparable to solar photovoltaics. Battery recycling will play a role, but not a large one in the near term because there is a lag between when batteries are manufactured and when they become available for recycling.

Audience Participant: What is the outlook for battery recycling and for storage capacity on the energy grid?

Micah Ziegler: The amount of storage capacity needed depends heavily on assumptions about other technologies, including nuclear power, transmission, hydropower, natural gas with carbon capture, and cost trajectories over time.

Audience Participant: How do federal funding, policy changes, and renewable incentives factor into this analysis?

Micah Ziegler: We considered these factors, but it is difficult to separate public and private R&D contributions precisely. Lithium-ion battery development was driven significantly by private-sector demand from consumer electronics, as well as public-sector research.

Audience Participant: How does the global ecosystem affect battery development, and where does the United States fit?

Micah Ziegler: The United States is investing heavily through subsidies, tax credits, electric vehicle policies, and incentives for domestic battery manufacturing. These policies can influence company decisions around research, manufacturing, and deployment.

Audience Participant: What would happen if no more storage were introduced into the stationary energy system?

Micah Ziegler: The system would adapt by relying more on other options, such as expanded solar and wind capacity, transmission, backup generation, geothermal, and demand-side management. If storage were not available, innovation would likely shift toward those other technologies.

Moderator: Thank you so much. This has been very insightful.

Learning to LEED: Ecolabels, Innovation, and Green Market Transformation 

Video Summary: Learning to LEED explores the economics of information in the context of innovative energy technology adoption. By leveraging the marketing benefits of early adoption, early adopters help build supply chains and economies of scale while reducing uncertainty and risk associated with new technologies. Efforts such as demonstration projects, government procurement, and ecocertification can help accelerate sustainability transitions and transform markets.

Speaker: Daniel Matisoff, Professor, School of Public Policy, Georgia Tech; Director, Sustainable Energy and Environmental Management (SEEM) Master’s Program.

Bio: Daniel Matisoff’s research focuses on the intersection of corporate sustainability and energy policy. His work examines energy policy, technology adoption and diffusion, corporate sustainability strategy, and the economics of renewable energy.

Daniel Matisoff, Professor, School of Public Policy: We begin with the story of Mercedes-Benz Stadium, a LEED Platinum and zero-waste-certified facility often described as one of the greenest stadiums in the world. Projects like this illustrate how high-profile sustainability initiatives can help transform markets and influence future building practices.

One of the major sustainability challenges we face is deep decarbonization of the built environment. Buildings are responsible for roughly 40 percent of global carbon dioxide emissions through electricity use, heating, cooling, refrigeration, construction materials, and building operations. Buildings also influence water use, indoor air quality, human health, material selection, and waste generation.

Traditional environmental economics often assumes that getting prices right—such as through carbon pricing—will solve environmental problems. However, in the building sector there are many additional market failures and barriers that carbon pricing alone is unlikely to address.

Energy efficiency decisions are affected by information asymmetries, imperfect information, high transaction costs, uncertainty, risk, principal-agent problems, and highly localized knowledge. Building technologies often depend on specialized expertise that is not evenly distributed across regions or industries.

One example is chilled-beam HVAC technology, which was common in parts of Europe for decades before beginning to spread through Georgia Tech and the Atlanta area. Adoption depended not only on the technology itself, but also on local knowledge, experience, and confidence.

This observation led to a key insight: multiple interacting market failures often require multiple policy tools. Simply relying on market prices is unlikely to drive large-scale sustainability transitions.

Current policies such as the Inflation Reduction Act and the Bipartisan Infrastructure Law provide subsidies, incentives, grants, research funding, and demonstration projects. The question becomes how to structure these programs so that they not only support individual technologies, but also transform markets more broadly.

The motivating example for this research was Georgia Tech’s Kendeda Building for Innovative Sustainable Design. The building was designed to be a living laboratory and to transform the way buildings are designed and constructed in the southeastern United States.

This raised several research questions. Can ecolabeling programs transform markets? What mechanisms drive market transformation? What supporting policies make transformation more likely? And how can demonstration projects influence technology adoption?

To answer these questions, we examined the Leadership in Energy and Environmental Design (LEED) certification system developed by the U.S. Green Building Council. LEED is one of the most widely adopted green building certification programs in the world and provides a useful case study for understanding market transformation.

LEED uses a multi-tier certification system, including Certified, Silver, Gold, and Platinum levels. We found evidence that these certification thresholds create incentives for additional environmental investment. Organizations often make improvements beyond what would otherwise be economically justified in order to reach the next certification tier and gain the associated recognition.

This creates what we describe as a “race to the top,” where organizations compete to demonstrate environmental leadership. The certification itself becomes a marketing signal that encourages further adoption and investment.

Our analysis included more than 75,000 LEED-registered buildings and over 1,100 LEED pilot projects spanning two decades. These pilot projects provided a unique opportunity to examine how demonstration projects influence broader market adoption.

We found that the presence of a LEED pilot project approximately doubled the likelihood that similar LEED-certified projects would subsequently be developed in the same area. Pilot projects appear to reduce uncertainty, build local supply chains, lower transaction costs, and demonstrate the feasibility of innovative technologies.

Government projects and university-led projects were particularly influential because they often encouraged adoption by private-sector organizations. Demonstration projects created opportunities for contractors, architects, engineers, and developers to gain experience with new technologies and practices.

Another important finding was that market transformation appears to be cumulative. Areas with more green buildings tend to experience even more green building development over time. Rather than reaching saturation quickly, adoption appears to reinforce itself through knowledge sharing, supply chain development, and reduced costs.

Information-sharing networks also play a significant role. LEED-certified professionals, organizations, and firms participate in learning networks that spread information about technologies, design practices, and successful implementation strategies.

We observed that organizations operating in multiple locations frequently transfer knowledge from one project to another. A company that builds one highly certified green building often applies those lessons to future projects in different regions.

Public policy also provides important support. Federal, state, and local governments have implemented a variety of incentives, procurement requirements, and certification preferences that encourage green building adoption. Public-sector projects often serve as demonstrations that influence private-sector decisions.

These observations suggest that we should think of market transformation as a process that combines multiple tools. Research and development funding helps create technologies. Pilot and demonstration projects help prove them. Ecolabels provide market recognition and incentives for early adopters. Procurement programs create demand and help build economies of scale. Finally, subsidies and market incentives help move technologies into widespread adoption.

This framework helps explain how technologies can move through what is often called the “Valley of Death,” the difficult stage between laboratory success and widespread commercialization. Demonstration projects, ecolabels, procurement programs, and supportive policies can help bridge that gap.

Looking ahead, climate policy is increasingly moving toward what Nobel Prize winner Elinor Ostrom described as a polycentric approach. Rather than relying on a single global solution, progress comes through actions by governments, businesses, universities, communities, and individuals operating at multiple scales.

Future research should focus on understanding how different policy tools interact, how they should be sequenced, and how they can work together to accelerate sustainability transitions. Evaluating individual policies in isolation may miss the cumulative and reinforcing effects that occur when multiple tools are used simultaneously.

During the discussion period, participants explored topics including green building design costs, market diffusion, demonstration project effectiveness, mass timber construction, public procurement, green technology adoption, policy evaluation, and the role of the Kendeda Building as a catalyst for innovation in sustainable building design.

Daniel Matisoff: The central lesson is that sustainability transitions require more than pricing carbon. They require learning, experimentation, demonstration, information sharing, and coordinated policy tools that help innovative technologies move from early adoption to widespread market transformation.

Biologically-Inspired Design: What Can We Learn? 

Video Summary: This seminar explores how principles derived from natural systems can inform the design of more sustainable, resilient, and efficient human systems. Drawing from biology, ecology, and engineering, the presentation examines how 3.8 billion years of natural selection have generated strategies that can inspire innovations in technology, manufacturing, organizational design, and sustainability. Examples range from Velcro and whale-fin-inspired engineering to industrial ecology, ecological networks, resilience, and self-organizing systems.

Speaker: Marc Weissburg, Professor, School of Biological Sciences, Georgia Tech.

Marc Weissburg, Professor, School of Biological Sciences: Today I want to discuss biologically inspired design and what we can learn from nature when thinking about the sustainability and resilience of human systems. The central idea is that biological systems have been shaped by approximately 3.8 billion years of natural selection. In many ways, evolution functions as an optimization process that continuously tests and refines solutions to environmental challenges.

Biologically inspired design seeks to identify useful principles from nature and apply them to human problems. Rather than copying nature directly, the goal is to understand how biological systems solve challenges and adapt those solutions to engineering, technology, and organizational contexts.

One of the classic examples is Velcro. Swiss engineer George de Mestral became interested in the way burrs attached themselves to clothing and animal fur. By studying their microscopic hook structures, he developed the hook-and-loop fastening system that became Velcro. This illustrates how careful observation of biological structures can inspire technological innovation.

Another example comes from whale fins. Researchers studying humpback whales discovered that the bumps, or tubercles, on the leading edges of whale flippers improve hydrodynamic performance. These structures have inspired improvements in turbine blades, fans, and aerodynamic surfaces by increasing efficiency and reducing drag.

Many bio-inspired designs focus on physical structures or individual organisms, especially mammals. However, this focus is often too narrow. Nature provides lessons not only from individual organisms but also from entire ecosystems, networks, and communities.

A major challenge in biologically inspired design is broadening our perspective. We often concentrate on visible, charismatic species while overlooking the immense diversity of biological systems. Valuable insights may come from insects, microbes, plants, marine organisms, and ecological interactions rather than only from large animals.

Another limitation is that bio-inspired design frequently focuses on products and objects rather than systems. Yet many sustainability challenges involve networks of relationships, flows of materials, and interactions among multiple components. Ecosystems offer important lessons in these areas.

Ecological network analysis provides one framework for understanding these interactions. Food webs, for example, describe how energy and materials move through ecosystems. By examining the structure of these networks, we can identify principles that contribute to efficiency, stability, and resilience.

These ideas have applications in industrial ecology and industrial symbiosis. In nature, waste from one organism often becomes a resource for another. Ecosystems rarely produce true waste streams. Instead, materials are continuously recycled and reused through interconnected networks.

Industrial systems can adopt similar principles. By designing networks where the outputs of one process become inputs for another, industries can reduce waste, improve resource efficiency, and lower environmental impacts.

One example involves manufacturing systems that increase material cycling. Companies such as Interface have explored approaches that mimic ecological cycles by reusing materials, reducing waste, and creating more circular production systems.

Ecological network analysis allows us to evaluate how resources move through systems and identify opportunities to increase cycling and reduce losses. These methods provide a way to translate ecological principles into industrial and economic contexts.

Another critical lesson from biology concerns resilience. Natural systems often contain redundancy. Multiple species may perform similar ecological functions, and multiple pathways may exist for moving resources through a system.

Engineers frequently view redundancy as inefficiency because it introduces additional costs. However, biological systems demonstrate that redundancy can provide resilience when disturbances occur. Systems that are optimized solely for efficiency may become vulnerable to unexpected disruptions.

Research comparing biologically inspired network designs with traditional cost-minimized engineering designs has shown that biologically inspired structures often perform better under stress. They may be less efficient under ideal conditions but are more capable of maintaining function when components fail or conditions change.

This tradeoff between efficiency and resilience is an important consideration for sustainability. Highly optimized systems can become fragile, whereas systems with some redundancy may be better equipped to handle uncertainty and shocks.

Self-organization provides another powerful lesson from nature. Many biological systems achieve complex outcomes without centralized control. Individual organisms follow relatively simple rules, yet collectively generate sophisticated and adaptive behaviors.

Honeybee colonies are a well-known example. Individual bees respond to local information and interactions, but the colony as a whole efficiently allocates labor, responds to environmental changes, and makes collective decisions.

Similar principles can be applied to computational and organizational systems. Researchers have developed algorithms inspired by self-organizing biological systems to improve tasks such as server allocation, resource management, and distributed decision-making.

These approaches often outperform centralized systems in dynamic environments because they allow local adaptation and rapid responses to changing conditions.

A broader lesson from biologically inspired design is that nature often solves problems differently than traditional engineering. Rather than maximizing a single objective such as efficiency, biological systems balance multiple objectives simultaneously, including adaptability, robustness, resource conservation, and long-term persistence.

This perspective can help us rethink sustainability challenges. Instead of asking how to maximize efficiency alone, we can ask how to create systems that remain functional, adaptable, and resilient over long periods of time.

During the discussion period, participants explored practical applications of biologically inspired design, including industrial systems, resource management, infrastructure networks, and opportunities for implementing ecological principles in human-designed systems.

Questions focused on identifying “low-hanging fruit” where biological principles could be applied immediately, as well as the challenges of translating ecological concepts into engineering and policy contexts.

Marc Weissburg: The ultimate lesson is that nature offers a vast library of solutions developed through billions of years of experimentation. By studying not only organisms but also ecological systems, networks, and processes, we can discover new ways to design technologies and institutions that are more sustainable, resilient, and effective. Designing systems more like nature may help us address some of our most significant environmental and societal challenges.

Electric Vehicles in Households: A Decade of Benefits 

Video Summary: This seminar examines more than a decade of research into integrating electric vehicles (EVs), photovoltaic (PV) systems, home energy storage (HES), and household energy systems. Beginning with a collaboration with Ford Motor Company in 2012, the research explores the financial and greenhouse gas implications of electrified lifestyles, evaluates different utility rate structures, and investigates how EVs, solar energy, and home energy storage can work together to optimize household energy use.

Speaker: Bert Bras, Brook Byers Professor, George W. Woodruff School of Mechanical Engineering, Georgia Tech.

Bert Bras, Brook Byers Professor, Woodruff School of Mechanical Engineering: I've been working in sustainability for about thirty years, and this particular project has occupied our research group for more than a decade. The work began in 2012 through a collaboration with Ford Motor Company to investigate what they initially called the "Home of the Future" and later branded as "My Energy Lifestyle."

Traditionally, houses and automobiles have been designed independently. The home and the vehicle shared little more than a garage and the people who used them. Ford challenged us to think differently by asking what would happen if the home and vehicle became integrated energy systems sharing electricity as a common fuel source.

The original project brought together partners from multiple industries, including Ford, Whirlpool, SunPower, Eaton, and Infineon. The idea was to move beyond a vehicle-centric perspective and examine how electric vehicles, smart appliances, photovoltaic systems, and energy management technologies could function as an integrated system.

We developed a simulation framework called My Energy Lifestyle, or MEL, to compare conventional households with households adopting electric vehicles, solar energy, smart appliances, and advanced energy management systems.

Early analyses focused on California households and compared baseline homes using conventional appliances and internal combustion vehicles with upgraded homes featuring photovoltaic systems, smart appliances, and electrified transportation.

The results showed significant reductions in greenhouse gas emissions and energy consumption, particularly when gasoline-powered vehicles were replaced by electric vehicles. Vehicle electrification contributed a substantial share of the overall environmental benefits.

However, one of the first lessons we learned involved the rebound effect. In one demonstration project, a homeowner who received an energy-efficient retrofit commented that the savings allowed them to operate their hot tub more frequently. This illustrated how efficiency gains do not always translate directly into reduced energy consumption.

Over time, the MEL framework evolved through multiple generations. The project expanded to include direct current home systems, battery-electric vehicles, home energy storage, microgrids, climate-specific analyses, Chinese residential applications, and eventually more advanced optimization and control capabilities.

One early finding involved residential microgrids. While connecting multiple homes together produced some benefits, the gains were relatively modest compared with the benefits of installing solar energy systems and improving household efficiency measures at the individual home level.

More recent work focuses on developing an interactive decision-support tool that allows prospective electric vehicle owners to evaluate the financial and environmental impacts of integrating EVs, solar photovoltaics, and home energy storage systems.

The model incorporates utility rate structures, photovoltaic generation, electric vehicle charging behavior, battery storage systems, household energy consumption, weather data, and regional electricity emissions factors. Users can evaluate different configurations based on their location and energy usage patterns.

A key feature of the model is its ability to evaluate vehicle-to-home (V2H) strategies. In these systems, electric vehicles can potentially provide electricity to homes during peak pricing periods or outages.

However, the practical implementation of vehicle-to-home systems remains challenging. Most vehicles were not originally designed for bidirectional power transfer, and regulatory, certification, warranty, and safety issues remain important considerations.

The model also considers battery degradation. Every charging and discharging cycle contributes to battery wear, which creates an economic cost that should be included when evaluating vehicle-to-home energy strategies.

Simulations show that utility rate structures strongly influence optimal system design. Net-metering policies often favor larger photovoltaic systems because excess electricity can be exported back to the grid at favorable rates.

In contrast, wholesale buyback arrangements typically favor smaller photovoltaic systems paired with energy storage because exported electricity receives much lower compensation.

Results vary significantly across locations. Cities such as Phoenix benefit from abundant solar resources, while Atlanta and Portland produce different economic and environmental outcomes because of differences in climate, electricity prices, and grid carbon intensity.

One important finding is that bigger systems are not always better. Beyond certain sizes, additional photovoltaic capacity or storage may provide diminishing returns and lower overall cost effectiveness.

In many scenarios, a photovoltaic system of approximately five kilowatts combined with a moderate-sized storage system appears to offer a favorable balance between cost and performance.

The model also evaluates greenhouse gas reductions. Carbon savings vary considerably depending on the local electricity grid. Regions with carbon-intensive electricity generation often realize greater environmental benefits from solar deployment than regions already relying on low-carbon electricity sources.

Surprisingly, the economic benefits of vehicle-to-home systems are often relatively small. In many cases, annual savings amount to only a few hundred dollars. Once battery degradation and equipment costs are considered, vehicle-to-home strategies may not generate substantial financial returns under current market conditions.

Similarly, home energy storage systems frequently provide limited economic benefits unless they are paired with favorable electricity pricing structures or used primarily for backup power during outages.

One consistent conclusion is that photovoltaic systems generally deliver stronger returns than residential battery storage under current conditions, particularly where net metering remains available.

The research also suggests that future opportunities may exist in commercial applications and fleet management. Large organizations with electric vehicle fleets may be better positioned to take advantage of vehicle-to-building energy strategies because vehicles remain available during peak demand periods.

Future improvements to the model include better battery degradation modeling, more sophisticated optimization strategies, machine-learning-based forecasting, location-specific installation costs, and additional utility pricing structures.

During the discussion period, participants explored topics including distributed energy resources, bundled energy products, utility rate design, net metering, vehicle-to-grid applications, commercial fleet electrification, battery economics, thermal energy systems, and policy incentives.

A recurring theme was that sustainable household energy systems often deliver environmental benefits and some financial savings, but those savings are frequently smaller than many consumers expect. Policy structures, utility incentives, and local conditions remain critical factors influencing economic outcomes.

Bert Bras: The most important lesson is that these systems must be evaluated locally and holistically. What works in Phoenix may not work in Atlanta, and what works in Atlanta may not work in Portland. Sustainable energy decisions require integrated analysis that considers technology, behavior, utility rates, climate, and infrastructure together rather than evaluating each component in isolation.

Conserving the Fabric of Life in Global Change 

Video Summary: Climate change and human-driven landscape modification are reshaping ecosystems across the globe. This seminar explores how biodiversity responds to these interacting pressures and discusses emerging conservation strategies designed to help species persist on a rapidly changing planet. Drawing on ecology, conservation biology, paleontology, and spatial modeling, Jenny McGuire examines climate connectivity, biodiversity resilience, species movement, and the role of historical data in informing conservation decisions.

Speaker: Jenny McGuire, Associate Professor, School of Biological Sciences, Georgia Tech.

Jenny McGuire, Associate Professor, School of Biological Sciences: Biodiversity is fundamentally important to both natural systems and human societies. While many people recognize the ecological value of biodiversity, it can be difficult to quantify its full contribution to human well-being. It is even more difficult to quantify the ethical implications of human-caused extinctions affecting species that evolved over millions of years and might otherwise have continued to exist long into the future.

Biodiversity also plays a critical role in carbon sequestration. While forests are often highlighted as important carbon sinks, research increasingly demonstrates that all components of biodiversity contribute to carbon storage and ecosystem functioning.

Plants, animals, and entire ecological communities influence nutrient cycling, ecosystem productivity, fire regimes, vegetation structure, and carbon storage processes. Biodiversity contributes both directly and indirectly to the functioning of ecosystems that help regulate Earth's climate.

For example, large herbivores can influence plant productivity, which affects carbon sequestration. Animal communities can alter fire dynamics, nutrient cycling, and vegetation composition. These interactions help maintain the ecological systems that store carbon and support ecosystem resilience.

At the same time, biodiversity faces unprecedented challenges. Climate change is accelerating, while human modification of landscapes continues to expand. Urbanization, agriculture, roads, fences, and other forms of development increasingly transform natural environments.

Today, more than half of terrestrial land surfaces have been directly modified by humans, and most remaining landscapes experience some degree of human influence. Climate change and land-use change interact in complex ways to alter species distributions and ecosystem dynamics.

To address these challenges, conservation science has increasingly shifted from static approaches toward dynamic conservation strategies. Traditional conservation often focused on protecting specific places or species under the assumption that ecosystems would remain relatively stable through time.

However, climate change means that species ranges and ecological communities are increasingly dynamic. Conservation strategies must therefore accommodate movement and adaptation rather than simply preserving current conditions.

One important concept is climate connectivity. Climate connectivity refers to the degree to which landscapes allow species to move and track suitable climatic conditions as climates change.

Many species are expected to shift their geographic distributions in response to warming temperatures. However, movement may be limited by human-modified landscapes that create barriers between suitable habitats.

To investigate this issue, we examined climate connectivity across natural land areas throughout the contiguous United States. We asked whether species could move through connected natural landscapes to reach climates that remain suitable in the future.

Our analysis showed that only about 41 percent of natural areas currently provide sufficient climate connectivity for species to successfully track projected climate change. Human land-use patterns significantly limit movement opportunities.

We then explored scenarios in which connectivity among natural areas was improved. Under those conditions, approximately 65 percent of natural areas could achieve climate connectivity, demonstrating that landscape connectivity can substantially improve conservation outcomes.

These findings have influenced efforts to identify climate corridors and conservation priorities throughout the United States. Organizations such as The Nature Conservancy increasingly use connectivity and resilience frameworks to guide land acquisition and conservation planning.

Climate-resilient landscapes are another key concept. These are areas with high environmental heterogeneity that provide diverse microclimates, allowing species to persist locally even as broader climatic conditions change.

Conservation strategies increasingly focus on both protecting climate-resilient landscapes and connecting them through corridors that facilitate movement over larger distances.

To better understand how species respond to environmental change, we also look to the past. Climate and habitat distributions have changed dramatically over thousands of years, providing natural experiments that can inform conservation today.

Advances in paleoclimate reconstruction and large biodiversity databases now allow researchers to combine fossil records with environmental data to examine long-term responses to climate change.

Conservation paleontology uses fossil data to understand how species and ecosystems responded to previous environmental changes. These historical insights can help identify strategies for future conservation.

One major resource consists of extensive fossil and pollen databases containing millions of records spanning thousands of years. These datasets provide information about species distributions, community composition, and environmental conditions through time.

Using pollen records from hundreds of sites across North America, we examined how plant communities changed during the last 20,000 years. Pollen preserved in sediment cores allows researchers to reconstruct vegetation composition and ecosystem dynamics through time.

Our analyses revealed that plant communities have historically undergone substantial turnover. Forests and other ecosystems frequently transitioned between different biome types as climates changed.

On average, North American biomes transitioned every few hundred years, with turnover rates increasing during periods of more rapid climate change. These findings suggest that ecological change is a normal feature of dynamic climates.

We also examined whether plant species maintained climate fidelity through time. Climate fidelity refers to the tendency of species to occupy similar climatic conditions even as those conditions move across the landscape.

The results showed that most plant species have historically tracked climate quite closely. As climates shifted, species often moved to remain within their preferred climatic conditions.

This finding supports the idea that many plant species may need opportunities for movement if they are to persist under future climate change.

We then extended this work to mammals using fossil records and species distribution data. Mammals showed more complex patterns, with some species shifting away from climates increasingly associated with human land use while others expanded into human-dominated environments.

Larger mammals such as bison, bears, and pumas often exhibited contractions or shifts away from human-modified landscapes. Smaller mammals frequently expanded into urban and agricultural environments that provide novel habitats and resources.

These findings suggest that species differ substantially in their responses to anthropogenic change. Some taxa are highly sensitive to habitat fragmentation, while others are more capable of adapting to human-modified environments.

This variation highlights the need for conservation strategies that consider species-specific vulnerabilities rather than assuming all species respond similarly to environmental change.

Locally, these ideas have implications for urban planning and ecological connectivity. The Atlanta region has begun incorporating connectivity concepts into planning efforts aimed at preserving ecological function and biodiversity.

Our research group is also conducting biodiversity surveys and ecological monitoring projects within the Atlanta region. Camera-trap studies at locations such as Stone Mountain reveal surprisingly rich biodiversity, including deer, coyotes, flying squirrels, opossums, armadillos, river otters, beavers, and numerous bird species.

These observations demonstrate that urban and suburban green spaces can support substantial biodiversity if appropriate habitat and connectivity are maintained.

Future research will continue investigating climate fidelity, species vulnerabilities, ecological connectivity, and the traits that enable species to persist under changing environmental conditions.

During the discussion session, participants explored topics including species adaptation, climate refugia, assisted migration, rewilding, conservation planning, urban biodiversity, social dimensions of conservation, and the integration of ecological and community knowledge.

Questions also addressed how species adapt to changing climates, whether humans should actively relocate species to suitable habitats, and how historical ecological information can help inform future conservation decisions.

Jenny McGuire: Conserving biodiversity in a rapidly changing world requires moving beyond static conservation approaches. By identifying climate-sensitive species, understanding vulnerabilities, promoting connectivity, and learning from the past, we can develop strategies that help the fabric of life persist and thrive despite the challenges of climate change and human landscape modification.

Data-Driven Waste Valorization Processes for Biochemicals and Functional Biomaterials 

Video Summary: This seminar explores the conversion of low-cost bio-based waste resources into value-added functional biomaterials, biochemicals, and renewable fuels. Julene Tong discusses research focused on waste valorization, sustainable materials, food safety technologies, lignin conversion, circular bioeconomy strategies, and the use of machine learning and process optimization to improve bioprocess performance under feedstock uncertainty.

Speaker: Julene (Julian) Tong, Associate Professor, School of Chemical and Biomolecular Engineering, Georgia Tech.

Julene Tong, Associate Professor, School of Chemical and Biomolecular Engineering: My research focuses on sustainability and the development of technologies that convert waste materials into valuable products. As we think about climate change, decarbonization, food security, water security, and resource sustainability, it becomes increasingly important to consider how we use and reuse available resources.

One major opportunity lies in bio-based waste streams. The United States generates hundreds of thousands of metric tons of solid waste every day, including agricultural residues, forestry residues, food waste, paper products, plastics, and many other materials that contain potentially valuable resources.

Much of this waste contains useful components such as cellulose, lignin, proteins, fatty acids, starches, and other compounds that can serve as feedstocks for value-added products. Rather than treating these materials as waste, we can view them as resources for a circular bioeconomy.

My research group works on two major themes. The first involves developing functional biomaterials from renewable resources. The second focuses on converting biomass and waste streams into platform chemicals, fuels, and other value-added products.

Within the biomaterials area, we have developed projects involving ultraviolet-protective greenhouse films, smart food packaging, nanoscale biomaterials, environmental remediation materials, sustainable fertilizers, and nutrient recovery technologies.

One example involved a NASA-funded project focused on greenhouse films for potential future use on Mars. Because ultraviolet radiation levels on Mars are significantly higher than on Earth, we developed UV-protective coatings derived from renewable materials that could help protect crops grown in extraterrestrial environments.

Another major area of research involves intelligent food packaging systems. These systems use bio-based materials and sensing technologies to provide real-time information about food freshness and spoilage.

A key challenge in food safety is determining freshness quickly, accurately, and affordably. Existing methods often require laboratory analysis, bacterial testing, chemical measurements, or time-consuming procedures that are impractical for everyday consumers.

To address this challenge, we developed a pH-sensitive smart packaging film derived from glycerol, a major byproduct of biodiesel production. Approximately one-third of biodiesel production results in glycerol, creating an abundant and inexpensive feedstock.

Using glycerol-derived materials, we synthesized pH-sensitive nanostructures capable of encapsulating food-safe dyes. These structures respond to pH changes associated with food spoilage and release color indicators that provide visual signals of freshness.

The resulting films exhibit highly sensitive responses to small pH changes. Unlike many existing freshness indicators that only detect large changes, our materials can identify subtle variations that occur during early stages of spoilage.

These materials can be incorporated into packaging films and used either through direct food contact or by detecting volatile compounds released during spoilage.

To improve usability, we combined these films with QR-code-based sensing systems. Standardized color references are included alongside the sensing film, allowing smartphone applications to compensate for variations in lighting conditions and environmental factors.

Machine learning algorithms then analyze color changes over time and classify food freshness levels based on calibrated models.

Using pork as a test case, we developed databases linking storage time, pH, temperature, and visual indicators. The system enables rapid freshness assessment using only a smartphone and a low-cost sensing label.

Future work aims to expand these databases to include additional food types, improve machine learning models, and incorporate arrays of sensing dyes capable of detecting multiple spoilage indicators simultaneously.

Beyond food packaging, another major area of research focuses on lignin valorization. Lignin is one of the most abundant renewable sources of aromatic compounds in nature, yet much of it is currently underutilized and often burned as a low-value energy source.

We are developing methods for converting lignin into valuable aromatic chemicals that can serve as precursors for plastics, fuels, and specialty chemicals. This work is particularly important because global demand for aromatic compounds continues to increase.

Traditional lignin depolymerization methods often require harsh reaction conditions, expensive catalysts, and produce complex mixtures that are difficult to separate. Our goal is to develop more selective and efficient conversion pathways.

One approach uses graphene oxide and environmentally friendly oxidants to selectively break specific chemical bonds within lignin while preserving aromatic ring structures. This enables production of valuable aromatic acids and related compounds.

Through optimized reaction conditions, we have achieved high conversion rates and substantial yields of aromatic products while minimizing waste generation.

A growing focus of our work involves incorporating data science and machine learning into waste valorization systems. Biomass feedstocks are inherently variable because they originate from diverse sources, seasons, and environmental conditions.

Feedstock variability creates challenges for process control and product consistency. Traditional process engineering approaches often struggle to accommodate this level of uncertainty.

To address this issue, we are developing reinforcement-learning-based control systems for bioprocesses. One example involves anaerobic digestion systems that convert organic waste into biogas.

In these systems, feedstocks such as food waste, agricultural residues, and municipal waste vary significantly in composition. We use machine learning models to estimate feedstock characteristics and optimize operating conditions in real time.

Reinforcement learning algorithms evaluate process performance and adjust feedstock ratios to maintain stable biogas production while minimizing storage issues and process disruptions.

By integrating process models, feedstock uncertainty, and machine learning control strategies, we can improve resilience and efficiency in waste conversion systems.

More broadly, this work illustrates how data-driven approaches can help transform waste valorization processes from static operations into adaptive systems capable of responding to changing conditions.

The long-term vision is a circular bioeconomy in which agricultural residues, food waste, municipal waste, and other renewable resources are continuously converted into valuable chemicals, materials, fertilizers, fuels, and environmental products.

During the discussion session, participants explored topics including food waste reduction, food supply chains, commercialization opportunities, machine learning applications, smart packaging, military food storage systems, consumer acceptance, environmental impacts, and regulatory considerations.

Additional questions focused on whether freshness monitoring technologies could reduce food waste, how food producers might respond to greater transparency about spoilage, and the role of government agencies in supporting deployment of these technologies.

Julene Tong: Waste should not be viewed simply as a disposal problem. Through innovative materials, chemical conversion technologies, machine learning, and systems engineering, we can transform waste streams into valuable resources that support sustainability, reduce environmental impacts, and contribute to a circular bioeconomy.

Reimagining Computing for Sustainability

Video Summary: Computing technologies play a growing role in both contributing to and addressing sustainability challenges. This seminar examines the environmental footprint of computing, from data centers and cloud infrastructure to mobile devices and the Internet of Things. Josiah Hester discusses emerging research focused on reducing computing's environmental impact through sustainable hardware design, battery-free devices, energy harvesting, and community-centered cyberinfrastructure that supports environmental monitoring and climate resilience.

Speaker: Josiah Hester, Assistant Professor, School of Interactive Computing and School of Computer Science, Georgia Tech.

Josiah Hester, Assistant Professor, College of Computing: Today I want to talk about sustainability and computing, both in terms of how computing contributes to environmental challenges and how computing can help address them. My work spans computer engineering, embedded systems, interactive computing, and sustainable computing systems.

Climate change and environmental degradation are among the defining challenges of our time. While mitigation and adaptation efforts are increasing, many climate impacts are already being felt by communities around the world. This creates an urgent need for new approaches that can help reduce environmental impacts while improving resilience.

Computing plays an important role in this conversation. Increasingly, every aspect of society depends on computational systems, including data centers, cloud computing, mobile devices, sensors, scientific computing, weather forecasting, artificial intelligence, and communication networks.

Computing infrastructure contributes significantly to global carbon emissions. Estimates suggest that the computing sector's environmental footprint is comparable to a substantial fraction of the aviation industry's emissions, and demand continues to grow rapidly.

The growth of computing is driven by multiple factors, including expanding internet access, increasing numbers of connected devices, growing data demands, artificial intelligence applications, and the continued deployment of Internet of Things technologies.

At the same time, improvements in computing efficiency are becoming more difficult to achieve. For decades, advances in semiconductor technology delivered automatic gains in efficiency and performance. Today, those gains are slowing, making sustainability challenges even more important.

To understand computing's environmental impact, it is useful to think about both operational energy consumption and embodied carbon. Operational energy includes the electricity used while devices and data centers are running. Embodied carbon includes emissions associated with manufacturing, transportation, and material extraction.

For consumer electronics such as phones and laptops, embodied carbon often dominates the total footprint. Manufacturing these devices requires significant energy and resources, making device longevity and reuse particularly important.

Data centers present a different challenge. In data centers, operational energy consumption often exceeds manufacturing impacts because systems operate continuously for years while processing enormous quantities of information.

The rapid growth of connected devices introduces another sustainability challenge. Industry projections suggest that billions, and potentially trillions, of Internet of Things devices may eventually be deployed worldwide.

Traditional approaches rely heavily on batteries to power these devices. However, batteries create challenges related to manufacturing, replacement, maintenance, recycling, and environmental impacts.

Much of my research focuses on rethinking this model by developing battery-free computing systems powered through energy harvesting.

Energy harvesting involves collecting small amounts of energy from environmental sources such as sunlight, movement, vibration, radio frequency signals, wind, or other naturally available sources. Instead of relying on large batteries, devices operate using harvested energy stored in small capacitors.

This creates a new challenge because harvested energy is intermittent. Devices frequently lose power and restart. Traditional computing systems are not designed for this environment.

Our work develops hardware and software systems that continue functioning despite frequent power interruptions. We call this intermittent computing.

By eliminating batteries and designing systems to tolerate interruptions, we can create devices that potentially operate for decades with little maintenance. This approach can significantly reduce environmental impacts while expanding access to computing in difficult environments.

We describe these systems as sustainable computational things. The goal is to create computing devices that are resilient, maintainable, energy-efficient, and useful throughout their entire operational lifetime.

Examples include battery-free sensors, wearable devices, environmental monitoring systems, educational technologies, and interactive computing platforms.

One project developed a battery-free Game Boy powered through user interaction. The device serves as both a research platform and a way to encourage people to think differently about energy and computing.

Other projects focus on wearable systems that harvest energy while monitoring health indicators such as respiration, movement, or physiological signals.

We have also developed educational platforms that help students and communities learn about sustainable computing through hands-on experiences with battery-free technologies.

A major area of our work involves environmental monitoring and climate resilience. We collaborate with Indigenous communities and environmental organizations to develop cyberinfrastructure that supports ecosystem monitoring and conservation efforts.

One example is our work with Indigenous communities in the Great Lakes region. These communities are deeply connected to wild rice ecosystems that hold ecological, cultural, and subsistence importance.

Wild rice habitats are increasingly threatened by climate change, development, changing hydrology, and other environmental pressures. Understanding these changes requires long-term monitoring and data collection.

We are developing networks of battery-free sensors capable of collecting environmental data over long periods with minimal maintenance requirements.

These systems gather information about water conditions, vegetation, wildlife activity, weather patterns, ice cover, and ecosystem dynamics. The resulting data supports both scientific research and community decision-making.

By combining environmental sensing, remote sensing technologies, machine learning, mobile applications, and community engagement, we can create tools that support conservation and climate adaptation efforts.

This work demonstrates how computing can move beyond simply consuming resources and instead become a tool for environmental stewardship, community empowerment, and sustainability.

Beyond small devices, significant efforts are also underway to improve sustainability within large-scale computing systems. Researchers are exploring carbon-aware scheduling, renewable-energy-powered computing, specialized processors, improved data center efficiency, and longer equipment lifetimes.

These approaches recognize that sustainability challenges require changes across the entire computing stack, from materials and hardware to software, infrastructure, and policy.

During the discussion session, participants explored topics including field-based computing research, climate resilience, carbon accounting, embodied emissions, energy harvesting, biodegradable electronics, device longevity, indigenous partnerships, sustainable cyberinfrastructure, and the future of low-power computing systems.

Questions also focused on balancing durability with technological obsolescence, opportunities for deploying sustainable sensor networks at Georgia Tech, and how computing can help communities respond to climate-related challenges.

Josiah Hester: Computing is both part of the sustainability challenge and part of the solution. By reimagining how we design, deploy, and use computing systems, we can reduce environmental impacts while creating powerful tools that help communities understand and respond to climate change. There is still much work to do, but there are many reasons to be optimistic about what sustainable computing can achieve.