The market for oil is global, which is why events like the war in Iran affect oil prices – and prices of the wide range of products made from oil – literally everywhere. Federal data shows that the price at the primary crude oil hub in the U.S. was US$66 a barrel in late February 2026 – before the U.S. and Israel attacked Iran – and $101 a barrel on April 13. Similar price increases have reverberated around the globe.

As an energy economist and an international trade economist, we field a lot of questions during such episodes, because when oil prices go up, manufacturers, businesses and ultimately consumers pay more.

Some basic economics

Crude oil may be the most important commodity in the global economic system.

It’s a literal fuel for the industrial economy. It powers the engines that drive transportation and paves the roads vehicles drive on. It’s a source for plastics from which the world’s products get made and packaged, and a key ingredient at some point in almost every supply chain. Even fertilizers that boost the food supply are made from it. In short, it is difficult to imagine modern life without oil and its derivatives.

And when its supply changes, its price changes. Economists explain this using a fundamental model of our field: the supply-demand diagram. When there’s less of something to go around, competition among consumers who want it and companies that need it can drive the price up.

Sometimes this process can play out over time, allowing people to adjust their purchasing or activities to dampen price shocks. But when a significant source of the world’s oil is effectively blocked without much advance notice, such as when the the U.S. and Israeli attacks on Iran closed the Strait of Hormuz, prices can rise sharply in a short period of time.

A natural question many people ask when oil prices spike is: Where does all that additional money go, and who benefits from it?

Some people have written entire books dissecting all the places that money goes when it leaves consumers’ pockets. But ultimately, the bulk of the money heads in the direction of the source of the oil itself – the oil companies.

What they do with the money varies widely, depending on where in the world an oil company is operating and who owns it. What also matters is the business environment – the set of laws and regulations – in which the company operates.

Middle East faces danger

Oil producers in the Middle East face significant new risk because of the war in Iran, including threats to production, processing locations and shipping routes. These risks raise their costs for insurance, security and transportation.

But production costs in the region are relatively low, so higher global oil prices typically still translate into strong profits.

For a major exporter such as Saudi Arabia, the government owns and controls nearly all oil production, so high prices generally benefit the government’s finances and investments, even during a war. In Saudi Arabia, oil revenue has historically been used to fund public spending.

West Texas gets a windfall

The Permian Basin, the largest oil field in the U.S., is a long way from the Persian Gulf. When global oil prices rise because of the war in Iran, oil companies operating in West Texas effectively get a windfall gain: Prices rise more quickly than costs, at least in the short run.

The immediate effect is more income from higher prices. The money largely goes to company owners – meaning shareholders – through dividends, debt reduction, company-backed purchases of its own stock, and reinvestment in drilling and production. Over time, companies may decide to spend some of that windfall on building more production capacity or pipelines to get more oil and gas to market.

North Sea boosts government revenue

In the North Sea, between the island of Great Britain and Scandinavia, a mix of multinational and government-owned companies produce most of the oil.

In the U.K., private shareholders are the primary beneficiaries of higher profits from increased oil prices, though an additional tax on oil and gas companies’ profits means the government also collects a significant share of the money, which it uses to help pay public expenses.

In Norway, oil revenues flow into the Government Pension Fund Global, the world’s largest sovereign wealth fund, valued at over $2 trillion. Laws govern how much, and for what purposes, money can be withdrawn from the fund, supporting public spending and preserving wealth for future generations. This is a similar model to Alaska’s state-owned program, funded by oil revenue, that pays for government services and sends an annual dividend to every permanent resident.

Russian oligarchs get rich

Russian oil is subject to stringent economic sanctions imposed by major industrial countries as a response to the Russian invasion and occupation of parts of Ukraine. While the U.S. cannot control how much Russia charges for its oil, it can control services needed to move Russian oil around the world. Under current price sanctions, Western shipping, insurance and financing can be used to ship and sell Russian crude oil only if the price is below $60 per barrel.

Russia’s oil industry is dominated by government-controlled companies whose leaders maintain close ties to President Vladimir Putin. The dealings of those shadowy figures are often shrouded in secrecy, but it is likely that they and Putin’s military-industrial complex – not the Russian people – are the main beneficiaries of high oil prices.

What this means for you

Everyday U.S. consumers may not like the idea of their hard-earned cash going into the already deep pockets of any of these groups. But in the short run, there’s not much to do but pay the price. For the long run, however, people around the world are already thinking and talking about, and opting for, sources of energy that don’t depend on fossil fuels.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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The U.S. Energy Information Administration expects nationwide retail gasoline prices to average near US$4.30 a gallon for April 2026 – the highest monthly average of the year. The political response has been familiar. Georgia has suspended its state gas tax, other states are weighing their own tax holidays, and the White House has issued a temporary waiver of a law known as the Jones Act in hopes of moving more domestic fuel to East Coast ports.

As an energy economist, I am often asked about what contributes to gas prices and what different policies can do to affect them.

The price of a retail gallon of gas is the sum of four things: the cost of crude oil, refining, distribution and marketing, and taxes.

In nationwide figures from January 2026, crude oil accounted for about 51% of the pump price, refining roughly 20%, distribution and marketing about 11% and taxes about 18%. That mix shifts with conditions: When crude oil prices spike, that can drive more than 60% of the price; when the price drops, taxes and logistics are larger shares of the cost.

Crude oil is the biggest ingredient

Because the price of crude oil is the largest element, most of the price at the pump is derived from the global oil market.

Usually, big swings in crude prices come mainly from shifts in global demand and expectations – not from supply disruptions, according to widely cited research in 2009 by the economist Lutz Kilian.

But what is happening in early 2026 with the war in Iran is one of the exceptions: a classic supply shock. Severe disruptions to shipping through the Strait of Hormuz and attacks on Middle East oil infrastructure have taken millions of barrels a day off the global market.

Most drivers generally can’t quickly reduce how much they drive or how much gas they use when prices rise, so gasoline demand doesn’t change much in the short run. That means a jump in crude costs tends to result in people paying more rather than driving less.

Refining, regulations and the California puzzle

Refining turns crude into gasoline at industrial scale. The U.S. doesn’t have a single gasoline market, though. Roughly a quarter of U.S. gasoline is a cleaner-burning blend of petroleum-derived chemicals called “reformulated gasoline,” which is required in urban areas across 17 states and the District of Columbia to reduce smog.

California uses an even stricter formulation that few out-of-state refineries make. California is also geographically isolated: No pipelines bring gasoline in from other U.S. refining regions.

California’s gasoline prices have long run above the national average, explained in part by higher state taxes and stricter environmental rules. But since a refinery fire in Torrance, California, in 2015 reduced production capacity, the state’s prices have been about 20 to 30 cents a gallon higher than what those factors would indicate.

Energy economist and University of California, Berkeley, professor Severin Borenstein has called this the “mystery gasoline surcharge” and attributes it to the fact that there isn’t as much competition between refineries or gas stations in California as in other states. California’s own Division of Petroleum Market Oversight says the surcharge cost the state’s drivers about $59 billion from 2015 to 2024. It’s not exactly clear who is getting that money, but it could be gas stations themselves or refineries, through complex contracts with gas stations.

Getting the gas into your car

The distribution and marketing category covers the costs of everything involved in getting the gasoline from the refinery gate to your tank.

Gasoline moves by pipeline, ship, rail and truck to wholesale terminals, and then by local delivery truck to service stations.

At the retailer’s end, the key factors are station rent and labor, the cost to buy gasoline in bulk to be able to sell it, credit card fees of as much as 6 to 10 cents a gallon at current prices, and franchise fees paid to the national brand, such as Sunoco or ExxonMobil, for permission to put their branding on the gas station.

Most gas station operators net only a few cents per gallon on fuel itself – which is why many gas stations are really convenience stores with pumps out front. Borenstein and some of his collaborators have also documented that retail gas prices rise quickly when wholesale costs climb but fall slowly when wholesale costs drop.

The question of gas tax holidays

The federal government charges a tax on fuel, of 18.4 cents a gallon for gasoline and 24.3 cents a gallon for diesel. States charge their own taxes, ranging from 70.9 cents a gallon for gas in California to 8.95 cents in Alaska.

When gas prices rise, many politicians start talking about temporarily suspending their state’s gas tax. That does reduce prices, but not as much as politicians – or consumers – might hope. Research on past gas tax holidays has found that consumers get about 79% of the reduction in gas taxes. That means oil companies and fuel retailers keep about one-fifth of the tax cut for themselves rather than passing that savings to the public.

Gas tax holidays also reduce funding for what the taxes are designed to pay for, typically roads and bridges. That pushes road and bridge upkeep costs onto future drivers and general taxpayers.

There is an additional problem, too: Taxes on gasoline are supposed to charge drivers for some of the costs their driving imposes on everyone else – carbon emissions, local air pollution, congestion and crashes. But Borenstein has found that U.S. fuel tax levels are already far below the true cost to society. Removing the tax on drivers effectively raises the costs for everyone else.

The Jones Act: A small number that adds up

The 1920 Jones Act is a federal law that requires cargo moving between U.S. ports to travel on vessels built and registered in the U.S., owned by U.S. citizens, and crewed primarily by U.S. citizens and permanent residents. Of the world’s 7,500 oil tankers, only 54 meet this requirement. Only 43 of these can transport refined fuels such as gasoline.

So, despite significant refining capacity on the Gulf Coast, some U.S. gasoline is exported overseas even as the Northeast imports fuel, in part reflecting the relatively high cost of moving fuel between U.S. ports.

Economists Ryan Kellogg and Rich Sweeney estimate that the law raises East Coast gasoline prices by about a penny and a half per gallon on average, costing drivers roughly $770 million a year. In light of the war’s effect on gas prices, the Trump administration has temporarily suspended the Jones Act requirements – an action more commonly taken when hurricanes knock out Gulf Coast refineries and pipeline networks.

What moves the number

The result of all these factors is that the price that drivers see at the pump mostly reflects the global price of crude, plus a stack of domestic costs, only some of which are inefficient.

Tax holidays give a partial, short-lived rebate. Jones Act waivers trim pennies, though permanent repeal may cause more fundamental changes, such as reduced rail and truck transport of all goods, which could lower costs, emissions and infrastructure damage associated with cargo transportation. Harmonizing fuel blends across states and seasons may lower prices somewhat, but likely at the expense of increased emissions.

Ultimately, the best protection against oil price shocks is a more efficient gas-burning vehicle, or one that doesn’t burn gasoline at all. In the meantime, the best I can offer as an economist is clarity about what that $4.30 actually buys.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Investment is the best word that summarizes Agam Shah’s journey as a graduate student at Georgia Tech.

That is clearest on the surface, where Shah studied how public statements by businesses and financial institutions shape market behavior. At a deeper level, though, his success was buoyed by support from professors and his mentorship of younger students.

Shah’s ability to connect and invest in others led him to partner with Georgia Tech colleagues and start a financial technology business. He returns to campus this week to officially graduate from Tech, giving us a chance to catch up about his grad school experience and life as an entrepreneur.

Graduate: Agam Shah

Research Interests: Quantitative and computational finance, artificial intelligence, natural language processing, large language models (LLMs)

Education: Ph.D. in Machine Learning, home unit in the School of Computational Science and Engineering (CSE)

Faculty Advisors: Scheller College of Business Professor Sudheer Chava and School of CSE Associate Professor Chao Zhang

What persuaded you to attend graduate school at Georgia Tech?

Georgia Tech’s dedicated College of Computing strongly appealed to me. I was particularly drawn to the interdisciplinary nature of its machine learning Ph.D. program and the School of Computational Science and Engineering, both of which align well with my research interests. 

What research project(s) from Georgia Tech are you most proud of and why?

I am proud of all 20-plus research papers I have had the opportunity to contribute to at Georgia Tech. However, if I had to choose one, it would be my work on Federal Open Market Committee (FOMC) text analysis, which was also highlighted in the news. 

This work is not only well-cited in academic literature, but the language model developed in the paper is also actively used by economists at many of the world’s top central banks, including researchers at the FOMC and the Bank of England. It is also used by leading financial institutions such as BlackRock and Daiwa Securities. Since its release, the model has achieved over 100,000 downloads on Hugging Face. 

What can you tell us more about your startup, ZettaQuant?

 ZettaQuant aims to solve one of the biggest challenges in using LLMs and agents: working effectively with massive underlying datasets. We serve as a layer between raw data and LLMs, helping distill billions of tokens into the relevant context that models can use. 

As a deep-tech startup, we are actively engaging with industry practitioners to better understand how to design and engineer our system to integrate seamlessly with their evolving AI workflows. Given the complexity of the problem we are tackling, particularly in advancing document intelligence systems, we are currently very focused on research and foundational development. 

How did your Georgia Tech education prepare you for starting ZettaQuant?

Not just my education, but my entire experience at Georgia Tech, extending beyond the classroom, prepared me for this journey. I met my co-founders at Georgia Tech, and many of the initial use cases we are exploring at ZettaQuant are built on open-source research I conducted there. 

In addition to research, I mentored more than 300 students through the Vertically Integrated Project “NLP for Financial Markets.” This experience taught me how to manage teams and think about building systems with a long-term vision. 

What advice would you give someone interested in graduate school?

 Most people pursue graduate school after already completing more than 15 years of education. Also, people who are admitted to a top school like Georgia Tech are often already well-positioned to secure strong job opportunities. So, graduate school should provide value beyond what you could learn outside the classroom. 

Before deciding, think carefully about what you hope to gain from graduate school that you cannot otherwise. Once you enroll, take full advantage of the faculty, research labs, networks, and seminars. Many students underutilize these opportunities during their undergraduate and graduate years. 

I would also like to quote the epilogue of my Ph.D. thesis: ‘Advice is abundant; conviction must be your own.’ Build a strong conviction about what you want to achieve from graduate school before committing to it. 

What did you do for fun and relaxation while attending Georgia Tech? Do you still keep up with these now?

 This may sound unconventional, but I spent a significant amount of time mentoring and teaching throughout my Ph.D. Many of my mentees went on to gain admission to top graduate programs. This included two students I mentored for all four years of their undergraduate studies who later joined the ML Ph.D. program at Georgia Tech. They are now teaching and mentoring students, completing a full-circle journey. 

Working with mentees and supporting their growth gives me a strong sense of fulfillment and serves as a form of relaxation. In addition, I enjoy listening to music, especially while coding, and I continue to do that today. 

What is your favorite Georgia Tech memory?

 If I had to choose one favorite memory, beyond the many exciting late nights in the lab, it would be proposing to my wife on Tech Green at Georgia Tech. She is also a Yellow Jacket, having completed her undergraduate degree here and currently pursuing her Ph.D. Our home truly is a hive of Yellow Jackets. 

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The Energy Policy and Innovation Center (EPIcenter) at Georgia Tech has awarded funding to a new cohort of faculty through its ACCELERATE program, an initiative designed to strengthen Georgia Tech’s thought leadership and real‑world impact in energy policy, decision‑making, and innovation across the Southeast. 

Eight faculty members received funding for projects that advance Georgia Tech energy research by generating early insights, expanding shared research tools, and exploring solutions related to energy policy, grid reliability, clean energy incentives, and industry‑driven innovation shaping Georgia’s energy future.

By supporting timely, policy-relevant research and engagement that connect Georgia Tech expertise with pressing regional energy challenges, the ACCELERATE program encourages collaboration across the Institute and with external partners, supports graduate student involvement, and amplifies research outputs that inform policy, regulatory, and market decisions. 

“ACCELERATE is designed to help early- and mid-career faculty move quickly on ideas that can shape energy policy and practice,” said Laura Taylor, director of EPIcenter. “By supporting both early‑stage collaboration and more developed policy research, the program enables Georgia Tech researchers to engage decision‑makers and stakeholders when it matters most.”

Proposals considered for funding were grounded in policy and behavioral research, including studies that examined how past or potential policies and regulations worked, and analyses of current market and behavioral outcomes that revealed management, policy, or regulatory gaps and opportunities.  

Funded projects span a range of disciplines and policy‑focused topics aligned with EPIcenter’s mission, with a strong emphasis on challenges facing Georgia and the Southeast. Collectively, the awards support research development, data creation, stakeholder engagement, and public-facing thought leadership intended to inform energy policy and implementation.

"As electricity demand grows, it is increasingly important to understand how industrial processes could use energy flexibly to enable efficient use of renewable resources like solar and wind,” said Micah Ziegler, assistant professor in the School of Chemical and Biomolecular Engineering and the Jimmy and Rosalynn Carter School of Public Policy. “Support from the EPIcenter ACCELERATE program enables us to ask fundamental questions about how to design flexible systems and supply chains."

Awards ranged from $5,000 to $75,000. Projects that received ACCELERATE funding include:

Measuring the Alignment Between Legislators’ Energy Bill Votes and Their District Characteristics in the Georgia House of Representatives
Faculty Researcher: Clio Andris, Associate Professor, School of City and Regional Planning and School of Interactive Computing

Strengthening Georgia Tech’s National Energy Modeling of Priority Research Areas
Faculty Researcher: Marilyn Brown, Regents' Professor and Brook Byers Professor of Sustainable Systems, Jimmy and Rosalynn Carter School of Public Policy

Protecting Consumers From Price Volatility: Evidence and Policy Lessons From Georgia's Natural Gas Market
Faculty Researcher: Dylan Brewer, Assistant Professor, School of Economics

Can Place-Based Incentives Accelerate the Energy Transition?
Faculty Researcher: Gaurav Doshi, Assistant Professor, School of Economics

The Revolving Door in Utility Regulation
Faculty Researcher: Michelle Graff, Assistant Professor, Jimmy and Rosalynn Carter School of Public Policy 

How Do Data Centers Affect Tradeoffs Between Reliability and Decarbonization?
Faculty Researchers: Tony Harding, Assistant Professor, Jimmy and Rosalynn Carter School of Public Policy, and Brian An, Assistant Professor, Jimmy and Rosalynn Carter School of Public Policy

Calculating the Emissions Cost of the Solar Rebound for the United States
Faculty Researcher: Matt Oliver, Associate Professor, School of Economics

Evaluating Long-Duration Flexibility of Industrial Demand in Electric Power Systems
Faculty Researchers: Micah Ziegler, assistant professor, School of Chemical and Biomolecular Engineering and the Jimmy and Rosalynn Carter School of Public Policy, and Constance Crozier, Assistant Professor, H. Milton Stewart School of Industrial and Systems Engineering

ACCELERATE is an annual program open to all Georgia Tech faculty, focusing on policy‑ and decision‑relevant research that advances energy affordability, reliability, resilience, and decarbonization in the region.

More information about EPIcenter’s research areas and programs is available at epicenter.energy.gatech.edu.

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Primarily driven by the rapid construction of data centers nationwide amid the artificial intelligence boom, total electricity usage in the United States is projected to grow by 32% by 2030, according to the Connected Grid Initiative. 

Nuclear power currently supplies roughly 20% of U.S. electricity, but because of its reliability compared to wind and solar power and its potential to reduce carbon emissions, the industry is positioned to expand its role in reshaping the future of energy. When Southern Company officially connected Units 3 and 4 at the Alvin W. Vogtle Electric Generating Plant to the grid, Georgia became home to the country’s largest nuclear power facility and to the first nuclear units built in the U.S. in more than 30 years. 

With Georgia Tech alumni playing critical roles at the plant, students entering the field, and faculty conducting innovative research, the Institute’s influence can be felt throughout the industry. 

Read more on the Georgia Tech Newscenter Page

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A recent review published in Energy Research & Social Science by EPIcenter public policy affiliates – Ryan Anthony, Brian An, Marilyn A. Brown, Michelle Graff, and Daniel C. Matisoff – examines five decades of low-income weatherization program evaluations. The researchers systematically analyzed 17 retrospective, outcome-focused evaluations to identify how assessment methods have shifted from early pre-post energy comparisons to more rigorous causal inference research designs. While the literature consistently finds low-income home retrofit programs, such as the Weatherization Assistance Program (WAP), reduce energy burdens, many earlier evaluations are limited by research designs, including selection-biased control groups and minimal community engagement in the evaluation process.

To address these limitations, the authors recommend that future evaluations prioritize the construction of appropriate control groups or adopt quasi-experimental approaches, such as propensity score matching, to better isolate causal impacts. They also highlight the value of modern difference-in-difference estimators for strengthening causal identification. In addition, the review emphasizes the importance of leveraging available and emerging technologies, such as smart meters, thermostats, and sensors, to provide timely, precise data for evaluating both energy consumption and savings as well as non-energy impacts, like health and safety.

Read more on the EPIcenter Research Page

Listen to a Podcast on the Research Here

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Generative artificial intelligence (AI) is best known for creating images and text. Now, it is helping industries make better planning decisions.

Georgia Tech researchers have created a new AI model for decision-focused learning (DFL), called Diffusion-DFL. Recent tests showed it makes more accurate decisions than current approaches.

Along with optimizing industrial output, Diffusion-DFL lowers costs and reduces risk. Experiments also showed it performs across different fields. 

Diffusion-DFL doesn’t just surpass current methods; it also predicts more accurately as problem sizes grow. The model requires less computing power despite these high-performance marks, making it more accessible to smaller enterprises.

Diffusion-DFL runs on diffusion models, the same technology that powers DALL-E and other AI image generators. It is the first DFL framework based on diffusion models.

“Anyone who makes high-stakes decisions under uncertainty, including supply chain managers, energy operators, and financial planners, benefits from Diffusion-DFL,” said Zihao Zhao, a Georgia Tech Ph.D. student who led the project. 

“Instead of optimizing around a single forecast, the model evaluates many possible scenarios, so decisions account for real-world risk and become more robust.”

[Related: GT @ ICLR 2026]

To test Diffusion-DFL, the team ran experiments based on real-world settings, including:

• Factory manufacturing to meet product demand
• Power grid scheduling to meet energy demand
• Stock market portfolio optimization

In each case, Diffusion-DFL made more accurate decisions than current methods. It also performed better as problems became larger and more complex. These results confirm the model’s ability to make important decisions in real-world scenarios with noisy data and uncertainty.

The experiments also show that Diffusion-DFL is practical, not just accurate. Training diffusion models is expensive, so the team developed a way to reduce memory use. This cut training costs by more than 99.7%. As a result, Diffusion-DFL can reach more researchers and practitioners.

“Our score-function estimator cuts GPU memory from over 60 gigabytes to 0.13 with almost no loss in decision quality, reducing the requirement for massive computing resources,” Zhao said. “I hope this expands Diffusion-DFL into other domains, like healthcare, where decisions must be made quickly under complex uncertainty."

Beyond decision-making applications, Diffusion-DFL marks a shift in DFL techniques and in the broader use of generative AI models. 

In supply chain management, planners estimate future demand before deciding how much product to stock. In this DFL problem, engineers align ML models with predetermined decision objectives, like minimizing risk or reducing costs. 

One flaw of DFL methods is that they optimize around a single, deterministic prediction in an uncertain future.

Diffusion-DFL takes a different approach. Instead of making a single guess, it determines a range of possible outcomes. This leads to decisions based on many likely scenarios, rather than on a single assumed future.

To do this, the framework uses diffusion models. These generative AI models create high-quality data from images, text, and audio. 

The forward diffusion process involves adding noise to data until it becomes pure noise. Models trained via forward diffusion can reverse diffusion. This means they can start with noisy data and then produce meaningful insights from training examples. 

Real-world data is often noisy and uncertain. Traditional DFL methods struggle in these conditions, but diffusion models are designed to handle them.

Because of this, Diffusion-DFL can explore many possible outcomes and choose better actions. Like image-generation AI, the model works well with complex data from different sources. This enables its use across different industries.

“Diffusion models have achieved significant success in generative AI and image synthesis, but our work shows their potential extends far beyond that,” said Kai Wang, an assistant professor in the School of Computational Science and Engineering (CSE).

“What makes Diffusion-DFL unique is that the specific downstream application guides how the model learns to handle uncertainty.

“Whether we are scheduling energy for power grids, balancing risk in financial portfolios, or developing early warning systems in healthcare, we can explicitly train these highly expressive models to navigate the unique complexities of each domain.”

Zhao and Wang collaborated with Caltech Ph.D. candidate Christopher Yeh and Harvard University postdoctoral fellow Lingkai Kong on Diffusion-DFL. Kong earned his Ph.D. in CSE from Georgia Tech in 2024.

Wang will present Diffusion-DFL on behalf of the group at the upcoming International Conference on Learning Representations (ICLR 2026). Occurring April 23-27 in Rio de Janeiro, ICLR is one of the world’s most prestigious conferences dedicated to artificial intelligence research.

“ICLR is the perfect stage for Diffusion-DFL because it brings together the exact community that needs to see the bridge between generative modeling and high-stakes decision-making for real-world applications,” Wang said.

“Presenting Diffusion-DFL allows us to challenge the traditional training framework of diffusion models. It’s about sparking a broader conversation on how we can align the training objectives of generative AI directly with actual, downstream decision-making needs.”

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Savannah is built on history and hospitality, which makes the collaboration between Lamarr.AI — a company named after a historic inventor and actress — and the city a match made for the big screen.

Some of Savannah’s many old buildings are expensive to heat and cool, especially in Georgia’s humid summers. They develop leaks. They need routine maintenance. But how does a building owner know where to begin with renovations or repairs? Enter Lamarr.AI, one of the first companies supported by the Partnership for Innovation’s (PIN) new Community Investment program.

“The Community Investment program is matching up faculty-led, faculty-spinoff startup companies that have technology that could be relevant to a community, a government, or to the civic space,” said Katie O’Connor, PIN’s community investment manager. “The company’s product is something that can help a community in a smart cities kind of way.”

Lamarr.AI fits the bill to a T. Its technology and the company grew out of research at Georgia Tech. Lamarr.AI’s technology uses drones, imaging, and artificial intelligence (AI) to assess a building’s envelope and determine the best ways to make these structures more energy efficient.

“The technology is like giving a building an MRI using drones, infrared and regular images, and our own AI,” said Tarek Rakha, Lamarr.AI’s co-founder and CEO. The drones, he explained, detect missing insulation, water intrusion, air escaping, and physical damage. AI and machine learning translate that information into 3-D models that map the defects.

Read more on EI2 Webpage
 

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A new study by EPIcenter affiliate Jamal Mamkhezri examines how public preferences for solar‑energy policy have shifted over a six‑year period in New Mexico, offering one of the first long‑term repeated cross‑section analyses of willingness to pay (WTP) for renewable‑energy attributes. Using identical discrete choice experiment (DCE) tasks from surveys conducted in 2017 and 2023, Professor Mamkhezri evaluates how households value increases in Renewable Portfolio Standards (RPS), changes in rooftop versus utility‑scale solar shares, monthly credit‑banking rules, water usage in electricity generation, and smart‑meter information delivery options.

Across more than 1,100 combined respondents, the study uncovers selective temporal stability in energy preferences. Some attributes—such as support for higher RPS targets, reductions in water use, and preferences for online smart‑meter information—remain relatively stable over time. In contrast, others shift considerably: WTP for increasing the rooftop solar share declines by more than 40%, while WTP to protect monthly credit banking rises more than 200%, reflecting heightened awareness of net‑metering debates and rapid growth in rooftop solar adoption.

Importantly, the study reveals that environmental attitudes, measured through New Ecological Paradigm (NEP) scores, once strongly predicted preferences for rooftop solar and smart‑meter technologies in 2017, but these relationships fade or even reverse by 2023—signaling a shift as these technologies transition from niche, identity‑driven goods to mainstream infrastructure. Meanwhile, environmental attitudes continue to robustly shape preferences for RPS increases and water‑use reductions in both survey waves.

Read Full Story on the EPIcenter Webpage

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