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A new model from Johns Hopkins’ Net Zero Industrial Policy Lab uses machine learning to predict tomorrow’s industrial powerhouses.

It’s no secret that China, Japan, and Germany are industrial powerhouses, with vast potential in clean tech manufacturing. So how’s a less industrialized nation with an eye on the economy of the future supposed to compete? Are protectionist policies such as tariffs a good way to jumpstart domestic manufacturing? Should it focus on subsidizing factory buildouts? Or does the whole game come down to GDP?
According to a new machine learning tool from Johns Hopkins’ Net Zero Industrial Policy Lab, none of the above really matters all that much. Many of the policies that dominate geopolitical conversations aren’t strongly correlated with a country’s relative industrial potential, according to the model. The same goes for country-specific characteristics such as population, percentage of industry as a share of GDP, and foreign direct investment, a.k.a. FDI. What does count? A nation’s established industrial capabilities, and the degree to which they cross over to climate tech.
The purpose of the tool, named the Clean Industrial Capabilities Explorer, is to help policymakers “X-ray your country’s existing industrial base to identify what are your genuine strengths,” Tim Sahay, co-director of the lab, told me. The model, he explained, can identify “which core capabilities in your underlying industrial know-how are weak. That is like a diagnosis of what you should get into.”
The model calculates competitiveness across 10 clean energy technologies: solar, wind, batteries, electrolyzers, heat pumps, permanent magnets, nuclear, biofuels, geothermal, and transmission. That analysis ultimately surfaced five “core capabilities” that are most predictive of a country’s relative strength in each technology area: electronics, industrial materials, machinery, chemicals, and metals. Strength in geothermal, for example, is highly correlated with a machinery-focused industrial base, since building a geothermal plant requires expertise in making drilling rigs, heat exchangers, and steam turbines.
This “X-ray” of national capabilities not only confirms the dominance of leading Asian and European manufacturing economies, it also surfaces a group of lesser-known nations that appear well-positioned to become major future producers and exporters of key clean technologies. These so-called “future stars” include a handful of Central European countries — Czechia, Slovenia, Hungary, Slovakia, and Poland — plus the Southeast Asian economies of Malaysia, the Philippines, Thailand, and Vietnam. In Africa, Ethiopia emerges as the most promising economy.

Take Hungary as an example — its core competencies are machinery, electronics, and chemicals, making the country highly competitive when it comes to producing components for batteries, biofuels, and the machinery critical for geothermal power plants. The U.S., by comparison, excels at nuclear, electrolyzers, biofuel, and geothermal.
Many of the European future stars appear to benefit from their proximity to Germany, long an industrial stronghold in the region. “Poland, for example, received a huge amount of German FDI in the late 90s, early 2000s,” Sahay told me, explaining that countries in this region built up strength in their chemicals and metals sectors under the influence of the Soviet Union. Germany then set up these countries as key suppliers for its various industries, from autos to chemicals.
Of the 10 countries identified as rising stars, all of them received Chinese investment sometime in the past 10 years, Sahay said. “What we are seeing is decisions that have been made over the last couple of decades are bearing fruit in the 2020s,” he said, explaining that all of the countries on the list “were identified as places for potential investment by the world’s leading industrial firms in the 2000s or 2010s.”
This has led Bentley Allan, a political science professor and co-director of the policy lab, to think that China is likely doing some modeling of its own to determine where to direct its investments. Whatever the country is working with, it’s arriving at essentially the same conclusions regarding which nations show strong industrial potential, and are thus attractive targets for investment. “China isn’t the only one who can benefit from that strategy, but they’re the only ones being strategic about it at the moment,” Allan told me.
Allan’s hope is that the tool will democratize the knowledge that’s helped China dominate the global clean tech economy. “No one’s produced a global tool that enables not just China to invest strategically, but enables the U.S. to invest strategically, enables the UK to invest strategically in the developing world,” he explained. That’s critical when figuring out how to build an industrial base that can weather geopolitical tensions that might necessitate, say, a shift away from Chinese imports or Russian gas.
While it might not be particularly surprising that a country’s existing industrial capabilities strongly correlate with its potential industrial capabilities, the reality is that in many cases, getting a clear view of a country’s actual core competencies is not so straightforward. That’s because, as Allan told me, economists simply haven’t made widely available tools like this before. “They’ve made other tools for managing the macroeconomic environment, because for 60 years we basically thought that that was the only lever worth pulling,” he said.
Due to that opacity around industrial strength, model was able to yield some findings that the researchers found genuinely surprising. For example, not only did the tool show that countries such as the Philippines and Malaysia have stronger manufacturing bases than Allan would have guessed, it ranked Italy higher than Germany in overall competitiveness, showing solid potential in the nuclear, transmission, heat pump, electrolyzer, and geothermal industries.
That illustrates another complication the model solves for — namely that the countries with the most potential aren’t always the ones pursuing the most robust or intentional green industrial strategies. Both Italy and Japan, for instance, are well-positioned to benefit from a more explicit, structured focus on climate tech manufacturing, Allan told me.
Industrial strength will likely not be achieved through broad economic policies such as tariffs, subsidies, or grant programs, however, according to the model. Say for example that a country wants to deepen its expertise in solar manufacturing. “The things that you might want to invest in are things like precision machinery to produce the cutters that actually are used to cut the polysilicon into wafers,” Allan told me. “It’s more about making targeted investments in your industrial base in order to produce highly competitive niches as a way to then make you more competitive in that final product.”
This approach prevents countries from simply serving as final assemblers of battery packs or solar panels or other green products — a stage that provides low value-add, as countries aren’t able to capture the benefits of domestic research and development, engineering expertise, or intellectual property. Pinpointing strategic niches also helps countries avoid wasting their money in buzzy industries where they’re simply not competitive.
“The industrial policy race is very much hype-driven. It’s very much driven by, oh my god, we need a hydrogen strategy, and, oh my god, we need a lithium strategy,” Sahay told me. “But that’s not necessarily going to be what your country is going to be good at.” By pointing countries towards the industries and links in the supply chain where they actually could excel, Sahay and Allan can demonstrate they stand to benefit from the clean energy transition at large.
Or to put it more broadly, when done correctly, “industrial policy is climate policy, in the sense that when you advance industry generally, you are actually advancing the climate,” Allan told me. “And climate policy is industrial policy, because when you are trying to advance the climate, you advance the industrial base.”
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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation.What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.