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There is no dearth of advice on the internet about how to lower your personal carbon emissions, but if we had found any of it completely satisfying, we wouldn’t have embarked on this project in the first place.
Our goal with Decarbonize Your Life is to draw your attention to two things — the relative emissions benefits of different actions, as well as the relative structural benefits. (You’ll find everything you need to know about the project here.) For the first, we needed some help. So we shared our vision with WattTime, a nonprofit that builds data-driven tools to help people, companies, and policymakers figure out how to reduce emissions, and lucky for us, they were excited to support the project.
“So many people out there feel helpless when it comes to addressing the climate crisis, but we believe that anyone, anywhere should have the tools and information they need to make a difference,” Henry Richardson, a senior analyst at WattTime, told me as we were wrapping up this project. “So we love the idea of helping average consumers understand which actions actually available to them can meaningfully contribute to reducing climate pollution. We want to help people prioritize those higher-impact activities that can mitigate climate change faster.”
WattTime’s claim to fame is building an API that calculates the emissions impact of using the grid at a given time and place. Users can then shift their energy consumption to times when the grid is cleaner or to build renewables in places where they will reduce emissions the most.
In an ideal world, we would have taken a similar time- and place-based approach in calculating the emissions savings of each energy-related action on our list. Switching to an EV if you live somewhere with very clean power will reduce emissions more than if you live somewhere with lots of coal plants, and likewise, getting rooftop solar if you live somewhere with coal-fired electricity is more effective than in areas with a cleaner grid. But when we started to game it out, we realized that level of exactitude would be, if not exactly impossible, certainly insanity-inducing.
Instead, WattTime helped us calculate the effect of each action if it was undertaken by an “average American household” — that is, one that consumes an average amount of electricity per year, drives an average number of miles in an average car per year, uses an average amount of energy for space heating, et cetera. WattTime also pulled data from publicly available sources like the Environmental Protection Agency, the Department of Energy, and the Energy Information Administration, to estimate the baseline emissions and savings of a given action. We ultimately made two calculations for each action to account for two different ways of estimating the emissions from using the electric grid:
While the first method gives us a picture of how much good each action can do in an immediate sense, the second gives us a picture of how much good it can do over time. For example, using the first method, buying clean power came out on top, with rooftop solar offering the potential to cut CO2 by about 5.7 metric tons per year, while switching to an electric vehicle would cut about 3 metric tons per year. But using the second method, car-related actions won out, showing EVs cutting CO2 by 4.6 metric tons per year, and rooftop solar cutting 1.4 metric tons per year. The truth is probably somewhere in the middle.
To calculate the emissions savings from dietary changes and food waste management, we turned to two more partners: HowGood, a data platform for food system lifecycle analysis, and ReFED, which collects similar data for food waste. As with energy, we used federal data from the U.S. Department of Agriculture to estimate the average American diet and ReFED’s estimates for the average American food waste mix (though note that those are for an individual, not for a household). From there, WattTime helped us determine that, for instance, just by replacing the beef in your diet with chicken, you could save nearly 2.5 metric tons of emissions each year — almost as much as you could save by going vegan.
Because we used averages and sought to simplify our list with actions like “electrify your space heating system,” rather than estimating the impact of every permutation like “switch from a propane furnace in Colorado with X efficiency to a cold climate heat pump with Y efficiency,” our estimates of emissions reductions are rough approximations and not reflective of real-world scenarios.
You’ll see that while these calculations certainly informed our ranking, they were not the sole metric we used to arrange this list. A quantitative analysis alone could not answer our question about the most “high-leverage” actions, so we used our reporting and expertise as climate journalists to fill in that last, crucial gap. Car-related actions and rooftop solar were neck-and-neck by the numbers, but we are confident that getting an EV (if you need to have a car) is more unambiguously necessary for the energy transition than getting rooftop solar. Similarly, while eating less meat can hugely reduce the carbon tied to an individual’s diet, the ripple effect it has on agricultural carbon emissions is less direct and harder to parse than the effect you can have by electrifying all your appliances and shutting down your natural gas account.
Getting an EV:
WattTime — 2.9 mtCO2/yr
Cambium — 4.5 mtCO2/yr
Structural benefits: Destroying demand for oil; increasing demand for charging stations; improving local air quality and chipping away at the social license for operating an internal combustion engine.
Getting rooftop solar:
WattTime — 5.7 mtCO2/yr
Cambium — 1.4 mtCO2/yr
Structural benefits: Get clean energy on the grid faster than utility-scale projects; influence neighbors; reduce electric demand in your neighborhood; reduce strain on grid if paired with a battery and part of a “virtual power plant”
Air-sealing and insulation:
WattTime — 1.2 mtCO2/yr
Structural benefits: Reduce strain on grid and need for grid investment; level out electricity demand to avoid the need to activate dirty “peaker” gas plants; prepare your home for cheaper, more even, and efficient heating and cooling
Switching to a heat pump for space heating:
WattTime — 1.4 mtCO2/yr
Cambium — 1.6 mtCO2/yr
Switching from a gas stove to an induction stove:
WattTime — Roughly even
Cambium — 0.1 mtCO/yr
Switching to a heat pump for water heating:
WattTime — 0.8 mtCO2/yr
Cambium — 1.6 mtCO2/yr
Switching from a natural gas-powered dryer to a heat pump dryer:
WattTime — Roughly even
Cambium — 0.1 mtCO/yr
Structural benefits: Increase demand for and reduce price of electric and efficient appliances; build a case for policies that wind down fossil fuel use; if fully electrifying, sends signal to downsize gas system.
Getting rid of your car:
WattTime — 5.17 mtCO/yr
Structural benefits: Supporting public transit and bike lanes, enabling others to use their cars less, too.
Switching from an omnivorous to a vegetarian diet:
WattTime and HowGood — 2.8 mtCO2/yr
Switching from an omnivorous to a vegan diet:
WattTime and HowGood — 2.9 mtCO2/yr
Replacing the beef in an omnivorous diet with chicken:
WattTime and HowGood — 2.5 mtCO2/yr
Structural benefits: Reduce demand for high-emitting food products, which has the double-pump benefit of reducing the amount of land required to cultivate high-emitting products; if replacing beef with chicken, increase demand for more carbon-efficient proteins; add to the business case for developing efficient plant-based proteins.
Cutting food waste in half:
WattTime and ReFED — more than 0.1 mtCO2/yr
Structural benefits: Reduce demand across the food system; send less food waste to landfill, which helps reduce methane emissions.
Composting all food waste:
WattTime and ReFED — 0.03 mtCO2/yr
Structural benefits: Encourages the build-out of municipal composting programs; encourages responsible farming practices by lowering the cost of compost; reduces demand for nitrogen-based fertilizer.
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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.