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The $3.5 trillion budget bill could transform the US power sector—and slash climate pollution

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3 years ago

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The $3.5 trillion budget bill could transform the US power sector—and slash climate pollution


In the coming weeks, Congress may pass one of the most important climate policies in US history.

The $3.5 trillion budget plan includes a provision known as the Clean Electricity Payment Program, which would use payments and penalties to encourage utilities to increase the share of carbon-free electricity in the total they sell each year. If it works as hoped, the legislation would ensure that the power sector generates 80% of its electricity from sources like wind, solar, and nuclear plants by 2030, cutting more than a billion tons of annual greenhouse-gas emissions.

The measure would mark a foundational step in President Joe Biden’s ambitious climate plan, which aims to put the nation on track to eliminate climate pollution from electricity generation by 2035—and achieve net-zero emissions across the economy by midcentury.

There are real questions, though, about whether the program will achieve its aggressive targets. How the nation’s complex electricity sector actually responds will depend heavily on how the agency that implements the program designs it, and particularly where it sets the payments and penalties, some economists say.

It’s also still unclear if the measure will pass in anything like its current form—or at all.

How would it work?

The Clean Electricity Payment Program is a twist on a clean electricity standard, a regulation numerous states have implemented that requires utilities to reach certain levels of clean electricity by specific years. The proposal mainly opts for payments and penalties over binding mandates because that could enable it to pass under a legislative process known as budget reconciliation, which requires only a simple majority of votes in the Senate.

Once companies boost their share of clean electricity above an annual target, they would earn payments for every additional megawatt-hour of electricity they sell that comes from carbon-free sources, according to an analysis by the Clean Air Task Force. Those that fail to reach that threshold would have to pay a fee.

The program wouldn’t require all electricity suppliers to reach the same levels at the same times; it would adjust the yearly goals according to the point from which each is starting. But the overall target would be for the US power sector to produce 80% of its electricity from clean sources, on average, in the next nine years.

US Senator Tina Smith of Minnesota has championed the measure, which the Department of Energy would likely oversee.

The budget bill also includes federal tax incentives for building more clean electricity generation. With those credits, the program would be funded at around $150 billion to $200 billion, according to Third Way, a center-left think tank in Washington, DC.

Added together, the measures in the package would amount to “the biggest, most ambitious climate and clean energy policies that the US has ever enacted, by far,” says Josh Freed, head of the organization’s climate and energy program.

What would the program do?

If the measures achieve the goal of 80% clean electricity by 2030, it would more than double the share of carbon-free electricity in the US and significantly accelerate the pace of the transition to clean energy.

Currently, about 38% of the electricity generated in the US comes from carbon-free sources: 18% from renewables and 20% from nuclear power.

Pushing the power sector to 80% would cut carbon dioxide emissions by 86% from 2005 levels, according to an analysis by the Natural Resources Defense Council, included in an Evergreen Collective report published this month.

That would eliminate well over a billion tons of annual climate pollution in the next nine years. By comparison, the power sector reduced annual emissions by a little more than 800 million tons in the 14 years leading up to 2019, driven almost entirely by the shift from coal to natural gas and the increase in renewables.

How else does it help?

That takes a giant whack at one of the largest sources of US climate pollution. The electricity sector produces a quarter of the nation’s total greenhouse gases, second only to the transportation sector at 29%.  

Cleaning up the power sector also makes it easier to address other major emissions sources. It ensures, for instance, that far more of the electricity used to charge electric cars, trucks, and buses is carbon free. The same goes for things like heating and cooking if regulations require more homes and businesses to shift to electric stoves, heat pumps, and other cleaner technologies.

“If we want to achieve real, deep cuts in emissions, we’ve got to do it through clean electricity,” says Leah Stokes, an assistant professor of political science at the University of California, Santa Barbara, who has consulted on the policy.  

Meanwhile, other studies have found the shift to around 80% carbon-free electricity would spur $1.5 trillion of investments into clean energy, create hundreds of thousands of jobs, and save hundreds of thousands of lives over the coming decades through reduced air pollution.

But will it really get us to 80% clean electricity by 2030?

“Who knows?” says James Bushnell, an environmental and energy economist at the University of California, Davis.

The downside to going with incentives over strict mandates is that you can’t guarantee the end result. The government will need to make some imperfect predictions, or continually assess and refine how big the sticks and plentiful the carrots will need to be to bring about the desired changes, Bushnell says.

It will also have to carefully design the program to prevent the industry from gaming it. He sees scenarios where utilities could pack together big additions of clean electricity in certain years and narrow misses in others, in ways that could minimize penalties, maximize payments, and slow the progress of the program.

Another problem is that much of the data today on US electricity generation and sales is self-reported, while the “cleanness” of electricity purchased in real-time markets isn’t always clear. So the government will likely need to set up stringent processes for monitoring and verification, and develop reliable ways to certify or track where carbon-free electricity originates and where it ends up.

What would it mean for electricity prices?

Most assessments of the Clean Electricity Payment Program conclude it will drive down consumer prices. That’s because it’s funded by the federal government, and utilities would be required to use the payments to benefit customers.

“In a traditional [clean electricity standard], the cost is carried in electricity rates, and therefore by utility customers,” noted the Evergreen report, which Stokes co-wrote. In contrast, the payment program would shield Americans from rising electricity bills, the report said.

But Bushnell says that even if those performance payments are used to reduce prices, it’s still possible they could tick up in some instances. That’s because utilities will all be competing for limited sources of both old and new clean electricity, which would drive up prices. Prices for dirty electricity could fall for the same reasons of market demand and supply. The actual results from market to market remain to be seen, he says.

So why not just enact mandates?

While simply mandating utilities to sell set levels of clean electricity by certain times offers a clearer path to the desired result, the proposed payment plan has one powerful advantage: it’s politically feasible.

Specifically, it could enable legislators to include the proposal in the budget reconciliation process. That allows Congress to approve legislation on certain issues, related to taxes and spending, with 51 votes in the Senate—precisely the number Democrats have if Vice President Kamala Harris steps in to cast a tie-breaking vote.

A regulatory rule wouldn’t qualify for reconciliation, requiring it to secure 60 votes to overcome the threat of a filibuster.

So does that mean it will definitely pass?

Not at all.

There are tight restrictions on what sorts of measures can be included in the reconciliation process, under what’s known as the Byrd rule. The Senate can’t consider “extraneous” provisions requiring any proposals to alter federal spending or taxes in ways that are more than incidental to other policy aims, among other tests.

So there’s always a chance that the Senate parliamentarian could rule that certain measures don’t qualify, stripping them from the final bill altogether.

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Why I became a TechTrekker

Published

8 months ago

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22 August 2023

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group jumps into the air with snowy mountains in the background


My senior spring in high school, I decided to defer my MIT enrollment by a year. I had always planned to take a gap year, but after receiving the silver tube in the mail and seeing all my college-bound friends plan out their classes and dorm decor, I got cold feet. Every time I mentioned my plans, I was met with questions like “But what about school?” and “MIT is cool with this?”

Yeah. MIT totally is. Postponing your MIT start date is as simple as clicking a checkbox. 

Sofia Pronina (right) was among those who hiked to the Katla Glacier during this year’s TechTrek to Iceland.

COURTESY PHOTO

Now, having finished my first year of classes, I’m really grateful that I stuck with my decision to delay MIT, as I realized that having a full year of unstructured time is a gift. I could let my creative juices run. Pick up hobbies for fun. Do cool things like work at an AI startup and teach myself how to create latte art. My favorite part of the year, however, was backpacking across Europe. I traveled through Austria, Slovakia, Russia, Spain, France, the UK, Greece, Italy, Germany, Poland, Romania, and Hungary. 

Moreover, despite my fear that I’d be losing a valuable year, traveling turned out to be the most productive thing I could have done with my time. I got to explore different cultures, meet new people from all over the world, and gain unique perspectives that I couldn’t have gotten otherwise. My travels throughout Europe allowed me to leave my comfort zone and expand my understanding of the greater human experience. 

“In Iceland there’s less focus on hustle culture, and this relaxed approach to work-life balance ends up fostering creativity. This was a wild revelation to a bunch of MIT students.”

When I became a full-time student last fall, I realized that StartLabs, the premier undergraduate entrepreneurship club on campus, gives MIT undergrads a similar opportunity to expand their horizons and experience new things. I immediately signed up. At StartLabs, we host fireside chats and ideathons throughout the year. But our flagship event is our annual TechTrek over spring break. In previous years, StartLabs has gone on TechTrek trips to Germany, Switzerland, and Israel. On these fully funded trips, StartLabs members have visited and collaborated with industry leaders, incubators, startups, and academic institutions. They take these treks both to connect with the global startup sphere and to build closer relationships within the club itself.

Most important, however, the process of organizing the TechTrek is itself an expedited introduction to entrepreneurship. The trip is entirely planned by StartLabs members; we figure out travel logistics, find sponsors, and then discover ways to optimize our funding. 

two students soaking in a hot spring in Iceland

COURTESY PHOTO

In organizing this year’s trip to Iceland, we had to learn how to delegate roles to all the planners and how to maintain morale when making this trip a reality seemed to be an impossible task. We woke up extra early to take 6 a.m. calls with Icelandic founders and sponsors. We came up with options for different levels of sponsorship, used pattern recognition to deduce the email addresses of hundreds of potential contacts at organizations we wanted to visit, and all got scrappy with utilizing our LinkedIn connections.

And as any good entrepreneur must, we had to learn how to be lean and maximize our resources. To stretch our food budget, we planned all our incubator and company visits around lunchtime in hopes of getting fed, played human Tetris as we fit 16 people into a six-person Airbnb, and emailed grocery stores to get their nearly expired foods for a discount. We even made a deal with the local bus company to give us free tickets in exchange for a story post on our Instagram account. 

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The Download: spying keyboard software, and why boring AI is best

Published

8 months ago

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22 August 2023

By

🧠


This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How ubiquitous keyboard software puts hundreds of millions of Chinese users at risk

For millions of Chinese people, the first software they download onto devices is always the same: a keyboard app. Yet few of them are aware that it may make everything they type vulnerable to spying eyes. 

QWERTY keyboards are inefficient as many Chinese characters share the same latinized spelling. As a result, many switch to smart, localized keyboard apps to save time and frustration. Today, over 800 million Chinese people use third-party keyboard apps on their PCs, laptops, and mobile phones. 

But a recent report by the Citizen Lab, a University of Toronto–affiliated research group, revealed that Sogou, one of the most popular Chinese keyboard apps, had a massive security loophole. Read the full story. 

—Zeyi Yang

Why we should all be rooting for boring AI

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. It hopes they could improve intelligence and operational planning. 

But those might not be the right use cases, writes our senior AI reporter Melissa Heikkila. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases. 

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. The DoD’s best bet is to apply generative AI to more mundane things like Excel, email, or word processing. Read the full story. 

This story is from The Algorithm, Melissa’s weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The ice cores that will let us look 1.5 million years into the past

To better understand the role atmospheric carbon dioxide plays in Earth’s climate cycles, scientists have long turned to ice cores drilled in Antarctica, where snow layers accumulate and compact over hundreds of thousands of years, trapping samples of ancient air in a lattice of bubbles that serve as tiny time capsules. 

By analyzing those cores, scientists can connect greenhouse-gas concentrations with temperatures going back 800,000 years. Now, a new European-led initiative hopes to eventually retrieve the oldest core yet, dating back 1.5 million years. But that impressive feat is still only the first step. Once they’ve done that, they’ll have to figure out how they’re going to extract the air from the ice. Read the full story.

—Christian Elliott

This story is from the latest edition of our print magazine, set to go live tomorrow. Subscribe today for as low as $8/month to ensure you receive full access to the new Ethics issue and in-depth stories on experimental drugs, AI assisted warfare, microfinance, and more.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 How AI got dragged into the culture wars
Fears about ‘woke’ AI fundamentally misunderstand how it works. Yet they’re gaining traction. (The Guardian) 
+ Why it’s impossible to build an unbiased AI language model. (MIT Technology Review)
 
2 Researchers are racing to understand a new coronavirus variant 
It’s unlikely to be cause for concern, but it shows this virus still has plenty of tricks up its sleeve. (Nature)
+ Covid hasn’t entirely gone away—here’s where we stand. (MIT Technology Review)
+ Why we can’t afford to stop monitoring it. (Ars Technica)
 
3 How Hilary became such a monster storm
Much of it is down to unusually hot sea surface temperatures. (Wired $)
+ The era of simultaneous climate disasters is here to stay. (Axios)
+ People are donning cooling vests so they can work through the heat. (Wired $)
 
4 Brain privacy is set to become important 
Scientists are getting better at decoding our brain data. It’s surely only a matter of time before others want a peek. (The Atlantic $)
+ How your brain data could be used against you. (MIT Technology Review)
 
5 How Nvidia built such a big competitive advantage in AI chips
Today it accounts for 70% of all AI chip sales—and an even greater share for training generative models. (NYT $)
+ The chips it’s selling to China are less effective due to US export controls. (Ars Technica)
+ These simple design rules could turn the chip industry on its head. (MIT Technology Review)
 
6 Inside the complex world of dissociative identity disorder on TikTok 
Reducing stigma is great, but doctors fear people are self-diagnosing or even imitating the disorder. (The Verge)
 
7 What TikTok might have to give up to keep operating in the US
This shows just how hollow the authorities’ purported data-collection concerns really are. (Forbes)
 
8 Soldiers in Ukraine are playing World of Tanks on their phones
It’s eerily similar to the war they are themselves fighting, but they say it helps them to dissociate from the horror. (NYT $)
 
9 Conspiracy theorists are sharing mad ideas on what causes wildfires
But it’s all just a convoluted way to try to avoid having to tackle climate change. (Slate $)
 
10 Christie’s accidentally leaked the location of tons of valuable art 🖼📍
Seemingly thanks to the metadata that often automatically attaches to smartphone photos. (WP $)

Quote of the day

“Is it going to take people dying for something to move forward?”

—An anonymous air traffic controller warns that staffing shortages in their industry, plus other factors, are starting to threaten passenger safety, the New York Times reports.

The big story

Inside effective altruism, where the far future counts a lot more than the present

" "

VICTOR KERLOW

October 2022

Since its birth in the late 2000s, effective altruism has aimed to answer the question “How can those with means have the most impact on the world in a quantifiable way?”—and supplied methods for calculating the answer.

It’s no surprise that effective altruisms’ ideas have long faced criticism for reflecting white Western saviorism, alongside an avoidance of structural problems in favor of abstract math. And as believers pour even greater amounts of money into the movement’s increasingly sci-fi ideals, such charges are only intensifying. Read the full story.

—Rebecca Ackermann

We can still have nice things

A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)

+ Watch Andrew Scott’s electrifying reading of the 1965 commencement address ‘Choose One of Five’ by Edith Sampson.
+ Here’s how Metallica makes sure its live performances ROCK. ($)
+ Cannot deal with this utterly ludicrous wooden vehicle. 
+ Learn about a weird and wonderful new instrument called a harpejji.



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Why we should all be rooting for boring AI

Published

8 months ago

on

22 August 2023

By

Why we should all be rooting for boring AI


This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I’m back from a wholesome week off picking blueberries in a forest. So this story we published last week about the messy ethics of AI in warfare is just the antidote, bringing my blood pressure right back up again. 

Arthur Holland Michel does a great job looking at the complicated and nuanced ethical questions around warfare and the military’s increasing use of artificial-intelligence tools. There are myriad ways AI could fail catastrophically or be abused in conflict situations, and there don’t seem to be any real rules constraining it yet. Holland Michel’s story illustrates how little there is to hold people accountable when things go wrong.  

Last year I wrote about how the war in Ukraine kick-started a new boom in business for defense AI startups. The latest hype cycle has only added to that, as companies—and now the military too—race to embed generative AI in products and services. 

Earlier this month, the US Department of Defense announced it is setting up a Generative AI Task Force, aimed at “analyzing and integrating” AI tools such as large language models across the department. 

The department sees tons of potential to “improve intelligence, operational planning, and administrative and business processes.” 

But Holland Michel’s story highlights why the first two use cases might be a bad idea. Generative AI tools, such as language models, are glitchy and unpredictable, and they make things up. They also have massive security vulnerabilities, privacy problems, and deeply ingrained biases.  

Applying these technologies in high-stakes settings could lead to deadly accidents where it’s unclear who or what should be held responsible, or even why the problem occurred. Everyone agrees that humans should make the final call, but that is made harder by technology that acts unpredictably, especially in fast-moving conflict situations. 

Some worry that the people lowest on the hierarchy will pay the highest price when things go wrong: “In the event of an accident—regardless of whether the human was wrong, the computer was wrong, or they were wrong together—the person who made the ‘decision’ will absorb the blame and protect everyone else along the chain of command from the full impact of accountability,” Holland Michel writes. 

The only ones who seem likely to face no consequences when AI fails in war are the companies supplying the technology.

It helps companies when the rules the US has set to govern AI in warfare are mere recommendations, not laws. That makes it really hard to hold anyone accountable. Even the AI Act, the EU’s sweeping upcoming regulation for high-risk AI systems, exempts military uses, which arguably are the highest-risk applications of them all. 

While everyone is looking for exciting new uses for generative AI, I personally can’t wait for it to become boring. 

Amid early signs that people are starting to lose interest in the technology, companies might find that these sorts of tools are better suited for mundane, low-risk applications than solving humanity’s biggest problems.

Applying AI in, for example, productivity software such as Excel, email, or word processing might not be the sexiest idea, but compared to warfare it’s a relatively low-stakes application, and simple enough to have the potential to actually work as advertised. It could help us do the tedious bits of our jobs faster and better.

Boring AI is unlikely to break as easily and, most important, won’t kill anyone. Hopefully, soon we’ll forget we’re interacting with AI at all. (It wasn’t that long ago when machine translation was an exciting new thing in AI. Now most people don’t even think about its role in powering Google Translate.) 

That’s why I’m more confident that organizations like the DoD will find success applying generative AI in administrative and business processes. 

Boring AI is not morally complex. It’s not magic. But it works. 

Deeper Learning

AI isn’t great at decoding human emotions. So why are regulators targeting the tech?

Amid all the chatter about ChatGPT, artificial general intelligence, and the prospect of robots taking people’s jobs, regulators in the EU and the US have been ramping up warnings against AI and emotion recognition. Emotion recognition is the attempt to identify a person’s feelings or state of mind using AI analysis of video, facial images, or audio recordings. 

But why is this a top concern? Western regulators are particularly concerned about China’s use of the technology, and its potential to enable social control. And there’s also evidence that it simply does not work properly. Tate Ryan-Mosley dissected the thorny questions around the technology in last week’s edition of The Technocrat, our weekly newsletter on tech policy.

Bits and Bytes

Meta is preparing to launch free code-generating software
A version of its new LLaMA 2 language model that is able to generate programming code will pose a stiff challenge to similar proprietary code-generating programs from rivals such as OpenAI, Microsoft, and Google. The open-source program is called Code Llama, and its launch is imminent, according to The Information. (The Information) 

OpenAI is testing GPT-4 for content moderation
Using the language model to moderate online content could really help alleviate the mental toll content moderation takes on humans. OpenAI says it’s seen some promising first results, although the tech does not outperform highly trained humans. A lot of big, open questions remain, such as whether the tool can be attuned to different cultures and pick up context and nuance. (OpenAI)

Google is working on an AI assistant that offers life advice
The generative AI tools could function as a life coach, offering up ideas, planning instructions, and tutoring tips. (The New York Times)

Two tech luminaries have quit their jobs to build AI systems inspired by bees
Sakana, a new AI research lab, draws inspiration from the animal kingdom. Founded by two prominent industry researchers and former Googlers, the company plans to make multiple smaller AI models that work together, the idea being that a “swarm” of programs could be as powerful as a single large AI model. (Bloomberg)

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