New York City was supposed to terminate its congestion pricing program last Friday because, well, Trump told them to. But they didn't do it and so harsh words were exchanged and then the deadline was extended for another 30 days. (This sounds oddly familiar.) Who knows what happens next month, but we are able to accurately quantify the benefits of nearly 3 months of congestion pricing.

Firstly, it's generating a lot of money. In the first two months of operation, congestion pricing has already brought in over $100 million in new revenue for the city. This is important because it's money that can be used for transit and other infrastructure improvements.

Equally important is the fact that this money was generated by creating measurable value for drivers. For all of the river crossings that lead into the CBD, average weekday travel times this past January are lower compared to January 2024. And in some cases, they're lower by a lot. The Holland Tunnel, for example, saw travel times drop by 48%.

Lastly, it's encouraging more people to take public transit. Here's a chart from Sam Deutsch over at Better Cities showing the increases in ridership since the program was implemented:

The MTA as a whole is now averaging about 448,000 more public transit riders per day. And to put this number into perspective, Sam reminds us that Washington DC has the second most-used public transit system in the US and that it sees an average of about 304,000 total riders per day (January 2024 figure). So in other words, New York's congestion pricing bump alone was nearly 1.5x DC's entire ridership base.

Some critics will argue that NYC's subway is dangerous and that this program unfairly pushes people toward it. But crime data suggests otherwise. New York's subway also saw over a billion rides in 2024! So I don't know how you argue that less people should be taking it. It's pretty clear that this is what moves the city. Imagine if the above went the opposite way and 448,000 more people started driving to work.

Some people may not like it, but the reality is that congestion pricing is doing exactly what it's intended to do: reduce traffic congestion, make money, and encourage more sustainable forms of urban mobility.

Cover photo by Wells Baum on Unsplash

Solar energy's share of total US electricity generation was only about 3.9% as of 2023. So it's not powering all that much today. However, the cost of PV modules continue to come down and installed capacity is growing very quickly. Here's an excerpt from a recent post by Brian Potter about solar energy:

By some metrics, solar PV has been deployed faster than any other energy source in history, going from 100 terawatt-hours of generation to 1,000 terawatt-hours in just 8 years, compared to 12 years for wind and nuclear, 28 for natural gas, and 32 for coal. In the US, solar PV projects are by far the largest share of planned new electrical generation capacity.

And here's a chart:

It's also interesting to look at which US states have the highest "capacity factors." The average for the entire US is 23%, which means that, on average, solar panels produce 23% of what they would if the sun were shining 24 hours a day. You might also think that the "sunshine state" would be one of the highest. But in fact, the top states are Utah and Arizona:

I'm highly interested in solar and we want to deploy it as much as we can on our projects going forward. If you're also interested in solar and want to learn more, Brian's post is an excellent place to start.

Images: Construction Physics

Last month, the Berkeley National Laboratory, under contract with the US Department of Energy, published this report estimating total data center energy usage across the country. It also forecasted future demand out to 2028.

As you can see, in 2018, total electricity consumption by US data centers was estimated at approximately 76 TWh or 1.9% of the US total. In 2023, consumption more than doubled to 176 TWh or 4.4% of the US total. And by 2028, this is expected to further jump to somewhere between 6.7-12% of the US total.

Here's some commentary from the report:

With significant changes observed in the data center sector in recent years, owing to the rapid emergence of AI hardware, total data center energy use after 2023 is presented as a range to reflect various scenarios. These scenarios capture ranges of future equipment shipments and operational practices, as well as variations in cooling energy use. The equipment variations are based on the assumed number of GPUs shipped each year, which depends on the future GPU demand and the ability of manufacturers to meet those demands. Average operational practices for GPU-accelerated servers represent how much computational power, and how often AI hardware in the installed base is used, to meet AI workload demand. Cooling energy use variations are based on scenarios in cooling system selection type and efficiency of those cooling systems, such as shifting to liquid base cooling or moving away from evaporative cooling. Together, the scenario variations provide a range of total data center energy estimates, with the low and high end of roughly 325 and 580 TWh in 2028, as shown in Figure ES-1.

This strikes me as being an important macro trend and a big deal. All signs point to more data centers being needed. And before we know it, they're going to represent a meaningful chunk of total electricity usage.

Note: TWh = terawatt hour = one trillion watt hours