When we first created the Koomey.com site circa 2010, we used Tumblr, which was a capable blogging site. We customized the site (with some difficulty) but it mostly performed well for a long time (almost 15 years).
This past summer we started investigating other options, and soon settled on Ghost. Many companies use it to handle newsletters with subscriptions, but it also works well for blog site hosting. It's open source and pricing is flat fee subscription, rather than a percentage of revenues like Substack (although tiers for bigger orgs and sites cost more).
One of the important learnings from recent technology developments is that commercial sites have a life cycle, and in their end stages undergo what Cory Doctorow has called "enshittification". The idea is that new sites launch to please users, but over time they move more and more to please their investors, which hurts the user experience as the company sucks more and more revenue from customers. It's not a universal law, but it is often true.
Our shift to Ghost insulates us somewhat from enshittification. Their business model is subscriptions and hosting and if their hosting becomes problematic we can just spin up our own Ghost instance (it's open source).
We don't anticipate doing paid newsletters, but Ghost will make that easy if we decide to go that route. The switch involve a bunch of futzing, but the site is looking better than ever, and now we can start thinking about how to tweak structure and content to better serve our clients.
As we worked to convert the site to Ghost, we also realized that the nature and purpose of the site had to shift, from being Jon Koomey's personal site to being a corporate site for Koomey Analytics, the small research company that Jon leads. That led to some obvious changes, but we think it holds together.
Expect more changes and improvements in the near future. Please do get in touch with ideas, suggestions, and new data sources. We're always happy to hear from like-minded data and analysis geeks.
The modern data center lies at the heart of today’s digital global economy, performing computing tasks like e-commerce, communications, search, financial modeling, and artificial intelligence (AI). Data centers undergo constant change, both in the workloads they run 24x7 and the hardware that runs those applications.
Lack of adequate planning and management can lead to under-provisioning, over-heating, and lost capacity, all of which undermine the profitability and sustainability of these critical facilities. Today’s AI and high-performance compute nodes can exacerbate these problems.
When IT loads deviate from the original data center design, stranded power and cooling capacity are the result. A simple analogy helps explain the problem. Most people are familiar with the game of Tetris TM, in which blocks fall at a regular pace, and the player’s task is to place those blocks in the correct orientation, filling up the space as thoroughly as possible.
In the simplest case, the blocks are of uniform size and shape (i.e., they conform precisely to what data center designers specified initially), and it’s easy for the user to fill up the space completely. The example on the left-hand side of Figure 1 illustrates this case. On the right-hand side, the TetrisTM player cannot make the shapes fit perfectly because their shapes are random, and they just keep coming. That leaves gaps (white space) between the shapes, which represent lost capacityin the data center. White space above the colored bricks represents unused capacity.
Figure 1: Lost capacity as illustrated by the game of Tetris
Lost data center capacity is exactly analogous to what are often called “zombie servers” in data centers, which are servers using electricity but doing nothing useful. This time it’s part of the data center itself (the cooling and power infrastructure) that is costing money (and lots of it) but not enabling any useful computing.
In this paper, we describe the challenges data center planners face and the potential for digital twins to help better manage data centers over their useful lives. Combining digital twins with computational fluid dynamics software (models that simulate and predict the behavior of airflow and heat in data centers) helps planners and managers save millions of dollars, reduce energy waste, increase profitability, improve data center reliability, predict failures, and lengthen the useful lifespan of costly data center equipment.
As the famous quote from George Box goes, “All models are wrong, but some are useful.” Bottom-up AI data center models will never be a perfect crystal ball, but energy analysts can soon make them much more useful for decisionmakers if our identified critical data needs are met. Without better data, energy analysts may be forced to take several shortcuts that are more uncertain, less explanatory, less defensible, and less useful to policymakers, investors, the media, and the public. Meanwhile, all of these stakeholders deserve greater clarity on the scales and drivers of the electricity use of one of the most disruptive technologies in recent memory. One need only look to the history of cryptocurrency mining as a cautionary tale: after a long initial period of moderate growth, mining electricity demand rose rapidly. Meanwhile, energy analysts struggled to fill data and modeling gaps to quantify and explain that growth to policymakers—and to identify ways of mitigating it—especially at local levels where grids were at risk of stress. The electricity demand growth potential of AI data centers is much larger, so energy analysts must be better prepared. With the right support and partnerships, the energy analysis community is ready to take on the challenges of modeling a fast moving and uncertain sector, to continuously improve, and to bring much-needed scientific evidence to the table. Given the rapid growth of AI data center operations and investments, the time to act is now.“
I worked with my longtime colleagues Eric Masanet and Nuoa Lei on this article.
You shouldn’t worry at all about your digital footprint, as we discussed in the show. It’s small and constantly improving, and much of the equipment uses the same amount of electricity when it’s idle as when it’s fully loaded, so your actions won’t change electricity use or emissions.
If you want to take personal action on climate, you should
* Vote against climate deniers and fossil fuel apologists. * Replace fossil fuel equipment at end of life with electrified equipment. That’s when it’s most cost effective. Buy heat pumps instead of furnaces, heat pump water heaters instead of normal water heaters, induction cooktops instead of gas cooktops, heat pump dryers instead of gas dryers, and electric vehicles instead of gasoline or diesel vehicles (if not ready for full electric, buy a plug in hybrid). * Fly less. * Drive less. * Eat less red meat. * Vote against climate deniers and fossil fuel apologists again!
Much of what needs to happen is to change our SYSTEMS, which is not under the control of most individuals, but the actions above are both under individual control and highly impactful. For more ideas, see our 2022 book:
Koomey, Jonathan, and Ian Monroe. 2022. Solving climate change: A guide for learners and leaders. Bristol, UK: IOP Publishing. [http://www.solveclimate.org]
The frenzy over new projections of electricity growth continues to escalate. This excellent episode of the Energy Transition Show is the best counterweight to that crisis mentality that I’ve found. The show notes themselves are extensive for those who want to dig in further.
Short summary: There are many reasons to believe that the utilities who are fanning the crisis mentality are doing it for self interested reasons based on data that are at best incomplete. Don’t take any of these claims at face value.
Related: Our Nature commentary on the need for scenarios to understand the effects of AI on electricity use in the face of deep uncertainty:
Luers, Amy, Jonathan Koomey, Eric Masanet, Owen Gaffney, Felix Creutzig, Juan Lavista Ferres, and Eric Horvitz. 2024. “Will AI accelerate or delay the race to net-zero emissions?” Nature. vol. 628, April 22. pp. 718-720. [https://doi.org/10.1038/d41586-024-01137-x]
I worked with a stellar team of the world’s top experts on computing’s effect on energy and emissions to craft this commentary for Nature, which came out today (April 22, 2024):
Luers, Amy, Jonathan Koomey, Eric Masanet, Owen Gaffney, Felix Creutzig, Juan Lavista Ferres, and Eric Horvitz. 2024. “Will AI accelerate or delay the race to net-zero emissions?” Nature. vol. 628, April 22. pp. 718-720. [https://doi.org/10.1038/d41586-024-01137-x]
Here’s the bottom line summary:
“Artificial Intelligence (AI) is one of the most disruptive technologies of our time. It’s imperative that decisions around its development and use — today and as it evolves — are made with sustainability in mind. Only through developing a set of standard AI-driven emissions scenarios will policymakers, investors, advocates, private companies and the scientific community have the tools to make sound decisions regarding AI and the global race to net-zero emissions.”
This conversation was a fun one. here’s the description:
Will the rise of machine learning and artificial intelligence break the climate system? In recent months, utilities and tech companies have argued that soaring use of AI will overwhelm electricity markets. Is that true — or is it a sales pitch meant to build more gas plants? And how much electricity do data centers and AI use today? In this week’s episode, Rob and Jesse talk to Jonathan Koomey, an independent researcher, lecturer, and entrepreneur who studies the energy impacts of the internet and information technology. We discuss why AI may not break the electricity system and the long history of anxiety over computing’s energy use. Shift Key is hosted by Robinson Meyer, executive editor of Heatmap, and Jesse Jenkins, a Princeton professor of energy systems engineering.
In 2011, we replaced lighting cans with LED inserts in our house, instantly reducing lighting energy use by 50% or more. The inserts looked like the ones on the left in the photos below.
Recently (September 2023) I needed to buy a few more to replace some of the old ones that failed. The new ones look like the one on the right in the photos. Both give 700 lumens of light output.
The old ones (with the little wire that screws into the socket) weigh 486 grams, use 11 W, have a color temperature of 3000 K, are about 11.9 cm high, and cost $50 each.
The newest ones weigh 226 grams, use 10 W, have a more pleasing color temperature of 2700 K, are about 6.3 cm tall, cost $11 each, and occupy less than half the volume of the 2011 version.
In a dozen years the price has come down a factor of nine, volume and weight are down by a factor of two (making shipping easier and less expensive), efficiency has improved about 9%, and lighting quality has improved. Not too shabby!
Technological progress like this is why Amory Lovins calls efficiency a renewable resource. It keeps getting better and cheaper over time!
For an intermediate look at the state of this technology in 2019, go here.
Every year since Chris Nelder started the Energy Transition Show, he’s interviewed me for the annual roundup episode, and this year is no exception. We discuss the proper role of government in a capitalist economy, climate change doomism, how the fossil fuel industry rigs the system, and the difficulties of the mid-transition as we shift away from conventional energy systems.
I, along with colleagues at World Resources Institute and Koomey Analytics, just had a commentary published in WIRES Climate Change. It’s titled “Abandon the idea of an ‘optimal economic path’ for climate policy”.
Many economic modelers think that if given enough time, money, graduate students, and coffee they can estimate an “optimal economic path” for climate mitigation that extends far into the future. They further argue that this path is the correct or best way to guide climate policy design.
The most prominent example is that of Nobel prize winning professor William Nordhaus, the father of cost-benefit or benefit-cost analysis for climate [1]. In his 2018 Nobel acceptance speech, Nordhaus [2] said:
[I]n the view of most economists, balancing of costs and benefits is the most satisfactory way to develop climate policy.
[O]ne of the most amazing results of Integrated Assessment Models (IAMs) is the ability to calculate the optimal carbon price…This concept represents the economic cost caused by an additional ton of carbon dioxide emissions (or more succinctly carbon) or its equivalent…In an optimized climate policy (abstracting away from various distortions), the social cost of carbon will equal the carbon price or the carbon tax.
Nordhaus argues that IAMs can estimate carbon prices that optimize global consumption, emissions, and climate change, balancing mitigation or abatement costs against benefits of reducing emissions (like risk reduction and avoided climate damages). Similar analyses, focused on damage costs, are used to assess appropriate social costs of carbon for regulatory purposes [3].
This way of framing the problem can be summarized in the following graph, which depicts benefit and cost curves in stylized fashion. It characterizes the place where the two curves cross as the “optimal” level of GHG reductions, where the marginal cost of reducing emissions is equal to the marginal benefits from reducing them. The point also suggests the optimal carbon price, as in the Nordhaus quotation above. In this view, reducing emissions beyond that point would imply that we are paying too much for emissions reductions because the costs for incremental emissions reductions would exceed the benefits.
This commentary focuses attention on underlying ideas about “optimal paths” that are in our view not widely enough understood and are often unstated, namely that
(1) there IS a single unique optimal path to solving the climate problem,
(2) this path exists independent of human choices, and
(3) society can discover this path in advance through better data collection, analysis, and logical thinking.
These beliefs are at odds with our current understanding of the forces driving the development of real economic and technological systems, which are dominated by increasing returns to scale, network externalities, learning curves, and other non-linear effects. Real non-linear systems are subject to “sensitive dependence on initial conditions”, which leads to chaotic and often unpredictable behavior of such systems in the face of imperfect measurements, randomness, and human choices [4, 5, 6, 7, 8]. Models of non-linear systems are also strongly affected by uncertainties in model structure, complicating things still further [9].
The full reference for the commentary is
Koomey, Jonathan, Zachary Schmidt, Karl Hausker, and Dan Lashof. 2023. “Abandon the idea of an “optimal economic path” for climate policy.” Invited Commentary for WIREs Climate Change. vol. e850, July 2. [http://doi.org/10.1002/wcc.850]
To download a pre-publication version of the article, click here.
3. US EPA. 2022. Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances. Washington, DC: U.S. Environmental Protection Agency. September. [https://www.epa.gov/environmental-economics/scghg]
7. Pluchino, Alessandro, Alessio Emanuele Biondo, and Andrea Rapisarda. 2018. “Talent versus luck: The role of randomness in success and failure.” Advances in Complex Systems. vol. 21, no. 03n04. pp. 1850014. [https://www.worldscientific.com/doi/abs/10.1142/S0219525918500145]
9. Thompson, Erica. 2022. Escape from model land: How mathematical models can lead us astray and what we can do about it. New York, NY: Basic Books. [https://amzn.to/3HDxH5t]
On June 19th and 20th, 2023, students and colleagues of Arnulf gathered at Oxford for a symposium to honor him and discuss data and methods for understanding technology and global change for the next twenty five years.
The workshop was organized by Charlie Wilson of Oxford and Greg Nemet at the University of Wisconsin, Madison, and was attended by a stellar cast of researchers and practitioners, some of whom I knew, others I was glad to have met for the first time at the workshop.
Ian Monroe and I talked with Allyson Klein at Tech Arena for her podcast about our book, Solving Climate Change: A Guide for Learners and Leaders. It’s a half hour conversation that covers many of the key lessons from our textbook. We think you’ll enjoy it.
Allyson was at Intel for many years and I had talked with her back in 2012 (and probably more recently) about ICT electricity use on her “Chip Chat” interview show.
On May 10, 2023, Ian Monroe and I gave a live webinar for the Institute of Physics (IOP) about our latest book, Solving Climate Change: A Guide for Learners and Leaders. The webinar is still posted at the Physics World website, and if you register you can watch it on demand:
Instructors can request an examination copy from IOP publishing: shorturl.at/qrLM0
After that webinar, four questions came back from the audience. Ian and I answered them, and I thought it would be good to post them here as well.
If you listen to the webinar or read the book and have questions, Email me and we’ll add them to the list of answered questions so others can benefit.
Question 1: For an individual with limited reach, do you have any advice when it comes to dealing with friends and family that are unwilling to listen to these facts and change their behavior to minimize their impact? It can be quite frustrating while having to deal with climate anxiety as well.
Getting people to change their habits is hard, but we need all levels of action to decarbonize our global economy. One of the most important things is to vote for politicians who promise real climate action, because the SYSTEM needs to change to get the emissions reductions we need. Politicians are much more likely to enact good climate policies if individual voters are demanding them, and individual spending and investment decisions can also influence companies who then influence politicians. Most people already support renewable energy and energy efficiency because they save people money and are cleaner than fossil alternatives.
To change people’s behavior, we’ve found that leading with the benefits of new technology (distinct from emissions reductions) can often be effective. Electric vehicles are cheaper to run, are cleaner, and are more fun to drive. Electric heat pumps generate no carbon monoxide, are quieter, are often cheaper to run, and are cheaper to install when replacing a furnace/AC combo (because heat pumps replace two pieces of equipment with one). Switching to electric cooking also improves indoor air quality, reducing the likelihood of asthma. Eating plant-based foods and reducing red meat generally improves overall health.
There are some online resources that can help. This one is great (and funny):
Question 2: In Mexico City we have students that spend a lot of time getting to college, sometimes 1 or 2 hours to arrive there. To promote remote work and less commuting, do you recommend online courses, in particular for engineering and sciences students? Teachers are reluctant to this change.
We are huge fans of remote work and study. As Amory Lovins says, move the electrons, leave the heavy nuclei at home! It’s vastly less emissions intensive to conduct lessons remotely. It takes different preparation for professors but it’s not clear that it takes more preparation, and the benefits are big, not just for climate, but also for quality of life. While it can be hard to fully replace the benefits of in-person instruction, hybrid systems where remote instruction is paired with limited in-person meetings can provide similar benefits, and we have increasingly better tools for replicating in-person experiences with online alternatives (which younger generations often prefer).
Question 3: You didn’t seem to mention reducing energy use. Is it wise not to assume this will happen?
Using energy more efficiently is great, but in the book we focus on what we call emissions efficiency and optimization, because energy efficiency is too narrow a frame for this problem. There is no question that we can reduce waste and eliminate unproductive uses of energy, but when energy is produced renewably, it may be just fine from an emissions perspective to use more.
In addition, the switch to electricity, which eliminates many sources of losses in combustion when electricity is generated from renewables, will result in a substantial increase in electricity use while significantly reducing fossil fuel energy use. Combustion losses are so significant in the current economy (somewhere around 20-30 % of total primary energy) that eliminating them will result in substantial energy savings for society even as electricity use goes up.
Question 4: I wish to know what actions can be taken in developing economies that depend on oil so well & are not anywhere near the expected green electrification needed to achieve a net zero carbon emissions footprint.
There is no need for developing countries to repeat our mistakes, especially since the alternatives to fossil fuels are now cheaper in societal terms virtually everywhere and cheaper in direct cost terms in many cases. There is no case for expanding fossil fuel infrastructure anywhere on the planet (with very few exceptions). Most electrification, renewable energy, and other climate solution technologies have even greater economic, health, and wellbeing benefits for developing economies that currently suffer proportionally more from existing fossil fuel and unsustainable agriculture pollution and economic distortion. Most fossil infrastructure expansion proposals are now being driven by fossil fuel interests because they want to lock in users as much as possible before serious emissions reductions begin. Their strategy is what the futurist Alex Steffen correctly calls “predatory delay”.
Electrifying two wheeled vehicles is already happening rapidly in the developing world, as is deployment of renewables in some places. China is now by far the world’s leader in scaling up electric vehicle, and while China now leads in electric car, bus, and truck production, China started by producing hundreds of millions of electric scooters and bikes, which cost less to run than the fossil-fueled vehicles they replace. The key is to overcome the power of vested interests, who want to delay action for as long as possible (because it benefits them).
Another step many countries can take is to protect natural areas from further destruction, to maintain the ecosystem services they provide while responsibly developing industries (like tourism) that thrive when forests and other natural systems are healthy.
Solving Climate Change: A Guide for Learners and Leaders, was released in late December 2022. The publisher, IOP, recently made if freely downloadable through May 21, 2023, so get it while it’s still free!
My talk on February 9, 2023 for the Salinas Rotary club is an expansion of points made in a commentary article by me and Professor Eric Masanet, UCSB, in Joule in 2021:
In the talk, I presented nine different high-profile misconceptions about electricity use and emissions associated with computing, explored four pitfalls that lead to such misconceptions, and suggested four ways we can do better in the future.
Here is a graph illustrating that substantial increases in information technology services, in this case data flows, does not necessarily imply increases in energy use.
Here is the conclusions slide:
You can download a PDF of the slides (which include three pages of references) HERE.
