Credit: Unsplash/CC0 Public Domain
Wind and solar power have grown faster than almost
anyone predicted, but projecting their future expansion remains surprisingly
difficult. Researchers at Chalmers University of Technology, Sweden, have
developed what they call a computational "time machine"—a model that
outperforms existing projection methods by using AI techniques to analyze
historical growth patterns across countries. Their central projection shows
that onshore wind is likely to supply around 25% of global electricity by 2050,
with solar reaching about 20%. This is consistent with the 2°C target, but
falls short of what is required for 1.5°C. The work appears in Nature Energy.
Predicting the future is particularly challenging for
technologies like wind and solar, where rapid cost declines are offset by
growing barriers such as public opposition, infrastructure constraints and policy shifts.
"Existing models are very good at identifying
what needs to happen to reach climate targets, but they can't tell us which
developments are most likely. That's the gap we wanted to fill," says
Jessica Jewell, Professor at Chalmers University of Technology.
Across more than 200 countries, the researchers
identified a recurring pattern in how wind and solar power grow: long periods
of relatively steady expansion punctuated by sudden growth spurts often triggered by policy shifts.
"Most models assume a smooth S-shaped growth
curve, but that's not how it actually looks in the real world. Growth often
comes in bursts, and if you ignore that, you can misjudge how fast technologies
will expand," says Avi Jakhmola, Ph.D. Student at Chalmers University of
Technology and first author of the paper.
13,000 virtual worlds for the future
So, with the goal of improving the predictions,
Jakhmola created a model built on 13,000 virtual worlds. In each of these
worlds, solar and wind power develop in different ways—from the fastest
possible expansion to the slowest—and everything in between. A machine learning
algorithm was then trained on all these worlds to learn to predict global
outcomes from early national trends.
"When we apply the model to real-world data, it
can tell us what is the most probable outcome for the future—given what we have
seen so far and given all the virtual worlds it has seen," says Jakhmola.
By 2050, the model projects onshore wind reaching
around 26% of global electricity (central range: 20–34%), and solar around 21%
(15–29%). This broadly aligns with 2°C-compatible pathways but falls short of
what's needed for 1.5°C.
The projections also put the COP28 pledge to triple renewables capacity by 2030 in
perspective. The pledge falls near the 95th percentile, meaning that it would
require growth rates rarely observed.
"The tripling of the renewables pledge is not
impossible, but it would require everything to go extremely well in all
countries," says Jewell.
The researchers also tested what would actually be
required for us to reach the 1.5°C goal.
"If we start now, the required growth rates are
demanding but not unprecedented, comparable to what the EU targets for wind
with REPowerEU and what India has planned for solar power," says Jakhmola.
"But if we delay until 2030, the acceleration needed becomes much steeper
and much more abrupt. The window for ramping up closes quickly."
Going back in time to ensure the model's
reliability
The researchers also used the model to test the
reliability of its projections—by going back in time.
"We wanted to know if our projections will hold
up ten or twenty years from now. When we fed the model only data from 2015, we
found that it correctly predicts what has happened since then. This is what we
mean by a computational time machine, and it gives us real confidence in the
projections going forward," says Jakhmola.
The study points toward a broader ambition to develop
scientifically-rigorous methods for projecting the most likely growth paths for
other low-carbon technologies, not just wind and solar.
Jewell says, "It's long been a joke how bad
technology forecasts are. But if you're a decision maker, trying to figure out
how hard to push for change, you need a realistic baseline. Our study is the
first step towards developing such a realistic view of the future."
Provided by Chalmers University of Technology
by Chalmers University of Technology
edited
by Stephanie
Baum, reviewed by Robert Egan
Source: Computational 'time machine' shows
solar and wind power on track for 2°C target, but not for 1.5°C
