{"id":114052,"date":"2026-04-16T10:29:18","date_gmt":"2026-04-16T08:29:18","guid":{"rendered":"https:\/\/www.sonnenseite.com\/?p=114052"},"modified":"2026-04-16T10:29:19","modified_gmt":"2026-04-16T08:29:19","slug":"computational-time-machine-shows-solar-and-wind-on-track-for-2c-target-but-not-for-1-5c","status":"publish","type":"post","link":"https:\/\/www.sonnenseite.com\/en\/future\/computational-time-machine-shows-solar-and-wind-on-track-for-2c-target-but-not-for-1-5c\/","title":{"rendered":"Computational \u201ctime machine\u201d shows solar and wind on track for 2\u00b0C target but not for 1.5\u00b0C"},"content":{"rendered":"\n

Wind and solar power have grown faster than almost anyone predicted but projecting their future expansion remains surprisingly difficult. Researchers at Chalmers have developed what they call a computational \u201ctime machine\u201d \u2013 a model that outperforms existing projection methods by using AI techniques to analyse historical growth patterns across countries. Their central projection shows that onshore wind is likely to supply around 25 per cent of global electricity by 2050, with solar reaching about 20 per cent. This is consistent with the 2\u00b0C target, but falls short of what is required for 1.5\u00b0C.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cExisting models are very good at identifying what needs to happen to reach climate targets, but they can\u2019t tell us which developments are most likely. That\u2019s the gap we wanted to fill\u201d, says Jessica Jewell, Professor at Chalmers University of Technology.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cMost models assume a smooth S-shaped growth curve, but that\u2019s 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,\u201d says Avi Jakhmola, PhD Student at Chalmers University of Technology and first author of the paper published in Nature Energy.<\/em><\/p>\n\n\n\n

13,000 virtual worlds for the future<\/h4>\n\n\n\n

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 \u2013 from the fastest possible expansion to the slowest \u2013 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.<\/p>\n\n\n\n

\u201cWhen we apply the model to real-world data, it can tell us what is the most probable outcome for the future \u2013 given what we have seen so far and given all the virtual worlds it has seen\u201d, says Jakhmola.<\/p>\n\n\n\n

By 2050, the model projects onshore wind reaching around 26 per cent of global electricity (central range: 20-34 per cent), and solar around 21 per cent (15-29 per cent). This broadly aligns with 2\u00b0C-compatible pathways but falls short of what\u2019s needed for 1.5\u00b0C.<\/p>\n\n\n\n

The projections also put the COP28 pledge to triple renewables capacity by 2030 in perspective. The pledge falls near the 95th<\/sup> percentile meaning that it would require growth rates rarely observed.<\/p>\n\n\n\n

\u201cThe tripling of renewables pledge is not impossible, but it would require everything to go extremely well in all countries\u201d, says Jewell.<\/p>\n\n\n\n

The researchers also tested what would actually be required if we are to reach the 1.5\u00b0C goal.<\/p>\n\n\n\n

\u201cIf we start now, the required growth rates are demanding but not unprecedented, comparable to what the EU targets for wind with REPowerEU<\/a> and what India has planned for solar power,\u201d says Jakhmola. \u201cBut if we delay until 2030, the acceleration needed becomes much steeper and much more abrupt. The window for ramping up closes quickly.\u201d<\/p>\n\n\n\n

Going back in time to ensure the model\u2019s reliability<\/h4>\n\n\n\n

The researchers also used the model to test the reliability of its projections \u2013 by going back in time.<\/p>\n\n\n\n

\u201cWe 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 \u2018computational time machine\u2019 and it gives us real confidence in the projections going forward\u201d, says Jakhmola.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Jessica Jewell says: \u201cIt\u2019s long been a joke how bad technology forecasts are. But if you\u2019re 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.\u201d<\/p>\n\n\n\n

More about the research<\/h4>\n\n\n\n

The paper ‘Probabilistic projections of global wind and solar power growth based on historical national experience<\/a>‘,  has been published in Nature Energy<\/em>. The authors are Avi Jakhmola,  Jessica Jewell, Vadim Vinichenko and Aleh Cherp. The researchers are active at Chalmers University of Technology and Lund University in Sweden, University of Bergen in Norway, International Institute for Applied Systems Analysis and Central European University in Austria.<\/p>\n\n\n\n

The researchers have also made an online visualisation tool of the results<\/a>, available at the Energy Technology and Policy website.<\/p>\n\n\n\n

Fun fact: The idea for the model was partly inspired by a colleague in a different field. During the early days of his PhD studies, Avi Jakhmola shared an office with a theoretical ecologist in the same division, whose work on simulating ecosystems sparked the idea of creating thousands of \u201cvirtual worlds\u201d to study how technologies evolve.<\/p>\n\n\n\n

More about the targets and the Paris Climate Agreement:<\/h4>\n\n\n\n

The Paris Climate Agreement<\/a>\u00a0is a\u00a0legally binding international treaty on climate change. It was adopted by 196 Parties at the UN Climate Change Conference (COP21) in Paris, France, on 12 December 2015 and entered into force on 4 November 2016. Its overarching goal is to hold \u201cthe increase in the global average temperature to well below 2\u00b0C above pre-industrial levels\u201d and pursue efforts \u201cto limit the temperature increase to 1.5\u00b0C above pre-industrial levels.\u201d<\/p>\n\n\n\n