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Decarbonising the energy system by 2050 could save trillions

Decarbonising the energy system by 2050 could save trillions

13 September 2022

For decades, scientists have called for a transition to clean energy to prevent the worst impacts of climate change but fears that such a transition would be costly and harm the economy have held back progress. However, a study published in Joule shows the reverse: an ambitious, decisive transition to green energy technologies such as solar, wind, and batteries, will likely save the world significant sums of money.

The research shows a win-win-win scenario, in which a transition to nearly 100% clean energy by 2050 results in lower energy system costs than a fossil fuel system, while providing more energy to the global economy, and expanding energy access to more people around the world. This result is based purely on the economics of different energy technologies, even without accounting for the costs of climate damages and climate adaptation that would be avoided by such an energy transition.

Since Russia’s invasion of Ukraine, the costs of fossil energy have skyrocketed, causing inflation around the world. This study, conducted before the current crisis, takes account of such fluctuations using over a century’s worth of fossil fuel price data. The current energy crisis underscores the study’s findings and demonstrates the risks of continuing to rely on expensive, insecure, fossil fuels. The research confirms that the response to the crisis should include accelerating the transition to low cost, clean energy as soon as possible, as this will bring benefits both for the economy and the planet.

For decades, standard models got clean energy costs badly wrong. The researchers analysed thousands of transition cost scenarios produced by major energy models and found that the real cost of solar energy dropped twice as fast as the most ambitious projections in these models. Over 20 years all of these models badly overestimated the future costs of key clean energy technologies versus reality.

Our team used a different approach, developing a ‘probabilistic model’ to estimate the costs of possible future energy systems more accurately based on past data. Probabilistic models are used widely throughout industry and research to estimate the likelihood of future events. The betting industry, for example, uses probabilistic models to make forecasts that, while never perfect, on average get the odds right and that enabled them to make £5.8 billion in UK profits in 2020 alone.

The team tested the forecasting method against decades of historical data for 50 different technologies, using a technique called backtesting, which is routinely used in the financial industry. The data used in this study includes 45 years of solar energy costs, 37 years of wind energy costs and 25 years for battery storage. Using probabilistic models, this study shows that the probability of further cost reductions in key green energy technologies is now so high that our best bet is to push ahead rapidly with the energy transition.

A common objection to scenarios for a rapid transition to net-zero carbon is the need for large amounts of energy storage to handle intermittent renewables. The study showed that the costs for key storage technologies, such as batteries and hydrogen electrolysis, are also likely to fall dramatically. Meanwhile, the costs of nuclear have consistently increased over the last five decades, making it highly unlikely to be cost competitive with plunging renewable and storage costs.

The study’s ‘Fast Transition’ scenario shows a realistic future of a near fossil-free energy system by 2050, providing 55% more energy services globally than today, with large amounts of wind, solar, batteries, electric vehicles, and clean fuels such as green hydrogen (made from renewable electricity). Such a net-zero carbon future is not only technically feasible, but the research shows it is expected to cost the world $12 trillion less than continuing with the polluting fossil fuel-based system we have today.

Image by Peter Dargatz from Pixabay

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