Green technology to burst 'carbon bubble' in catastrophe for fossil-fuel economies, new research predicts

Researchers’ model suggests between $1tn (£750bn) and $4tn could be wiped off value of global fossil fuel assets by 2035

Trillions of dollars of fossil fuel wealth will be wiped out at some point over the next 17 years even if governments fail to impose binding carbon emissions limits on industry to curb global warming, according to a major new study.

Environmentalists and policymakers have long warned of the threat of a “carbon bubble” and “stranded assets” for listed energy companies, based on the possibility they will never be able to realise the value of their vast stores of oil, gas and coal if politicians actually deliver on their decarbonisation promises.

But today a group of scientists and analysts from Cambridge, Nijmegen, Macao and the Open University take that warning a step further by arguing that these assets are destined to be stranded regardless of official policies to discourage the use of fossil fuels because clean energy technologies are now developing so rapidly that those polluting assets will be worthless in any case.

“Our analysis suggests that, contrary to investor expectations, the stranding of fossil fuels assets may happen even without new climate policies. This suggests a carbon bubble is forming and it is likely to burst,” said Professor Jorge Viñuales from Cambridge University.

If policymakers did deliver on the decarbonisation programmes, the loss for investors would be even more rapid.

The research is at odds with work from the International Energy Agency, which projects steady price rises for fossil fuels until 2040. And Donald Trump’s decision last year to pull the United States out of the Paris Agreement on climate change has also done nothing to persuade most investors to take the stranded assets warning seriously.

But the researchers’ new “simulation-based, energy-economy-carbon-cycle climate” model suggests investing in fossil fuel firms today is likely to prove a disastrous bet, suggesting that between $1 trillion (£750bn) and $4 trillion (£3 trillion) could be wiped off the value of global fossil fuel assets by 2035.

They also break down the impact of this climate bubble bursting by region and country, finding that major fossil fuel exporters such as the United States, Canada and Russia will see significant falls in GDP and major increases in unemployment, as employees in the sector are laid off.

“Mass unemployment from carbon-based industries could feed public disenchantment and populist politics,” said Professor Viñuales.

However, countries that are currently net importers of fossil fuel energy, such as China, India, Japan and the nations of the EU, will get a GDP boost from the collapse of the carbon industry, according to the researchers.

At the level of the global economy, the researchers estimate that the regional boom and bust effects will cancel out leaving international GDP growth unchanged.

The Paris Agreement aims to limit the increase in global average temperatures to below 2C above pre-industrial levels.

The global “carbon budget” estimated to be consistent with meeting that target is between one fifth and one third of the world’s reserve of oil, gas and coal.

The Governor of the Bank of England, Mark Carney, warned in 2015 that the bursting of a carbon bubble could potentially destabilise financial markets and hit insurers especially hard.

The price of solar panels around the world has been falling much more rapidly than previous expected, meaning that the costs of decarbonising energy production is also lower. The numbers of people switching to electric or hybrid vehicles, helped by state subsidies and regulation, has also been rising faster than previously projected.

The researchers’ model assumes that solar energy partially displaces the use of coal and natural gas for power generation before 2050 and that global petrol use also peaks in the coming decades

It says: “These results are robust and driven by historical data rather than by exogenous modelling assumptions”.