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Policy
| 06/27/18

What role for artificial intelligence in the fight against climate change?

TCE

Digital technologies are enabling clean technologies, from the energy sector to transport, but many of the same innovations could also enable the fossil fuel industry.

A raft of digital technologies that have come into their own in the past decade, from machine learning to data analytics, offer both opportunity and risk to climate change, warns an extensive new report from some of the sector’s key thinkers.

Headline writers have been wowed by the feats performed and the changes wrought by advances in artificial intelligence, the declining cost of sensors, system automation, and the Internet of things. But up to now, it has been rare to consider how the new digital revolution might impact climate and energy.

Earlier this year, the Council on Foreign Relations, a US international affairs think-tank convened a workshop on the subject, inviting scientists, former government officials and the business community to explore the different ways these technologies might assist with the clean transition. Last week, the organization published their findings in a 138-page book, Digital Decarbonization.

The participants identified ways in which digital tech was already supporting clean technologies, but they warned that the same processes could enable the fossil fuel industry as well. Both sides of the ledger need to be carefully attended to by policymakers, they argue in the book, in order to avoid serious risks.

Digital communication, data analytics and system automation software in particular are allowing electric companies to run their grids more efficiently and allow consumers to use less energy. At the same time, these utilities are producing eye-watering volumes of data but not in standard formats between or even inside firms, meaning that there are still many more efficiencies to be achieved, and thus potentially lower emissions, with better wrangling of this data.

The major stumbling block that many renewable energy sources such as solar and wind face is their variability. But utilities could upgrade their grids with digital sensors and new communications systems as consumers begin to move over to smart appliances and programmable thermostats. This should allow them to better match supply and demand and ameliorate to some extent the intermittency problem.

But coal, oil and gas companies can also use many of the same techniques to achieve efficiencies in production such as hiking extraction from shale wells and in coal plant maintenance, resulting in a lengthening of their lifetimes.

The workshop attendees focussed on the mitigation side of the climate conundrum, but they did not look at adaptation to global warming. As it happens, the Pacific Institute for Climate Solutions is supporting the application of artificial neural networks, which involve digital models based on the neural structure of the brain that learn from experience, to improve the trustworthiness of flood impact analyses.

There is a great amount of uncertainty in the scale of flooding as a result of rising sea levels. If a city only comes up with an adaptation strategy for one or a handful of flooding scenarios, then the strategy is unlikely to work, as the probability of one scenario occurring out of many possibilities is low. This means there needs to be an assessment of a much wider range of scenarios. This would be very difficult and labour intensive for humans to do. So researchers are developing a new method via machine learning to discover impacts across hundreds of scenarios. The technique is first being applied to the City of Vancouver.

In depth

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