Governments, investors and business leaders need to adopt practical solutions that can be deployed across the world at scale. The arrival of 5G along with wider adoption of AI technology into the physical world will make it possible to substantially enhance the opportunities to scale cleaner energy generation technologies, enable efficiency gains in manufacturing, our homes, retail stores, offices and transportation that will enable substantial reductions in pollution. Policies that incentivise the accelerated development and deployment of Industry 4.0 solutions will require politicians and regulators to better understand the opportunities that 5G alongside AI will enable. The OECD published a paper "What works in Innovation Policy" and observed that "Policies ignoring or resisting the industrial transition have proven to be not just futile but result in an innovative disadvantage and weak economic performance." Entering the new year will allow us to develop and deploy solutions for the 2020s that make use of the next industrial revolution with 5G and AI to enable dramatic efficiency gains across all sectors of the economy and to enhance renewable energy generation. The emergence of India, China and others as industrial economic powers is occurring at a time when we now know the damage that such pollution causes and hence there is a need to work together, collaboratively to solve a global problem. Embracing technological change and enhancing its capabilities to deliver better living standards alongside sustainable development is the best option for those who really want to make an impact on climate change at scale in the 2020s and beyond. I wish to thank Henry Derwent, former advisor to Prime Minister Margaret Thatcher and former CEO of IETA for his efforts to promote technological innovation and scaled up financing with Green Bonds.
Climate change is one of the most pressing issues of our time. Despite increasing global consensus about the urgency of reducing emissions since the 1980s, they continue to rise relentlessly. We look to technology to deliver us from climate change, preferably without sacrificing economic growth. Our optimistic--some would say techno-utopian--visions of the future involve vast arrays of solar panels, machines that suck carbon dioxide back out of the atmosphere, and replacing fossil fuels for transport and heating with electricity generated by renewable means. This is nothing less than rebuilding our civilization on stable, sustainable foundations.
This article was first published in Branch magazine, an online collaboration between EIT Climate-KIC, Mozilla Foundation and Climate Action.tech A global pandemic has shocked the world, leading to thousands of deaths, economic hardship and profound social disruption. While we worry about our immediate needs, we should remember that another crisis is looming: climate change. The lockdown made it clear that staying at home and slowing down the economy is far from enough to solve the climate crisis. We're still emitting more than 80 per cent as much CO2 as normal, despite having 17 per cent fewer emissions compared to 2019 -- which is one of the most significant drops in recent years (1).
In December 2020 we launched a focus series AI for Good: UN sustainable development goals (SDGs). Each month we pick a different sustainable development goal (SDG) and highlight work in that area. February was the turn of UN SDG number 13: climate action. In this summary article we highlight some of work at the intersection of AI and climate science. Climate Change AI (CCAI) is a volunteer-led effort bringing together people from academia, industry, and the public sector.
Some of the biggest names in AI research have laid out a road map suggesting how machine learning can help save our planet and humanity from imminent peril. The report covers possible machine-learning interventions in 13 domains, from electricity systems to farms and forests to climate prediction. Within each domain, it breaks out the contributions for various subdisciplines within machine learning, including computer vision, natural-language processing, and reinforcement learning. Recommendations are also divided into three categories: "high leverage" for problems well suited to machine learning where such interventions may have an especially great impact; "long-term" for solutions that won't have payoffs until 2040; and "high risk" for pursuits that have less certain outcomes, either because the technology isn't mature or because not enough is known to assess the consequences. Many of the recommendations also summarize existing efforts that are already happening but not yet at scale.