Data Science in Clean Technology: Looking back and looking forward to 2021
Happy 2021 everyone! The year certainly started with a bang - if there’s one thing we can’t say - it’s that January has been a boring month.
With the change in administrations in the United States, we’ve seen several changes in climate and clean technology policy which will have significant impacts on the state of the market, startups in the field, funded research and technology developments. And this is likely to extend to the use of machine learning, AI, smart sensors and the other aspects of data science in clean technology. So, let’s take a quick look at what happened at the intersection of data science and clean technology in 2020 and what the prospects for 2021 look like.
Looking back at 2020:
2020 of course, is the year like no other - divided between pre-pandemic and what happened as the pandemic took hold globally. So, how did that influence the trends in clean technology and the application of data science in the different sectors?
Pre-pandemic, there were some clear breakouts in the clean technology sectors where machine learning, AI and data science in general were being used extensively. Energy and agriculture - as has been the case for the last couple of years - were the big players. There were several new companies entering the field, existing startups in prediction and monitoring being acquired by Fortune 500 companies and researchers and scientists around the world were continuing to develop new algorithms and new technology using satellite data, drone data, biomimicry and other aspects of data science. There were several hundred jobs at the intersection of data science and clean technology that were posted globally every week - and in general, it seemed that the sector was poised for a stellar year.
As the pandemic struck, we saw an immediate impact on new projects and construction in all the clean tech sectors. New renewable energy plants were put on hold, new construction in the water and sustainable manufacturing sectors were delayed and existing upgrades and improvements were evaluated to see how the systems would function in a world where many workers had to work from home. Additionally, sectors like the food and agricultural sector, transportation and urban sustainability, had to reevaluate existing supply chains, labor requirements and in some cases, like the transportation sector - reevaluate the entire sector itself and the long-term impacts from the pandemic.
Naturally, this impacted hiring in the sector - job postings dropped to almost zero and did not recover until sometime in August. At present, there are several roles that are open - fewer than we saw in January and February of 2020, but we’re seeing a return to job growth - albeit slower than it was before.
However, one of the most interesting aspects of the pandemic in the clean technology field was the acceleration of trends with respect to how automation, robotics, smart sensors, machine learning and data science are penetrating the different sectors. One of the biggest winners in 2020 was the rise of predictive maintenance and smart sensors in the water, energy and construction sectors. As work-from-home orders and lockdowns became more frequent and in some cases hard to predict, utilities and companies started focusing on using machine learning with smart sensors in order to augment their limited workforce. This meant that among other issues - there was a lot of interest in figuring out better maintenance systems through anomaly detection and sensor data that was stored on the cloud, understanding supply chains and predicting and managing potential disruptions from the pandemic, and in general moving towards targeted investments in data science and robotics that were likely to yield high returns in such uncertain times.
Probably the most interesting aspect was seeing the water sector break out in 2020. Water, of course, as a human necessity and a public good - has been classified as an essential industry throughout the world. But 2020 was probably the first year that water managers and companies as a whole saw the value of data science and analytics in improving and managing their systems for the customers and for the operators.
Looking forward to 2021
If the water sector was a surprising star in 2020, climate and climate resilience are shaping up to be the big breakouts in 2021. Some of that, of course, is related to the changing administration in the United States. The last week or so has seen the USA rejoin the Paris accords, institute climate bonds and look at incorporating clean tech investment in budgets as a source of economic benefits, including job creation at the local scale.
Additionally, the pandemic has shown the world that our systems are fragile - and as climate related disasters increase around the globe, countries are interested in building systems that are likely to withstand these shocks over time. So, that has meant far more interest from the large companies, governments and other players in clean technology and the application of data science in the field.For example, General Motors (GM), has announced that it will stop manufacturing gasoline powered vehicles by 2035. That’s quite a change for a company that only last year was suing the state of California over its emission standards! Electric vehicles (EVs) are the fastest growing segment of the automotive market and we’ve been seeing moves from all the major players towards autonomous cars, buses and trucks - so, another huge opportunity where data science, smart sensors and clean technology will come together.
As the vaccines roll out globally, and we come to terms with the “new normal” or whatever returns by mid to late 2021, it’s worth watching which countries are going to be taking the lead in data science, clean technology and climate resilience and which ones are hanging on to old systems. It’s quite likely that we’re in for a decade where we’ll see countries leapfrogging their development - simply by creating opportunities for these new technologies to emerge and be deployed.
We're in the processes of building a couple of fantastic new offerings that many folks in our community have asked for - so blog posts will be limited for a few months. Our jobs portal will still be updated regularly to make sure that all our members can keep up with what's happening in the sector. We can't wait to share what's happening at our end!
The last couple of months have been interesting from a climate viewpoint - we’ve seen a record number of climate related disasters around the globe - drought, floods, fires, heat waves…..and it looks like this is probably going to be what our planet will look like in the near future. Add to that the COP26 conference that is scheduled for October 31st - and climate, sustainability and technology are front page news! So, let’s talk about one of the technologies in the news - artificial intelligence (AI) and its impact on climate, water, agriculture, energy, forestry, ecosystems and other sectors in clean technology . AI and its subset of tools - machine learning (ML), data science and statistics - are being touted as one of the key technologies in solving the problems facing the planet today. And while these technologies are certainly powerful - applying them effectively to solve problems in clean tech is another issue altogether. AI has been used by scientists in different clean tech se
Will AI transform water, energy, agriculture, climate and all the other clean tech sectors? Can AI transform these sectors? Some version of these questions always gets asked at any meeting or conference in clean technology. Of course, part of that is because there’s been so much hype around AI and the whole “software is eating the world” interviews that came out a couple of years ago. But part of it is also because these tools are so powerful that professionals working in these sectors can see the potential - but just aren’t sure if it’s applicable to their sector yet. So, let’s start by asking a couple of fundamental questions. Why do we need AI at all? Or any models for that matter? Models are used to understand the world - to estimate the impacts of changes in systems and to try and predict what will happen in the future. Typically, the approaches used in building models can be classified into three broad categories - physical or mechanistic approaches, statistical approaches and