As with all new ideas, the important thing is to identify a clear purpose and a market. Which sustainability issues can be addressed and what case studies can back up their credibility?
The technology marketplace is full of new ideas delivering functionality to solve real world problems. 2018 was the year of Bitcoin and blockchain. 2019 starts to see some of the challenges associated with delivering services. However, there are examples of sustainability solutions associated with Blockchain.
Artificial intelligence and machine learning are attracting huge investment as a way of delivering technology services on the scale of the digital revolution. Big data analytics has certainly been around a little longer and is now establishing itself as a cornerstone for many organisations’ technology services, as well as delivering data solutions.
How can we make more sustainable decisions?
Cryptocurrencies and their distributed ledgers have seen a huge surge in popularity. Although a lot of this was driven by speculation, there are some very useful technologies which can support sustainability. From my perspective, I’m an advocate of cryptocurrencies, just not Bitcoin. Bitcoin mining consumes more energy than Switzerland1. The way Satoshi Nakamoto defined the algorithm’s creation of future coins, requires more computer capacity. Blockchain, on the other hand, provides the possibility to manage very long transactional chains (both monetary and physical assets) across geographical regions.
Historically, ledgers of accounts were physical books managed by clerks within companies. The early computerisation within banks resulted in digital ledgers. We now have a huge number of unconnected central ledgers, all reconciling accounts either through batch jobs or real-time settlement using networks such as VISA or SWIFT.
Centralised ledgers do not support a currency without borders such as Bitcoin, so developers reused an existing concept of a distributed ledger and this created an ‘aha’ moment for people working on complicated supply chains and closed loop manufacturing processes.
Blockchain, saving the world?
Each organisation associated with the supply chain can install blockchain and become part of the chain. Rather than settling transactions, the software can be used to track and manage component parts of the supply chain. Theoretically, both the chain and its distribution are infinite, making it ideal for complex global manufacturing. Supermarkets are testing blockchain as a way of managing food supply chains, which will enable them to identify problems and reduce food waste. The United Nations Food and Agriculture Organisation (FAO)2 estimates that in 2016, food waste accounted for 8% of global greenhouse gas emissions. Roughly a third of all food is wasted globally.
The construction industry has identified blockchain as a way of improving resource management. Generally, there is a wide variety of well-developed information domains, however data is not generally shared across domains, resulting in fragmentation. Real estate services are changing, bare spaces with long-term leases and little support are now competing with short lived, flexible offices and co-living spaces making ownership of the resources more complex. In a building, 40%of the total energy and 50% of the raw materials are associated with the construction process3.
To improve building sustainability, environmental labelling and circular solutions will reduce the overall environmental footprint. Blockchain supports this model in the following way: by including the components’ environmental footprint in the chain, the total footprint of the building can be better understood, resulting in positive actions. Similarly, each component’s characteristic is recorded, allowing reuse at a future date. For example, a steel beam has an extremely long life and could make up a component of multiple buildings, rather than being sent for recycling after each use. Blockchain shares each record across the domains.
Using AI to make faster decisions
We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. Similarly, we develop ideas which are then continuously improved through cycles and iterations. It is well known that James Dyson breaks down the creative process that went into creating the bag-free cyclone technology design, including the 15 years and 5,127 prototypes it took before the first model, DC01, ultimately proved successful in 1993.
There is a limit on how many decisions and how rapidly a human can make these decisions, which is where computers with their vast ability to carry out calculations can help. When we talk about handing decisions to AI, we expect it to do the same sort of process but to do it better. How can machine learning and AI support the delivery of sustainability goals?
One example is in our energy supply sector. Wind power is being developed as a key alternative zero carbon energy source. The big challenge is the unpredictability of wind which reduces its benefit. Deepmind4 and Google utilise machine learning algorithms on Google’s wind farms to help predict wind strengths 36 hours into the future. By providing greater accuracy, the data supports grid planning, which is important when using renewable energy sources.
Joining up the data
Data sits at the heart of most computer systems. Extracting useful information from multiple data sources has, over time, become known as big data analytics. Being able to capture both structured and unstructured data, refining and sorting the data in a way which discards the junk and standardises the useful, then providing data scientists with the tools to create data linkage and structure, provides huge benefits to companies and organisations.
When looking at sustainability and climate change mitigation, being able to identify patterns in the data helps to make informed sustainable decisions. With the recent fires in the Amazon and the realisation of its importance in controlling both weather patterns and acting as a carbon dioxide sink, big data analytics was key to the validation of ranchers’ and slaughterhouses’ response to Zero-Deforestation Agreements in the Brazilian Amazon5.
The historical challenge was that ranchers had been cutting down the rainforest on their land to allow cattle grazing. Ranchers needed to be encouraged to protect the forest on their land. Most Amazon cattle are processed by five major meatpacking companies. These companies signed an agreement to stop accepting meat products from farms who had recently cleared forests.
Satellite images of the Amazon Forest were analysed to provide both current and historic land use data. By cross-referencing with farm specific information sources, changes in forest coverage by individuals could be understood and action taken where appropriate. Deforestation rates dropped by more than 80% between 2004 and 2014. Without a big data analytics platform, it would have been virtually impossible to complete this analysis manually.
Using the data for good
The recurring theme across all the emerging technologies covered today is how computers can rapidly interrogate data to support sustainable decision-making and help deliver the United Nations Sustainable Development Goals. It is this ability which supports humans to find answers to difficult questions.
This article covers a small subset of possible technologies, many more of which are available. On a cautionary note, some of these technologies may not achieve what is expected. The technology industry must also address its own sustainability issues relating to natural resource and energy consumption. If we are going to deliver against the United Nations Sustainable Development Goals, data must be one of the cornerstones in realising our objectives.
