How can our companies and Artificial Intelligence help in fighting climate change?

Source: De Tijd (28/01/2021); Author: Professor Kristof Stouthuysen

In the Paris climate agreement, more than 100 countries – including Belgium – have committed to reducing net greenhouse gas emissions to zero by 2050. In concrete terms, being climate-neutral means that you only emit as many emissions as you can absorb. There are two ways of doing this: increasing absorption capacity (e.g. through afforestation, the construction of bogs, mangroves, healthy soils and underwater ecosystems) or significantly reducing greenhouse gas emissions.

Although the new US President Joe Biden is putting climate change back on the political agenda, whether or not this goal is achieved will mainly depend on the vigour of the business community. And that makes sense: although governments can regulate and stimulate, it is up to society to tackle the problem. Many large companies such as Microsoft, Siemens, Ikea, Apple, Tesla and Heathrow Airport are already committed to reducing their net CO² emissions to zero by 2040 (or earlier). However, every company – whether large or small – has a role to play in halting climate change.

So the key question is: how can you accelerate this process? The proper use of artificial intelligence (AI) is the key to helping our companies become climate-neutral more quickly. A few possible applications with a great deal of potential are given below.

1. Improving energy forecasting and the energy consumption of buildings
A focus on renewable energy means that companies must be able to predict how much energy they need, both now and in the long term. Although such predictive algorithms already exist, there is room for improvement. Intelligent control systems can help companies with the energy consumption of their buildings by regulating aspects such as the temperature, ventilation and lighting on the basis of weather data, occupancy levels or other environmental factors. Smart buildings can also communicate with their internal or external energy sources via the Internet of Things (IoT) and adapt their consumption to the supply of green energy available at that time.

2. Discovering new materials
The research and development process for new and recyclable materials to enable the production of more energy-efficient products is often slow and both capital and labour-intensive. Machine Learning (ML), an AI application, can discover and evaluate new chemical structures with the desired functionality more quickly. The development of a COVID-19 vaccine is just one example.

3. Optimising transport and, by extension, the entire supply chain
Machine Learning can not only help unravel the complex tangle of delivery and departure points according to order size and transport type, but it can also organise transport in a more economical and environmentally friendly way by avoiding congestion or half-empty trucks. For example, HelloFresh saves nine journeys around the world every month using ML-driven route planning. In the same way, ML can reduce CO² emissions in the entire supply chain. After all, better supply and demand forecasts will lead to less transport and less waste throughout the value chain. AI applications also allow sensor data, satellite data, audio data and network data to be analysed in order to better assess the sustainability of suppliers and customers. This will allow companies to help customers and suppliers become more environmentally conscious, in addition to doing so themselves.

4. Accelerating the roll-out of an electric company car fleet
A lack of trust in the range of electric cars is often a major problem when making vehicle fleets greener. Not only can AI algorithms help employees use their batteries more efficiently, but they can also predict charging behaviour and thus help improve the charging infrastructure in consultation with external partners (such as leasing companies and network operators).

5. Sustainable value creation
Both companies and their stakeholders (investors, customers and suppliers) increasingly need data that maps the sustainability of their value creation. For example, satellite images can estimate the wear and tear of buildings, the emission of harmful gases, land management, water pollution, deforestation, overfishing or other violations of environmental and/or human rights.

6. Smart robots reduce waste and duplicate efforts
Robots are already the norm for more efficient production in the manufacturing industry today. However, companies are also increasingly digitising and robotising administrative or simple repetitive tasks. If these robots also became smart, they could learn from their errors and new data, leading to even greater efficiency and less paperwork and waste.

7. Making investment decisions greener
All too often, financial parameters, such as the expected return and payback period, still form the basis of an investment decision. Non-financial parameters that measure the impact on the environment and society must also be taken into account. Machine Learning can easily handle these kinds of complex evaluation exercises, allowing companies to make well-considered, greener decisions.

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