Digital transformation supports decarbonisation of the economy

Digital technologies support decarbonisation across all sectors of the economy. Global emissions from fossil fuels could be reduced by as much as 35% by 2030 through digitalisation. This reduction potential can be even greater due to rapid progress in technology. Renewables alone are not enough to decarbonise the economy. Digitalisation is equally important to achieve that goal.

Digital technologies drive decarbonisation in three ways:

Analysis of raw material and energy consumption data provides a better insight into environmental impacts. It enables carbon footprint tracking and reporting.

Decarbonisation is achieved through data tracking and monitoring processes geared towards reducing carbon footprint. Tracking and reporting reliable data is at the heart of investigating and reducing the environmental impact of industry.

One technology that enables advanced traceability of product sourcing at every stage of production is blockchain (distributed data ledgers). The key advantage of blockchain is that collected data is reliable and cannot be manipulated.

Blockchain can be used to track the carbon footprint of products, allowing it to be monitored from manufacturer to point of sale. It also facilitates transactions in and the operation of the distributed energy market.

Production efficiency is improved through more rational use of resources needed for production. This saves energy and raw materials, boosting earnings and reducing greenhouse gas emissions. Production resources are used more efficiently, which improves energy efficiency.

Fewer redundant processes, less raw material and energy usage and lower inventories directly contribute to reducing plant emissions. According to estimates, smart industrial solutions have the potential to save 4.2bn MWh of energy and 81bn litres of water by enabling more efficient production processes.

Industrial process optimisation starts with sensors in a single machine. With artificial intelligence, the entire production control and management system is analysed. Artificial intelligence is capable of identifying the elements or areas of production worth investigating and optimising. This may involve reducing energy consumption where excess energy usage has been detected. All variables are analysed in real time.

Predicting failures and accidents minimises the risk of machine downtime and raw materials and energy waste. It also reduces the risk of leaks and spills, prevents water, air and soil contamination and improves human safety.

One of the greatest applications of artificial intelligence in industry is modeling and predicting future events. The process involves analysing large sets of historical data as input to make useful predictions (forecasts). Prediction can be used in many ways, including for predicting resource and energy consumption, customer demand and behaviour, and defects and failures in production facilities.

Image analysis supported by intelligent algorithms enables highly accurate, automatic detection of defects and anomalies. Accurate image data analysing tools quickly identify leaks and spills of hazardous substances or harmful gases. The best known example of using AI for prediction is predictive maintenance. With AI, industrial machines can be closely monitored and failures can be predicted by analysing vibration levels, temperature fluctuations, power supply and other parameters.

Three key technologies driving decarbonisation

Artificial intelligence (AI) stands among the top technologies offering the greatest potential to support decarbonisation. AI is the main driver of the digital transformation occurring across the economy and the key contributor to the achievement of Agenda 2030, Green Deal and Paris Agreement goals. It permeates most Industry 5.0 technologies and addresses global climate issues – with its applications ranging from the monitoring of climate trends and predicting weather events, to dedicated solutions designed to mitigate or eliminate greenhouse gas emissions.

The new mobile communications standard called 5G NR (New Radio) was developed as a successor to 4G technology and addresses challenges that could not be solved in previous-generation networks. Compared with its predecessors, the technology promises higher data rates, lower latency (down to one millisecond) and reliable communications.

With its unique architecture and improved radio spectrum utilisation, 5G helps to improve energy efficiency and reduce carbon footprint.

5G networks can make a real difference in reducing carbon emissions by operators in several ways.

  • The networks will be more efficient in terms of the amount of data transmitted per unit of energy. With 5G, the energy consumption per bit is on average 90% less than on 4G.
  • While 5G networks could save up to 0.5 billion tonnes of CO2 globally by 2030, about 50% of that is attributable to effects not directly related to 5G.

Public and private clouds are a computing power sharing solution. As the server power is used by multiple users, it is constantly enhanced and adjusted to specific needs, which contributes to reducing total demand for electricity.

Access to cloud services can help organisations reduce their carbon footprint. Service providers already offer reports detailing the carbon footprint generated by a service used by an organisation. Migrating data from on-premises locations to public cloud can reduce carbon emissions. This would be achieved through:

  • automation and autonomous adjustment of computing power to current demand, computing power sharing and real-time allocation,
  • more efficient cooling and heat recovery from server cooling systems, powering data centres with clean energy sourced from large wind farms or high-efficiency solar farms.

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Digital transformation

Key digital technologies

Digital transformation

Transformation of business models