How Technology Helps Businesses Reduce their Carbon Emissions
Environmental impact
Carbon emissions are rapidly rising and need to be cut by at least 45% if the world is to avoid heating by 1.5℃, resulting in flooding of all major coastal cities [1].
Governments have signed the Paris Agreement to reduce carbon emissions by 2050, however the rate of growth is making this difficult. AI applications can help companies reduce its environmental impact and get back on track in reaching the agreed objectives.
A report by PwC and Microsoft suggests that using AI could reduce the worldwide gas emissions of greenhouse gases by 4% by 2030, equivalent to the predicted annual emissions of Australia, Canada and Japan combined for the same year [2]. There are many use cases for AI and IoT to help organisations reduce their carbon footprint. New technologies can help monitor the emissions, predict future emissions, and with the right knowledge and data, make adjustments leading to reduction of the amount of carbon being generated.
Technology Infrastructure
Technologies used for carbon emission monitoring and reduction include, but are not limited to, industrial IoT, distributed storage, dynamic pricing, waste reduction and smart meters.
Machine Learning models can be trained to monitor emissions generated by company’s operations and make recommendations on how to make substantial reductions. The more data is fed into a ML system the more accurate these recommendations tend to become. Let’s take a look at how these technologies are being applied across industries.
Data Analytics for Transport
AI applications in the energy and transport sectors have an increasing impact on reducing carbon emissions.
AI leads to an estimated 2.2% decrease in gas emissions in the energy sector, and 1.7% in transport [3]. Data analytics streamlines operations to drive both carbon and cost savings, resulting in higher operational efficiency.
AI-powered systems use data generated by logistics activities including planning, routing, and detention tracking. This day-to-day information can provide insights into how shippers and carriers can be optimised, such as finding patterns in routes where traffic delays lead to fuel waste, or common locations where trucks sit idle waiting for pick-ups or drop-offs. AI can be trained to optimise routes so that the most fuel-efficient routes and schedules are used. One of the most common AI solutions to the so-called ‘Travelling Salesman Problem’ applies genetic programming, which allows for quick identification of the most optimal route by analysing thousands of possibilities in a systematic way.
Smart Construction
Residential and Commercial Buildings produce almost one-fifth of the world’s carbon emissions.
This is mainly due to poor planning, and buildings’ heating, ventilation, and air conditioning (HVAC) systems being incredibly energy intensive. With the correct AI and IoT strategies, the carbon footprints of our buildings can be reduced by up to 90% [4]. AI can utilise the data of HVAC systems to analyse what times heating and colling is used, and for how long, to create use patterns, which can then be optimised. Recently DeepMind has pioneered in introduction machine learning solutions to optimise the data of cooling systems to reduce the energy consumption of buildings by 40% [5].
Precision Agriculture
Agriculture is unfortunately a major driving force of climate change, accounting for up to 15% of greenhouse gas emissions.
Fertilisers alone are responsible for 2.5% of emissions, polluting the world with nitrous oxide which warms the atmosphere 300 times more than carbon footprint [6].
One approach to reducing this problem is the use of computer vision. Aerial imagery can give farmers real-time insights of their crops, assessing where fertilisers are required and how many. Semios uses on-the-ground hardware sensors to manage crops by optimally deploying resources such as fertiliser use, fixing irrigation pipes and weeding. The use of AI in agriculture can reduce emission by 10% and water use by 15% [7].
To summarise, new technologies such as IoT, AI and ML have the power to help organisations become more environmentally friendly. The way in which this is done comes down to three key components:
Monitoring emissions – to find trends in release of carbon emissions and areas of improvement
Predicting emissions – using the monitored data to create future scenarios, telling us when and to act
Reducing emissions – providing detailed insights into the most optimal ways of decreasing carbon footprints.
These technologies can be applied to all industries and present a great way find new ways to improve business’ sustainability. It is estimated that AI could generate $1-$3 trillion in value when applied to corporate sustainability8. If you think your company could do better in terms of its environmental impact and want to find ways to generate more value through higher operational efficiency, we recommend getting in touch with an AI expert to discuss the technological opportunities tailored to your business.
2 https://www.pwc.com/gx/en/news-room/press-releases/2019/ai-realise-gains-environment.html
5 https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40
8 https://www.bcg.com/en-gb/publications/2021/ai-to-reduce-carbon-emissions