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Software can help reduce carbon emissions in three ways

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today! With the climate change crisis looming, many are betting on novel technological solutions such as carbon capture. We certainly need these sorts of solutions…

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. For insightful talks and networking opportunities, join AI and data leaders. Register today!


With the climate change crisis looming, many are betting on novel technological solutions such as carbon capture. These types of solutions are urgently needed. Software may be the solution if we are looking to reduce emissions immediately.

About 51 billion tons of greenhouse gases are added to the atmosphere every year, the bulk of which are in manufacturing and energy. Global producers of goods like cement, steel and plastic account for roughly a third of greenhouse gases (GHGs), while the electricity sector comprises a quarter. Some emissions are obvious. Others result from indirect activity (scope 3) which makes them difficult to measure or reduce.

As researchers try to make carbon capture more efficient ,, it is crucial to identify ways to cut emissions now. One such possibility is software. Aided by software, processes can become as much as 10% more efficient, reducing the amount of carbon that continues to accumulate in the atmosphere.

Here are three ways businesses can implement software to reduce their environmental impact:

1. Eliminating waste

The production of industrial and consumer goods has not been optimized to reduce waste. To put it another way, there has been a certain amount of waste that is necessary to produce products economically on a large scale. Engineers who are reluctant to produce inferior products tend to use more raw materials, which can lead to overestimating the actual amount of ingredients that is needed.

Software is able to transform this process. Machine learning allows a piece software to learn the complex production process of an item within minutes. It can do this across many sectors, including a steel beam or a pint ice cream. This software then can determine how to make the same item with minimal waste, thereby reducing carbon emissions.

2. Optimizing energy consumption

Software has already been a key driver of efficiency in the energy sector. Once a small area of investments, software now serves as a means of controlling the entire energy process, from generation and storage to distribution and consumption. Last year, the power sector spent an estimated $3 billion on software to optimize the performance and costs of generation and grid assets.

Other industries that heavily rely on electricity and fuel are following their lead. AI software is used to optimize logistics and trucking routes. In freight shipping, many companies are adopting machine learning (ML)-based route optimization. These software tools can help shippers maximize profits and reduce fuel consumption by designing routes that are most efficient.

Traditional production also relies upon methods that produce high levels of greenhouse gases, such as the burning of coal and other fossil fuels. Software can help optimize energy consumption and allow for more efficient ways to produce. This would require a lot of experimentation and time, which is why software is so important. Software allows us to reduce the time it takes to perform experiments and can latch onto low-energy methods of manufacturing high-quality goods.

3. Minimizing resource dependence

Businesses rely on adding materials and resources to their production. Software can help reduce emissions in the supply chain by allowing them to better use these resources.

In agriculture, digital monitoring and planning can more efficiently deploy fertilizers while increasing crop yields. Similarly, steelmakers are reducing dependence on mined alloys by more than a third by using new machine learning technology. These efficiency improvements allow existing resources to go further, reducing the environmental impact of production.

Software has prompted efficiency impro

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