Many companies are making the challenging commitment to become carbon neutral in the coming decades. Executives and employees will face difficult decisions in their journey to reduce emissions. Making climate-friendly choices will require ongoing acquisition of scientific and empirical knowledge.
Digital technologies are promising to help enterprises to reduce Greenhouse gas emissions (GHG). In particular, machine learning applications can enhance business processes by saving energy demand, optimizing delivery routes or developing sustainable materials.
But machine learning also has major downsides. It is expensive, requires a lot of energy and has a terrible carbon footprint. Training a single model can emit as much CO2 as five cars in their lifetimes. In response, researchers promote energy efficient hardware, algorithms and deployment times and locations.
Recommendations for businesses who want to develop knowledge for reducing their footprint: data competencies and leadership are major influencing factors. Focusing on actual challenges to mobilize engagement among all stakeholders and building partnerships with research organizations to share experiences and identify best practices can be vital to facilitate novel approaches.
Machine learning is not a silver bullet in the fight against climate change, it is much rather a double-edged sword. The technology has vast potential to contribute to solving complex challenges, but in its current form it also generates substantial environmental costs.
As a bonus, a text-generating neural network provides its own perspective on how machine learning can help businesses reduce carbon emissions on the last page of the recommendations. Take a look at it.
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Special Report on the Risks and Opportunities for Organizations using Machine Learning Methods to reduce the Corporate Carbon Footprint.
- Special Report on the Risks and Opportunities for Organizations using Machine Learning Methods to reduce the Corporate Carbon Footprint.