Quantification of a Swedish Digitalization Company´s GHG Emission : A Single Case Study
Sammanfattning: Research shows that the warming of the climate over the last century is extremely likely due to human activities. Furthermore, there is a need for an understanding how business activities counteract or contribute to climate change. In particular, the digitalization industry is often introduced as an important player in climate challenge. However, research also concludes that the digitalization industry’s impact on the climate is ambiguous, since it in some cases contributes to climate change and in other cases counteracts it. In order to understand the interplay between greenhouse gas emissions and digital solutions, it is necessary to outline and quantify the emissions from particular digitalization projects and furthermore the industry itself. The thesis takes off in a single case study at a Swedish digitalization consultancy company in order to investigate how both internal greenhouse gas emissions and greenhouse gas emissions from customer projects can be quantified as accurate and as often as possible. The findings disclose that greenhouse gas (GHG) emissions can be tracked with an extremely short time step, practically continuously, especially if the tracking is integrated with the company’s ERP1 . Furthermore, the findings show that greenhouse gas emissions from customer projects can be quantified if interpreting and implementing the GHG Protocol with a soft system methodology (SSM) approach. The thesis contributes with (1) a general interpretation of the Corporate Standard (part of the GHG Protocol) in the context of digitalization; (2) a specific example of that interpretation and implementation; (3) a practical interpretation and implementation of the Project Protocol in the context of digitalization and its avoided or caused greenhouse gas emissions; and (4) a general and an in-depth analysis on the topic of quantifying a Swedish digitalization company’s greenhouse gas emissions and feasible approaches to assumption making.
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