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A data-driven approach for estimating travel-related carbon emissions with high spatial and temporal granularity
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A4 Artikkeli konferenssijulkaisussa
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en
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4
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AGILE : GIScience series, Volume 5
Abstract
The transport sector is the second-largest contributor to greenhouse gas emissions in the EU, with a high reliance on fossil fuels and increasing demand for transportation. In Finland, cars are the most common means of travel, accounting for 60% of overall domestic travel. Considering that a significant portion of the work-related trips in Finland (70%) are done by car, there exists great potential to reduce travel-related emissions by substituting short-distance work trips with active transport. Typically, the potential for reducing travel related carbon emissions are studied using travel surveys or other national level statistics. However, there is little geographical evidence regarding the extent of carbon reduction potential achieved through the shift to active transportation, i.e. where the carbon-reduction potential is highest? This study aims to develop a data-driven method to assess the potential reduction of carbon emissions from commuters switching from car to more sustainable travel modes. As a case study, we investigate the carbon reduction potential in Otaniemi, Espoo (Finland) which is a major commuting hub with large business corporations and research institutes. More specifically, we analyze and simulate the influence of replacing short car-based commuting trips with trips done by electric bikes. Furthermore, we investigate the change in commute-related emissions to Otaniemi before and after the pandemic. Our results show that there were major changes in the work-related travel demand that significantly reduced the carbon footprint from commuting. In addition, our simulation shows that there exists significant potential to reduce travel-related emissions if short car trips (less than 5 km distance) would be done by e-bikes instead of cars. Our approach shows great potential for providing crucial place-based information for employers and policymakers to promote sustainable mobility cultures and effectively target climate mitigation policies.
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Tenkanen, H, Chau, N & Dey, S 2024, 'A data-driven approach for estimating travel-related carbon emissions with high spatial and temporal granularity', AGILE : GIScience series, vol. 5, 45. https://doi.org/10.5194/agile-giss-5-45-2024