Project Details
Description
Carbonwise Jukola project created and piloted a sustainable and carbonwise learning and operating model for a public mass event. This was done together with the companies and partners involved in the project. The operating and learning model was concretized and tested at the Porvoo Jukola event in June 2023. Jukola is the world's biggest orienteering relay event and the biggest adult exercise event in Finland. Jukola has about 20 000 competitors from 30 countries and 50 000 visitors every year. The project examined the carbon neutrality models of previous mass events as well as Jukola events. The project analyzed the estimated carbon footprint of the Porvoo Jukola event, and determines how the event can be organized as carbon-neutrally as possible. This included an examination of different and new compensation models. The project will reduce the carbon dioxide emissions caused by Porvoo Jukola event by prioritizing and selecting the key activities and measures that can be most effectively achieved by developing the target. Porvoo Jukola event offers a perfect testbed for creating and piloting the model on a different levels of wide range of operations. The main objective of the development project was to improve carbon neutral event organization and try new bold solutions, so that the Jukola event has the smallest possible carbon footprint. During the project, a model of carbon-wise events was built that can be used and utilized in other events. Partners participating in the Carbonwise Jukola project, will have the opportunity to build and develop a more carbon-neutral business. During the project, the preconditions of the climate partnership model between companies and the city of Porvoo was also be studied. The project will increase public awareness and discussion of carbon neutrality as part of the production of mass events across industry boundaries, and will assess and analyze the project's impact from the perspective and learning model. The target groups of the Carbonwise Jukola project are the companies responsible for the production of future mass events and other key partners and stakeholders involved in the implementation of the events. The result of the project is a feasible, verifiable, measurable and reproducible carbonwise learning and operating model to support the production of a mass event.
The project was implemented in cooperation between the development company Posintra Oy, the orienteering club OK Trian and Aalto University during 2022 and 2023.
Coordinator: Posintra Oy (Joint applicant) (lead)
Partner: Orienteringsklubben Trian rf (Joint applicant)
Partner: Aalto University (Joint applicant)
The project was implemented in cooperation between the development company Posintra Oy, the orienteering club OK Trian and Aalto University during 2022 and 2023.
Coordinator: Posintra Oy (Joint applicant) (lead)
Partner: Orienteringsklubben Trian rf (Joint applicant)
Partner: Aalto University (Joint applicant)
Key findings
The Hiiliviisas Jukola project involved the planning and implementation of Jukola's Viesti -event with the aim of reducing the event's carbon footprint and developing new solutions with the companies that participated in the project. New low-carbon solutions were utilized in the implementation of the event. The carbon footprint was estimated to have been smaller compared to previous implementation years. Interviews with project companies, broader stakeholders of the events, and those responsible for organizing the Jukolan Viesti -event showed that the organizations had developed practices related to carbon wisdom. The learning and operating model of the project was published and the model was widely presented to the bodies responsible for the responsibility of sports events.
Acronym | - |
---|---|
Status | Finished |
Effective start/end date | 14/10/2022 → 31/12/2023 |
Funding
- EU: Other EU funding (structural funds)
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.