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The idling of forest machines reduces the energy and cost efficiency of wood-harvesting operations. This study estimated the idling times of forest machines using automatically collected machine big data. The study also examined the impact of machinery type, harvesting enterprise, seasonal variation, engine power, and machine manufacturing year on fuel consumption during idling. Furthermore, a scenario approach was used to describe the influence of idling times on the volume and price of carbon dioxide (CO2) emissions. The dataset included monthly data from 2021–2024, covering ten Finnish logging enterprises with a total of 28 harvesters and 23 forwarders.
The results showed that the proportion of idle time of harvesters and forwarders regarding the total operating hours was 14.0% and 11.1%, respectively. Significant differences were observed between harvesting enterprises, averaging from 6.7% to 18.2%. Idling proportions were also found to be around 14%-unit higher in winter months (November–February) than in summer months (May–August). Average fuel consumption during idling was 4.28 L h–1 for harvesters and 3.38 L h–1 for forwarders. Greater engine power was associated with increased fuel consumption when idling, and vice versa newer machines exhibited greater fuel consumption during idling.
Annual idle-related CO2 emissions for wood-harvesting machinery in Finland were estimated at approximately 10,700 metric tonnes (t) with costs equivalent to €48 million. Reduction in idling by 25–50% could cut emissions by approximately 2,700–5,300 t CO2 and generate additional revenue up to €55 million when the reduced idle engine hours were transferred into productive hours. If idling is reduced by 100% and all the reduced idling time was assumed to be productive working hours, CO2 emissions from harvesters and forwarders would be 30,309 t CO2 greater, whereas the annual revenue would exceed €120 million.
Based on the study, it can be concluded that understanding, as well as acknowledging, the influence of idling has a positive impact on energy and cost-efficiencies, while also ensuring more environmentally friendly wood-harvesting operations. The research strengthens the foundation for low-emission and efficient machine operations, but further studies on idling are still needed outside the forest sector as well. Therefore, we recommend that future research focus on assessing the impacts of idling in other machine segments both in Finland and globally, utilizing automatically collected machine data.
| Keywords | CO2-emissions; machine-data; idle; wood-harvesting |
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