Abstract

The EU’s climate and energy framework and Energy Efficiency Directive drive European companies to improve their energy efficiency. In Finland, the aim is to achieve carbon neutrality by 2035. Stora Enso Wood Supply Finland (WSF) had a target, by 2020, to improve its energy efficiency by 4% from the 2015 level. This case study researches the use of the forest machine fleet contracted to Stora Enso WSF. The aims were to 1) clarify the forest machine fleet energy-efficiency as related to the engine power; 2) determine the fuel consumption and greenhouse gas (GHG) emissions from wood-harvesting operations, including relocations of forest machines by trucks; and 3) investigate the energy efficiency of wood-harvesting operations. The study data consisted of Stora Enso WSF’s industrial roundwood harvest of 8.9 million m3 (solid over bark) in 2016. The results illustrated that forest machinery was not allocated to the different cutting methods (thinning or final felling) based on the engine power. The calculated fuel consumption totalled 14.2 million litres (ML) for harvesting 8.9 million m3, and the calculated fuel consumption of relocations totalled 1.2 ML, for a total of 15.4 ML. The share of fuel consumption was 52.5% for harvesters (cutting), 39.5% for forwarders (forest haulage), and 8.0% for forest machine relocations. The average calculated cubic-based fuel consumption of wood harvesting was 1.6 L/m3, ranging from the lowest of 1.2 L/m3 for final fellings to the highest of 2.8 L/m3 in first thinnings. The calculated fuel consumption from machine relocations was, on average, 0.13 L/m3. The calculated carbon dioxide equivalent (CO2 eq.) emissions totalled 40,872 tonnes (t), of which 21,676 t were from cutting, 16,295 t were from forwarding, and 2,901 t from relocation trucks. By cutting method, the highest calculated CO2 eq. emissions were recorded in first thinnings (7340 g CO2 eq./m3) and the lowest in final fellings (3140 g CO2 eq./m3). The calculated CO2 eq. emissions in the forest machine relocations averaged 325 g CO2 eq./m3. The results underlined that there is a remarkable gap between the actual and optimal allocation of forest machine fleets. Minimizing the gap could result in higher work productivity, lower fuel consumption and GHG emissions, and higher energy efficiency in wood-harvesting operations in the future.

Highlights

  • In the 2020s, climate change is one of the biggest issues in the world

  • This study focused on the energy efficiency of the Stora Enso WSF wood-supply chain, in the wood-harvesting operations and forest machine relocations in Finland

  • The results illustrate that the forest machinery researched in the study was not directed at different cutting methods and harvesting sites based on the engine power

Read more

Summary

Introduction

In the 2020s, climate change is one of the biggest issues in the world. Human activities are estimated to have caused approximately 1.0°C of global warming above pre-industrial levels, with a likely range of 0.8°C to 1.2°C because of greenhouse gas (GHG) emissionsCroat. j. for. eng. 43(2022)1– among others carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) (Masson-Delmotte et al 2018). Human activities are estimated to have caused approximately 1.0°C of global warming above pre-industrial levels, with a likely range of 0.8°C to 1.2°C because of greenhouse gas (GHG) emissions. Human GHG emissions were over 36 billion tonnes of carbon dioxide equivalent. (CO2 eq.) in 2018 (World Economic Forum 2019) In this respect, the EU’s GHG emissions totalled 4483 ­million tonnes of CO2 eq, the lowest level since 1990 (European Commission 2019). The EU’s climate and energy framework (European Union 2014) has set three challenging targets for 2030: 1) decrease GHG emissions by at least 40%, 2) increase share of renewable energy sources (RESs) by at least 27%, and 3) improve energy efficiency by at least 27% from the 1990 level. There is an even more ambitious target for 2050: to cut GHG emissions 80% below the 1990 level (European Union 2011)

Objectives
Methods
Results
Discussion
Conclusion

Full Text

Published Version
Open DOI Link

Get access to 115M+ research papers

Discover from 40M+ Open access, 2M+ Pre-prints, 9.5M Topics and 32K+ Journals.

Sign Up Now! It's FREE

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call