Luc LeBel

Hybrid-electric powertrains have emerged as a promising solution for reducing fuel consumption and greenhouse gas (GHG) emissions in mechanized forest operations. This study presents a cradle-to-grave life cycle assessment (LCA) comparing a conventional diesel engine and a hybrid-electric powertrain used in forestry harvesters. The assessment was conducted in SimaPro using the IMPACT World+ midpoint method and the Average Dissipation Rate (ADR) approach for mineral resource dissipation. Two functional units were used: a primary, productivity-normalized unit of 1 m³ of harvested wood, and a secondary engine-level unit of one harvester engine system over a lifetime of 15,000 operating hours. Results showed that the operation phase dominated most environmental impact categories. On the engine-hour basis, the hybrid-electric powertrain reduced climate change impacts by 6.9% and fossil and nuclear energy use by 6.6% relative to the conventional diesel engine; when normalized per m³ of harvested wood using the average productivity of each system, these reductions increased to 26.4% and 26.4%, respectively, reflecting the hybrid system’s combined advantage in fuel efficiency and productivity. Most other operation-driven categories showed a similar pattern, including ozone layer depletion, terrestrial acidification, freshwater eutrophication, marine eutrophication, particulate matter formation, photochemical oxidant formation, and water scarcity. Land occupation, which increased by 10.0% for the hybrid system on an engine-hour basis, instead decreased by 13.2% on a per-m³ basis. However, freshwater ecotoxicity, human toxicity non-cancer, ionizing radiation, and mineral resource dissipation remained higher for the hybrid system on both bases, because of the additional electric motor, power electronics, and supercapacitor. Overall, the hybrid-electric powertrain improves the environmental performance of forestry harvesters on a like-for-like service-output basis, although increased material requirements and resource use for specific categories remain important trade-offs for future technology development.

Climate change poses an increasing threat to the functionality of forest transportation infrastructure, particularly in northern regions where seasonal access and ground conditions are critical for wood mobilization. The objective of this study was to assess how projected changes in temperature and precipitation may compromise accessibility to forest resources. In addition, it aimed to develop targeted adaptation recommendations to support resilient transportation systems. These actions are essential to ensure the continuity of wood supply under future climatic conditions. Climate projections were extracted from the climatedata.ca platform based on the CMIP6 (CanDCS-M6) model under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Using a GIS-based workflow, projected temperature and precipitation data were spatially matched to the selected Forest Management Units (FMUs) in Quebec, Canada, and the study area was divided into three latitudinal subregions to capture spatial temperature variation. Classified road network maps were then overlaid with projected climate data for 2020, 2040, 2060, and 2080 to evaluate winter road usability, precipitation-related exposure of road classes, and changes in effective winter road density. Results showed a consistent shortening of the winter road operational period under all scenarios, with the most severe reductions under SSP5-8.5. In highly affected areas, the winter road usability window may decrease from 90 days in 2020 to only 21 days by 2080. Increased precipitation is also expected to affect numerous road segments, raising risks of erosion, sedimentation, and loss of accessibility. A reduction of approximately 7% in effective winter road density is projected across the study area under the high-emission scenario (SSP5-8.5), reflecting the most severe impact of future temperature increases. Based on these findings, targeted road upgrades, climate-informed infrastructure design, and alternative access planning are proposed to help sustain wood flow and support year-round forest operations under future climatic conditions.

It has become increasingly important to incorporate carbon metrics in the forest harvest planning process. The Generic Carbon Budget Model (GCBM) is a well-recognized tool to evaluate the potential impact of management decisions on carbon sequestration and storage, supporting sustainable forest management planning. Although GCBM is effective in carbon budgeting and estimating carbon metrics, its computational complexity makes it difficult to integrate into forest planning with multiple scenarios. In this regard, this study proposes using machine algorithms to expedite the output generated by GCBM. XGBoost was implemented to estimate the carbon pool and NEP in managed forests of Quebec. Furthermore, polynomial regression was also implemented to serve as a validation benchmark. Datasets with total sizes of 13.53 million and 7.56 million samples were compiled for NEP and carbon pool forecasting to run the model. The results indicate that XGBoost was able to accurately replicate the performance of the GCBM model for both NEP forecasting (R² = 0.883) and carbon pool estimation (R² = 0.967 for aboveground biomass). Although machine learning approaches are comparatively faster, GCBM still offers better accuracy. Hence, the decision on which method to use, either machine learning or GCBM, should be dictated by the specific objectives and the constraints of the project.

Nordic forests, like those found in Canada, used to consist of large and relatively homogeneous mature stands. Such a spatial pattern allows for harvest operations to be highly concentrated, minimizing procurement costs. However, the growing fragmentation of these forests makes planning difficult and increases the costs of road building and machinery relocation. While operational solutions have been developed in regions with small harvest areas, their transferability to different settings is unknown. Finding the most suitable combination of equipment for a given context is challenging considering the multitude of possibilities. The objective of this study is to identify, from all possible options, a subset of harvest systems expected to perform well in fragmented boreal forests. The results from this research are two-fold. First, a comprehensive review of forest machines and harvest systems is provided. Second, a multi-criteria decision analysis (MCDA) methodology is proposed to evaluate the alternatives. In a boreal forest context, the conventional harvester-forwarder system (CTL) was ranked among the best solutions, along with mild adaptations of the usual configurations. Several whole-tree (WT) system configurations were also highly ranked. While the results are specific to the case studied, the review and selection methodology can serve in different operational contexts.

Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This article proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.