Assessing the Climate Benefits of Hybridization in Forest Harvesters: A Life Cycle Perspective

Abstract

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.

1. Introduction

Over the past decade, global warming has accelerated significantly compared to the early 2000s, driven by rising global greenhouse gas (GHG) emissions, which reached a record 36.8 Gt in 2024 [1]. In the context of global warming, forests and wood-based products play a crucial role in mitigating GHG through carbon sequestration, long-term carbon storage in biomass and harvested wood products, and the substitution of fossil-intensive materials and energy sources [2]. Despite these benefits, forest harvesting operations are associated with considerable fuel consumption by heavy forest machinery. According to Kärhä et al. (2024) [1], global CO₂ emissions from fully mechanized cut-to-length (CTL) operations are estimated at approximately 4 million tonnes. Of this total, harvesters account for about 57% of emissions, while forwarders, machines used for log transportation within forest stands, contribute the remaining 43% [1]. The reliance on internal combustion engines in conventional harvesters has raised environmental concerns, as emissions such as carbon dioxide and methane are closely linked to operating conditions and fuel use [3]. In response to these challenges, hybrid harvesting technologies have emerged as a promising approach to improve energy efficiency and reduce emissions in forest operations [4,5]. However, evaluating the overall environmental benefits of these technologies requires a comprehensive assessment approach capable of considering impacts across the entire life cycle of the machinery, including material production, operation, and end-of-life treatment.

Life cycle assessment (LCA) provides a systematic framework to quantify the environmental impacts associated with all stages of a product or system, from raw material production to operation and end-of-life management [6,7,8]. According to ISO 14040 [9], LCA is the “compilation and evaluation of the inputs, outputs, and the potential environmental impacts of a product system throughout its life cycle system” [10]. In ISO 14040 standards, four LCA phases are included: (1) goal and scope definition; (2) life cycle inventory; (3) life cycle impact assessment; and (4) interpretation. The system boundaries and functional unit are first determined. Data for the inventory is then collected and calculated by considering the functional unit and in the life cycle impact assessment phase, the potential environmental impacts are identified and qualified. In the life cycle impact assessment (LCIA), emissions results of the Life Cycle Inventory (LCI) are linked to the environmental categories to which they contributed [10].

LCA approaches allow the evaluation of a broader set of environmental impacts over the life cycle of wood production, addressing not only forest operations but also the production of energy and materials consumed [6]. By identifying the life cycle stages and harvesting processes that contribute most significantly to environmental impacts, including GHG emissions, LCA can support the development of technical, operational, and economic strategies aimed at improving sustainability [11,12,13]. Consequently, LCA has been widely applied to evaluate the environmental performance of forest harvesting systems [14]. Studies have shown that over 85% of the energy inputs in forest harvesting operations are attributed to fuel consumption by forest machinery. Berendt et al. (2018) [7] assessed the environmental impact of remote-controlled mini forestry crawlers with categories that include global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), and human toxicity potential (HTP). They showed that the GWP of the harvester was related to the diesel consumption for machine use (40%), electricity (26.6%), and district heat (11.5%) during machine manufacturing, assembling, and maintenance [7]. In the concept of system boundary, most of the studies followed a cradle-to-gate methodology, with two distinct system boundaries identified: from the forest-to-forest road (25%) and from forest-to-plant gate (61%). Only 14% of the studies considered a cradle-to-grave approach, or at least either the consumer stage or recycling, respectively [15,16,17,18].

Schweier et al. (2016) [10] reported that, when different silvicultural treatments were evaluated separately, the semi-mechanized harvesting system (harvester, chainsaw, and forwarder) exhibited the lowest environmental impacts for thinning operations. For final felling, the fully mechanized system (harvester and forwarder) generated the lowest environmental impacts compared with the motor-manual system (chainsaw and forest tractor), based on global warming potential (GWP), eutrophication potential (EP), and acidification potential (AP) indicators [10]. Furthermore, Alzamora et al. (2022) [13] compared semi-mechanized, mechanized, and tower logging to evaluate their environmental impact on harvesting activities. These activities included felling, logging, processing, sorting, loading, and transportation. They concluded that, based on the LCA approach, the mechanized harvesting system demonstrated the highest fuel-use efficiency and the lowest GHG emissions under average monthly production conditions, with emissions estimated at approximately 11 kg CO₂-eq m⁻³ of harvested timber [13].

However, Abbas and Handler (2018) [12] showed that through the LCA approach, the chainsaw-based harvesting system had lower GHG emissions and fossil energy demand per green tonnes of wood compared to feller buncher-based harvesting. Despite their lower productivity, the reduced consumption of fuel and lubricants, together with lower machinery requirements, contributed to improved environmental performance. Abbas and Handler (2018) [12] used the information of fuel consumption and lubricants to compare the environmental impacts of these two harvesters and only estimated the operation phase and the repair factors for maintenance of these harvesters without considering end-of-life and manufacturing process [12]. Additionally, Kühmaier et al. (2022) [8] found, based on an LCA, that the GHG emissions for felling, delimbing, and crosscutting were significantly lower when using a chainsaw compared to a harvester [8]. Despite the growing body of LCA research on harvesting systems, limited attention has been paid to hybrid harvesting technologies. Therefore, this study applied an LCA approach to compare hybrid and conventional harvester systems and assess their potential for reducing fuel consumption and associated GHG emissions.

Hybridization has also been investigated in other forest operations. Zemánek et al. (2022) [19] evaluated a timber tractor-trailer equipped with an electric hybrid drive and showed that the electric system assisted the combustion engine during high-power-demand conditions, particularly when transporting loaded timber uphill. These findings demonstrate the potential of hybrid technologies to support energy-demanding operations in forest machinery [19]. Hybrid harvesters typically integrate internal combustion engines with electric power systems, enabling more efficient energy use and reduced fuel consumption compared to conventional machines [5,20]. However, their life cycle environmental performance under real forestry conditions remains insufficiently quantified. Previous studies have mainly addressed simulated hybrid non-road machinery or the operational impacts of forest machines. This study is the first cradle-to-grave LCA of a real hybrid-electric forest harvester powertrain that includes hybrid-system manufacturing materials. This study presents a comparative life cycle assessment of a forest harvesting system equipped with either a conventional internal combustion engine (ICE) or a hybrid-electric powertrain. The analysis evaluates the environmental performance of both configurations across the full life cycle, with a focus on energy use and Global Warming Potential (GWP). We hypothesize that the additional manufacturing burdens of the hybrid-electric powertrain are offset by lower operational impacts as utilization increases, resulting in lower overall life cycle impacts than the conventional system.

The study addresses the following questions:

• What is the difference in life cycle environmental impacts between a hybrid harvester engine and a conventional baseline engine?
• Can reductions in environmental impacts during the use phase offset the additional burdens associated with the production of the hybrid-electric powertrain?

2. Materials and Methods

This study follows the framework for life cycle assessment (LCA) as defined by International Organization for Standardization standards ISO 14040 and ISO 14044 [21]. The methodology consists of four main phases: goal and scope definition, life cycle inventory (LCI) analysis, life cycle impact assessment (LCIA), and interpretation.

2.1. Goal and Scope Definition

The goal of this study was to evaluate and compare the environmental impacts associated with the life cycle of a conventional diesel engine and a hybrid-electric powertrain used in a forestry harvester. To fulfill this objective, an LCA methodology was applied, and a cradle-to-grave analysis was conducted with a focus on energy consumption and GHG emissions, expressed as Global Warming Potential (GWP). These indicators were prioritized because fuel combustion during forest harvesting operations represents the main source of operational environmental impacts and contributes directly to climate change. In addition, reducing GHG emissions and fossil energy consumption was consistent with recent decarbonization pathways proposed by the IPCC [22]. The machine under evaluation was a Logset 12H GTE Hybrid harvester (Logset, Koivulahti, Finland) that combines a diesel engine with a hybrid-electric powertrain (Figure 1).

The diesel engine operates at a mostly constant speed within its optimal efficiency range, while the electric motor supplies additional power during peak loads. The hybrid-electric powertrain includes an electric motor, supercapacitor-based energy storage, and a generator control unit (GCU) that manages energy flow between the engine and the hybrid unit (Figure 2). During low power demand, the electric motor functions as a generator to recharge the energy storage system, enabling stable and efficient operation even under demanding harvesting conditions [23].

The technical and powertrain specifications of the evaluated Logset 12H GTE Hybrid harvester are presented in

Table 1. Technical and powertrain specifications of the evaluated Logset 12H GTE Hybrid harvester.

Category	Parameter	Specification
Machine	Machine model	Logset 12H GTE Hybrid
	Base machine weight	24,500 kg (54,000 lbs)
	Harvesting head model	TH75
	Fuel tank capacity	500 L (132 US gal)
	DEF capacity	40 L (11 US gal)
Diesel engine	Power at 2100 rpm	205 kW (280 hp)
	Power at 1900 rpm	220 kW (300 hp)
	Max torque at 1500 rpm	1200 Nm
Electric motor (hybrid unit)	Max power at 2100 rpm	175 kW (230 hp) for 4.5 s
	Power at 1500 rpm	125 kW (170 hp) for 6 s
	Max torque at 100–2100 rpm	800 Nm
Hybrid-electric powertrain (diesel + electric motor)	Max power at 2100 rpm	380 kW (510 hp) for 4.5 s
	Power at 1500 rpm	313 kW (420 hp) for 6 s
	Max torque at 1500 rpm	2000 Nm

.

Figure 3 presents the system boundary applied in this engine-level LCA, encompassing all major life cycle stages from raw material acquisition and manufacturing to operation and end-of-life treatment within a “cradle-to-grave” framework. The cradle-to-grave boundary applies to the engine/powertrain systems rather than the complete harvester; common machine components, infrastructure, relocation between job sites, and routine maintenance were excluded. During the use phase, only energy and material flows required for engine operation were considered. These included diesel fuel consumption and Diesel Exhaust Fluid (DEF) for both diesel and hybrid engines, with the latter adjusted according to its operational characteristics.

The functional unit provides the reference to which all inputs and outputs in an LCA are normalized and may differ depending on the stage of the supply chain being assessed [24]. Two complementary functional units were used in this study. The secondary functional unit is defined as “one engine system for a forest harvester” operated over a lifetime of 15,000 h, enabling a consistent comparison of engine configuration and isolating the environmental performance of the powertrain itself from field productivity, stand conditions, and operator effects. The primary functional unit is defined as 1 m³ of wood harvested, enabling a comparison on the basis of equivalent service output between the two systems, which differ in productivity. Results are reported on the recalculated-to-the-per-m³ basis using the average productivity of each system: 16.3 m³/h for the diesel-powered harvester and 20.62 m³/h for the hybrid-electric harvester, derived from the field measurements reported in the companion study by Yadegari et al. (2026) [5]. Because raw material, manufacturing, transport, and end-of-life burdens are fixed per engine system regardless of hours operated, and operation-phase burdens scale linearly with operating hours, the per-m³ results were obtained by dividing each system’s total lifetime impact by the total volume of wood harvested over its 15,000 h operational lifetime: 244,500 m³ for the diesel-powered harvester (15,000 h × 16.3 m³/h) and 309,300 m³ for the hybrid-electric harvester (15,000 h × 20.62 m³/h). This rescaling does not alter the relative contribution of each life cycle stage to total impact, since every stage is divided by the same constant for a given system. The impact assessment focused on GHG emissions and global warming potential.

2.2. Life Cycle Inventory Analysis

The LCI is organized around four stages: (i) raw materials for the diesel and hybrid harvester engines, (ii) energy inputs required for engine manufacturing and assembly, (iii) the use phase of the engines, and (iv) the end-of-life phase of both engines. The detailed life cycle inventory (LCI), including the material composition and mass of the main powertrain components, is provided in

Table A1. The ecoinvent process used throughout the study.

Life Cycle Inventory Modeled of Engines (Stand Software for Data Reference in Ecoinvent 3)		Amount	Unit	Uncertainty
1. Diesel engine components
Output	Camshaft	30	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for cast iron|Conseq, U	30	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron sand casting	30	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	21.4	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	48.27	MJ	Lognormal
Output	Conrods	50	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for steel, low-alloyed|APOS, U	50	kg	Lognormal
Inputs from Technosphere: material/fuels	Forging, steel $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for forging, steel|Cut-off, U	50	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	35.66	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	80.49	MJ	Lognormal
Output	Crankshaft	105	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for steel, low-alloyed|APOS, U	105	kg	Lognormal
Inputs from Technosphere: material/fuels	Forging, steel $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for forging, steel|Cut-off, U	105	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	74.9	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	169.05	MJ	Lognormal
Output	Cylinder block	420	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum alloy, AlLi $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for aluminium alloy, AlLi|Cutt-off, U	20	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron, aluminum, low-wax $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for casting, aluminum, low-wax|Cut-off, U	400	kg	Lognormal
Inputs from Technosphere: material/fuels	Casting, aluminum, low-wax $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for casting, aluminum, low-wax|Cut-off, U	20	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron sand casting	400	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	299.6	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	676.33	MJ	Lognormal
Output	Cylinder head	180	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for cast iron|APOS, U	180	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron sand casting	180	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	128.4	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	289.836	MJ	Lognormal
Output	Flywheel	70	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for cast iron|APOS, U	70	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron sand casting	70	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	49.93	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	112	MJ	Lognormal
Output	Oil pressure relief valve	15	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for steel, low-alloyed|APOS, U	15	kg	Lognormal
Inputs from Technosphere: material/fuels	Forging, steel $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for forging, steel|Cut-off, U	15	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	10.7	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	24.12	MJ	Lognormal
Output	Pistons	100	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for cast iron|APOS, U	55	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for steel, low-alloyed|APOS, U	30	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum alloy, AlLi $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for aluminium alloy, AlLi|Cut-off, U	15	kg	Lognormal
Inputs from Technosphere: material/fuels	Forging, steel $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for forging, steel|Cut-off, U	30	kg	Lognormal
Inputs from Technosphere: material/fuels	Casting, aluminum, low-wax $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for casting, aluminum, low-wax|Cut-off, U	15	kg	Lognormal
Inputs from Technosphere: material/fuels	Cast iron sand casting	55	kg	Lognormal
Inputs from Technosphere: electricity/heat	Electricity, production mix FI/FI U	71.33	kWh	Lognormal
Inputs from Technosphere: electricity/heat	Heat, district heating, manufacturing	160.92	MJ	Lognormal
2. Diesel engine transportation
Processes	Transport, freight, lorry 16 –32 metric ton, EURO3 $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	684	tkm	Lognormal
Processes	Transport, freight, sea, transoceanic ship $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|transport, freight, sea, transoceanic ship|APOS, U	6587.27	tkm	Lognormal
3. Diesel engine use phase
Output	Use phase diesel engine	1	hr	Lognormal
Inputs from Technosphere: material/fuels	Diesel, low-Sulphur, at refinery/RER U	21.2	kg	Lognormal
Inputs from Technosphere: material/fuels	Urea $\left\{\mathrm{R}\mathrm{E}\mathrm{R}\right\}$|market for urea|Cut-off, U	0.20	kg	Lognormal
Inputs from Technosphere: material/fuels	Water, deionized $\left\{\mathrm{R}\mathrm{O}\mathrm{W}\right\}$|market for water, deionized|Cut-off, U	0.43	kg	Lognormal
Emission to air	Particulates, <2.5 um	1.25	g	Lognormal
Emission to air	Carbon dioxide	66.70	kg	Lognormal
Emission to air	Methane	0.276	g	Lognormal
Emission to air	Nitrogen oxides	40.60	g	Lognormal
Emission to air	Carbon monoxide	127.37	g	Lognormal
Emission to air	NMVOC, non-methane volatile organic compounds, CA	11.49	g	Lognormal
Emission to air	Carbon black	0.191	g	Lognormal
Emission to air	TSP	1.25	g	Lognormal
Emission to air	Sulfur dioxide, Ca	0.424	g	Lognormal
Emission to air	Particulates, <10 um	1.25	g	Lognormal
Emission to air	Dinitrogen monoxide	2.95	g	Lognormal
Emission to air	Ammonia, CA	0.170	g	Lognormal
4. Hybrid-electric powertrain components
Output	Converter	23	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum alloy, AlLi $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for Aluminium alloy, AlLi|Cut-off, U	8	kg	Lognormal
Inputs from Technosphere: material/fuels	Copper, cathode $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for copper, cathode|Cut-off, U	8	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, electric, un- and low-alloyed, at plant/RER U	5	kg	Lognormal
Output	Cooling system	10	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum, cast alloy $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	3	kg	Lognormal
Inputs from Technosphere: material/fuels	Ethylene glycol $\left\{\mathrm{R}\mathrm{o}\mathrm{W}\right\}$|market for ethylene glycol|Cut-off, U	1	kg	Lognormal
Inputs from Technosphere: material/fuels	Water, deionised $\left\{\mathrm{E}\mathrm{u}\mathrm{r}\mathrm{o}\mathrm{p}\mathrm{e} \mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{t} \mathrm{S}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{z}\mathrm{e}\mathrm{r}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{d}\right\}$|market for water, deionised|Cut-off, U	1	kg	Lognormal
Inputs from Technosphere: material/fuels	Other components	5	kg	Lognormal
Output	DC/AC Convertor	14	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum alloy, AlLi $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for Aluminium alloy, AlLi|Cut-off, U	7	kg	Lognormal
Inputs from Technosphere: material/fuels	Copper, cathode $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for copper, cathode|Cut-off, U	3	kg	Lognormal
Inputs from Technosphere: material/fuels	Other components	4	kg	Lognormal
Output	Electric motor	172	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum alloy, AlLi $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for Aluminium alloy, AlLi|Cut-off, U	42	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, electric, un- and low-alloyed, at plant/RER U	65	kg	Lognormal
Inputs from Technosphere: material/fuels	Copper, cathode $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for copper, cathode|Cut-off, U	32	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	20	kg	Lognormal
Inputs from Technosphere: material/fuels	Mischmetal $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|neodumium oxide to generic market for mischmetal|Cut-off, U	7	kg	Lognormal
Inputs from Technosphere: material/fuels	Other components	6	kg	Lognormal
Output	HV cables and connectors	15	kg	Lognormal
Inputs from Technosphere: material/fuels	Synthetic rubber $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	4	kg	Lognormal
Inputs from Technosphere: material/fuels	Copper, cathode $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for copper, cathode|Cut-off, U	9	kg	Lognormal
Inputs from Technosphere: material/fuels	Other components	2	kg	Lognormal
Output	Control unit	8	kg	Lognormal
Inputs from Technosphere: material/fuels	Electronics, for control unit $\left\{\mathrm{R}\mathrm{E}\mathrm{R}\right\}$|production|Alloc Rec, U	8	kg	Lognormal
Output	Mounting frame	150	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	150	kg	Lognormal
Output	Supercapacitor	150	kg	Lognormal
Inputs from Technosphere: material/fuels	Activated carbon, granular $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for activated carbon, granular|Cut-off, S	35	kg	Lognormal
Inputs from Technosphere: material/fuels	Aluminum, wrought alloy $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	45	kg	Lognormal
Inputs from Technosphere: material/fuels	Copper, cathode $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for copper, cathode|Cut-off, U	12	kg	Lognormal
Inputs from Technosphere: material/fuels	Steel, low-alloyed $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	10	kg	Lognormal
Inputs from Technosphere: material/fuels	Polylactic acid, granulate $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for polylactic acid, granulate|Cut-off, U	20	kg	Lognormal
Inputs from Technosphere: material/fuels	Printed wiring board, surface mounted, unspecific, Pb free $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	7	kg	Lognormal
Inputs from Technosphere: material/fuels	Electrolyte, for Li-ion battery $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for electrolyte, for Li-ion battery|Cut-off, U	18	kg	Lognormal
Inputs from Technosphere: material/fuels	Other components	6	kg	Lognormal
5. Hybrid-electric powertrain transportation
Processes	Transport, freight, lorry 16–32 metric ton, EURO3 $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|market for|Alloc Rec, U	1065.96	tkm	Lognormal
Processes	Transport, freight, sea, transoceanic ship $\left\{\mathrm{G}\mathrm{L}\mathrm{O}\right\}$|transport, freight, sea, transoceanic ship|APOS, U	10,267.992	tkm	Lognormal
6. Hybrid-electric powertrain use phase
Output	Use phase hybrid-electric powertrain	1	hr	Lognormal
Inputs from Technosphere: material/fuels	Diesel, low-sulphur, at refinery/RER U	19.59	kg	Lognormal
Inputs from Technosphere: material/fuels	Urea $\left\{\mathrm{R}\mathrm{E}\mathrm{R}\right\}$|market for urea|Cut-off, U	0.41	kg	Lognormal
Inputs from Technosphere: material/fuels	Water, deionized $\left\{\mathrm{R}\mathrm{O}\mathrm{W}\right\}$|market for water, deionized|Cut-off, U	0.85	kg	Lognormal
Emission to air	TSP	1.16	g	Lognormal
Emission to air	Carbon dioxide	61.64	kg	Lognormal
Emission to air	Methane	0.255	g	Lognormal
Emission to air	Nitrogen oxides	37.53	g	Lognormal
Emission to air	Carbon monoxide	117.68	g	Lognormal
Emission to air	NMVOC, non-methane volatile organic compounds, CA	10.62	g	Lognormal
Emission to air	Carbon black	0.176	g	Lognormal
Emission to air	Particulates, <2.5 um	1.16	g	Lognormal
Emission to air	Particulates, <10 um	1.16	g	Lognormal
Emission to air	Dinitrogen monoxide	2.72	g	Lognormal
Emission to air	Sulfur dioxide, CA	0.392	g	Lognormal
Emission to air	Ammonia, CA	0.157	g	Lognormal

(Appendix A).

2.2.1. Raw Material Acquisition

The LCI begins with the quantification of material inputs required for the production of the engine components. The material composition of both the conventional diesel engine and the hybrid-electric powertrain was established using a combination of manufacturer data from Logset, literature sources, and representative datasets from the ecoinvent database. The inventory includes major material groups such as cast iron, steel, aluminum, copper, plastics, and electronic components. The material inputs were modeled using datasets from the ecoinvent database, which already include upstream processes such as raw material extraction and processing. Therefore, these stages were not assessed separately, and the system boundary was limited to material production and component manufacturing.

For the conventional configuration, the inventory consisted of the diesel engine block, cylinder head, and crankshaft system, and associated mechanical parts. In contrast, the hybrid configuration included additional components such as an electric motor/generator unit, power electronics (e.g., DC converter and generator control unit), and an energy storage system. These additional components require greater quantities of material, particularly in copper, steel, and electronic materials, thereby contributing to higher environmental burdens during the manufacturing phase. As a general consideration, inventory modelling focused on the components that contribute most significantly to the engine weight, in order to reduce data collection efforts, while maintaining the reliability and consistency of the results. Despite excluding lighter components, the model still accounted for more than 90% of the total engine mass. Small components representing less than 1–5% of the total component mass were excluded or represented using proxy datasets when no specific ecoinvent process was available. Figure 4 presents the material composition of the diesel and hybrid-electric powertrain, along with the cumulative contribution of individual components to the total engine mass.

2.2.2. Manufacturing

The manufacturing phase was represented using a simplified level of detail, in which engine components were aggregated into major subsystems. Each subsystem consists of one or more components, allowing a structured representation of the engine architecture. The material production and manufacturing processes were modeled using datasets from the ecoinvent database within the SimaPro version 1.00 environment.

2.2.3. Use Phase Emissions and Calculation Methodology

The use phase of the life cycle was modeled based on the operational fuel consumption of the machinery, as this parameter directly determines emissions during operation. Routine machine maintenance, including hydraulic oils, lubricants, filters, and other common maintenance activities, was excluded from the system boundary. Manufacturer information indicated no additional routine maintenance requirement for the hybrid-specific components during the considered operational period; therefore, servicing or replacement of the electric motor, power electronics, and supercapacitor was not included. Fuel consumption values for both the conventional diesel and hybrid harvester configurations were derived from extensive field studies conducted by Yadegari et al. (2026) [5], which evaluated hybrid harvester performance under real operating conditions in Quebec, Canada. The study was based on approximately 4 months of data collection across multiple seasons, including summer, autumn, and winter, ensuring representative operating conditions. Based on these results, the average fuel consumption was set to 21.2 kg/h (1.59 L/m³) for conventional diesel harvesters and 19.59 kg/h (1.26 L/m³) for hybrid harvesters. Further details of the experimental design and statistical analysis are reported in Yadegari et al. (2026) [5]. These values, together with DEF consumption, were used as the basis for estimating use phase emissions, as described in the following subsection [5].

DEF, also known as AUS 32, is a non-toxic aqueous solution composed of 32.5% high-purity urea and 67.5% deionized water, used in Selective Catalytic Reduction (SCR) systems to reduce nitrogen oxide (NOx) emissions (ISO 22241 [25]; EPA).

An operational lifetime of 15,000 h was adopted as the base-case scenario based on the service-life assumption provided for the studied Logset harvester. This value is also consistent with assumptions and empirical estimates reported for CTL harvesting machinery. Ackerman et al. (2021) [26] assumed an economic lifetime of 15,000 h for harvesters, while Spinelli et al. (2011) [27], based on more than 1000 used-machine records from Europe and North America, estimated an economic life of approximately 18,000 h for harvesters and forwarders [26,27]. To assess the influence of this assumption on the results, a sensitivity analysis was conducted over an extended lifetime range, as discussed in Section 3.4 [28].

Emissions during the operation phase were estimated using a fuel-based (Tier 2) approach in accordance with the European Monitoring and Evaluation Programme/European Environment Agency (EMEP/EEA) air pollutant emission inventory guidebook. This approach was selected due to the availability of fuel consumption data for both the conventional diesel and hybrid harvester configurations, allowing direct quantification of emissions based on actual fuel use. The method is particularly suitable for this study, as the primary objective is to evaluate the environmental benefits associated with reduced fuel consumption of the hybrid-electric powertrain during operation. To ensure consistency, the emission factors were aligned with Stage V emission standards.

Emissions for each engine were calculated using Equation (1), which is a modified version of the EMEP/EEA formulation adapted to the scope of this study. For each pollutant $i$, emissions were calculated as:

E_i = EF_i * FC

(1)

where E_i represents the emission of pollutant i, FC is the fuel consumption (kg/h), and EF_i is the corresponding emission factor expressed per unit of fuel consumed. This formulation enables a transparent comparison between the diesel and hybrid configurations, as differences in emissions are directly driven by differences in fuel consumption. The baseline emission factors used in this study for the forestry sector and Stage V technology are presented in

Table 2. Baseline fuel-based emission factors used for Stage V forestry machinery [29,30,31].

Pollutant	BC	CH4	CO	N2O	NH3	NMVOC	NOX	PM10	PM2.5	PM
Amount	9	13	6008	139	8	542	1915	59	59	59
Units	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel	g/tonnes fuel

.

The resulting hourly emissions estimated using the Tier 2 method are presented in

Table 3. Hourly emissions (g/h) of conventional diesel and hybrid harvester engines estimated using fuel consumption data and Tier 2 emission factors.

Pollutant	Diesel Engine	Hybrid-Electric Powertrain
CH4	0.276	0.255
N2O	2.95	2.72
NH3	0.170	0.157
CO	127.37	117.68
NMVOC	11.49	10.62
NOx	40.60	37.53
BC	0.191	0.176
PM10	1.25	1.16
PM2.5	1.25	1.16
TSP	1.25	1.16

. The hybrid configuration results in consistently lower emissions across all pollutants compared to the conventional diesel engine. This reduction (approximately 7–8%) is directly proportional to the decrease in fuel consumption, as identical emission factors were applied. The emission indicates that hybridization reduces emissions primarily through improved fuel efficiency, leading to lower environmental impacts across both climate change and air pollution categories.

CO₂ and SO₂ emissions are primarily associated with fuel combustion and therefore depend on engine characteristics and equipment technology. CO₂ emissions were estimated using the carbon intensity of diesel fuel, assumed to be 3146 g CO₂ per kg of fuel combusted. In addition, diesel fuel density was assumed to be 0.85 kg/L. SO₂ emissions were estimated based on the sulfur content of diesel fuel. A maximum sulfur concentration of 10 ppm was assumed, with complete oxidation of sulfur to SO₂ during combustion. Both CO₂ and SO₂ were therefore treated as fuel-dependent emissions during the operation phase.

Using this approach, CO₂ emissions were calculated at 66.70 kg/h for the conventional diesel engine and 61.64 kg/h for the hybrid configuration. Corresponding SO₂ emissions were estimated at 0.424 g/h for the diesel engine and 0.392 g/h for the hybrid-electric powertrain.

2.2.4. End-of-Life Disposal

The end-of-life phase was modeled using a material-specific waste treatment approach based on recycling, incineration, and landfill scenarios. Recycling rates for key materials were derived from Quebec-specific waste management statistics, reflecting the geographic location where the harvesters are used and scrapped. Rates were taken from RECYC-QUÉBEC’s Bilan 2023 de la gestion des matières résiduelles au Québec (RECYC-QUÉBEC, 2025), which reports material-specific recycling rates calculated as the ratio of quantities generated to quantities directed to recycling for the municipal collection [32].

Waste treatment processes were implemented using datasets from the ecoinvent database within the SimaPro environment, following the cut-off system model. Where a representative dataset was unavailable within the cut-off system model, datasets from other available ecoinvent system models (e.g., APOS or Alloc Rec) were used to represent the required process or material. The energy requirements and emissions associated with recycling and residual waste treatment processes were also obtained from the corresponding ecoinvent datasets. For materials not recovered through recycling, residual waste was allocated to municipal waste treatment based on Quebec-specific disposal statistics, with 96.0% disposed of in engineered landfill and 4.0% treated through incineration (RECYC-QUÉBEC, 2025) [32].

2.3. Study Assumption

Due to limitations in the availability of detailed manufacturer-specific data and the complexity of modeling hybrid forest machines, a set of assumptions was adopted to ensure consistency and transparency in the life cycle assessment. Foreground inventory data were prioritized from system-specific primary sources where available. Material and component information for the hybrid-electric powertrain was obtained directly from Logset, while operational fuel-consumption data were derived from the field measurements reported by Yadegari et al. (2026) [5]. In the absence of manufacturer-specific inventory data for the conventional diesel engine, its material composition was modeled using the published literature. The 15,000 h operational lifetime was based on Logset technical documentation. Secondary background data for material production and manufacturing were obtained from ecoinvent, while emission factors and end-of-life parameters were derived from published guidelines and official statistics, respectively. A summary of the key assumptions applied in this study is presented in

Table 4. Key assumptions used in the life cycle assessment model.

Category	Parameter	Assumption	Source/Justification	Data Type
Lifetime	Operational lifetime	15,000 h (base case); 5000–20,000 h (sensitivity range, Section 3.4)	Logset documentation, [26,27,28,29]	Secondary—manufacturer documentation and literature
Fuel consumption	Diesel engine; Hybrid engine	21.2 kg/h; 19.59 kg/h	[5]	Primary—field measurements
Emission factors	Use phase	Tier 2 (fuel-based approach)	EMEP/EEA guidelines [30]	Secondary—emission-factor database
DEF consumption	Both systems	Included (urea + deionized water)	Field study data [5]	Primary—field measurements
Diesel engine material composition	Components	Major components representing >90% of total mass	Logset company, literature [31,33,34]	Secondary—literature
Hybrid-electric powertrain	Configuration	Parallel hybrid-electric powertrain	Logset and Danfoss documentation, literature [35,36]	Primary—manufacturer
Background manufacturing processes	Source	ecoinvent database implemented in SimaPro	Standard LCA practice	Secondary—LCI database
End-of-life	Recycling rates	(RECYC-QUÉBEC, 2025) [32]	[32]	Secondary—official statistics

.

2.4. Life Cycle Impact Assessment

The IMPACT World+ midpoint + ADR method was implemented in SimaPro to quantify environmental impacts. Impact categories were considered in the analysis, covering climate change, fossil and nuclear energy use, mineral resources use, photochemical oxidant formation, ozone layer depletion, freshwater and terrestrial ecotoxicity, human toxicity (cancer and non-cancer), ionizing radiation, land occupation and transformation, marine and freshwater eutrophication and acidification, particulate matter formation, and water scarcity. The environmental impact categories, corresponding units, and their descriptions used in the life cycle impact assessment are presented in Table S1.

Dissipation-based indicators were applied to evaluate mineral resource use in the hybrid-electric powertrain and diesel engines. The Average Dissipation Rate (ADR) method was used to quantify the dissipation of metals through emissions, disposal, and non-functional recycling, reflecting how quickly materials become unavailable for future use after extraction [37,38]. Expressed relative to iron, the ADR indicator was applied to key materials in the engine systems, including aluminum, copper, and steel. This approach enables the evaluation of how material choices in the hybrid-electric powertrain, particularly in the electric motor, power electronics, and supercapacitor-based energy storage, influence long-term resource availability.

3. Results

3.1. Overall Environmental Performance of Diesel and Hybrid Harvester Engines

The environmental impacts associated with the diesel engine and the hybrid-electric powertrain are summarized in

Table 5. Environmental impacts of the raw materials and manufacturing, transport, operation, and end-of-life stages of diesel and hybrid harvester engines.

				Contribution (%)
Impact Category	Unit	Engine	Total	Raw Materials and Manufacturing	Transport	Operation	End-of-Life
Climate change	kg CO2 eq	Diesel engine	1,195,744.6	0.80	0.02	99.18	<0.01
		Hybrid-electric powertrain	1,113,757.9	1.14	0.03	98.83	<0.01
Fossil and nuclear energy use	MJ deprived	Diesel engine	17,462,203.0	0.65	0.02	99.34	<0.01
		Hybrid-electric powertrain	163,105,439	0.94	0.03	99.03	<0.01
Mineral resource use	kg deprived	Diesel engine	4485.2	16.22	0.07	83.71	<0.01
		Hybrid-electric powertrain	4472.6	19.93	0.11	79.96	0.01
Photochemical oxidant formation	kg NMVOC eq	Diesel engine	2011.39	1.8	0.10	98.09	<0.01
		Hybrid-electric powertrain	1892.03	2.69	0.17	97.13	<0.01
Ozone layer depletion	kg CFC-11 eq	Diesel engine	0.19	0.16	0.01	99.82	<0.01
		Hybrid-electric powertrain	0.18	0.36	0.04	99.6	<0.01
Freshwater ecotoxicity	CTUe	Diesel engine	429,385,070.0	34.26	0.14	64.18	1.42
		Hybrid-electric powertrain	1,179,783,000.0	30.64	0.04	23.1	46.23
Human toxicity cancer	CTUh	Diesel engine	0.028	76.28	0.05	23.65	0.02
		Hybrid-electric powertrain	0.028	76.93	0.03	22.87	0.18
Human toxicity non-cancer	CTUh	Diesel engine	0.022	20.99	0.08	78.92	0.01
		Hybrid-electric powertrain	0.035	50.49	0.08	48.78	0.06
Freshwater acidification	kg SO2 eq	Diesel engine	0.0056	1.56	0.08	98.35	<0.01
		Hybrid-electric powertrain	0.0054	3.11	0.14	96.74	<0.01
Terrestrial acidification	kg SO2 eq	Diesel engine	4.32	1.66	0.09	98.25	<0.01
		Hybrid-electric powertrain	4.16	3.31	0.16	96.53	<0.01
Freshwater eutrophication	kg PO4 eq	Diesel engine	58.14	0.36	0.01	99.63	<0.01
		Hybrid-electric powertrain	54.04	0.50	0.02	99.48	<0.01
Marine eutrophication	kg N eq	Diesel engine	31.14	4.69	0.12	95.18	<0.01
		Hybrid-electric powertrain	30.93	5.54	0.19	94.27	<0.01
Particulate matter formation	kg PM2.5 eq	Diesel engine	165.7	3.07	0.09	96.84	<0.01
		Hybrid-electric powertrain	163.58	4.99	0.14	94.86	0.01
Ionizing radiation	Bq C-14 eq	Diesel engine	2,090,869.7	5.14	0.06	94.80	<0.01
		Hybrid-electric powertrain	2,054,481.8	7.22	0.13	92.64	0.01
Land transformation, biodiversity	m2 yr arable	Diesel engine	231.08	0.43	0.02	99.55	<0.01
		Hybrid-electric powertrain	215.92	0.92	0.03	99.04	0.01
Land occupation, biodiversity	m2 yr arable	Diesel engine	1221.2	9.98	0.38	89.63	0.04
		Hybrid-electric powertrain	1342.82	20.49	0.54	78.89	0.08
Water scarcity	m3 world eq	Diesel engine	8,308,617.1	1.90	<0.01	98.01	<0.01
		Hybrid-electric powertrain	7,694,099.2	1.95	<0.01	98.05	<0.01
Average Dissipation Rate (ADR)	kg Fe-eq.	Diesel engine	4665.2	35.34	0.28	64.38	<0.01
		Hybrid-electric powertrain	6294.3	54.17	0.10	45.73	<0.01

. The results indicate that hybridization improves environmental performance in several operation-related impact categories through reduced fuel consumption; however, additional materials and electronic components introduce higher impacts in some categories associated with manufacturing and upstream processes.

Overall, the operation phase dominates most environmental impact categories for both engine types. Climate change, fossil and nuclear energy use, ozone layer depletion, freshwater eutrophication, particulate matter formation, terrestrial acidification, and water scarcity are primarily driven by fuel combustion during operation, with operational contributions generally exceeding 94% of total impacts.

The hybrid configuration reduced several operation-related impacts compared with the conventional diesel engine. Climate change impacts decreased from 1.20 × 10⁶ to 1.11 × 10⁶ kg CO₂ eq, corresponding to a reduction of approximately 6.9%. Similarly, fossil and nuclear energy use decreased by approximately 6.6%, while reductions were also observed for ozone layer depletion, terrestrial acidification, freshwater eutrophication, particulate matter formation, ionizing radiation, and water scarcity. These reductions are mainly attributed to the lower fuel consumption achieved by the hybrid-electric powertrain during operation. The results therefore demonstrate that hybridization can improve operational efficiency and reduce fuel-related environmental burdens in forestry machinery.

However, the hybrid-electric powertrain exhibits higher impacts in several categories associated with material production and upstream processes. Freshwater ecotoxicity increased from 4.29 × 10⁸ to 1.18 × 10⁹ CTUe, while human toxicity non-cancer increased from 0.0227 to 0.0358 CTUh. Land occupation also increased from 1221.2 to 1342.8 m²·yr arable. These increases are mainly associated with the additional components required for hybridization, including the electric motor, power electronics, and energy storage system. In contrast, mineral resource use remained relatively similar between the two systems, decreasing slightly from 4485.3 to 4472.6 kg deprived. Marine eutrophication also showed only a marginal reduction, while photochemical oxidant formation decreased from 2011.4 to 1892.0 kg NMVOC eq.

3.2. Contribution of Life Cycle Stages

To further visualize the relative contribution of each life cycle stage across the evaluated impact categories, Figure 5 presents the percentage contributions of raw materials and manufacturing, transport, operation, and end-of-life processes for both the diesel and hybrid-electric powertrain.

The results show that the operation phase is the dominant contributor to most impact categories for both systems. In particular, climate change, and fossil and nuclear energy use, are overwhelmingly influenced by operational fuel consumption, with the operation stage contributing between approximately 95% and 100% of total impacts for these categories. For human toxicity cancer, both systems share the same underlying driver: electric arc furnace slag treatment, a landfill-disposal byproduct of cast iron and low-alloyed steel production, dominates the manufacturing-stage impact for both configurations (Figure S1). The diesel engine’s larger cast iron mass (420 kg cylinder block, 180 kg cylinder head) yields a marginally higher total (0.0288 CTUh) than the hybrid configuration (0.0282 CTUh, driven primarily by a 105 kg crankshaft and downsized engine core), a difference of roughly 2%. Here, hybridization does not change the mechanism generating the impact, only slightly reducing the ferrous mass responsible for it.

In contrast, upstream processes associated with raw materials and manufacturing contribute substantially to several toxicity and resource-related categories, especially in the hybrid-electric powertrain. For freshwater ecotoxicity, raw materials and manufacturing contribute 30.6% of total impacts in the hybrid configuration, compared with 34.3% for the conventional diesel engine; as discussed below, the hybrid system’s end-of-life stage now contributes more to this category than either raw materials and manufacturing or operation. Process-contribution analysis (Figure S2) provides mechanistic insight beyond the aggregate values reported in Table 5. Average Dissipation Rate (ADR) results indicate that manufacturing processes dominate mineral resource dissipation impacts in the hybrid-electric powertrain, mainly due to the additional metals and electronic components associated with the electric system.

The end-of-life stage remains negligible (below 1%) for most impact categories for the diesel engine. Transport contributes only marginally across all categories for both systems, generally remaining below 1% of total impacts.

3.3. Comparison of Selected Environmental Impact Categories

The environmental trade-offs associated with hybridization can be further examined by comparing the contributions of individual life cycle stages. Figure 6 presents the absolute contributions of these stages for selected impact categories, including climate change, fossil and nuclear energy use, freshwater ecotoxicity, and land occupation.

As shown in Figure 6, the hybrid-electric powertrain substantially reduces climate change and fossil energy impacts during the operation phase due to lower fuel consumption. In contrast, freshwater ecotoxicity and land occupation are higher in the hybrid-electric powertrain because of increased contributions from raw material extraction and manufacturing processes associated with hybrid components.

The results indicate a clear trade-off between operational benefits and upstream material-related burdens. While the hybrid-electric powertrain reduces fuel and energy-related impacts through lower fuel consumption during operation, these benefits are partly offset by increased impacts from material extraction, manufacturing, and end-of-life processes. This highlights the importance of considering both operational efficiency and material-related burdens when evaluating cleaner production strategies for hybrid forestry machinery.

3.4. Sensitivity Analysis of Accumulated-Utilization-Hours Scenario

A sensitivity analysis was performed by varying the lifetime from 5000 h up to 20,000 h [28] to assess the influence of operational lifetime on the environmental performance of the studied systems. The results were evaluated for both diesel and hybrid configurations to examine changes in total impacts and relative differences between the systems. It should be noted that the sensitivity analysis evaluated the effect of operational lifetime on a single engine system. These scenarios represent accumulated utilization rather than equipment durability, and no component replacement was assumed.

As expected, prolonging machine use from 5000 to 20,000 h resulted in higher cumulative impacts across all categories, mainly due to increased fuel consumption and operational inputs. The effects of operational lifetime on the environmental impacts of the diesel engine and hybrid-electric powertrain are presented in Figure 7.

In most categories, the diesel engine and hybrid-electric powertrain followed similar increasing trends. However, the hybrid-electric powertrain showed lower impacts than the diesel engine at longer lifetimes for several key categories, including climate change, fossil and nuclear energy use, freshwater eutrophication, land transformation, and water scarcity. This indicates that the environmental benefit of the hybrid-electric powertrain becomes more evident as machine lifetime increases.

Marine eutrophication, mineral resource use, particulate matter formation, and ionizing radiation showed similar environmental impacts for both powertrain configurations across the evaluated lifetime. Some categories, particularly freshwater ecotoxicity, human toxicity non-cancer, and land occupation, remained higher for the hybrid-electric powertrain. These higher impacts were primarily attributable to the additional materials and components required for the hybrid-electric powertrain, including the electric motor, control system, and energy storage system. The accumulated-utilization-hours sensitivity results indicated that increasing operational lifetime improves the relative environmental performance of the hybrid harvester in energy- and climate-related categories, while some toxicity- and material-related impacts remain higher. Therefore, the environmental advantage of the hybrid-electric powertrain is most evident when the machine is used over longer service lifetimes.

3.5. Uncertainty Analysis

In addition to the contribution and sensitivity analyses, an uncertainty analysis was conducted to evaluate the robustness and reliability of the life cycle impact assessment (LCIA) results. The uncertainty analysis focused on the variability of the developed life cycle inventory (LCI) data and its influence on the total impact assessment results. Uncertainty in the input and output flows of each process was quantified using the pedigree matrix approach [39]. This approach evaluates data quality through six qualitative indicators, namely reliability, completeness, temporal correlation, geographical correlation, technological correlation, and sample size. Based on these indicators, uncertainty factors were assigned to all relevant flows within the modeled system. Monte Carlo simulation was performed within the SimaPro software version 1.00 environment using 1000 iterations to propagate uncertainties through the model. The results are presented as distributions of impact category indicators, reflecting the variability of the outcomes [29].

The uncertainty analysis results are presented in Figure 8 and Figure 9. Figure 8 shows the normalized mean environmental impacts together with the corresponding 95% confidence intervals obtained from 1000 Monte Carlo simulation runs. Figure 9 presents the comparative Monte Carlo probabilities for the diesel engine and hybrid-electric powertrain. For each impact category, the comparison represents the proportion of simulation runs in which one alternative produced a lower impact than the other, thereby providing an indication of the robustness and direction of the observed differences. Figure 8 and Figure 9 differ in simulation structure. Figure 8 derives from two independent single-system Monte Carlo runs, while Figure 9 uses SimaPro’s comparative Monte Carlo function, which evaluates both systems together within each iteration and samples shared background processes identically across systems; this paired sampling correctly accounts for correlation and underlies the robustness claims in this section.

Pedigree-based uncertainty was concentrated on hybrid-specific inputs, where data uncertainty is comparatively elevated due to proxy substitution and technology-specific characterization. The diesel engine’s core components are common to both configurations and were treated as fixed values based on manufacturer specifications, given that Figure 9’s paired sampling draws shared inputs identically across systems.

The uncertainty ranges of several impact categories overlap considerably between the two systems, indicating substantial uncertainty in their absolute LCIA results. However, overlap between the individual confidence intervals alone does not determine the robustness of the comparative difference. Therefore, the direction and robustness of the differences were further evaluated using the comparative Monte Carlo analysis presented in Figure 9. Categories with comparative probabilities close to an even split between the alternatives indicate no clear environmental advantage, whereas probabilities strongly favoring one alternative indicate a more robust directional difference under the modeled uncertainty. Categories such as freshwater ecotoxicity, land occupation biodiversity, ionizing radiation and land transformation biodiversity exhibit particularly large confidence intervals, reflecting high uncertainty in background datasets and land-use-related emissions. Marine eutrophication and mineral resource use also show substantial variability, suggesting sensitivity to assumptions regarding material extraction and upstream supply chains. Despite these uncertainties, the hybrid-electric powertrain generally trends toward reduced environmental burdens or similar performance relative to the diesel engine configuration.

Figure 9 presents the comparative Monte Carlo probabilities between the diesel engine (A) and the hybrid-electric powertrain (B). For each impact category, the bars represent the percentage of Monte Carlo iterations in which A < B and A ≥ B, rather than the percentage difference in LCIA scores between the two systems. A strong predominance of one outcome indicates a robust directional difference under the modeled uncertainty, whereas a more balanced probability indicates substantial uncertainty regarding which alternative has the lower environmental impact. The comparative analysis showed robust lower impacts for the hybrid-electric powertrain in several operationally dominated categories, particularly short and long-term climate change, fossil and nuclear energy use, ozone layer depletion, freshwater and terrestrial acidification, freshwater eutrophication, and water scarcity. In contrast, freshwater ecotoxicity, ADR, land occupation, and ionizing radiation showed a high probability of greater impacts for the hybrid-electric powertrain. Other categories showed less decisive comparative probabilities and should therefore be interpreted as trends rather than robust differences. For short-term climate change, the robustness of the comparison was further examined using the Monte Carlo distribution of the difference between the diesel and hybrid systems (Diesel − Hybrid; Figure S3). The distribution remained entirely above zero and was centered at approximately 8.2 × 10⁴ kg CO₂ eq, indicating that the diesel system consistently produced a higher climate change impact than the hybrid-electric powertrain across the simulated uncertainty range.

4. Discussion

4.1. Comparison of Environmental Impact Results

The magnitude and distribution of environmental impacts across the life cycle stages observed in this study are generally consistent with findings reported in previous LCA studies on diesel engines and forestry machinery. Similar to earlier studies, the operation phase was identified as the dominant contributor to most environmental impact categories, particularly climate change, fossil energy use, particulate matter formation, and acidification-related impacts, while the raw material and manufacturing stages represented the second-largest contribution [33,34,40].

Direct comparison between the present study and previous LCA studies remains difficult because of differences in engine applications, power capacities, operational conditions, functional units, and system boundaries. Most previous studies focused on automotive, agricultural, or construction machinery engines, whereas the present study evaluated a heavy-duty hybrid forestry harvester operating under highly variable load conditions. In addition, several earlier studies adopted cradle-to-gate boundaries or excluded raw materials, manufacturing, and end-of-life stages, while the present study followed a cradle-to-grave approach [8,14,41,42]. These methodological differences influence the absolute magnitude of environmental impacts and limit direct quantitative comparison.

Nevertheless, comparison of climate change impacts with previous diesel engine studies provides useful context. Li et al. (2013) evaluated the life cycle impacts of a six-cylinder diesel bus engine and reported that the use phase contributed approximately 97.7% of total climate change impacts, while raw material acquisition and manufacturing accounted for less than 1% each [33]. Similarly, the present study showed that the operation phase dominated climate change impacts, contributing 99.18% and 98.83% of total impacts for the diesel and hybrid-electric powertrains, respectively. The contribution of the manufacturing stage remained comparatively small but increased in the hybrid configuration because of the additional electric motor, power electronics, and energy storage components. These findings are consistent with the conclusions of Li et al. (2013) [33], who identified fuel combustion during operation as the primary environmental hotspot of diesel engines.

The dominance of the operation phase is also consistent with studies conducted on forestry and non-road mobile machinery. Berendt et al. (2018) reported that diesel consumption during machine operation was the major contributor to global warming potential in forestry machinery [7], while Khan and Huang (2023) observed that operational fuel use remained the primary source of climate impacts in diesel and hybrid construction excavators [31]. Similarly, Alzamora et al. (2022) found that mechanized forest harvesting systems with improved fuel efficiency exhibited lower greenhouse gas emissions compared with less efficient harvesting alternatives [13]. The reductions in climate change and fossil energy impacts observed for the hybrid harvester in the present study therefore align with previous findings regarding the environmental benefits of hybridization and reduced fuel consumption in heavy-duty machinery.

Engine-hour functional unit environmental impacts during the use phase were calculated based on hourly fuel consumption (kg/h), resulting in a 6.9% reduction in climate change impacts for the hybrid-electric powertrain. As anticipated from the field-measured fuel consumption reduction of approximately 20% per cubic meter of harvested wood reported by Yadegari et al. (2026) [5].

Unlike conventional diesel engine studies, the hybrid powertrain in this study exhibited higher impacts in some categories associated with material production and upstream processes, particularly freshwater ecotoxicity and human toxicity non-cancer. Similar trends were reported by Khan and Huang (2023), who demonstrated that hybrid and electric excavators may reduce operational emissions while simultaneously increasing impacts associated with material extraction, electronics manufacturing, and battery-related processes [31]. In the present study, these increases were primarily linked to the additional electric motor, power electronics, supercapacitor system, and associated materials used in the hybrid drivetrain.

Although the hybrid-electric powertrain exhibited higher impacts in several manufacturing-related categories, these results should be interpreted in the context of the substantially higher power capability provided by the hybrid configuration. The conventional diesel engine evaluated in this study delivers 205 kW, whereas the hybrid-electric configuration combines the diesel engine with a 175 kW electric motor, resulting in a maximum short-term combined power output of approximately 380 kW. Consequently, the comparison represents a relatively conservative assessment of the hybrid system, since the hybrid machine provides significantly greater peak power while maintaining lower fuel consumption during operation.

In forestry applications, achieving a comparable power output using a conventional diesel-only configuration would require a substantially larger engine with greater displacement, additional cylinders, larger cooling and exhaust after-treatment systems, and increased structural requirements [43]. Such modifications would increase both material requirements and operational fuel consumption, thereby leading to higher environmental burdens during both manufacturing and use phases. Therefore, the additional manufacturing impacts associated with the hybrid components should be interpreted alongside the functional advantage of delivering substantially higher peak power and improved power management capability without relying exclusively on a larger diesel engine. The lifetime sensitivity analysis assumes that both powertrains remain operational for up to 20,000 h. This assumption was adopted to compare the influence of operational lifetime on environmental performance, although the long-term durability of hybrid-specific components remains uncertain because hybrid forestry harvesters are still a relatively recent technology. Although the inventory covers the major powertrain components, a complete manufacturer BoM was unavailable, and minor components representing approximately 1–5% of component mass were excluded. Because environmental relevance is not necessarily proportional to mass, this exclusion may influence some material- and toxicity-related categories, particularly for electronics and specialized materials. However, the contribution analysis indicates that operation dominates most fuel-related impact categories and the principal comparative conclusions.

The comparison also highlights the importance of considering multiple environmental indicators rather than focusing solely on greenhouse gas emissions. While the hybrid-electric powertrain improved environmental performance in climate change, fossil energy use and water scarcity categories trade-offs were observed in several toxicity- and resource-related impacts. Comparable conclusions were reported by Jiang et al. (2014), who emphasized that improvements in one environmental category do not necessarily correspond to overall environmental superiority across all impact categories [34].

Finally, although differences in methodology and machine configuration prevent exact comparison with previous studies, the general trends observed in the present work are consistent with the existing LCA literature on diesel engines and heavy-duty machinery systems. The results confirm that fuel consumption during operations remains the primary driver of climate-related impacts, while hybridization can reduce operational emissions at the expense of increased upstream impacts associated with additional materials and electronic components.

4.2. Mineral Resource Dissipation Results

To complement the IMPACT World+ midpoint + ADR assessment, the Mineral Resource Dissipation method developed by Poncelet et al. (2022) [38] was applied to evaluate the dissipation potential of mineral resources associated with the diesel engine and hybrid-electric powertrains. This method assesses the loss of mineral resource accessibility after extraction due to dissipative flows, providing additional insight into long-term resource availability [38].

The Average Dissipation Rate (ADR) indicator was incorporated into the midpoint impact assessment as an additional category. The hybrid-electric powertrain showed an ADR value more than the conventional diesel engine in

Table 6. Average Dissipation Rate results for the diesel engine and hybrid-electric powertrains based on the Mineral Resource Dissipation method developed by Poncelet et al. (2022) [38].

Impact Category	Unit	Diesel Engine	Hybrid-Electric Powertrain
ADR	kg Fe-eq.	4662.23	6294.35

, indicating substantially greater mineral resource dissipation associated with hybrid technologies. Higher ADR values represent increased dissipation rates of metals after extraction and lower long-term availability within the Technosphere [38].

The higher ADR impact of the hybrid-electric powertrain is likely related to the additional electronic components, batteries, and specialty metals required for electrification. These materials are often associated with lower recycling efficiencies and higher dissipative losses. Therefore, although the hybrid-electric powertrain may reduce impacts in several climate and energy-related categories, it also increases the potential loss of valuable mineral resources, highlighting a trade-off between operational environmental benefits and long-term resource sustainability.

4.3. Productivity-Normalized Comparison

Table 7. Environmental impacts of the diesel engine and hybrid-electric powertrain, normalized per m³ of harvested wood (primary functional unit).

Impact Category	Unit	Diesel Engine (per m3)	Hybrid-Electric Powertrain (per m3)
Climate change	kg CO2 eq	4.8906	3.6008
Fossil and nuclear energy use	MJ deprived	71.57	52.70
Mineral resources use	kg deprived	0.01836	0.01445
Photochemical oxidant formation	kg NMVOC eq	0.008221	0.006111
Ozone layer depletion	kg CFC-11 eq	8.160 × 10−7	5.917 × 10−7
Freshwater ecotoxicity	CTUe	1756	3815
Human toxicity cancer	CTUh	1.178 × 10−7	9.117 × 10−8
Human toxicity non-cancer	CTUh	9.284 × 10−8	1.157 × 10−7
Freshwater acidification	kg SO2 eq	2.331 × 10−8	1.769 × 10−8
Terrestrial acidification	kg SO2 eq	1.771 × 10−5	1.345 × 10−5
Freshwater eutrophication	kg PO4 eq	2.376 × 10−4	1.746 × 10−4
Marine eutrophication	kg N eq	1.272 × 10−4	9.990 × 10−5
Particulate matter formation	kg PM2.5 eq	6.789 × 10−4	5.302 × 10−4
Ionizing radiation	Bq C-14 eq	8.552	6.642
Land transformation, biodiversity	m2yr arable	9.448 × 10−4	6.984 × 10−4
Land occupation, biodiversity	m2yr arable	0.004990	0.004332
Water scarcity	m3 world eq	33.99	24.86
Average Dissipation Rate (ADR)	kg Fe-eq.	0.01910	0.02035

recasts the same results on the primary, productivity-normalized functional unit of 1 m³ of harvested wood, using the conversion described in Section 2.1 (16.3 m³/h for the diesel-powered harvester and 20.62 m³/h for the hybrid-electric harvester; 244,500 m³ and 309,300 m³ total lifetime volume, respectively).

Normalizing the life cycle impacts by harvested volume strengthens the environmental advantage of the hybrid-electric powertrain in most impact categories by accounting for its higher observed productivity. For climate change, the impact decreased from 4.89 kg CO₂ eq/m³ for the diesel engine to 3.60 kg CO₂ eq/m³ for the hybrid-electric powertrain, corresponding to a reduction of approximately 26.4%. Similar reductions were observed for fossil and nuclear energy use (26.4%), ozone layer depletion (27.5%), freshwater eutrophication (26.5%), photochemical oxidant formation (25.7%), terrestrial acidification (24.1%), and water scarcity (26.9%). These differences are substantially greater than those obtained using the 15,000 h powertrain-level comparison, demonstrating the importance of considering harvesting productivity when comparing the environmental performance of the two systems. Notably, land occupation, which was higher for the hybrid system on the 15,000 h basis, became lower when expressed per m³ because the fixed life cycle burdens were distributed over a larger lifetime harvesting output.

The productivity-normalized results also show that the environmental advantage of hybridization is not uniform across all categories. Despite the larger lifetime harvesting output of the hybrid system, freshwater ecotoxicity remained substantially higher (3814 versus 1756 CTUe/m³), as did human toxicity non-cancer and ADR. In contrast, ionizing radiation decreased from 8.55 Bq C-14 eq/m³ for the diesel engine to 6.64 Bq C-14 eq/m³ for the hybrid-electric powertrain, corresponding to a reduction of approximately 22.3%. Land occupation also changed from being higher for the hybrid system on the 15,000 h basis to approximately 13.1% lower when expressed per m³. Overall, productivity normalization reinforces the hybrid system’s benefits in operation-dominated categories while showing that some material-intensive categories remain higher. The comparative Monte Carlo analysis further showed that the environmental advantages of hybridization are not equally robust across all impact categories. The reduction in climate change impact was maintained across the simulated uncertainty range, supporting the robustness of the climate-related benefit. In contrast, several toxicity, resource-use, and land-related indicators exhibited greater uncertainty and less decisive comparative probabilities. Consequently, deterministic differences in these categories should be interpreted as tendencies rather than conclusive environmental advantages or disadvantages. These results emphasize that the environmental benefit of hybridization is most clearly supported for operationally driven categories, whereas conclusions for material-sensitive categories remain more dependent on inventory and background-dataset assumptions.

The present study is limited to environmental LCA and does not evaluate the social or economic dimensions of hybridization. Social aspects such as occupational exposure, noise, ergonomics, employment, and community impacts, as well as economic factors including acquisition, maintenance, component replacement, and utilization, should be addressed in future social-LCA and life cycle costing studies. Detailed information on the datasets used for the modeled materials and components, including the proxy datasets applied where exact matches were unavailable, is provided in

Table A2. Material data sources and proxy substitutions used in the life cycle inventory.

System/Component	Material or Process Represented	Dataset Used in SimaPro	Geography	Basis/Classification
Diesel engine	Engine material composition	Literature-derived material inventory + ecoinvent (cast iron, low-alloyed steel)	GLO	Direct material representation; not a proxy
Electric motor	Electrical steel	Steel, electric, un- and low-alloyed, at plant	RER	Direct material representation; not a proxy
Electric motor/Converter/DC/AC Converter	Copper conductors, busbars and cables	Copper, cathode—market for copper, cathode, Cut-off, U	GLO	Direct material representation; not a proxy
HV cables and connectors	Cable insulation	Synthetic rubber—market for, Alloc Rec, U	GLO	Direct material representation; not a proxy
Control unit	PCB and electronic components	Electronics, for control units—production, Alloc Rec, U	GLO	Direct, purpose-specific dataset; not a proxy
Electric motor	Permanent magnets (NdFeB)	Mischmetal, neodymium oxide to generic market for mischmetal, Cut-off, U	GLO	Proxy, no NdFeB-specific process exists in ecoinvent; mischmetal used as the closest available rare-earth-content analogue
Supercapacitor	Electrode/electrolyte material	Electrolyte, for Li-ion battery—market for, Cut-off, U	GLO	Proxy, no supercapacitor-specific process exists in ecoinvent; Li-ion battery electrolyte used as the closest available electrochemical analogue
Supercapacitor	Separator/polymer components	Polylactic acid, granulate—market for, Cut-off, U	GLO	Proxy, no supercapacitor separator-material dataset exists in ecoinvent; generic biopolymer used as the closest available material analogue
Supercapacitor	PCB/electronic control components	Printed wiring board, surface mounted, unspecified, Pb free—market for, Alloc Rec, U	GLO	Direct, generic representation of embedded electronics; not a proxy

(Appendix A). Most materials were represented using direct ecoinvent datasets, while proxies were required for a limited number of inputs. The results for material-sensitive impact categories should therefore be interpreted with this limitation in mind, and more component-specific inventory data would improve the robustness of future assessments. Unexpected replacement or maintenance of hybrid-specific components was not modelled and could influence material-related impacts if such interventions occur during the assumed lifetime. A further limitation is that identical fuel-based Stage V emission factors were applied to both powertrains; therefore, differences in NO_x, PM, CO, CH₄, and other exhaust emissions reflect differences in fuel consumption rather than measured engine-specific emission behavior.

5. Conclusions

This study evaluated and compared the environmental performance of conventional diesel and hybrid-electric powertrains through a powertrain-level life cycle assessment framework. The results showed that the operation phase dominated most environmental impact categories for both systems due to fuel consumption during machine use. The hybrid-electric powertrain showed clear environmental advantages in several operation-related impact categories. Reductions were observed in climate change, fossil and nuclear energy use, ozone layer depletion, freshwater eutrophication, terrestrial acidification, photochemical oxidant formation, ionizing radiation, and water scarcity. These reductions were substantially larger when normalized per m³ of harvested wood rather than over the 15,000 h engine-level lifetime (e.g., 26.4% versus 6.9% for climate change), reflecting the hybrid system’s combined advantage in fuel efficiency and productivity. Despite these operational benefits, the hybrid-electric powertrain generated higher impacts in some categories associated with material extraction, manufacturing, and upstream supply chains, particularly freshwater ecotoxicity, human toxicity non-cancer, and mineral resource dissipation. Land occupation also increased on the engine-hour basis but decreased by approximately 13.1% for the hybrid system once results were normalized per m³ of harvested wood. The ADR assessment highlighted the greater dissipation potential of mineral resources in the hybrid configuration, emphasizing the importance of considering long-term resource availability alongside climate-related benefits.

Sensitivity analysis showed that the environmental advantages of the hybrid-electric powertrain became more pronounced as operational utilization increased, indicating that extended machine utilization can improve the relative sustainability of hybrid forestry machinery. However, uncertainty analysis also demonstrated that several impact categories remain sensitive to assumptions regarding material production, land use, and background inventory datasets.

Overall, the findings confirm that hybrid-electric powertrain represents a promising pathway toward reducing fuel consumption and greenhouse gas emissions in mechanized forest operations. Nevertheless, achieving broader environmental sustainability will require not only improvements in operational efficiency, but also advances in material selection, component recycling, and sustainable supply-chain management for hybrid technologies. Future developments should therefore focus on improving both the operational efficiency and material sustainability of hybrid forestry machinery.