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Introduction
Energy is an essential input in most economic activities and is a crucial pillar in the development of the economy (Xing et al., 2023). Increasing climate threat and volatility of energy prices, mainly caused by the conflict in Ukraine and the SARS-CoV-2 virus pandemic, have led economic leaders to prioritize the optimization of their energy use patterns (Hamit-Haggar, 2012; Lee & Birol, 2020; McKinsey & Company, 2022).
One of the most recurring challenges for optimizing energy models is the precise determination of final energy consumption, and even more complex, demonstrating improvements in energy performance (Navarrete & Labelle, 2023). The term 'final energy consumption' refers to the form and application of energy consumed directly at a point of use (International Energy Agency [IEA], 2016; International Organization for Standardization [ISO], 2018). The accuracy of final energy consumption is influenced by the quality and availability of activity data (IEA, 2016).
In 2018, timber production in Mexico reached 8.3 million cubic meters of round wood (m3r), with Durango and Chihuahua as the main producers, accounting for 30.2 % and 19.9 %, respectively (Secretaría de Medio Ambiente y Recursos Naturales [SEMARNAT], 2021). On the other hand, the total final consumption (TFC) for the sector was 4 442 772.53 tons of oil equivalent (toe), of which diesel represented 68.46 %, followed by electrical energy with 28.38 % (Secretaría de Energía [SENER], 2021).
To achieve desirable levels of timber production, it is essential to involve a wide range of machinery and equipment in logging and mechanical wood processing operations. The implementation of these operations implies the use of some form of energy source and often represents a significant cost not only in financial terms but also in environmental issues (Food and Agriculture Organization [FAO], 1991).
The energy consumption in forest enterprises accounts for up to 50 % of operating costs in forest supply activities (Reyes et al., 2022), while the carbon footprint in primary wood processing centers is 710.62 tCO2e·yr-1 (tons of carbon dioxide equivalent per year) (Meza et al., 2022). For this reason, the current energy situation in forestry demands a set of actions focused on managing accurate data on the use and final consumption of energy. This information will help identify opportunities for improvement in energy performance to address climate and economic challenges. Therefore, the objective of this study was to estimate the amount of final energy consumed in logging and sawing.
Materials and Methods
Characteristics of forestry companies
In the study, six forestry companies participated, all dedicated to primary wood processing, five were community-based and one was private. These companies are in the municipality of Pueblo Nuevo, Durango, within a radius of approximately 60 km from the central area of the city of El Salto. Participation in the study was voluntary, and the identity of the companies is preserved, keeping the information confidential.
The community sawmills operate with one production line, while the private company has two lines; each company has a recovery mill. The average installed capacity of the sawmills is 70 m3 per week, equivalent to 29 664 board foot (BF), and in the recovery mills it is 20 m3 (8 476 BF). The main forest resource is round wood from Pinus spp. with dimensions ranging from 0.30 to 0.65 m in diameter and 4.88 to 6.10 m in length.
Logging activities in the felling areas consist of felling and transporting round wood to the sawmills, while the sawmill production line configuration consists of six sequentially developed mechanized operations. The process begins with the storage and handling of round wood, followed by sawmilling, sanitation and sizing activities, and then the sawn timber is sorted and handled and stored.
The timber products with the highest production levels are boards with commercial dimensions ranging from 4 to 12 inches (in) wide by 7/8 thick by 8 to 20 feet (ft) long and planks of 4 to 12 in by 5/4 in by 8 to 20 ft. The products with lower production are beams (6-10 in * 3-4 in * 8-20 ft), bars (3-4 in * 5/4 in * 8-20 ft), wooden sleepers (7 in * 8 in * 8 ft), polines (3-4 in * 3-4 in * 8-20 ft) and waldras. The dimensions of the latter depend on demand. In addition, by-products such as decking, packing box material, broom handle squares, mulch, firewood and sawdust are also manufactured. The latter two are sometimes considered residues.
Finaly energy use coverage
The coverage boundaries of final energy consumption were established using predefined homogeneity criteria by the IEA (2015 and 2016): determination of the main economic activity, geographical location, definition of a time series, and breakdown of the type of energy consumed.
The main economic activity of forestry companies was determined based on the highest value added or contribution in the production chain (>50 %), according to the top-down approach method as suggested by the International Standard Industrial Classification (ISIC) of All Economic Activities Revision 4 (United Nations, 2009). The value added was adjusted based on information from Pro Floresta (2008), to ensure that the main economic activity referred to the same boundaries in terms of final energy consumption.
Table 1 shows the main economic activity of forestry companies and a summary of the application of the top-down approach. This follows a hierarchical principle where economic activities are subdivided into categories consisting of four mutually exclusive levels (section, division, group, and class). The section is the highest level, while the class is the lowest or most detailed level. Sections A and C were the sectors in which the companies participated.
Table 1 Main economic activity of forestry companies studied using the top-down approach.
| Section | Division | Group | Class | Description | Value added (%) |
|---|---|---|---|---|---|
| A | Agriculture, livestock, forestry and fishing | ||||
| 02 | Forestry and timber extraction | ||||
| 021 | 0210 | Forestry and other forestry activities | 4 | ||
| 022 | 0220 | Logging* | 13 | ||
| 024 | 0240 | Forestry support services | 7 | ||
| C | Manufacturing industries | ||||
| 16 | Production of wood and manufacture of timber products, except furniture | ||||
| 161 | 1610 | sawing | 67 |
*Logging is carried out successively in the same unit and when the product of one process serves as an input for the next. That is the case of the combination of forestry and logging with the subsequent transportation of round wood in the forest.
Division 02 comprised the production of round wood for industries using forest products. Classes 0210 and 0220 refer to activities such as logging and the production of forest products requiring minimal processing, such as round wood, firewood, wood chips, as well as round wood for direct use (pulpwood). Class 0240 included activities such as forestry management consulting services, timber stock assessment, and transportation of round wood in the forest for remuneration.
Division 16 refers to the manufacture of wood products such as lumber, plywood, veneer sheets, and other derivatives. Class 1610 includes activities such as sawing, planing, and debarking of wood. The highest value added corresponded to this class. It also includes drying, impregnation and chemical treatment of wood.
Energy review
To identify the significant energy uses (SEUs) in the sawmills, a level 1 energy diagnosis or audit was carried out as established by ASHRAE (American Society of Heating Refrigerating and Air Conditioning Engineers, 2011). The audit consisted of plant and felling area tours, as well as analyzing the companies' activity data or records for two calendar years. The audit involved no direct measurement of energy variables in the field.
Logging operations (class 0220) considered in the audit were: forest survey, construction or rehabilitation of logging roads, tree felling, wood chopping, timber skidding, loading, transportation of roundwood standing on a forestry road to the sawmill. On the other hand, sawmilling operations (class 1610) were: management of round wood, debarking of wood, sawing, sanitation and sizing, classification of sawn wood, processing of by-products and management of sawn wood. Office activities were also considered part of this process.
Data collection
A census of activity was carried out due to the relevance of data accuracy, i.e., physical and digital records for two calendar years to serve as a primary source of information to determine energy consumption in forestry companies (ASHRAE, 2011).
The number of records varied significantly and depended largely on the information access policies of each company. A detailed analysis of these records (accounting reports, invoices, receipts, purchase orders, accountability reports, and modules of the MicroSIP® digital administrative system) allowed for the collection of data associated with purchased electrical energy consumption (in Mexican pesos, MXN) in kilowatt-hours (kWh) and the use of fossil fuels expressed in volume units (L), including diesel, gasoline, and additive lubricants. Some records also provided insight into the use of woody forest biomass (kg).
Since the activity data involved diverse units of energy measurement, it was necessary to standardize them using a set of conversion factors proposed by Capehart et al. (2012). Additionally, average daily prices (MXN) reported by retail fuel stations' operators were consulted to determine the volume (L) of fossil fuel used (Comisión Reguladora de Energía [CRE], 2022).
Total final energy consumption or TFC is the sum of all final energy accounted for in the activity data for the set of companies during the time series. For convenience, TFC units were expressed in tons of crude oil equivalent (toe), as this is the form in which national energy balances are expressed (SENER, 2021). Energy consumption was expressed as a percentage (%) and in terajoules (TJ); one TJ (billion Joules) is equivalent to 23.88 toe.
Greenhouse gas (GHG) emissions from energy consumption in forestry companies were expressed in tCO2e and estimated using the documented Tier 1 emission factors method (Intergovernmental Panel on Climate Change [IPCC], 2006; World Business Council for Sustainable Development & World Resource Institute, 2005). Global warming potentials were taken from the fifth assessment report (AR5) of the IPCC (2015). Also, the emission factors of the national electricity system disseminated and estimated annually by the CRE (2018 and 2019) were required for the years 2017 and 2018 corresponding to the time series of this study.
Statistical Analysis
Companies and their establishments were defined as statistical unit E1, E2, E3, E4, E5 and SP in accordance with ISIC (UN, 2009). The statistical unit facilitated the consistency and analysis of energy use and consumption in a standardized way.
The dataset was treated by levels of disaggregation, which consisted of separating and hierarchizing the activity data in a pyramidal fashion, thereby determining the maximum and minimum level of disaggregation (IEA, 2016), which was conditioned by the availability and quality of the activity data. The level with the highest data disaggregation was the form and application of energy, followed by activity (ISIC class) and the most aggregated level was the statistical unit. The highest level of disaggregation required the most activity data. TFC is also presented by level of disaggregation.
Chebyshev's theorem was used to determine the fraction or proportion of observations (at least 75 % and more likely 95 % of the observations) that were at an interval k = 2 standard deviations from the arithmetic mean of the data (Mendenhall et al., 2010).
An ANOVA was conducted at each level of energy consumption disaggregation to identify differences among statistical units. Whenever significant differences were detected, the Tukey's HSD test for equal samples (P = 0.05) was applied. Data analysis was carried out using the InfoStat software version 2019 (Di Rienzo et al., 2019).
Results and Discussion
Energy form and applications
Logging of timber forest resources in the six statistical units required energy in the form of fossil fuel, electric and thermal energy. The use of renewable energy was non-existent. Table 2 shows in detail the forms and types of energy per activity (ISIC class).
Table 2 Description of energy use in six sawmills in Pueblo Nuevo, Durango, México.
| Activity (ISIC) | Diesel in ORHDV |
Gasoline in LDV&PICEM |
Additive Lubricants in PICEM |
Electrical in EM | Thermal in ADS |
|---|---|---|---|---|---|
| Logging (0220) | Cranes Trucks Tractors Excavators Backhoe loaders | Pick-up trucks Brushcutters Chainsaws | Brushcutters Chainsaws | Not identified | Not identified |
| Sawing (1610) | Articulated boom loaders Front loaders Forklifts | Chainsaws | Chainsaws | Electric motors Lighting equipment Computer and telecommunication equipment Power tools | Wood burning boilers |
ISIC: International Standard Industrial Classification, ORHDV: off-road heavy-duty vehicles, LDV&PICEM: light-duty vehicles and portable internal combustion engines, PICEM: portable internal combustion engines, EM: electric machines, ADS: artificial drying stoves.
Forest machinery consumed fossil fuels. Diesel fuel (No. 2 fuel oil) was used in all types of off-road heavy-duty vehicles (ORHDV). Gasoline was utilized in light-duty vehicles and portable internal combustion engine machines (LDV&PICEM). Additive lubricants (synthetic oils with a ratio of 250 mL of oil per 12.5 L of gasoline) were employed in portable internal combustion engine machines (PICEM) with both four-stroke and two-stroke engines. The use of liquefied petroleum gas, natural gas, and propane was not observed in the processes of the statistical units.
Purchased electrical energy was used in all classes of electrical machines and thermal energy in artificial drying stoves (ADS). The application of thermal energy to generate steam was identified in facilities for the artificial drying of wood; however, there was no evidence that the facilities were in operation.
On-site energy generation from indigenous renewable energy resources (solar, wind, hydro or forest biomass) was also not observed in forestry activities.
For logging, gasoline-powered chainsaws were used to fell and cut down trees. This type of machinery was also used for forestry support services (pruning and shredding of branches and tree tips in the felling area). The pick-up trucks with gasoline engines were used for forest routes and forest management activities. On the other hand, fuel oil number two (diesel) was required for excavators and backhoes for construction or rehabilitation operations of forest roads; draggers and cranes for haulage and loading operations; and for trucks to transport roundwood standing on a forestry road to the sawmill.
Sawing required articulated boom loaders, front loaders, and forklifts to stack, move and load round wood and sawn wood in the sawmill yard. These types of vehicles consumed diesel, while gasoline-powered chainsaws were used to cut round wood.
Squirrel cage motors mechanically coupled to band saws and toothed circular saws assisted in debarking, sawing, sanitation, sizing and boarding operations.
Significant energy use
The use of energy sources in the six statistical units resulted in a total final consumption (TFC) of 854.30 toe (35.77 TJ) over two years. Figure 1 shows the SEUs for each statistical unit.
Figure 1 Energy consumption (toe: tons of oil equivalent) per type of machinery and vehicles in six sawmills in Pueblo Nuevo, Durango. ORHDV: off-road heavy-duty vehicles, LDV&PICEM: light-duty vehicles and portable internal combustion machines, PICEM: portable internal combustion machines, EM: electric machines, ADS: artificial drying stoves
The predominant energy source in the six statistical units was diesel. The consumption of this fuel in ORHDV was substantial when compared to other energy uses, representing around three-quarters (74.9 %) of TFC. The use of gasoline in LDV&PICEM and electricity in machinery accounted for 14.2 % and 10.7 %, respectively, while less than 1 % of the energy came from additive lubricants used in two-stroke gasoline engine PICEM.
Similarly, Donahue et al. (2021) demonstrated that diesel usage also recorded the highest energy consumption in sawmills in the state of Oregon. Diesel and natural gas together represented 93.9 % of the fossil fuels used. In the sawmill industry of Arizona, Colorado, and New Mexico, Loeffler et al. (2016) found that 61 % of the energy used came from diesel, 35 % from electricity, and 3 % from gasoline. The rest came from propane and wood residues.
Studies show a variety of results. Berg and Lindholm (2005) indicated that diesel was the major source of energy used in forestry operations in Sweden, although gasoline and electricity were also used substantially. On the other hand, in Malaysia, Ratnasingam et al. (2017) note that 63 % electric energy was used for sawing and cutting timber, while diesel (36 %) was used in round wood transportation.
In Canadian sawmills, Meil et al. (2009) reported that diesel dominated for logging and transportation with 30 % of TFC, while in the sawmilling process, bioenergy use accounted for 51 % followed by purchased electricity (24 %) and natural gas (17 %). Gasoline and liquid propane gas accounted for less than 1 % of TFC.
At eight sawmills in California, Morgan et al. (2019) used wood and bark residues for energy production, which accounted for 87.6 % of the TFC, while 1.2 % of the electrical energy came from renewable sources; at sawmills in Montana, 77.3 % of the TFC corresponded to bioenergy and fossil fuels such as diesel, gasoline and natural gas participated with only 3.8 %, 0.1 % and 5.6 %, respectively. In sawmills in Norway, Olsson (2020) found that 5 % of TFC was diesel, 16 % electricity and 79 % biomass.
According to Wan et al. (2012), some Finnish sawmills have invested in bioenergy production for diversifying their business and increasing added value at the company level. The use of bioenergy in the studied companies could represent an opportunity to use clean and renewable energy sources while contributing to the mitigation of greenhouse gas emissions. Sawmill residues could be used in bioenergy microgeneration and cogeneration plants, primarily in the form of wood chips (Dudziec et al., 2023).
This study revealed that most of ORHDVs, LDVs and PICEM were used for logging stage. Therefore, logging can be categorized as a Significant Energy-Using (SEU) Unit in the timber forestry production process. Fow sawing, machines classified as squirrel cage motors used up to 98 % of the electrical energy, so they can also be considered as SEUs in this stage of the process.
Since electric motors consumed a large fraction of the electrical energy, energy conservation and energy efficiency strategies would have to be targeted at these SEUs to achieve improved energy performance. Saidur et al. (2009) reported that the use of efficient electric motors can reduce the cost of operation and maintenance. Moreover, replacing inefficient motors with high-efficiency models can significantly decrease emissions.
Among the factors observed that could hinder the implementation of energy-saving and efficient energy use strategies are deep-rooted tendencies to continue using obsolete or standard efficiency technologies; lack of knowledge about efficient technologies; absence of regulatory schemes and government obligations, as well as high costs of high efficiency technologies.
Total final energy consumption disaggregation levels
In terms of energy application (the most disaggregated level), the highest diesel consumption in ORHDV occurred in unit SP, with around 6.6 TJ (Table 3), approximately double the consumption in E3 and E5, while the lowest diesel consumption was observed in E4 (2.93 TJ). Both E4 and E2 had the highest gasoline consumption in LDV&PICEM with around 1.8 and 1.4 TJ, respectively. These values were similar to the combined gasoline consumption in LDV&PICEM of E1, E3, E5, and SP and even higher than the electrical energy consumed in electric machines of these statistical units. E3 and SP had the highest consumption of electrical energy in electric machines, each with around 1 TJ. The remaining statistical units consumed between 0.35 and 0.65 TJ. The use of additive lubricants in PICEM was less than 0.025 TJ in all statistical units.
Table 3 Disaggregation of total final energy consumption (TFC) in six sawmills in Pueblo Nuevo, Durango.
| Statistical unit |
ORHDV (TJ) |
LDV&PICEM (TJ) |
PICEM (TJ) |
EM (TJ) |
ADS (TJ) |
Logging (TJ) |
Sawing (TJ) |
TFC*
(TJ) |
|---|---|---|---|---|---|---|---|---|
| E1 | 4.8011 | 0.4631 | 0.0019 | 0.5464 | N/A | 4.30 | 1.52 | 5.81 |
| E2 | 5.2136 | 1.4144 | 0.0184 | 0.6441 | N/A | 5.74 | 1.55 | 7.29 |
| E3 | 3.6017 | 0.4325 | 0.0027 | 0.9872 | N/A | 3.57 | 1.46 | 5.02 |
| E4 | 2.9255 | 1.8014 | 0.0020 | 0.3841 | N/A | 4.03 | 1.08 | 5.11 |
| E5 | 3.6609 | 0.2287 | 0.0036 | 0.3509 | N/A | 3.33 | 0.91 | 4.24 |
| SP | 6.6464 | 0.7034 | 0.0241 | 0.9093 | N/A | 3.84 | 4.44 | 8.28 |
| TFC | 26.85 | 5.04 | 0.05 | 3.82 | 24.81 | 10.95 | 35.77 |
*TFC may not match due to rounding. ORHDV: off-road heavy-duty vehicles, LDV&PICEM: light-duty vehicles and portable internal combustion machines, PICEM: portable internal combustion machines, EM = electric machines, ADS = artificial drying stoves. N/A: not available, SP: sole proprietorship
Regarding the activity (ISIC class), logging required 24.81 TJ (592.67 toe), approximately two-thirds of the final energy use (69 % of TFC). This activity consumed a little over twice the energy compared to sawing (10.95 TJ = 261.63 toe). The unit that consumed the most energy in logging was E2 with 5.74 TJ, which consumed around 2 TJ more than E3, E5, and SP. Most units required between 1 and 1.5 TJ for sawing. In all units, energy consumption for logging was higher than for sawing, except for SP, which recorded the highest TFC with 8.28 TJ, while E5 had the lowest TFC with 4.24 TJ. The aggregated TFC was around 35.77 TJ.
The range of disaggregated data variability and distribution spanned from 0.08 to 0.57 TJ, with an average of 0.25 TJ. The interval k > 2 standard deviations in absolute value comprised approximately 95 % of the observations. Application of Chebyshev's theorem was a conservative estimate.
The TFC varied significantly among statistical units (P = 0.0018); in SP, it was significantly higher than in the other units (0.35 ± 0.19 TJ), while the TFC in E1 (0.24 ± 0.14 TJ), E2 (0.30 ± 0.16 TJ), E3 (0.21 ± 0.12 TJ), E4 (0.21 ± 0.20 TJ), and E5 (0.18 ± 0.09 TJ) was statistically similar among them
However, the data behavior at more disaggregated levels was different. The comparison of TFC disaggregation levels is shown in Table 4. Energy consumption in ORHDV, LDV, PICEM, and EM showed significant differences among sawmills (P < 0.05). In contrast, during logging, energy consumption showed no significant differences between units, but it did during sawing, where consumption was higher in SP.
Table 4 Comparison of the disaggregated levels of total final energy consumption (TFC) among six sawmills in Pueblo Nuevo, Durango
| Statistical Unit | Average (TJ) | Standard Deviation (±TJ) | Median (TJ) | F | P |
|---|---|---|---|---|---|
| ORHDV | |||||
| E1 | 0.2001 ab | 0.1242 | 0.2405 | 4.25 | 0.0013 |
| E2 | 0.2172 ab | 0.1281 | 0.2636 | ||
| E3 | 0.1501 a | 0.1169 | 0.2260 | ||
| E4 | 0.1219 a | 0.1379 | 0.0509 | ||
| E5 | 0.1525 a | 0.0847 | 0.2045 | ||
| SP | 0.2769 b | 0.1908 | 0.3323 | ||
| LDV&PICEM | |||||
| E1 | 0.0193 a | 0.0079 | 0.0201 | 10.66 | 0.0001 |
| E2 | 0.0589 bc | 0.0344 | 0.0541 | ||
| E3 | 0.0180 a | 0.0062 | 0.0184 | ||
| E4 | 0.0751 c | 0.0868 | 0.0280 | ||
| E5 | 0.0095 a | 0.0050 | 0.0121 | ||
| SP | 0.0293 ab | 0.0182 | 0.0255 | ||
| PICEM | |||||
| E1 | 0.0001 a | 0.00004 | 0.0001 | 41.14 | 0.0001 |
| E2 | 0.0008 bc | 0.0004 | 0.0010 | ||
| E3 | 0.0001 a | 0.0001 | 0.0002 | ||
| E4 | 0.0001 c | 0.0001 | 0.0001 | ||
| E5 | 0.0001 a | 0.0001 | 0.0002 | ||
| SP | 0.0010 ab | 0.0006 | 0.0008 | ||
| EM | |||||
| E1 | 0.0228 a | 0.0136 | 0.0216 | 27.46 | 0.0001 |
| E2 | 0.0268 b | 0.0091 | 0.0295 | ||
| E3 | 0.0411 c | 0.0136 | 0.0399 | ||
| E4 | 0.0160 a | 0.0099 | 0.0177 | ||
| E5 | 0.0146 a | 0.0074 | 0.0166 | ||
| SP | 0.0379 c | 0.0057 | 0.0386 | ||
| Logging | |||||
| E1 | 0.1790 a | 0.1111 | 0.2103 | 1.82 | 0.1133 |
| E2 | 0.2393 a | 0.1361 | 0.2914 | ||
| E3 | 0.1486 a | 0.1213 | 0.2288 | ||
| E4 | 0.1681 a | 0.1722 | 0.1261 | ||
| E5 | 0.1389 a | 0.0819 | 0.1852 | ||
| SP | 0.1600 a | 0.1382 | 0.2743 | ||
| Sawing | |||||
| E1 | 0.0632 a | 0.0320 | 0.0686 | 43.98 | 0.0001 |
| E2 | 0.0645 a | 0.0258 | 0.0663 | ||
| E3 | 0.0607 a | 0.0136 | 0.0594 | ||
| E4 | 0.0450 a | 0.0335 | 0.0335 | ||
| E5 | 0.0380 a | 0.0179 | 0.0460 | ||
| SP | 0.1851 b | 0.0800 | 0.1632 | ||
ORHDV: off-road heavy-duty vehicles, LDV&PICEM: light-duty vehicles and portable internal combustion machines, PICEM: portable internal combustion machines, ADS = artificial drying stoves, EM = electric machines. Means with different letters are significantly different according to Tukey's test (P < 0.05).
It should be emphasized that the energy consumption in each statistical unit depends on several factors such as the number of equipment, the effective operating time and the diversity in electrical and mechanical characteristics (power, performance and efficiency). In addition to these factors, Lijewski et al. (2017) stated that the type of wood (log size, diameter and length), terrain conditions, machine operator skills, environmental conditions, as well as the type of machine used, and its technical condition are also elements of influence in energy consumptionFurthermore, it is possible that the decision-making process in social enterprises (E1, E2, E3, E4 and E5) influences TFC when compared to the sole proprietorship (SP). This could be attributed to differences in legal, institutional and operational structures that exist between community forest enterprises and private owners.
Some factors influencing logging may be the surface distribution of the felling areas or the variation in the distances traveled when transporting roundwood standing on a forest road to the sawmill. In the case of sawing, the difference may be the result of two production lines in the SP unit, which would represent a significant additional demand for fossil fuels and electrical energy in the respective machinery.
The difference in fossil fuel consumption at ORHDV, LDV and PICEM could also be influenced by degrees of preventive maintenance and good or bad practices in equipment operation. Fuel efficiency depends strongly on following the operation and maintenance recommendations provided by the manufacturer. The data plates of the electrical machines revealed that the efficiency of the motors ranged from standard to high electrical efficiency. Furthermore, a significant number of motors had been rewound at least once throughout their lifespan. Additionally, noise and vibration levels in the motors, as well as load misalignment, could also contribute to differences in energy consumption.
Thollander et al. (2007) showed that, through the implementation of energy efficiency programs, energy efficiency improvements of between 16 % and 40 % can be achieved in small and medium-sized enterprises, respectively. Improving energy efficiency reduces GHG emissions and promotes sustainable development (Cai et al., 2022).
Annual Energy Consumption and Greenhouse Gas Emissions
Energy consumption per statistical unit was estimated at 2.98 TJ·yr-1 (71.19 toe·yr-1), of which 2.07 TJ·yr-1 (49.39 toe·yr-1) corresponded to logging and 0.91 TJ·yr-1 (21.80 toe·yr-1) to sawing. The average annual consumption of diesel, gasoline, additive lubricants and electricity was estimated at 2.24 TJ·yr-1 (53.44 toe), 0.42 TJ·yr-1 (10.04 toe), 0.004 TJ·yr-1 (0.10 toe) and 0.32 TJ·yr-1 (7.61 toe), respectively. The TFC of the six companies was approximately 35.77 TJ (854.30 toe).
The mass of GHG emissions, derived from energy consumption per statistical unit, corresponds to 260.14 tCO2e·yr-1. Logging was the activity responsible for the release of the largest number of emissions with 164.21 tCO2e·yr-1. Sawing released 95.94 tCO2e·yr-1. The main direct emission source was the use of fuel oil number two (diesel) in mobile sources, i.e., SEUs in ORHDV. TFC from the companies released 3 121.71 tCO2e over the time series.
Conclusions
This study estimated the final energy consumption derived from logging and sawing activities in six forestry companies. The disaggregation of data facilitated the identification of significant energy uses; only in logging it was possible to accept the hypothesis that energy consumption was not significantly different. The study demonstrated the urgent need for an effective energy management program to enable savings and efficient use of fossil fuels and electricity, thus leading to reduced costs and greenhouse gas emissions. The use of dendroenergy could present a viable alternative for optimizing energy consumption in sawmills.
