Abstract
The sawmilling sector is the backbone of the Malaysian wood-based industry. Sawn timber is used extensively for further manufacturing of secondary wood-based products. The conversion of saw-logs into sawn timber releases several gases into the atmosphere, and these may contribute to environmental burdens as well as environmental impacts. Thus, this study aims to determine the environmental performance from gate-to-gate in the sawmilling industry using the life cycle assessment technique. Data pertaining to the saw-logs and energy consumption was calculated, and the environmental performance was assessed. The study focused on two different size sawmills and two tropical hardwood species. The findings concluded that several types of gases namely, CO2, CH4, NOx, N2O, SO2, and CO were discharged to the environment as a result of sawmilling processes. The discharge of these gases impacted the environment in the form of global warming, acidification, human toxicity, eutrophication, and photo-oxidant formation potentials.
Download PDF
Full Article
Assessment of Environmental Emissions from Sawmilling Activity in Malaysia
Geetha Ramasamy,a,* Jegatheswaran Ratnasingam,a Edi Suhaimi Bakar,a Rasmina Halis,a and Neelakandan Muttiah b
The sawmilling sector is the backbone of the Malaysian wood-based industry. Sawn timber is used extensively for further manufacturing of secondary wood-based products. The conversion of saw-logs into sawn timber releases several gases into the atmosphere, and these may contribute to environmental burdens as well as environmental impacts. Thus, this study aims to determine the environmental performance from gate-to-gate in the sawmilling industry using the life cycle assessment technique. Data pertaining to the saw-logs and energy consumption was calculated, and the environmental performance was assessed. The study focused on two different size sawmills and two tropical hardwood species. The findings concluded that several types of gases namely, CO2, CH4, NOx, N2O, SO2, and CO were discharged to the environment as a result of sawmilling processes. The discharge of these gases impacted the environment in the form of global warming, acidification, human toxicity, eutrophication, and photo-oxidant formation potentials.
Keywords: Environmental burdens; Environmental impacts; Life cycle; Sawmilling; Meranti
Contact information: a: Universiti Putra Malaysia, Faculty of Forestry, 43400 UPM, Serdang, Selangor, Malaysia; b: Centre for Energy Sustainability, Energy Consultants Pte. Ltd., No. 8-Block 2 Selegie Centre, Brass Road, 54360 Singapore; *Corresponding author: gita209@gmail.com
INTRODUCTION
The Malaysian wood-based industry began in the early 1900s. While it was primarily focused on meeting the domestic demand at the time, it has been transformed into a large, export-oriented industry, producing a wide variety of value-added products (National Timber Policy 2009). The industry has gained a prominent socio-economic status by contributing more than RM 20 billion in annual export earnings and providing employment to almost 226,000 workers over the last few years (MTIB 2012). In 2011, a total of 3975 manufacturing entities were operating in the Malaysian wood-based industry (Table 1).
The wood-based industry has emerged as one of the most important, prominent, and fastest growing manufacturing sectors in the Malaysian economy. Despite the growing importance of value-added wood product manufacturing, the sawmilling sector remains the backbone of the wood-based industry (Baharuddin 1984; National Timber Policy 2009). Sawmilling produces sawn timber and wood waste that are exploited by the other wood-based industries to be further processed into value-added products, such as furniture, wood-based panels, moulding, joinery, etc. Since the implementation of the 1st Industrial Master Plan (IMP) in 1986 by the Malaysian government, the sawmilling industry has been accorded lesser importance (Menon 2000).
Table 1. Number of Wood-Based Industries in Malaysia
With a reduced supply of saw-logs from natural forest in the country, which practices Sustainable Forest Management (SFM), the capacity for utilization of wood within the sawmilling industry in Malaysia has also suffered (Table 2).
Table 2. The Capacity Utilization of Sawmilling in Malaysia
Furthermore, the technology used in the Malaysian sawmilling sector is old and obsolete (Ong 1986; Ho and Gan 2003). In general, sawmills fail to modernize and automate because of a lack of finances. It has been noted that the different characteristics of the tropical hardwood and softwood saw-logs, together with the variable market demands for these sawn timbers, makes the application of new technology in the Malaysian sawmilling sector uneconomical (Yap 2004).
As a result, the Malaysian sawmilling sector suffers from low productivity and generates a large volume of waste. According to the ITTO-CITES Project report (2010), the generation of wood waste in the sawmilling sector of Peninsular Malaysia was approximately 45 to 50% of the total volume of saw-log input. On the other hand, the sawmilling sector is also energy-intensive. As reported by Mahlia (2002), the electrical energy consumed during the sawmilling processes is generated off-site at power stations that burn fossil fuels. During the sawmilling process, a substantial amount of thermal energy is also produced on-site. The combustion of fossil fuels for energy generation discharges gases, such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), mono-nitrogen oxides (NOx)—which include nitric oxide (NO) and nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2), non-methane volatile organic compounds (NMVOC), and particulates to the environment (Mahlia 2002; Ong et al. 2011; Rosnazri et al. 2012). These gases have a negative impact on the environmental air quality.
A number of studies have investigated the effects of resource consumption in sawmilling and the resultant environmental profiles. These studies have revealed that the consumption of resources results in the discharge of a variety of gases in different quantities into the environment (Kinjo et al. 2005; Eshun et al. 2010; Puettmann et al. 2010; Bergman and Bowe 2012; Tellnes et al. 2012). Eshun et al. (2010) highlighted that the environmental emissions are different between countries and sawmills as a result of the different technologies, methods, and environmental standards applied. It is widely believed that the effects of sawing softwoods is less environmentally damaging compared to hardwoods, although no conclusive reports are available at this time (Bergman and Bowe 2012). In addition, the emission of several gases subsequently impacts the environment in the form of global warming, acidification, human toxicity, ozone depletion, photo-oxidant formation, material depletion, energy depletion, and eutrophication potentials (Kinjo et al. 2005; Puettmann et al. 2010; Eshun et al.2011; PE International AG 2012; Tellness et al. 2012). Consequently, the issue of environmental performance from the sawmilling industry has become a topic of intense debate both at the national and international levels (Eshun et al. 2010).
Although alternative materials, such as steel, plastic, and concrete, can replace wood for many applications, this practice is not desirable, as these materials have been reported to contribute to greater environmental burdens compared to wood (González-Garcia et al. 2012). In view of this difference, research on the environmental performance of the sawmilling sector is of high interest, especially in Malaysia, which has a large wood-based industry. Therefore, a study of the environmental performance of the sawmilling sector in Peninsular Malaysia using the life cycle assessment (LCA) technique was carried out. This study will help to fill the existing knowledge-gap in the environmental performance assessment of the sawmilling sector in Peninsular Malaysia. The results from this study will provide benchmark values for the environmental profiles contributed by the sawmilling industries in the country, which will help formulate the necessary strategies for the overall improvement of the industry.
EXPERIMENTAL
The LCA analytical tool was used in this study to evaluate the environmental performance as a result of the resources consumption during the production of rough green sawn timber. The assessment of the burdens and potential environmental impacts was determined on the basis of LCA methodological framework, which consists of four phases that are the goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and life cycle interpretation. The methodological framework was evaluated in accordance to the revised ISO 14040 (2006) Standards for Framework and Guidance and the ISO 14044 (2006) Standards for Technical Requirements and Guidelines.
Goal and Scope Definition Phase
The goal and scope definition phase framed the LCA study, and the methodological choices were clearly defined. The methodological framework was developed corresponding to the study scope, establishment of system boundaries, description of functional units, and the selection of the allocation approach.
Scope of the study
The study assessed the environmental performances from the sawmilling sector. Two sawmills were chosen for this study. The selection of sawmills in this environmental burdens assessment was presumed to be representative of the overall sawmilling sector that produces rough green sawn timber of Meranti species in Peninsular Malaysia. In addition, the technology applied in the sawmilling sector in Peninsular Malaysia was comparable (Ho and Gan 2003). Hence, the findings of the environmental performances in this investigation provided benchmark values for the sawmilling sector, which is considered the oldest wood-processing industry in the country.
The first sawmill, referred to as sawmill A, is the biggest sawmill in Peninsular Malaysia. Sawmill A was set as the base scenario for this study. Meanwhile, the second sawmilling, referred to as sawmill B, is a medium-sized sawmill. The purpose to include medium-sized sawmill in this study was to determine any notable differences from the resources consumption to the environmental performance when compared to sawmill A. Small-size sawmills do exist, but mills of this type provide custom wood products only. In addition, smaller mills tended not to keep accurate production records, and even some of the large hardwood sawmills did not have primary mill data requested. The combination of sawmill A and sawmill B produced 25% to 30% of sawn timber out of the total output in Peninsular Malaysia. Table 3 summarizes the differences between the two sawmills.
Table 3. Differences Between Sawmill A and Sawmill B
Meanwhile, sawmills A and B were similar in terms of the sawing operational parameters. Saw blade properties for the head saw, re-saw, and cross-cut saw are as specified in Table 4. This is also fixed for both Light Red Meranti and Dark Red Meranti wood species. In view of the fact that the sawing operational parameters and saw blade properties were similar for both sawmills, as well as wood species, these were treated as the constant factors.
Table 4. The Fixed Factors of Sawing Operational Parameters and Saw Blade Properties
Description of the sawmills under study
The assessment of environmental performance associated with the production of rough green sawn timber in both sawmills used the gate-to-gate approach, which assessed the saw-logs as they entered the mill for cutting up until the production of rough green sawn timber. The primary breakdown process cut the saw-logs into flitches. These flitches were then moved along the conveyor to be re-sawn into sawn timber in the secondary breakdown process. The quality control process ascertained that all defects spotted on the sawn timber were cross-cut and removed. The rough green sawn timbers were then ready for shipment. Off-road transportation activities, including the transportation of the saw-logs and sawn timbers within the sawmills, were included in this research assessment.
Several types of hardwood saw-log species were used in sawmill A and sawmill B for rough green sawn timber production. In this study, however, the assessment of environmental performances focused primarily on the Meranti species. According to Blaser et al. (2011), the Meranti species is largely exploited for sawn timber production in Peninsular Malaysia. Furthermore, Meranti sawn timber is well established in the local and international markets. Therefore, this study focused on the Light Red Meranti and Dark Red Meranti saw-logs in view to the fact that the sawmills chosen for this study had a consistent supply of these species year-around.
Meanwhile, the analysis of energy consumption was identified to be electrical energy and diesel fuel energy. Electrical energy was used to operate the primary breakdown, secondary breakdown, quality control, and conveyor belts for the conversion of saw-logs into sawn timber, while diesel fuel energy was used for off-road transportation activities.
System boundary
The system boundary was set up to display the flow of the inputs, outputs, and the environmental releases during the rough green sawn timber production within the gate-to-gate production. The inclusion and exclusion of certain aspects in the setting up of the system boundary is presented in Table 5.
Table 5. The Inclusions and Exclusions in System Boundary
The formation of the system boundary, which was reflected in the inclusion and exclusion of the aspects shown in Table 5, consisted of the foreground (on-site) and background (cumulative) system boundaries, as shown in Fig. 1. Within the solid line contains the background system boundary, while the foreground system boundary is shown within the dotted line. The foreground system boundary evaluated the emissions that occurred in the sawmills from a set of unit processes (on-site emission). Meanwhile, the background system boundary included the emissions from the consumption of materials and electrical energy.
Fig. 1. System boundaries of sawn timber production
Functional unit
The investigated woods were Light Red Meranti and Dark Red Meranti rough green sawn timber. By definition, the functional unit describes the quantitative description of an investigated product (Finnveden et al. 2009). In this study, volume was used as the functional unit for the outputs. The functional unit used in this study for the environmental emission assessment was therefore standardized, on a as per-unit volume basis for 1.0 m3 of Light Red Meranti and Dark Red Merantirough green sawn timber.
Life Cycle Inventory (LCI)
The inputs used during the manufacturing process of rough green sawn timber were saw-logs and energy. The inputs data were collected representing one full calendar year in 2013. The inventory method quantified the inputs used in the sawmilling activities. The outputs consisted of products, wood residues, and environmental emission, which were then evaluated based on the resources consumed.
Data collection and data analysis of material flow
The first part of the data collection was to determine the flow of the saw-logs. The recovery approach was used to evaluate the yield of rough green sawn timber. The study applied the cubic recovery percent method in order to determine the yield of sawn timber. The calculation of sawn timber recovery (%) is shown in Eq. 1.
Once the flow of saw-logs was determined, the allocation approach was selected. The flow of saw-logs in the unit production processes not only produced rough green sawn timber, but it also produced wood residues in the form of off-cuts, shavings, sawdust, and splinters. These wood residues were not used by the sawmills. Instead, they were sold to other mills. Therefore, in this study, the physical relationship allocation was chosen, and sawn timber was regarded as the main and only product, while other products were regarded as waste for sawmill A and sawmill B.
Data collection and data analysis of energy consumption
Meanwhile, the analysis of energy consumption was categorized into the electrical energy and diesel fuel energy. The calculation of electrical energy consumption was based on Eq. 2 (Devaru et al. 2014), in which the data was collected using an electricity meter (Crystal Instrumentation P-04, Taiwan).
In Eq. 2, V is the average voltage (V), I is the average amperage (A), and cosine (Ф) is the power factor.
The electrical energy consumption (kWh) of the conveyor belt was estimated on the basis of the load factor, motor efficiency, and the operating hours of the motors, using Eq. 3 (Devaru et al. 2014).
The input of diesel fuel was converted into an energy value using the high heating value (HHV) concept. Equation 4 shows the calculation of the energy value for the diesel fuel.
Data collection and data analysis of environmental burdens
The release of CO2, CH4, N2O, NOX, CO, and SO2 components into the environment was calculated after the resource consumption was determined. The calculation for every component was done based on the activity data and emission factors, as shown in Eq. 5 (International Panel of Climate Change 2006). Activity data were associated with the measurement of electrical energy and diesel fuel energy during the sawn timber production. The emission factor was the representative value given for CO2, CH4, N2O, NOX, CO, and SO2 that was related to the activity.
Life Cycle Impact Assessment
The study continued to assess the potential environmental impacts once the elements discharged to the environment were identified. The assessment of potential environmental impacts associated to the production of Light Red Meranti and Dark Red Meranti rough green sawn timber in sawmill A and sawmill B was performed by means of the centre of Environmental Science, Leiden University (CML) baseline method. The selection of the CML method was due to the fact that this approach is widely used, internationally accepted, and well recognized in the life cycle study of timber products (Eshun et al. 2011). The CML methodology was accounted for based on the “Operational Guide to Life Cycle Assessment” (Guinée et al. 2001). The LCIA study was carried out on the basis of classification and characterization phases, as Rivela et al. (2007) mentioned that it is the least subjective approach.
Five potential environmental impacts categories were selected in this study, comprising of global warming, acidification, human toxicity, eutrophication, and photo-oxidant formation potentials. In the classification phase, the elements discharged to the environment, which are the CO2, CH4, N2O, NOX, CO, and SO2, were assigned into the potential environmental impacts categories as shown in Table 6.
Table 6. Classification of Substances into the Categories of Potential Environmental Impacts
Once the substances were assigned into the potential environmental impacts categories, the characterization step translated the related substances into the potential environmental impacts by using the equivalency factors. The calculation used was based on Guinee et al. (2001), as shown in Eq. 6,
where EF is the equivalency factor, while M represents the mass of the substance. The details of the equivalency factors for global warming, acidification, human toxicity, eutrophication, and photo-oxidant formation potentials are shown in Table 7.
Table 7. Equivalency Factors for Potential Environmental Impacts
Life Cycle Interpretation
The findings were explained through the life cycle interpretation in accordance with the study goal. In this study, the interpretation phase highlighted the effect of sawmilling and wood species factors on the environmental burdens. Further, this stage of LCA interpreted and discussed the potential environmental impacts from both sawmills and wood species. The results were statistically analyzed using the Statistical Package for the Social Science 20.0 (SPSS; IBM, USA).
RESULTS AND DISCUSSION
The findings from the LCI study focused on the Light Red Meranti and Dark Red Meranti rough green sawn timber production in sawmill A and sawmill B are presented in this section. All of the findings were analyzed on the basis of mean values per m3 of Light Red Meranti and Dark Red Meranti rough green sawn timber. The results of this study are presented in four parts: (1) product yield; (2) energy consumption of electricity and diesel fuel; (3) environmental burdens; and (4) potential environmental impacts.
Product Yield
The flow of saw-logs in the manufacturing process produced rough green sawn timber as the main product. The yield of Light Red Meranti and Dark Red Meranti rough green sawn timber was enumerated by applying the cubic recovery technique. The method of cubic recovery was selected for this study based on the report by Lin et al. (2011), who suggested that the cubic recovery method is more practical and more accurate than the lumber recovery factor (LRF).
Figure 2 shows the mean recovery of Light Red Meranti and Dark Red Meranti rough green sawn timber determined from sawmill A and sawmill B.
Fig. 2. The average recovery of sawn timber and co-products in sawmill A and sawmill B
The mean recovery of Light Red Meranti and Dark Red Meranti rough green sawn timber in sawmill A differed by 10.36%. Likewise, the mean of the sawn timber recovery for Light Red Meranti was 10.49% higher than Dark Red Meranti in sawmill B. On the other hand, in a comparison between sawmills, sawn timber recovery for both species in sawmill B yielded greater quantities than sawmill A by 3.23% and 3.05% for Light Red Meranti and Dark Red Meranti, respectively.
Apart from the production of rough green sawn timber, the flow of saw-logs also resulted in wood losses in the form of off-cuts, sawdust, shavings, and splinters. These wood losses were not further used in sawmill A and sawmill B. Off-cuts, sawdust, shavings, and splinters were eventually sold to other mills in which off-cuts were recovered for other wood products, while sawdust, shavings and splinters were used for energy generation in the boilers. As shown in Fig. 2, the proportion of off-cuts, sawdust, shavings, and splinters was slightly higher for the Dark Red Meranti in both sawmills. When comparing of Dark Red Meranti between sawmills A and B, a higher proportion of volume was observed in sawmill A, with a small difference of 0.73%, 0.72%, 1.02%, and 0.58% for off-cuts, sawdust, shavings, and splinters, respectively.
Energy Consumption
The sources of energy used in the sawmilling activities were identified as electrical energy and diesel fuel energy.
Electrical energy consumption
Electricity was used in sawmills A and B to run the motors for the sub-system unit processes comprising the primary breakdown, secondary breakdown, quality control, and conveyor belt. Energy used to operate the sub-system processes was defined as process energy (Vigon et al. 1993). Figure 3 shows the mean of the electrical energy used by sawmill A and sawmill B to cut 1 m3 of Light Red Meranti and Dark Red Meranti rough green sawn timber.
Fig. 3. The average of electrical energy consumption in sawmill A and sawmill B
Overall, the average electrical energy consumed in sawmill A was 30.70 MJ/m3 and 49.10 MJ/m3 for Light Red Meranti and Dark Red Meranti, respectively. Based on Fig. 3, the difference in electrical energy consumption between the Light Red Meranti and Dark Red Meranti in the primary breakdown, secondary breakdown, quality control, and conveyor belt sub-systems was observed to be 6.19 MJ/m3, 11.77 MJ/m3, 0.44 MJ/m3, and 0.006 MJ/m3, respectively. In sawmill B, the mean electrical energy consumed for Light Red Meranti and Dark Red Meranti was 29.08 MJ/m3 and 36.08 MJ/m3, respectively. Similar to sawmill A, the electricity used to operate the sub-system processes during the sawmilling activities was higher when cutting Dark Red Meranti. These differences were 0.31 MJ/m3, 7.31 MJ/m3, 0.004 MJ/m3, and 0.005 MJ/m3 in the primary breakdown, secondary breakdown, quality control, and conveyor belt sub-systems.
Diesel fuel energy consumption
Diesel fuel was used in sawmills A and B for off-road transportation activities. These activities involved the carrying of saw-logs and sawn timber boards within the mills themselves. Since this study was focused on the off-site transportation activity, the fuel used was the only aspect taken into consideration. The average input of diesel fuel to move the Light Red Meranti and Dark Red Merantisaw-logs and rough green sawn timber is shown in Fig. 4. The average consumption of diesel fuel for sawmill A was evaluated at 0.24 L/m3 and 0.38 L/m3 for Light Red Meranti and Dark Red Merantisawn timber, respectively. Meanwhile, the average consumption of diesel fuel for sawmill B was evaluated at 0.22 L/m3 and 0.34 L/m3 for Light Red Meranti and Dark Red Meranti sawn timber, respectively.
Fig. 4. The average of diesel fuel energy consumption in sawmills A and B
In addition, the energy value of diesel fuel was determined. The approach used to convert the volume of diesel fuel into the energy value was the high heating value (HHV) method. Table 8 presents the mean diesel fuel energy value for the Light Red Meranti and Dark Red Meranti for sawmills A and B, respectively.
Table 8. Average of Energy Value of Diesel Fuel
Environmental Burdens
The use of resources in sawmilling activities consequently discharges the wood residues, CO2, CH4, N2O, NOx, CO, and SO2 in different types and quantities into the environment. The environmental emissions considered in this study were those that led to environmental burdens.
Environmental burdens associated with saw-logs consumption
As mention earlier, the conversion of saw-logs not only produced rough green sawn timber, but also resulted in wood losses in the form of off-cuts, sawdust, shavings, and splinters. However, these wood residues were not considered as environmental burden, as they were not disposed of at the landfill. In fact, these wood residues were sold to other mills for energy generation and were recovered in other mills. Emissions to the environment were only considered when a by-product remained unused for another purpose (Ingerson 2011).
Environmental burdens associated with energy consumption
The off-site electricity was generated from the burning of fossil fuels in conventional power stations. Meanwhile, diesel fuel was combusted on-site, especially for transportation activities. Saidur et al.(2007) described that fuel is comprised of carbon, sulfur, nitrogen, or their compounds. Inevitably, these components were emitted into the environment in different amounts, depending on the quantities and types of fossil fuel used (Puettmann et al. 2010). The release of these gases as a result of fuel consumption is categorized as anthropogenic emission. Anthropogenic emissions consequently are related to environmental burdens (Milota et al. 2005; Kinjo et al. 2005; Bergman and Bowe 2012).
It was noticeable from this investigation that the consumption of electrical and diesel fuel energy during the production process discharged several gases, namely CO2, CH4, NOx, N2O, SO2, and CO. The observations in Tables 9 and 10 show that there were varied discharges of CO2, CH4, NOx, N2O, SO2, and CO, as a result of electrical energy and diesel fuel consumption, on the basis of the wood species and the sawmill used. The release of SO2 from diesel fuel could not be evaluated because of a lack of data. Overall, the average emission of CO2 was the largest.
Table 9. Environmental Burdens Associated with the Energy Consumption in Sawmill A
Similar finding have been reported in other studies such as Kinjo et al. (2005), Milota et al. (2005), Puettmann et al. (2010), and Bergman and Bowe (2012). It was then followed by SO2, NOx, and CO. Furthermore, the differences in the emission between CO2 compared to SO2, NOx, and CO were noteworthy. On the contrary, the release of CH4 and N2O into the environment was considered minimal.
Table 10. Environmental Burdens Associated with the Energy Consumption in Sawmill B
Effect of the Test Factors on the Environmental Burdens
The sawmills and wood species were classified as the categorical variables in this study. The effect of these categorical variables were analysed on the environmental burdens. The purpose was to carry out the analysis in order to identify any significant contribution that was made to the environmental burdens of CO2, CH4, NOx, N2O, SO2, and CO on the basis of the two sawmills of different sizes (sawmill A and sawmill B) and two types of wood species (Light Red Meranti and Dark Red Meranti). The normality test showed that the variables of CO2, CH4, NOx, N2O, and CO were observed to be normally distributed, except for SO2.
Statistical test for normal distribution variables
A multivariate analysis of variance (MANOVA) statistical test was applied for the normally distributed variables. The first part of the MANOVA test was to analyze the overall significance of the main effects of sawmilling and wood species, respectively, and also the interaction of sawmill and species. The overall multivariate test on the environmental burdens is shown in Table 11. The analysis shows that the main effect of the sawmill factor and the interaction of sawmill and wood species were evaluated to be non-significant, as the p-value was larger than 0.05. In the meantime, the wood species factor was the only variable with a significant effect on the environmental burden, since the p-value was less than 0.05. The possible reason that the sawmill factor had no effect on the environmental burdens was due to the similarity in the sawmilling conditions in terms of saw blade properties and sawing operational parameters in both sawmills. The interaction of sawmill and wood species factors was not significant and therefore the sawmill factor had little influence on the environmental burdens due to wood species.
Table 11. Multivariate Test of Variables on the Environmental Burdens
The null hypothesis of no difference in environmental burden between the Light Red Meranti and Dark Red Meranti was rejected. Hence, the analysis was continued to examine the main effects of wood species on the environmental burdens. The second part of the MANOVA statistical test was identical to the five separate factorial ANOVA test if MANOVA was not opted. Leech et al. (2005) pointed out that an experimental-wise alpha rate of 0.05 is required in the second part of the MANOVA statistical test. The main reason was that the p-values in the multivariate test did not take into consideration that the multiple ANOVAs have been carried out. Hence, the p-value of 0.05 was divided into five, according to the total of the dependent variables (CO2, CH4, NOx, N2O, and CO), to get an adjustable confidence level. As shown in Table 12, the mean values for CO2, CH4, N2O, NOxand CO, emissions in Dark Red Meranti were slightly greater than those for the Light Red Meranti.
Table 12. Mean Comparison of Environmental Burdens between Wood Species
The effect of Light Red Meranti and Dark Red Meranti in the discharge of CO2, CH4, NOx, N2O, and CO was considered to be statistically significant if the p-value was less than 0.01. Nevertheless, the analysis showed no significant differences in the emission between the Light Red Meranti and Dark Red Meranti, as the p-value was greater than 0.01 (Table 13). The two wood species differed in terms of sawn timber dimension produced, saw logs characteristics and physical properties.
Perhaps, the variation in energy consumption was not strong enough to provide a significant difference in the environmental emission. Therefore, it can be noted that the difference in the mean release of CO2, CH4, N2O, NOx, and CO was not influenced by the wood species.
Table 13. Effect of Wood Species on the Environmental Burdens
Statistical test for normal distribution variables
A non-parametric test (Mann-Whitney U test) was applied to determine the effect of the categorical factors on the SO2 emission. The Mann-Whitney U test was used because the normality of SO2 was violated. Table 14 depicts the results of the Mann-Whitney U analysis.
The findings of the statistical analysis showed that the variables, sawmill and wood species, respectively, were not statistically significant in the discharge of SO2 emissions, since the p-value was greater than 0.05.
Table 14. Effect of the Sawmill and Wood Species on the SO2 Environmental Burden
A Comparison of the Environmental Emissions in Wood Species
Generally, the release of CO2, CH4, NOx, N2O, CO, and SO2 from Dark Red Meranti was greater than that of Light Red Meranti. As a matter of fact, the result showed that wood species factor did not affect the emission of CO2, CH4, NOx, N2O, CO, and SO2, as the finding was non-significant between the wood species. Although the two variables did not show any differences in emissions between the Light Red Meranti and Dark Red Meranti, the overall factors that influenced these variations in emissions were highlighted.
The saw-log’s density, length, diameter, volume, moisture content as well as the dimensions of sawn timber produced, for both wood species, were different. The observations in this study with regards to the variability in the energy consumption, particularly electricity, for Light Red Meranti and Dark Red Meranti saw-logs was likely caused by the differences in saw-log characteristics and physical properties. Increasing the saw-log’s length and diameter would require additional energy during the cutting processes. Furthermore, cutting smaller dimension sawn timber would increase the energy demand (McCurdy et al. 2006).
Saw-logs of higher density require more energy and more material for a given cutting volume (Darmawan et al. 2008; Ratnasingam et al. 2008; Ratnasingam et al. 2009; Ramasamy and Ratnasingam 2010). In the meantime, the sawing volume of Dark Red Meranti in both sawmills was higher than the Light Red Meranti, which explains the higher use of diesel fuel energy.
Perhaps, the difference in the saw logs characteristics, physical properties, and number of logs does not have strong influence in the variation in energy consumption. As a result, the weak variability in the consumption of electrical and diesel fuel energy between the Light Red Meranti and Dark Red Merantidid not show any significant difference in the discharged amount of CO2, CH4, NOx, N2O, CO, and SO2.
Potential Environmental Impact Assessment
The release of CO2, CH4, NOx, N2O, CO, and SO2 is capable of impacting the environment (Kinjo et al. 2005; Eshun et al. 2011; Tellnes et al. 2012). Based on this fact, the potential environmental impacts were evaluated in this study, as Bovea and Gallardo (2006) pointed out that the output from the inventory study are normally not well defined in terms of environmental performance. Besides that, study based on inventory only proved to show unsupported conclusions. The findings of the potential environmental impacts are shown in Fig. 5.
Fig. 5. Potential Environmental Impacts: (a) global warming potential of 100 years; (b) acidification potential; (c) eutrophication potential; (d) human toxicity potential; and (e) photo-oxidant formation potential
The findings in Fig. 5 indicate that the global warming, acidification, human toxicity, eutrophication, and photo-oxidant formation potentials showed a mean difference between the sawmills and wood species. A statistical analysis was performed in order to determine any significant difference for each of the potential environmental impacts between the test factors. As the normality for each of the potential environmental impacts was observed to be accepted, MANOVA statistical test was applied for further analysis.
The first part of the MANOVA test was a multivariate test. As shown in Table 15, the multivariate test for sawmill, wood species, and interaction of sawmill and wood species was not statistically significant, as the p-value was larger than 0.05. According to Leech (2005), the statistical analysis was not continued to examine in detail the univariate analysis for main effect of sawmill factor, main effect of wood species factor, and interaction of sawmill and wood species because the finding for each of the potential environmental impact would be non-significant as well. Hence, each of the potential environmental impacts was not significantly different between the main effect of sawmill factor, main effect of wood species factor, and interaction of sawmill and wood species.
Table 15. Multivariate Test of Variables on the Potential Environmental Impacts
CONCLUSIONS
- This study evaluated the environmental burden by applying the gate-to-gate concept for the sawmilling process. The environmental burdens were determined using the life cycle assessment (LCA) methodological framework for the two most common tropical hardwoods in Peninsular Malaysia, namely Light Red Meranti (Shorea spp.) and Dark Red Meranti (Shorea spp.) cut in two different sawmills.
- The resource consumption of saw-logs and energy was evaluated in this study. The assessment of environmental burdens was carried out after determining the resource consumption measures. The discharged CO2, CH4, NOx, N2O, SO2, and CO was enumerated from the energy consumption. Overall, the emission of CO2 was the greatest, followed by SO2, NOx, and CO. The emission of CH4 and N2O from electrical energy and diesel fuel consumption was insignificant.
- The components were transformed into several potential environmental impacts. The assessment of potential environmental impacts resulted in the potential formation of global warming (CO2, CH4, and N2O), acidification (SO2 and NO2), human toxicity (NO2 and SO2), eutrophication (NO and NO2), and photo-oxidant formation (CO, CH4, SO2, and NO2).
- The analysis showed that wood species, sawmill or the interaction between wood species and sawmill, respectively, had no significant influence on the environmental performance.
REFERENCES CITED
Baharuddin, H. G. (1984). “Peninsular Malaysia’s timber industry in perspective,” The Malaysian Forester 47, 70-79.
Bergman, R. D., and Bowe, S. A. (2012). “Life-cycle inventory of manufacturing hardwood lumber in Southeastern US,” Wood and Fiber Science 44(1), 71-84.
Blaser, J., Sarre, A., Poore, D., and Johnson, S. (2011). Status of Tropical Forest Management 2011, ITTO Technical Series No. 38, International Tropical Timber Organization, Yokohama, Japan.
Darmawan, W., Usuki, H., Quesada, J., and Marchal, R. (2008). “Clearance wear and normal force of TiN-coated P30 in cutting hardboards and wood-chip cement boards,” Holz als Roh-und Werkstoff66(2), 89-97. DOI: 10.1007/s00107-007-0213-5
Devaru, D. G., Maddula, R., Grushecky, S. T., and Gopalakrishnan, B. (2014). “Motor-based energy consumption in West Virginia sawmills,” Forest Products Journal 64(1/2), 33-40. DOI: 10.13073/FPJ-D-13-00070
Eshun, J. F., Potting, J., and Leemans, R. (2010). “Inventory analysis of the timber industry in Ghana,” The International Journal of Life Cycle Assessment 15(7), 715-725. DOI: 10.1007/s11367-010-0207-0
Eshun, J. F., Potting, J., and Leemans, R. (2011). “LCA of the timber sector in Ghana: Preliminary life cycle impact assessment (LCIA),” The International Journal of Life Cycle Assessment 16(7), 625-638. DOI: 10.1007/s11367-011-0307-5
Finnveden, G., Hauschild, M. Z., Ekvall, T., Guinée, J., Heijungs, R., Hellweg, S., Koehler, A., Pennigton, D., and Suh, S. (2009). “Recent developments in life cycle assessment,” Journal of Environmental Management 91(1), 1-21. DOI: 10.1016/j.jenvman.2009.06.018
González-García, S., Lozano, R. G., Estévez, J. C., Pascual, R. C., Moreira, M. T., Gabarrell, X., i Pons, J. R., and Feijoo, G. (2012). “Environmental assessment and improvement alternatives of a ventilated wooden wall from LCA and DfE perspective,” The International Journal of Life Cycle Assessment 17(4), 432-443. DOI: 10.1007/s11367-012-0384-0
Guinée, J. B., Gorrér, M., Heijungs, R., Huppes, G., Kleijin, R., de Koning, A., van Oers, L., Sleeswijk, A. W., Suh, S., and Udo de Haes, H. A. (2001, updated 2015). Handbook on Life Cycle Assessment. Operational Guide to the ISO Standards, Kluwer Academic Publishers, Dordrecht.
Ho, K. S., and Gan, K. S. (2003). “Saw doctoring practices in Peninsular Malaysia,” Timber Technology Bulletin 27(8), 1-7.
Ingerson, A. (2011). “Carbon storage potential of harvested wood: Summary and policy implications,” Mitigation and Adaptation Strategies for Global Change 16(3), 307-323. DOI: 10.1007/s11027-010-9267-5
International Panel of Climate Change. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, H. S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.), IGES, Japan.
ISO 14040. (2006). “Environmental management-life cycle assessment-principles and framework,” Second Edition, International Organization for Standardization, Geneva, Switzerland.
ISO 14044. (2006). “Environmental management -life cycle assessment-requirements and guidelines,” International Organization for Standardization, Geneva, Switzerland.
ITTO-CITES Project. (2010). “Sawn timber and plywood recovery study of ramin (Gonystylus bancanus) in Peninsular Malaysia,” (http://www.itto.int/files/user/cites/malaysia/Sawntimber%20and%20plywood%20recovery%20study.pdf).
Kinjo, M., Ohuchi, T., Kii, H., and Murase, Y. (2005). “Studies of life cycle assessment of Sugi lumber,” Journal of the Faculty of Agriculture 50(2), 343-351.
Leech, N. L., Barrett, K. C. and Morgan, G. A. (2005). SPSS for Intermediate Statistics 2nd edition, Lawrence Erlbaum Associates, London.
Lin, W., Wang, J., Wu, J., and DeVallence, D. (2011). “Log sawing practices and lumber recovery of small hardwood sawmills in West Virginia,” Forest Products Journal 61(3), 216-224. DOI: 10.13073/0015-7473-61.3.216
Mahlia, T. M. I. (2002). “Emissions from electricity generation in Malaysia,” Renewable Energy 27(2), 293-300. DOI: 10.1016/S0960-1481(01)00177-X
MTIB. (2012). “Malaysian Timber Statistics 2009-2011,” Malaysian Timber Industry Board, Percetakan Nasional Malaysia Berhad, Kuala Lumpur.
McCurdy, M. C., Li, J., and Pang, S. (2006). “Modeling of energy demand in a sawmill,” in: Proceedings from the Conference of CHEMECA 2006, Auckland, pp. 17-20.
Menon, P. (2000). “Status of Malaysia’s timber industry,” Asian Timber 20(6), 12-15.
Ministry of Plantations Industries and Commodities. (2012). “Resources,” (http://www.kppk.gov.my/).
National Timber Policy. (2009). “NATIP National Timber Policy 2009-2020,” Ministry of Plantation Industries and Commodities, Malaysia.
Ong, M. S. (1986). “The implications of the industrial master plan on the development of wood-based industry in Malaysia,” The Malaysian Forester 49(3), 241-248.
Ong, H. C., Mahlia, T. M. I., and Masjuki, H. H. (2011). “A review on energy scenario and sustainable energy in Malaysia,” Renewable and Sustainable Energy Reviews 15(1), 639-647. DOI: 10.1016/j.rser.2010.09.043
PE International AG (2012). Life Cycle Assessment of Rough-Sawn Kiln-Dried Hardwood Lumber. Final report for American Hardwood Export Council (AHEC).
Puettmann, M. E., Wagner, F. G., and Johnson, L. (2010). “Life cycle inventory of softwood lumber from the Inland Northwest US,” Wood and Fiber Science 42(Special Issue), 52-66, (http://www.corrim.org/pubs/reports/2010/swst_vol42/52.pdf).
Ramasamy, G., and Ratnasingam, J. (2010). “A review of cemented tungsten carbide tool wear during wood cutting processes,” Journal of Applied Sciences 10(22), 2799-2804. DOI: 0.3923/jas.2010.2799.2804
Ratnasingam J., Tee, C. T., and Farrokhpayam, S. R. (2008). “Tool wear characteristics of oil palm empty fruit bunch particleboard,” Journal of Applied Sciences 8(8), 1594-1596. DOI: 10.3923/jas.2008.1594.1596
Ratnasingam, J., Ma, T. P., Ramasamy, G., and Manikam, M. (2009). “Tool wear characteristics of cemented tungsten carbide tools in machining oil palm empty fruit bunch particleboard,” Journal of Applied Sciences 9(18), 3397-3401. DOI: 0.3923/jas.2009.3397.3401
Rivela, B., Moreira, M.T., and Feijoo, G. (2007). “Life cycle inventory of medium density fibreboard,” The International Journal of Life Cycle Assessment 12(3), 143-150. DOI: 10.1065/lca2006.12.290
Rosnazri, A., Ismail, D., and Soib, T. (2012). “A review on existing and future energy sources for electrical power generation Malaysia,” Renewable and Sustainable Energy Reviews 16(6), 4047-4055. DOI: 10.1016/j.rser.2012.03.003
Saidur, R., Hasanuzzaman, M., Sattar, M. A., Masjuki, H. H., Irfan Anjum, M., and Mohiuddin, A. K. M. (2007). “An analysis of energy use, energy intensity and emissions at the industrial sector of Malaysia,” International Journal of Mechanical and Materials Engineering 2(1), 84-92, (http://eprints.um.edu.my/id/eprint/6887).
Tellnes, L. G. F., Nyrus, A. Q., and Flaete, P. O. (2012). “Carbon footprint of products from Norwegian sawmilling industry,” in: Proceedings of the Biennial Meetings of the Scandinavian Society of Forest Economics, C. L. Todoroki and E. M. Rönnqvist (eds.), Hyytiälä, Finland.
Vigon, B. W., Tolle, D. A., Cornaby, B. W., Latham, H. C., Harrison, C. L., Boguski, T. L., Hunt, R. G., and Sellers, J. D. (1993). Life Cycle Assessment: Inventory Guidelines and Principles, Risk Reduction Engineering Laboratory Office of Research and Development U.S. Environmental Protection Agency, Cincinnati, OH.
Yap, F. I. (2004). “Review of the current timber industry in Malaysia,” Bachelor’s Degree Thesis, University of Southern Queensland, Australia.
Article submitted: May 7, 2015; Peer review completed: July 20, 2015; Revised version received and accepted: August 10, 2015; Published: August 19, 2015.
DOI: 10.15376/biores.10.4.6643-6662