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Carbon Life Cycle Assessment on California-Specific Wood Products Industries: Do Data Backup General Default Values for Wood Harvest and Processing?

Spatial Informatics Group LLC (SIG), Pleasanton, CA 94566, USA
Gund Institute for Environment, University of Vermont, Burlington, VT 05405, USA
TSS Consultants, Sacramento, CA 95819, USA
Placer County Air Pollution Control District (PCAPCD), Auburn, CA 95603, USA
New Hampshire Agricultural Experiment Station, Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH 03824, USA
Geospatial Analysis Lab, University of San Francisco, San Francisco, CA 94117, USA
Author to whom correspondence should be addressed.
Forests 2021, 12(2), 177;
Submission received: 30 December 2020 / Revised: 25 January 2021 / Accepted: 28 January 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Forest Ecosystem Services and Products)


Carbon life cycle assessments (C LCA) play a major role in greenhouse gas (GHG)-related forest management analytics for wood products and consist of several steps along a forest to disposal path. Yet, input values for wood product C LCAs frequently rely on potentially outdated generic datasets for wood product outputs and mill efficiencies. Assumptions regarding sawmill efficiencies and sawmill-specific wood product outputs have a direct and significant impact on wood product C LCAs because these variables affect the net carbon footprint of the finished product. The goal of this analysis was to evaluate how well standard wood product C LCA inputs and assumptions for the two initial wood products LCA steps (i) forest operations and (ii) wood processing represent the current state of the wood processing industry in California. We found that sawmill efficiencies and wood product outputs both support and deviate from lookup tables currently used in publications supporting the climate-forest policy dialogue. We recommend further analysis to resolve the major discrepancies in the carbon fraction stored in durable wood products and production-related emissions to improve C LCA metrics and advance forest-related climate policy discussions in California and elsewhere.

1. Introduction

Carbon stored in wood products plays a major role in greenhouse gas (GHG) related forest management analytics such as required for national GHG inventories [1], forest, carbon offset markets [2], GHG impact analysis of forest management options [3,4], or quantifying nature-based climate solutions [5]. Wood product carbon inputs frequently rely on standardized inputs that might be outdated. Carbon life cycle assessments (C LCA) for wood products consist of several steps along a cradle to grave path: fossil fuel emissions related to harvest and in-forest processing activities, transportation to a manufacturing facility such as saw or paper mill, fossil fuel emissions and product groups manufactured at the processing site (including fossil fuel offsets by using mill residues for energy production), transportation to and from distribution centers, in-use (e.g., half-life of products) and post-use GHG emission profiles (e.g., energetic use, deposition fate, non-CO2 emission profiles), and displaced fossil fuel intensive non-wood products (e.g., concrete, steel, aluminum). Carbon dioxide (CO2) emissions can be used as a standardized metric for mass-balance equations. If non-CO2 GHG emissions are anticipated or stored carbon is reported on, CO2 equivalents (CO2e) can be used to further extend this standardization metric.
Assumptions on sawmill efficiencies and sawmill-specific wood product outputs have a direct and significant impact on wood product C LCA outcomes and the GHG footprint of the finished product. The accuracy of these assumptions impacts the in-use and post-use C LCA profiles for wood products. However, input values for wood product C LCAs frequently rely on generic datasets on wood product types and mill efficiencies for a given region, as well as product use lifespan and post-use fates that are likely outdated. For example, data predating 2006 [6] are frequently cited in harvested wood C LCA analyses. During the last 30 years wood producers and manufacturers have been under competitive market pressure, which has led to persistent incremental changes to the efficiency of wood manufacturing facilities and forest harvesting technology. The changes in these industry sectors requires a periodic review of such datasets to capture their current performance profiles.
The goal of this analysis was to evaluate how well standard wood product C LCA inputs and assumptions up to and including the manufacturing facility (Figure 1) represent the current state of the wood processing industry in California. In 2016, the closest date for which state-wide statistics are available, California’s timber harvest was 5.910 million m3 which was processed at 80 different processing facilities [7].

2. Materials and Methods

In 2016, we surveyed three large commercial sawmill owners who process sawlogs from their own as well as public timberlands at 11 sawmill sites. The primary reporting unit for sawlogs and processed lumber is a thousand board feet (mbf) in the US. One board foot equals a volume of a one-inch (0.0254 m) thick board with a length of one foot (0.305 m). Board feet expressed in “lumber tally” are actually produced volumes while “log scale” is an estimate of anticipated board foot volumes produced based on pre-defined algorithms such as set forth in the Scribner log scale [8]. Since the purpose of the study was to test standardized inputs and assumptions, all inputs relied on manual entries and no LCA database or program was used. Conversions from mbf to m3 were based on a conversion factor of 3.76 m3/mbf which in turn was based on a dataset-wide average specific gravity of 0.382 mg/m3. We gathered the following 2015 data:
  • Sawmill operations—Data were provided in Scribner log scale by dominant species for all participating sawmills. Sawmill wood products (lumber tally for lumber; tonnage and moisture content for all other products) and on-site energy production and consumption was also provided. A small fraction of the sawmill data (5% of lumber volume) was derived from 2014 data;
  • Harvest and in-forest processing—Data were provided on usage rates for fossil fuel (diesel and gasoline) engines for chainsaws, yarders, loaders, etc., in gallons/mbf and forest-to-mill production (number of loads, total fossil fuel consumption, average distance) was calculated as averages based on averaged metrics provided for 83% of the dataset. Another subset of the harvest data constituting of 15% of total processed sawlogs provided forest stand-level fossil fuel engine consumption for harvest and processing equipment, harvest area (in ha), slash fate if left on site, number of truck loads (forest to mill), and transport distance.
We used the following conversion factors to change each process variable to megagrams of carbon dioxide equivalents (mg CO2e) to perform GHG calculations. One megagram is equivalent to a metric tonne.
  • For fossil fuel emission factors: 0.00232 mg CO2e/liter for gasoline and 0.0317 mg CO2e/liter for diesel fuel [9] and 0.0042 mg C/MWh for natural gas [10];
  • 90% efficiency when converting natural gas to process heat;
  • For electricity generation from mill and forest harvest residues, we assumed a heating value of 18.5 Gigajoule per oven-dried mg (ODT) [11] and an electrical conversion efficiency of 29% as reported by the major participant of the sawmill survey;
  • To calculate avoided emissions from biomass-based electricity generation, we assumed each unit of electricity generated would offset one unit of grid based electricity which had an associated emission factor of 0.205 mg CO2e/MWh for California in 2016 [12];
  • A carbon fraction of 0.5 for biomass (0% moisture) which is representative for temperate conifer forests [1]. Specific gravity for wood species was taken from Miles & Smith (2009) [13].

3. Results and Discussion

3.1. Carbon Footprint of Wood Products Manufacturing

For the calendar year 2015, the surveyed entities processed a total of 3.764 million m3 in sawlogs (as shown in Table 1), equivalent to a total of 4.4 million mg CO2e. This included 3.823 million m3 in lumber products storing 2.6 million mg CO2e. Durable byproducts (e.g., paper, pulp, particle board) stored 0.333 million mg CO2e while 0. 393 million ODT of short-lived byproducts (sawdust, hogfuel, shavings, bark, pulp chips sold for landscaping, animal bedding, soil amendment, etc.) stored 0.720 million mg CO2e. Biomass sold for energy used off-site (0.191 million ODT) avoided emissions of 0.321 million mg CO2e. An additional 0.274 million ODT of biomass was used on-site for process heat and electricity generation and avoided 0.503 million mg CO2e in emissions. Forest harvest residues (“slash”) included around 0.957 million ODT. While most of this biomass was burnt on-site or scattered in the forest, around 0.266 million ODT were shipped to power plants and results in 0.081 million mg CO2e in avoided fossil fuel emissions.
Fossil fuel emissions associated with harvest and transport (diesel and gasoline) as well as processing at the sawmill (natural gas and grid electricity) equaled 5% and 1% of CO2e stored in lumber and durable byproducts, respectively. This relatively low proportion is consistent with other recent forest sector C LCA studies [15].
If the on-site electricity production using hogfuel (excluding the slash shipped to a biomass power plant) was appropriately considered to displace fossil fuel emissions, sawmill CO2e emissions would decrease to 2% of CO2e stored in lumber and durable byproducts. Only 2% (measured by CO2e emissions) of sawmill energy demand was satisfied with fossil fuels (natural gas for process heat; on-site electricity generation offsetting almost entirely the need for grid electricity). A total of 520 GWh/year of electricity was generated on-site from hogfuel from the three entities; satisfying 100% of on-site net electricity demand. Of total on-site electricity production, 58% or 301 GWh were sold to the grid and not used on-site. Although on-site electricity production exceeded on-site demand, 13 GWh were purchased in 2015 to bridge peak consumption times when demand exceeded production, resulting in a net of 288 GWh of excess biomass electricity production.
Electricity generation from slash was around 394 GWh derived from 266,000 ODT of biomass; offsetting around 0.081 million mg CO2e. Pile burning of slash at or near the harvest site emitted around 0.347 million mg CO2e while scattered slash will emit around 0.902 mg million CO2e in the short-term due to decay. See section “Slash: fate and fossil fuel use” below for more information regarding slash. No value-added pathway for slash was reported such as biochar [16] or briquetting [17] production.

3.2. Dataset as a Representative Snapshot of the 2015 Timber Industry in California

We collected calendar year 2015 Sawmill data derived from 11 sawmills. As a reference, Marcille et al. [7] counted a total of 32 California-based sawmills operating in 2016. These sawmills process logs sourced from forests in the northern California coastal region as well as mid to high-elevation forests in the northern and central Sierra Nevada and Cascade mountain ranges.
The sawmills surveyed processed a total of 3.764 million m3, which represents over 75% of all harvest volume from private and tribal lands in 2015. The dataset also represents 60% of the total 6.129 million m3 log harvest in California during 2015 [18].
The surveyed sawmills processed volumes of trees species that closely resembled the total California specific harvest volume species composition for 2016 [7]. While Ponderosa pine was somewhat overrepresented, Douglas fir was slightly underrepresented (Table 2). Because the lumber conversion rates for Ponderosa pine compared to Douglas fir are similar, we can conclude that from a sawmill data perspective, the dataset was representative of the total harvested volume and sawmill performance in California for 2015.
We received fossil fuel consumption data for harvest operations for 91% of the harvested volumes reported in Table 2. For forest-to-sawmill transport, we received data on mileage for 91% of the total volume and fossil fuel consumption records for 83% of the dataset. Stand-level detailed harvest data was provided to us for 4000 harvested hectares at 112 harvest sites; yielding 0.333 million m3 or a total of 9% of the total reported volume. In comparison, Stewart & Nakamura [19] derived their dataset from 28 sites covering 6870 hectares in northern California.

3.3. Harvest, Transport and Sawlog Processing Analysis

3.3.1. Harvest Operations

Type of harvest operations and fossil fuel use. Fossil fuel use across entities ranged from 3.9 to 6.8 L per m3 harvested sawlog volume and averaged at 6.6 L per m3 (Table 3). Gasoline, used for some trucks and chainsaws besides diesel, represented around 9% of the total fuel use. The stand-level harvest dataset (4000 hectares, 112 harvest sites; 0.334 million m3) suggested that 45% of volume was harvested using tractor-based logging and 53% using cable-logging systems. Helicopter based logging was minimal (2%) for the stand-level harvest dataset and absent for the other harvest dataset. Fuel use for ground-based systems exceeded reported values from other studies [15,20] by a factor of two. Skyline-based logging systems also exceeded values reported in the literature [15,20].
Slash: fate and fossil fuel use. Data on slash production was provided for the entire dataset on a per mbf (log scale) basis and averaged by surveyed entity. Slash constituted 29% of total harvested biomass. Slash production varied by entity from 0.24 to 0.35 ODT/m3; averaging at 0.29 ODT/m3. This is double the rate as reported by e.g., Stewart & Nakamura (2012). On average, 28% of slash was used for electricity production, 21% was pile-burnt and 51% was scattered on site. However, slash fate varied considerably between surveyed entities. Some entities scattering 95% of slash while shipping none to power plants and other entities shipped up to 40% to power plants while pile-burning 25%. These slash recovery rates are significantly lower than the slash production rates reported in the literature. For instance, Stewart & Nakamura [19] and Ince et al. [21] assumed slash recovery rates of 75% and 66%, respectively. In-forest processing emissions for slash destined for electricity generation was based on survey responses for 83% of total reported harvest volume and included 3.0 and 3.2 L per ODT for in-forest processing (handling, chipping, loading) and transport to a power plant, respectively. In comparison, Han et al. [15] suggests only 1.5 L per ODT for biomass processing and loading.

3.3.2. Sawlog Transport

Sawlog transport distance (one way) ranged from five to 109 km by entity and averaged 100 km for the entire dataset available for harvest volumes. The log volume transported averaged 16.4 m3/load, and had a range from 16.2 to 18.4 for all entities surveyed. Harvest site to sawmill fuel consumption was only reported directly for 83% of total sawlog volume surveyed and averaged 2.1 km per liter. In comparison, the average fuel economy for a truck of this size (class 8) in 2020 was 2.3 km per liter [22].

3.3.3. Sawmill Processing

Sawmill efficiency. Lumber overrun, the “amount of lumber actually recovered in excess of the amount predicted by the log scale, expressed as a percentage of the log scale” [8] averaged 1.6 and varied from 1.6 to 1.9 across entities. A value >1 means that more lumber was procured than initially estimated. This average value is identical with previously reported averages for California [8]. Sawmill efficiency, measured in the fraction of lumber and durable byproducts divided by processed volume was 67.6% across all surveyed entities. Across-entity variations were minimal with a range of 67.3 to 73.4% of entity-wide (average) sawmill efficiencies. This sawmill efficiency is essentially identical to typical lookup values such as the 67.5% provided by Smith et al. [6] for Californian softwood processing facilities and widely applied for wood product life cycle assessments [23]. It is considerably higher than the 61% suggested for the entire Californian durable wood products processing industry [24]. An industry-wide efficiency of 74.9% as applied by Stewart and Nakamura [19] is not supported by this data. The California timber processing industry’s efficiency applied by Stewart & Nakamura [19] was based on a 2006 California-wide forest product industry survey and has been superseded since then by a 2012 [24] and 2016 [7] survey.
Sawmill energy demand. A total of 82.3% of process heat demand, measured in mg CO2e emissions as a proxy to energy units, was covered by residual biomass internally sourced during sawlog processing. Another 15.3% was covered by biomass sourced from off-site. Only 2.4% of on-site process heat demand was derived from fossil fuels (natural gas). 100% of net electricity demand was produced internally. Corroborating on site energy demand for California sawmills is challenging since literature is sparse and is site-specific. A study close to California [25] suggests 48 MWh/m3 electricity use while our dataset suggests 74 MWh/m3 of wood products. This higher consumption cannot be further explored due to a lack of data but can potentially explained by more energy-intensive machine processing of lumber and byproducts. Most of the electricity use in Loeffler et al. [25] was purchased off-site which might be another driver to keep electricity use low. Comparing other on-site energy use such as for process heat between the two studies provides challenges since both regions differ significantly in their energy production and consumption mechanisms (combined heat and power generation at the California mills with a percentage of biomass bought off-site and a percentage of on-site electricity production sold to the grid vs. a fossil fuel reliant energy demand for the southwestern US dataset).
Wood products output. For reporting, the total durable volumetric product unit outputs from the surveyed sawmills were converted to mg CO2e to calculate their respective conversion rates. The total lumber conversion rate was 71% with another 9% stored in durable byproducts including raw material for paper and pulp. This result suggests considerable deviations from widely utilized lookup tables used for wood product life cycle assessments such as California Air Resource Board’s data for forest-based carbon offset calculations [23] which assume that lumber represents 97% to 99% of wood products generated in Californian sawmills with 0% to 1% stored in pulp/paper products and 0% to 3% stored in plywood. In contrast, the dataset more closely aligns with the wood product mix suggested by Smith et al. [6] for California which suggests that 67% of total durable outputs is stored in lumber. Where the Smith et al. and our dataset differs is in the fraction of short-lived wood products (landscape mulch, animal bedding, soil amendment, etc.) which constitutes 20% of total non-bioenergy products in our dataset. There is no comparable wood product category available in the Smith et al. [6] lookup tables.

4. Conclusions

Carbon LCA inputs and assumptions for wood products as currently used in a California context are generally supported by recent data. We recommend further analysis to resolve in particular the major discrepancies in the carbon fraction stored in durable wood products. Expanding the opportunities of slash for energetic use or other wood products could considerably enhance GHG benefits of the wood products industry. Improvement of production-related emissions and C LCA metrics could advance the climate policy discussion regarding the potential of forests to mitigate GHG emissions in California and beyond.

Author Contributions

Conceptualization, T.B., D.S., and T.M.; methodology, T.B., T.M.; formal analysis, T.B., B.S.; data curation, T.M., T.B.; writing—original draft preparation, T.B., B.S.; writing—review and editing, J.G.; project administration, B.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.


The analysis provided in this report was part of the 2015–2019 “Quantifying ecosystem service benefits of reduced occurrence of significant wildfires” (QEBROW). The goal of this project was to provide a scientifically rigorous and stakeholder-endorsed carbon accounting framework for avoided wildfire emissions from fuel treatments with the intent to tap into carbon markets to co-fund fuel treatments. Funders for the QEBROW project included Sierra Pacific Industries, CAL FIRE, Placer County Air Pollution Control District, Sacramento Municipal Utility District, US Forest Service, and the Coalition for the Upper South Platte. J.G. was supported by funding from the New Hampshire Agricultural Experiment Station.

Data Availability Statement

Restrictions apply to the availability of these data. Data was obtained from sawmills and are available from the authors with the permission of those sawmills.


We are grateful to the forest products manufacturing enterprises who contributed to the dataset for sharing vital information. Due to the proprietary nature of this data, these enterprises remain anonymous in this report but are known to the authors.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


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Figure 1. Carbon LCA boundary of study.
Figure 1. Carbon LCA boundary of study.
Forests 12 00177 g001
Table 1. Fate and CO2 profile of sawlogs processed by surveyed entities in 2015. CO2 volumes stored in wood products do not add exactly to 100% of CO2 volume of harvested sawlogs due to rounding and minimal reporting inconsistences (4% error). The quantity of CO2e is reported in millions of megagrams (106 mg). Carbon stocks and GHG emissions are expressed as positive values. GHG emission savings are expressed as negative values.
Table 1. Fate and CO2 profile of sawlogs processed by surveyed entities in 2015. CO2 volumes stored in wood products do not add exactly to 100% of CO2 volume of harvested sawlogs due to rounding and minimal reporting inconsistences (4% error). The quantity of CO2e is reported in millions of megagrams (106 mg). Carbon stocks and GHG emissions are expressed as positive values. GHG emission savings are expressed as negative values.
UnitValue106 mg
Harvested timber on trucks to mill106 m33.7644.387
Scattered on site106 ODT0.4920.902
Onsite open pile burning106 ODT0.1990.347 a
Used for electricity106 ODT0.266 (394 GWh)−0.081
Lumber mill products
Lumber106 m33.8232.633
Byproducts, durable (pulp, paper, particle board)106 ODT0.182 b 0.333
Byproducts, short-lived, non-energy106 ODT0.393 0.720
Byproducts, hog fuel sold offsite for energy c106 ODT0.191 0.321
Byproducts, hog fuel used onsite for energy106 ODT0.2740.503
Energy use for in-field processing and transport
Harvest (logging, yarding, in-forest processing, loading)106 L (91% diesel, 9% gasoline)44.9210.120
Slash processing and transport106 L (diesel)18.7040.051
Transport to sawmill (log trucks)106 L (diesel)8.0440.022
Energy use for sawmill operations
Fuel combustion onsite for heat and electricity
Natural gas106 MWh720.013
Hogfuel-own106 ODT19060.503
Hogfuel-purchased106 ODT5250.139
Electricity (excluding slash)
Onsite generation used at millGWh220−0.045
Onsite generation sold to grid for use offsiteGWh301−0.062
Purchased from grid to run millGWh630.13
(a) Assuming a pile burn combustion efficiency of 95% [14]. (b) Note: Biomass volume is not equivalent to output volume since products in this category can be of composite nature also including other raw material. (c) Hogfuel: Low-quality (mixed) biomass not meeting other byproduct definitions such as bark, pulp chips, shavings, sawdust.
Table 2. Timber volumes processed-surveyed (2015) vs. total volume (2016) in California.
Table 2. Timber volumes processed-surveyed (2015) vs. total volume (2016) in California.
SpeciesSurveyed VolumesTotal CA Volumes 2016 [7]
106 m3% of Total106 m3% of Total
Ponderosa pine (Pinus ponderosa)1.01127%1.35023%
Sugar pine (Pinus lambertiana)0.3469%0.4257%
Douglas fir (Pseudotsuga menziesii)0.79321%1.39424%
Redwood (Sequoia sempervirens)0.43211%0.82114%
Other species1.18131%1.91932%
Table 3. Average and range of fuel consumption recorded (liter per m3 of harvested sawlog volume). Ranges provided in brackets by surveyed entity. Ground-based systems reflect mechanized systems dominated by feller-bunchers, shovel yarders, boom processors, and (truck) loaders. Lubricant consumption was not considered (<1% of fuel use).
Table 3. Average and range of fuel consumption recorded (liter per m3 of harvested sawlog volume). Ranges provided in brackets by surveyed entity. Ground-based systems reflect mechanized systems dominated by feller-bunchers, shovel yarders, boom processors, and (truck) loaders. Lubricant consumption was not considered (<1% of fuel use).
Harvest SystemAverage and Range for Surveyed EntitiesOneil & Puett-Mann [20]; U.S. Pacific NorthwestHan et al. [15]; Even-Aged Redwoods
Ground-based (mechanized)8.5 (4.3–8.8)3.24.1
Skyline-based4.8 (3.4–5.0)3.03.5
Helicopter12.3 (12.1–14.1)n/a17.8 a
Weighted average6.6 (3.9–6.8)
(a) Uneven-aged harvest.
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Buchholz, T.; Mason, T.; Springsteen, B.; Gunn, J.; Saah, D. Carbon Life Cycle Assessment on California-Specific Wood Products Industries: Do Data Backup General Default Values for Wood Harvest and Processing? Forests 2021, 12, 177.

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Buchholz T, Mason T, Springsteen B, Gunn J, Saah D. Carbon Life Cycle Assessment on California-Specific Wood Products Industries: Do Data Backup General Default Values for Wood Harvest and Processing? Forests. 2021; 12(2):177.

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Buchholz, Thomas, Tad Mason, Bruce Springsteen, John Gunn, and David Saah. 2021. "Carbon Life Cycle Assessment on California-Specific Wood Products Industries: Do Data Backup General Default Values for Wood Harvest and Processing?" Forests 12, no. 2: 177.

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