5.5 Residential Wood Burning

    Category ID Description EIC
    289 Fireplace 61060202300000
    2761 Pellet Stove 61060002300000
    2762 Wood Stove (except Pellet Stove) 61060002300000

    Introduction

    This document outlines the methodology for estimating greenhouse gas (GHG) emissions from residential wood burning activities that cover the use of typical wood burning devices in a home, such as fireplaces (category 289), pellet stoves (category 2761), and cordwood stoves (category 2762) which in this methodology include fireplace inserts. Both pellet stoves and cordwood stoves are considered “woodstoves” here. The purpose of use of these devices can range from primary heat, supplemental heat, or ambiance. These activities are a source of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions. Emissions are reported in specific categories based on the type of wood burning device as shown in the table above.

    Fireplaces are the most common wood burning devices across the Bay Area and are generally less efficient for heating compared to woodstoves. They are used primarily for supplemental heating and for aesthetic appeal. Fireplace combustion is characterized by high air-to-fuel ratios and burn rates. A traditional masonry fireplace typically contains a large open firebox without any control on combustion air and is not highly efficient as a heating device. A net heat loss may occur in a residence if colder, outside air is drawn in to replace the inside air that is used for combustion and lost through the chimney draft. There are some prefabricated (metal) fireplaces which are slightly higher in energy efficiency than masonry fireplaces.

    Woodstoves are used primarily as domestic space heaters. They have enclosed fireboxes and dampers to reduce air-to-fuel ratios and control burn rates. Since they are stand-alone heating devices, the greater surface area radiates more heat than a fireplace does. Fireplace inserts that fit into the fireplace can increase the heating efficiency by either radiating the heat into the house or venting heated air into the house by circulating air around the insert with the help of a fan. They are more like woodstoves than fireplaces without inserts, due to their common operating and combustion characteristics. According to the emissions standards of criteria pollutants, there are three types of woodstoves: conventional, USEPA certified non-catalytic, USEPA certified catalytic.

    Pellet stove is a type of woodstove burning pellet of sawdust, wood products, and other biomass materials pressed into manageable shapes and sizes instead of wood log. It is treated as a separate source category as it has much higher heating efficiency and consequently, lower emission factors of criteria pollutants than woodstoves. This characteristic of pellet stoves has potential regulatory implications on mitigation of criteria emissions from residential wood burning activity.

    Following a similar inventory scope as California’s official statewide GHG emissions inventory, referred to as AB32 GHG Inventory, the regional GHG inventory for the San Francisco Bay Area (SFBA) includes only the CH4 and N2O emissions resulting from the combustion of biomass fuel such as wood logs since the CO2 emissions are considered to have occurred eventually and anyways, as biomass would have decayed in natural cycle. For residential wood burning, the CO2 emissions, classified as biogenic CO2 (CO2_bio), are estimated and tracked separately (CARB, 2016; CARB, 2023).

    Methodology

    This section describes how GHG emissions are estimated for residential wood burning categories. These categories are considered area source categories since they cover emission sources that are not directly permitted by the BAAQMD (also known as the Air District), so emissions are not systematically or annually cataloged and/or reported. The methodology used to calculate emissions for the base year(s) of residential wood burning area sources is as follows:

    Base Year(s) EmissionsBY, county, pollutant, category =

    Activity DataBY, county, category × Heat Content × Emission Factorpolutant × Control Factorpollutant × GWPpollutant

    Where:

    • EmissionsBY, county, pollutant, category is the annual total CO2 equivalent (CO2eq) emissions of an applicable base year (BY, which is 2010-2023 in this case), county, GHG pollutant, and source category.
    • Activity DataBY, county, category is the annual total activity data of an applicable base year, county, and source category. Currently, activity estimated for residential wood burning is grouped into the three categories described in the introduction: fireplaces (category 289), pellet stoves (category 2761), and cordwood stoves (category 2762).
    • Heat Content: is the higher heating value (HHV) of wood fuel (Btu / unit), adopted from the methodology of CARB’s AB 32 GHG Inventory (2023 Edition; CARB, 2023).
    • Emission Factorpollutant: is a factor quantifying the amount of emissions, in mass, of a particular pollutant resulting from a unit of activity data. For CH4, N2O and CO2_bio, the energy-based emission factors from the methodology index of CARB’s AB 32 GHG Inventory (2023 Edition; CARB, 2023) have been adopted. More details are provided in Emissions Factors subsection.
    • Control Factorpollutant: is a fractional ratio (between 0 and 1) that captures the estimated reduction in emissions because of Air District rules and regulations.
    • GWPpollutant is the Global Warming Potential (GWP) of a particular GHG pollutant. The current version of the GHG emissions inventory incorporates the GWP values reported in the Fifth Assessment report of the Intergovernmental Panel for Climate Change (IPCC, 2014). The GWPs for the principal GHGs are 1 for CO2 & CO2_bio, 34 for CH4, and 298 for N2O, when calculated on a 100-year basis with climate-carbon feedback included.

    With:

    • BY: is the year for which fine-grained activity estimates are directly supported by statistical modeling of survey data, 2010 – 2023 in this case.
    • county: is the one of the nine counties or partial counties (Sonoma and Solano Counties) in the Air District’s jurisdiction.
    • pollutant: is the GHG pollutant (CH4, N2O and CO2_bio).
    • category: is the source category as shown in the table at the start of this write-up, which is defined based on the types of wood burning devices.

    Once emissions of the base years 2010-2023 are determined for each pollutant, historical backcasting and forecasting of emissions relative to the base year emissions are estimated using growth profiles as follows:

    Emissions FY = Emission 2023 × Growth FactorFY

    Emissions HY = Emission 2010 × Growth FactorHY

    Where:

    • Emissions FY: is the annual county-total CO2 equivalent (CO2eq) emissions of a particular future year (FY) between 2023 and 2050.
    • Growth FactorFY: is the growth factor of a given future year in the corresponding growth profile.
    • Emissions HY: is the annual county-total CO2 equivalent (CO2eq) emissions of a particular historical year (HY) between 1990 and 2009.
    • Growth FactorHY: is the growth factor of a given historical year in the corresponding growth profile.

    More details on activity data/throughput, emission factors, emission controls, and backcasting/ forecasting are provided in the following subsections.

    Activity Data / Throughput

    Residential wood burning activity is estimated by modeling fuel consumption (in mass of wood burned) across the Bay Area using a data-driven, spatially resolved approach. The methodology leverages Spare the Air (STA) Winter Survey data, combined with U.S. Census data on housing stock, to estimate the average daily and annual wood fuel consumption by location and device type.

    The fuel consumption estimation process involves three key steps:

    Step 1: Estimate Wintertime Fuel Consumption at ZIP Code Level

    For each combination of ZIP code, year, device type (e.g., fireplace, woodstove, pellet stove), and housing characteristics (e.g., housing density, home age, building type), a set of trained statistical models is used to predict average daily wood consumption per household during the winter period (November–February).

    These models are based on data collected through the Spare the Air Winter Telephone Survey, which has been conducted annually by the Air District since the early 2000s. The survey collects responses from a statistically representative sample of Bay Area households, stratified by region, to understand:

    • Whether and how often wood is burned in the home,
    • What types of devices are used,
    • The amount and type of wood fuel burned,
    • The purpose of burning (e.g., primary heat, ambiance),
    • Awareness and response to Air District regulations and advisories.

    Survey results are then linked to ZIP-code level housing data from the U.S. Census and American Community Survey (ACS, U.S. Census Bureau, 2023), allowing the model to infer wood burning behavior across the entire housing stock using a multilevel regression framework (described further below). The result is a ZIP-level estimate of average mass of wood burned per home per day during the winter season, differentiated by device type. A conversion factor of 5 pounds per log is used to convert number of logs to mass of wood burned (BAAQMD, 2015).

    Step 2: Extend Estimates to Annual Fuel Use

    While the majority of wood burning occurs in winter, some burning still takes place in shoulder and non-winter months. To produce annual average fuel consumption estimates, a monthly adjustment profile is applied based on survey responses and climatological patterns. This converts winter-season predictions into year-round average daily fuel use, which is then multiplied by the estimated number of homes burning wood in each ZIP code.

    Step 3: Spatial Refinement to 1 km Resolution (for Air Quality Modeling)

    Although not critical for county-level emissions estimation, ZIP-level activity is further refined to a 1 km × 1 km grid using gridded U.S. Census data on housing density and, for woodstoves, primary heating fuel. This allows for more accurate emissions mapping in support of regional air quality modeling and spatial policy development. For inventory reporting at the county level, this refinement step has minimal impact and only affects ZIP codes that straddle county boundaries.

    Methodological Framework

    Four key aspects of the approach work together to produce reliable and unbiased estimates that still yield useful information about spatial variation in wood fuel consumption at ZIP code level:

    • Decomposition into a product of predictions from trained statistical models
    • Partial pooling (multilevel regression), reflected in model specifications, as a data-driven form of adaptive regularization, balancing over-smoothing vs. risk of outlier-driven predictions
    • Post-stratification, used to weight and sum conditional rates predicted by models in a way that adjusts for sample imbalance
    • Bayesian estimation, used to iteratively build and check models, generate posterior predictions, and propagate uncertainty

    More details regarding the four key aspects, composition of wood fuel types and device types, as well as converting winter fuel consumption to annual average are discussed in Appendix.

    County Distribution / Fractions

    The activity data are simulated by statistical models at ZIP code level which is granular than county-level. For each category, the activity data are aggregated to the county level within the Air District’s jurisdiction to estimate county distribution as shown in the table below.

    ID Description ALA CC MAR NAP SF SM SNC SOL SON
    2761 Pellet Stove 0.32 0.11 0.06 0.05 0.06 0.08 0.13 0.07 0.11
    2762 Wood Stove (except Pellet Stove) 0.16 0.13 0.10 0.09 0.02 0.07 0.13 0.05 0.26
    289 Fireplace 0.15 0.18 0.08 0.05 0.04 0.11 0.17 0.08 0.14

    Emission Factors

    At the time of developing the SFBA inventory, two potential data sources have been evaluated for generating the emission factors (EF) of GHG pollutants as shown in table below.

    Data Source

    Descriptions of EF

    CARB MRR - CARB Mandatory Reporting of Greenhouse Gas Regulation

    • Adopted in CARB’s current AB32 GHG Inventory (CARB, 2023).
    • Incorporated to the current CARB MRR (CARB, 2019) by referencing EFs and HHV in USEPA's MRR promulgated by USEPA and published in the Federal Register by April 25, 2011 (referred to as 2011 USEPA MRR).
    • Reflects energy-based EFs and High Heat Value (HHV) in 2011 USEPA MRR.

    USEPA MRR - USEPA's Final Rule on Mandatory Reporting of Greenhouse Gases

    • Incorporated in the current USEPA's MRR codified in Code of Federal Regulations Title 40 Part 98 (USEPA, 2025).
      • Table C-1 to Subpart C of Part 98—Default CO2 Emission Factors and High Heat Values for Various Types of Fuel
      • Table C-2 to Subpart C of Part 98—Default CH4 and N2O Emission Factors for Various Types of Fuel
    • Reflects changes in HHV and energy-based EFs of CH4 and N2O updated in 2014 USEPA MRR.

    Both data sources include energy-based EFs and a default HHV which can be multiplied to estimate mass-based EFs in gram per ton of wood fuel burned. In fact, they originated from the same source (USEPA MRR) but reflect the changes in EF & HHV from 2011 to 2014 as shown in the following table.

    Pollutant

    CARB MRR

    USEPA MRR

    EF (g/btu)

    HHV (btu/ton)

    EF (g/ton)

    EF (g/btu)

    HHV (btu/ton)

    EF (g/ton)

    CH4

    0.000032

    15,380,000

    492.16

    0.0000072

    17,480,000

    125.86

    N2O

    0.0000042

    15,380,000

    64.6

    0.0000036

    17,480,000

    62.93

    CO2_bio

    0.0938

    15,380,000

    1,442,644

    0.0938

    17,480,000

    1,639,624

    By comparing the two datasets and consulting with CARB’s AB 32 GHG Inventory team, it is found that:

    • The HHV from the latest USEPA MRR is based on dry mass while the one from CARB MRR (or 2011 USEPA MRR) is adjusted from dry basis HHV to a wet basis HHV assuming that the moisture content of wood fuel is 12%.
    • Fo CO2_bio, the higher HHV in the latest USEPA MRR results in a 14% higher mass-based EF.
    • For CH4, the mass-based CH4 EF derived from the latest USEPA MRR is much lower (-74%) than that from CARB MRR due to significant decrease in energy-based EF (-78%) and the use of dry basis HHV.

    For this version of SFBA GHG Inventory, the EFs from CARB MRR have been adopted for the following considerations:

    • It is consistent with the current CARB MRR and CARB's AB32 GHG Inventory. The CARB GHG Inventory team has mentioned that the current GHG Inventory uses data from CARB MRR, which currently references the older version of 40 CFR Part 98 (2011 USEPA MRR). This assumption keeps the CARB GHG inventory in agreement with the current CARB MRR. The CARB GHG inventory team is not actively planning to update it to the latest version.
    • Staff in the Air District do not have representative HHV or moisture content values for Bay Area Counties. However, the value (12%) used in CARB's MRR seems reasonable as the moisture content of seasoned firewood is less than 20% (BAAQMD, 2017).
    • The difference in EFs and HHVs has little impact on overall GHG inventory as the residential wood burning GHG emissions has little contribution to the regional GHG inventory and its dominant GHG, CO2_bio, is not included in the regional inventory but tracked separately.

    Control Factors / Emission Controls

    In July 2008, the District enacted Regulation 6 Rule 3 (Rule 6-3) to control emissions from wood-burning devices (BAAQMD, 2008). This rule limits emissions of particulate matter (PM) and visible emissions (VE) from wood-burning devices, including any wood-burning device, pellet-fueled wood heater or any indoor permanently installed device burning solid fuel for aesthetic or space-heating purposes which includes fireplaces. Though the rule aims to reduce PM emissions, it has co-benefits of reducing GHG emissions as it prohibits wood burning activity on Spare the Air days during the wintertime (November - February). In October 2015, the District amended the rule to prohibit the installation of indoor wood burning devices in new building construction starting November 1, 2016 (BAAQMD, 2015). In November 2019, the District amended the rule further to extend the periodic wood burning bans on STA days from wintertime only to year-round (BAAQMD, 2019).

    The control factors default to 1.0 across all residential wood burning categories reflecting no additional emission controls have been applied locally assuming that the reduction of GHG emissions over time is reflected in the reduction in number of actively used devices, consumption of fuel, and/or changes in the fleet mix (i.e., stove certification %), all of which are captured by the annual wood burning activity data predicted by the statistical models.

    Historical Emissions

    The STA survey started in the winter of 2005/2006 while the necessary Census housing stock data at ZACT level is available for 2010 and onward. For the years 1990-2009, the GHG emissions have been backcasted with a category-specific, activity-based growth profile adopted from previous regional inventory (BAAQMD, 2023).

    Future Projections

    For the years 2024–2050, GHG emissions are forecasted, assuming that there is no growth, i.e., same as the year 2023. This assumption is based on the District’s ban on the installation of indoor woodburning devices in new building construction since November 2016 according to the District’s Rule 6-3.

    Sample Calculations

    The table below shows an example calculation for calculating base year 2022 CH4, N2O and CO2_bio emissions from fireplaces (category 289) in Alameda County.

    Step 1

    Annual county throughput (ton/yr)

    9,813.222

    Step 2

    Heat content

    (Btu/ton, 12% of fuel moisture)

    15,380,000

    CH4

    N2O

    CO2_bio

    Step 3

    Emission Factor (g/Btu)

    0.000032

    4.2E-06

    0.0938

    Step 4

    Emissions, in mass (ton/yr)

    9,813.222

    × 15,380,000

    × 0.000032

    ÷ 907,185

    = 5.323804

    9,813.222

    × 15,380,000

    × 4.2E-06

    ÷ 907,185

    = 0.698749

    9,813.222

    × 15,380,000

    × 0.0938

    ÷ 907,185

    = 15,605.4

    Step 5

    GWP

    34

    298

    1

    Step 6

    GHG Emissions (MMTCO2eq)

    5.323804

    × 34

    × 0.907185

    ÷ 1,000,000

    = 0.000164

    0.698749

    × 298

    × 0.907185

    ÷ 1,000,000

    = 0.000189

    15,605.4

    × 1

    × 0.907185

    ÷ 1,000,000

    = 0.014157

    Assessment of Methodology

    Routine updates to the residential wood burning GHG inventory include incorporating the county-level throughput data for each year not covered in the previous iteration, updating EFs and GWPs for each greenhouse gas to match the most recently published IPCC assessment report. Additional updates to the inventory for the base year 2022 include adopting energy-based emission factors and heat content consistent with CARB’s AB 32 GHG Inventory.

    Base Year

    Revision

    Reference

    2022

    1. Incorporated throughput data estimated with a suite of statistical models based on the multi-year STA survey data and Census data on housing stock.
    2. Split woodstoves (category 288) into two categories: pellet stoves (category 2761) and non-pellet woodstoves (category 2762).
    3. Aligned the GHG emissions methodology to be consistent with CARB AB 32 GHG Inventory by using the same energy-based EFs and HHV adopted from the current CARB MRR.
    1. BAAQMD, 2024
    2. BAAQMD, 2024
    3. CARB, 2023

    2015

    1. Updated base year activity data to include activity for 2012-2015, which were extrapolated from annual activity of historical years.
    1. BAAQMD, 2023

    2011

    1. Updated base year and historical activity data to include annual activity for 2009-2011 by county estimated using statistical estimation methods based on the District’s STA survey starting from the winter of 2005/2006.
    2. Assuming the density of wood burned is five pounds per unit of log.
    3. GHG emission factors are obtained from the Department of Energy’s (DOE’s), Energy Information Administration (EIA).
    1. BAAQMD, 2014
    2. BAAQMD, 2014
    3. BAAQMD, 2014

    Emissions

    The table below shows the total GHG emissions by pollutant in metric tons of CO2 equivalents (MTCO2eq) for residential wood burning in 2022.

    ID Description CH4 CO2_bio N2O Total
    289 Fireplace 1105.2 95280.7 1271.3 97657.2
    2762 Wood Stove (except Pellet Stove) 657.6 56697.8 756.4 58111.8
    2761 Pellet Stove 138.3 11912.1 159.0 12209.4

    Summary of Base Year 2022 Emissions

    Although it is a significant source of PM2.5, residential wood burning contributes much less GHG emissions. The tables below show the contribution of residential wood burning GHG emissions to the overall regional total (0.006%) and to the Commercial and Residential sector (0.03%) in 2022. Please note that CO2_bio emissions are excluded in the following tables and tracked separately.

    Contribution of Residential Wood Burning Emissions by Sector
    Subsector Sector Subsector GHG Emissions (MMTCO2eq) Sector GHG Emissions (MMTCO2eq) % of Sector
    Residential Wood Burning Commercial + Residential 0.004 12.85 0.03%

    Contribution of Residential Wood Burning Emissions to Regional Total
    Subsector Subsector GHG Emissions (MMTCO2eq) Regional Total GHG Emissions (MMTCO2eq) % of Regional Total
    Residential Wood Burning 0.004 65.68 0.006%

    Trends

    The time series chart below shows the emission trends for residential wood burning categories.

    Summary of Trends

    Emissions and activity associated with residential wood burning have steadily increased from 1990 to 2007 in SFBA reflecting the rising activity level due to the growth in population and new housing units. From 2008 to 2023, emissions appear to have declined steadily and substantially, in large part due to steady and substantial decreases in activity. An observed decrease in the presence of actively used wood-burning devices in Bay Area housing stock is qualitatively consistent with the adoption of District’s Rule 6-3 and its 2015 and 2019 Amendments. To an unknown degree, the recent decrease in fuel consumption may also or instead reflect secular trends among Bay Area residents in terms of behavior (i.e., simply choosing to burn less wood) or infrastructural changes in terms of device prevalences and types (for example, substituting natural-gas fireplaces for wood burning fireplaces in remodels or new construction). Though staff believe the emissions will continue to decrease due to phase-out of older devices with less energy efficiency and more GHG emissions, dismantling of fireplaces due to house renovation, and the extension of periodic wood burning bans on STA days to year around, they have assumed zero growth for years 2024 and onward.

    Uncertainties

    The uncertainties of GHG emissions of residential wood burning categories are introduced by the underlying activity estimates and emissions factors. Table A-1 in the Appendix identifies various places where uncertainty appears and how it is handled within the Bayesian framework. Additional aspects of uncertainty are elaborated below.

    • Though the multi-year survey data is a practical way to understand the real-world winter wood burning activity in SFBA, survey data in general have limitations. To date, the Spare the Air survey has sampled about 1% of homes in the Bay Area. The approach described above (post-stratification) corrects for some response biases. No approach can account for unmeasured factors that may lead to bias. For example, it may be that something about non-response rates is changing over time in a way that is leading to over-estimation or under-estimation of trends, but the survey is not asking questions that could detect it. Improvements have been made over time to the survey sampling, which is now based on addresses rather than telephone numbers and administered in more than one language.
    • Model-building introduces uncertainty. Reasonable alternatives for the structure of some of the sub-models (for example, a structure that allows for non-zero asymptotic behavior) could be more accurate descriptors of reality. A different combination of terms could result in better predictions. However, the set of survey questions that have been asked over time, and the amount of observations, limit the data budget. A decision was made to spend the budget in a reasonably balanced way on both (a) spatial variation and (b) adjusting for secular trends, given the effort’s other aim of predicting fine-scale spatial variation for pollutants, like PM2.5, whose spatial variation matters more (for air quality modeling) than it does for a GHG inventory.
    • Conditional on the models’ specification being correct (or approximately so), predictive uncertainty is well captured, quantified, and propagated in the models’ predictions of activity, as explained throughout the text above.
    • Forecasts and backcasts (section “Trends” above) are independent of the 2011–2023 statistical modeling; no conceptual consistency has been enforced. Uncertainty in the future projection of “no growth” is not quantified or quantifiable.
    • The conversion factor of “5 pounds per log” is based on a rather dated precedent and the number does not have any associated uncertainty.
    • Questions about “number of logs” may not be translating well to pellet-stove users.
    • Different wood types have different energy and emission factor characteristics, but the mix of wood types being combusted in the Bay Area has not been well studied.
    • Survey respondents are asked about their “winter” activity. We interpret this as Nov–Feb, but survey respondents may have different interpretations.
    • The CH4 and N2O emissions could be lower if switched to the emission factors published in the latest USEPA MRR.
    • The mass-based emission factor of CO2_bio highly depends on the moisture content assumed. Though 12% is within the typical range of firewood, it seems to be on the lower bound which leads to lower CO2_bio emissions.

    However, these uncertainties have negligible impact on the regional GHG inventory considering the contribution of residential wood burning GHG emissions to the regional total.

    Contact

    Authors: Yuan Du and David Holstius

    Reviewer: Abhinav Guha

    Last Update: 08/27/2025

    References

    BAAQMD. 2008. Regulation 6 Particulate Matter and Visible Emissions Rule 3 Wood-Burning Devices. Available in District archive per request. Bay Area Air Quality Management District.

    BAAQMD. 2014. BY2011 Methodology. 7.3 Domestic Solid Fuel (Wood). Last updated in January 2014. Bay Area Air Quality Management District. https://baaqmd.github.io/BY2011-methodology/07-03-SS-combust-domestic-wood.html

    BAAQMD. 2015. Regulation 6 Rule 3: Wood-burning Devices - 2015 Amendment. Bay Area Air Quality Management District. https://www.baaqmd.gov/en/rules-and-compliance/rules/reg-6-rule-3-woodburning-devices?rule_version=2015%20Amendment

    BAAQMD. 2017. Frequently Asked Questions Regulation 6, Rule 3 Wood-Burning Devices Updated – October 2017. Bay Area Air Quality Management District. https://www.baaqmd.gov/~/media/files/compliance-and-enforcement/wood-burning/faqs-_10_1_2015-final-pdf.pdf

    BAAQMD. 2019. Regulation 6 Rule 3: Wood-burning Devices - 2019 Amendment (Current). Bay Area Air Quality Management District. https://www.baaqmd.gov/en/rules-and-compliance/rules/reg-6-rule-3-woodburning-devices?rule_version=2019%20Amendment

    BAAQMD. 2023. Base Year Inventory Methodology. 7.3 Domestic Solid Fuel (Wood). . Bay Area Air Quality Management District. Last updated on November 06, 2023. https://baaqmd.github.io/BY2015-methodology/07-03-SS-combust-domestic-wood.html

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    USEPA. 2025. USEPA’s Final Rule on Mandatory Reporting of Greenhouse Gases (USEPA MRR). United States Environmental Protection Agency. https://www.ecfr.gov/current/title-40/chapter-I/subchapter-C/part-98