Visitors   Views   Downloads

California air resources board forest carbon protocol invalidates offsets

View article
'Direct measurement forest carbon protocol: A commercial system-of-systems to incentivize forest restoration and management' has been accepted by @thePeerJ. CO2 flux data & comparison with CARB forest carbon protocols are reported to assist landowners. https://t.co/yR5LjwlMDS https://t.co/2vEwF17FLX
'Direct measurement forest carbon protocol: A commercial system-of-systems to incentivize forest restoration and management' has been accepted by @thePeerJ. CO2 flux data & comparison with CARB forest carbon protocols are reported to assist landowners. https://t.co/yR5LjwlMDS https://t.co/haV7rBkVDL
'Direct measurement forest carbon protocol: A commercial system-of-systems to incentivize forest restoration and management' has been accepted by @thePeerJ. CO2 flux data & comparison with CARB forest carbon protocols are reported to assist landowners. https://t.co/yR5LjwlMDS https://t.co/nCmkk2CBHM
'Direct measurement forest carbon protocol: A commercial system-of-systems to incentivize forest restoration and management' has been accepted by @thePeerJ. CO2 flux data & comparison with CARB forest carbon protocols are reported to assist landowners. https://t.co/yR5LjwlMDS https://t.co/o04ZqHcLNj
'Direct measurement forest carbon protocol: A commercial system-of-systems to incentivize forest restoration and management' has been accepted by @thePeerJ. CO2 flux data & comparison with CARB forest carbon protocols are reported to assist landowners. https://t.co/yR5LjwlMDS https://t.co/FUuZVS3wlE
'Direct measurement forest carbon protocol: A commercial system-of-systems to incentivize forest restoration and management' has been accepted by @thePeerJ. CO2 flux data & comparison with CARB forest carbon protocols are reported to assist landowners. https://t.co/yR5LjwlMDS https://t.co/Om41CpBuaA
The article 'California air resources board forest carbon protocol invalidates offsets' was one of the top 5 most viewed #BiosphereInteractions #ClimateChangeBiology #Forestry and #SoilScience articles published in @thePeerJ journal in 2019!
The article 'California air resources board forest carbon protocol invalidates offsets' was one of the top 5 most viewed #BiosphereInteractions #ClimateChangeBiology #Forestry and #SoilScience articles published in @thePeerJ journal in 2019!
The article 'California air resources board forest carbon protocol invalidates offsets' was one of the top 5 most viewed #BiosphereInteractions #ClimateChangeBiology #Forestry and #SoilScience articles published in @thePeerJ journal in 2019!
The article 'California air resources board forest carbon protocol invalidates offsets' was one of the top 5 most viewed #BiosphereInteractions #ClimateChangeBiology #Forestry and #SoilScience articles published in @thePeerJ journal in 2019!
The article 'California air resources board forest carbon protocol invalidates offsets' was one of the top 5 most viewed #BiosphereInteractions #ClimateChangeBiology #Forestry and #SoilScience articles published in @thePeerJ journal in 2019!
The article 'California air resources board forest carbon protocol invalidates offsets' was one of the top 5 most viewed #BiosphereInteractions #ClimateChangeBiology #Forestry and #SoilScience articles published in @thePeerJ journal in 2019!
The Word Nobody Wanted to Say at the UN Climate Action Summit — ProPublica https://t.co/aBVhVBxP19 Carbon offsets may be worthless without verification by direct measurement. California air resources board forest carbon protocol invalidates offsets https://t.co/yR5LjwlMDS
The Word Nobody Wanted to Say at the UN Climate Action Summit — ProPublica https://t.co/aBVhVBxP19 Carbon offsets may be worthless without verification by direct measurement. California air resources board forest carbon protocol invalidates offsets https://t.co/yR5LjwlMDS
The Word Nobody Wanted to Say at the UN Climate Action Summit — ProPublica https://t.co/aBVhVBxP19 Carbon offsets may be worthless without verification by direct measurement. California air resources board forest carbon protocol invalidates offsets https://t.co/yR5LjwlMDS
The Word Nobody Wanted to Say at the UN Climate Action Summit — ProPublica https://t.co/aBVhVBxP19 Carbon offsets may be worthless without verification by direct measurement. California air resources board forest carbon protocol invalidates offsets https://t.co/yR5LjwlMDS
Lack of direct measurement for REDD+ programs is subject to review and verification. Can we afford to get carbon accounting wrong?  California air resources board forest carbon protocol invalidates offsets https://t.co/yR5LjwlMDS https://t.co/z54qIyRq3c
Lack of direct measurement for REDD+ programs is subject to review and verification. Can we afford to get carbon accounting wrong?  California air resources board forest carbon protocol invalidates offsets https://t.co/yR5LjwlMDS https://t.co/2ErUULwDpg
185 days ago
RT @pemcarbon: Forest carbon offset protocol invalidation risk extends to vendors & policy platforms such as the United Nations Program on…
RT @pemcarbon: Forest carbon offset protocol invalidation risk extends to vendors & policy platforms such as the United Nations Program on…
RT @pemcarbon: Forest carbon offset protocol invalidation risk extends to vendors & policy platforms such as the United Nations Program on…
RT @pemcarbon: Forest carbon offset protocol invalidation risk extends to vendors & policy platforms such as the United Nations Program on…
Forest carbon offset protocol invalidation risk extends to vendors & policy platforms such as the United Nations Program on Reducing Emissions from Deforestation and Forest Degradation (REDD+) & the Paris Agreement. CARB protocol invalidates offsets https://t.co/yR5LjwlMDS https://t.co/yq1LkNQbeE
Involved in forest carbon?  Learn about invalidation risk- California air resources board forest carbon protocol invalidates offsets-https://t.co/8LiL6dMgPR. PeerJ Video Abstract https://t.co/0MTP4FmvEa
RT @pemcarbon: Forest carbon offset protocol invalidation risk extends to vendors & policy platforms such as the United Nations Program on…
Environmental Science
  1. February 17, 2020: Minor Correction: The authors have supplied an updated Supplemental Information 10 file. Howland forest carbon offset data (CAR681), reported in the authors' initial analysis, are updated with new offsets (CAR1161). The authors report that new data are inconsistent with previously published records and that the combined data are shown to be invalid based on comparison with known values for forest carbon sequestration and related criteria.
  2. View the video abstract & interviews with the authors for exclusive insights.
Note that a Preprint of this article also exists, first published June 13, 2019.

Introduction

We review and analyze California Air Resources Board (CARB) protocols representing net annual forest carbon sequestration (Marland et al., 2017) to characterize carbon accounting uncertainty, and ultimately how well the protocol reflects actual net forest carbon sequestration compared to independent and direct measurement of carbon. The CARB protocols must quantify net carbon sequestration as real, additional, permanent, verifiable, and enforceable (California Air Resources Board, 2011; California Air Resources Board, 2014; California Air Resources Board, 2015b), a requirement to ensure that erroneous offsets (e.g., Air Resources Board Offset Credits, or, ARBOC’s) do not compromise the integrity of California’s AB32, and its successor AB398, mandatory cap on emissions (Nunez, 2016; Schatzki, Stavins & Hall, 2018). The CARB issues carbon offsets based on determination of net forest carbon sequestration according to CARB and Climate Action Reserve (CAR) protocols (CARB-CAR); ∼123 million metric tons of CO2 equivalent (CO2e)1 have been issued since 2006 (California Air Resources Board, 2018a). Forest offsets account for up to ∼79% of CARB offsets issued, emphasizing the importance of their validation (California Air Resources Board, 2018a). The CARB-CAR compliance rules stipulate invalidation criteria for offsets that exceed 5% of actual net forest carbon (e.g., overcrediting) for a period of up to eight years (California Air Resources Board, 2015b; California Air Resources Board, 2015a) and correction of errors due to material misstatement (e.g., percentage error; California Air Resources Board, 2013b)2 ; these criteria have not been widely applied to CARB-CAR offsets. The CARB-CAR protocols and resulting registered offsets have not been independently validated approximately thirteen years since California passed the Global Warming Solutions Act (AB32) in 2006 with the goal of reducing its greenhouse gas (GHG) emissions to 1990 levels by 2020 (Nunez, 2016). Moreover, the lesson learned from false claims of emission reduction by the automotive industry is that all commercial claims of emission reduction must be verified by independent direct measurement (Li et al., 2018a) to ensure product value and integrity.

We address the validation gap by comparing CARB-CAR estimated results (e.g., CO2 is not observed directly) with a global database of in situ measured net forest carbon sequestration flux, or net ecosystem exchange (NEE) (Baldocchi, Chu & Reichstein, 2018; Baldocchi & Penuelas, 2019; FLUXNET, 2019). Three assumptions of the CARB-CAR protocol are evaluated in this review: (1) offsets represent complete forest carbon accounting, (2) results are compliant with CARB-CAR 5% invalidation rule for overcrediting, and (3) results reflect actual net annual forest carbon ecosystem dynamics resulting in verified forest carbon financial products. We present data for selected projects, such as the Howland Forest, Maine, USA, the only location where both methods were applied, and for statistical tests to compare CARB-CAR populations with directly measured forest carbon sequestration population data obtained in the field by the NEE method. The results characterize the uncertainty, accuracy and precision (e.g., interannual variance) of the CARB-CAR protocols, their effect on pricing of forest carbon products and their impacts on carbon market integrity. Protocols related to the CARB-CAR methodology are also assessed for uncertainty and impact on forest carbon markets.

Literature Survey, Data Sources and Methods

CARB-CAR

The CARB-CAR population data used in this study represent 63 sites covering 340 site years, primarily located in the US temperate zone, and have been assigned CAR serial numbers to project offsets (units: gC m−2yr) (Table 1, Supplemental Informations 1,37 for site information and data). The CARB-CAR projects listed in Table 1 were sourced from the California Air Resources Board and the California Environmental Protection Agency website pages as noted for “Early Action Projects” (California Air Resources Board, 2015c) and as ARBOC’s issued as of 09-01-2018, and must adhere to CARB-CAR forest management protocols. Forest project data were accessed through links to a Climate Action Reserve project identification number (CAR#) providing a project summary page, a document summary page and a cumulative performance report page. The data used in this analysis was sourced directly from the Cumulative Performance Report page and from the column of Verified GHG Reductions for each year of each project or as otherwise reported on the project page when a Cumulative Performance Report was not available (Table 1). We reviewed CARB-CAR registry records and history for each project noting discretionary inconsistencies in protocol reporting and process (Table 1). The CARB protocols and accounting requirements are described in three related primary documents published in 2011 (California Air Resources Board, 2014), 2014 (California Air Resources Board, 2014) and 2015 (California Air Resources Board, 2015b) entitled: “Compliance Offset Protocol U.S. Forest Projects”. The underlying equations for the CARB-CAR protocol are reviewed (Supplemental Information 8) to identify carbon accounting terms and to establish similarities with related protocols including the American Carbon Registry (ACR) (Winrock International, 2016), the Clean Development Mechanism (CDM) (Warnecke, 2014) and the Verified Carbon Standard (VCS) (Verified Carbon Standard, 2018). The CARB-CAR protocols employ limited forest mensuration practice (e.g., forest survey every six years or longer, up to 12 years; California Air Resources Board, 2015b; Marland et al., 2017), vegetation proxies (Cawrse et al., 2009), estimated baselines and growth simulation models (California Air Resources Board, 2011; Climate Action Reserve, 2018b) to determine net forest carbon sequestration. Direct measurement of CO2 in the field does not occur at any point of the protocol process. Additional details are available in the endnotes.3

NEE

NEE population data, described by Baldocchi, Chu & Reichstein (2018), and Baldocchi & Penuelas (2019), herein referred to as NEE1 and NEE2 (units: gC m−2yr−1), respectively, were employed to assess validity of CARB-CAR results representing direct measurement of CO2 flux. The NEE1 population data represent 59 eddy covariance tower sites, primarily within the US and Canada, with a total of 540 site years. The NEE1 project annual data, employed for the analyses presented, was sourced from each of the NEE1 site references and cross-checked with the NEE1 published data for each site. (see Supplemental Information 1, 37 for site information and data) and utilized for comparison with CARB-CAR project interannual data. NEE is also expressed as Net Primary Productivity (NPP) where the term—NEE is employed (Chapin et al., 2006). NEE is a meteorologically based direct measurement method, deployed in the field, integrating all forest CO2 fluxes (e.g., above and below ground) and is methodologically independent of the model and estimation-based CARB-CAR protocol. We provide a graphical interpretation of the NEE1 data in Supplemental Information 9, illustrating the relationship and relative magnitude for NEE (open rectangle, right axis), GPP and Reco (open circles), filled and gray rectangle symbols represent NEE1 data for the Howland Forest, Maine, USA (Ho-1), and the Wind River (Wrc), Washington, USA, sites, respectively, illustrating overlapping and narrow ranges for diverse forests (e.g., west coast redwoods versus east coast hardwoods). NEE, GPP and Reco values falling outside of the known ranges likely do not reflect natural and managed forest ecosystems and are a sign of inaccurate estimates and a basis for invalidation. NEE2 data, inclusive of NEE1 data, expand the GPP, Reco and NEE population database for ecologically diverse natural and managed forests across 155 global observation platforms representing 1,163 site years. NEE1,2 data are derived from measurement of gaseous molecular CO2 vertical fluxes (Baldocchi, Chu & Reichstein, 2018; Baldocchi & Penuelas, 2019) and are based on globally applied and standardized eddy covariance methods (Baldocchi, 2003; Baldocchi, 2014) to quantify NEE (Chapin et al., 2006; Luyssaert et al., 2009) as tCO2e or gC m−2yr−1. NEE data, available from the Fluxnet database (Fluxnet, 2019), are routinely employed to test significance of trends for forest growth (Ney et al., 2019), soil and ecosystem respiration (Bond-Lamberty & Thomson, 2010; Göckede et al., 2019), fire disturbance (Rocha & Shaver, 2011) and effects of climate change on diverse ecosystems (Yu et al., 2019), similar to the analytical approach employed in this report. Additional information for NEE projects is described in the endnotes.4

Statistical calculations

Individual annual records (units: gC m−2yr−1) were used in this analysis; trends in time series are not considered. Our analysis is based on population data for NEE, GPP and Reco (NEE1,2) representing primarily diverse temperate zone forest ecosystems (Supplemental Information 9) of the world. The tight coupling between GPP and Reco constrains NEE within relatively narrow boundaries (mean, standard deviation) considering the global spatial and annual time scales reported (Baldocchi & Penuelas, 2019); values falling outside the NEE1,2 boundaries are likely inaccurate, and with additional criteria may be considered invalid. Although only a single location, the Howland Forest, shares application of both NEE and CARB-CAR protocol, an accurate measurement of net carbon sequestration for any location is expected to comply with the NEE, GPP and Reco relationship (Supplemental Information 9, filled and gray rectangle symbols for Howland Forest and Washington, GPP and Reco, respectively). Each data point (whether in NEE or CARB-CAR) comes from the underlying population of the world’s forests (e.g., managed or conserved) providing the basis for the two-sample statistical tests presented. We employ standard statistical tests to compare population characteristics, including the accuracy in means and precision (i.e., interannual range) for the CARB-CAR protocol methods, relative to directly measured NEE1,2 sample annual values and variance. See Supplemental Information 2 for details of the statistical analyses.

Results

Box plots

Figure 1A shows the difference in distribution, measures of central tendency and outliers between the CARB-CAR and NEE1 populations for net forest carbon sequestration. A selected interval of the box plot excluding the CARB-CAR outliers is presented in Fig. 1B to illustrate the population differences (CARB-CAR median of −445.4 gC m−2yr−1 compared to the NEE1 median value of −172.5 gC m−2yr−1 ) corresponding to the larger spread and left-skewness of CARB-CAR values. The sample means and standard deviations (±), for all annual values, Fig. 1A, of the CARB-CAR and NEE1 datasets are, respectively, −948.8 ± 1,504.8 gC m−2yr−1 and −198.0 ± 261.6 gC m−2yr−1 representing an extreme range of ∼5× the value for CARB-CAR forest carbon sequestration mean and ∼6× standard deviation for interannual variance relative to the NEE1 population data (Baldocchi, Chu & Reichstein, 2018). The difference in mean values between the two populations is significant at the 95% confidence level, rejecting the null hypothesis that the CARB-CAR population mean falls within the NEE1 population mean. The mean and standard deviation of the NEE2 data are −156 ± 284 gC m−2yr−1, respectively (155 sites; 1,163 site year), supporting the comparison with CARB-CAR data and ∼6× the CARB-CAR mean and ∼5× the standard deviation characterizing interannual ranges for natural and managed forest ecosystems. NEE1,2 natural variation for global forests across management activities is constrained tightly by GPP and Reco (Supplemental Information 9) providing boundaries for NEE comparisons.

Box plots of annual records from CARB-CAR (340 years, 63 sites) and NEE1 (540 years, 59 sites) projects.

Figure 1: Box plots of annual records from CARB-CAR (340 years, 63 sites) and NEE1 (540 years, 59 sites) projects.

(A) The box plots show the median (white line through each box), the 25th percentile (bottom of lower box), the 75th percentile (top of upper box), the upper and lower whiskers represent the upper and lower values that are not outliers, and outliers (individual closed circles). The CARB-CAR data show outliers exceeding −12,000 gC m−2yr−1. CARB-CAR median is −445.4 gC m−2 yr−1 compared to the NEE1 median value of −172.5 gC m−2 yr−1. The means and standard deviations (±) are, −948.8 ± 1504.8 and −198.2 ± 261.6, for CARB-CAR and NEE1, respectively. (B) Box plots, described as above for CARB-CAR and NEE1 populations with CARB-CAR outliers removed.
Plot of 95% confidence interval for the difference in means between CARB-CAR and NEE1 annual data.

Figure 2: Plot of 95% confidence interval for the difference in means between CARB-CAR and NEE1 annual data.

The combined data set (All Years) consists of 340 CARB-CAR and 540 NEE1 data points. A formula for a large-sample confidence interval (described in Methods) is used for the unfilled bars and no assumption on equal standard deviations between the two data sets has been made. The top and bottom of the open bars represent the range of the difference between the CARB-CAR and the NEE1 means with a 95% confidence level. The filled square symbol below each bar represents the 5% estimation error allowed by CARB-CAR.

Difference between means

Figure 2 shows a plot of the 95% confidence interval for the difference in means between the CARB-CAR and NEE1 population of (Baldocchi, Chu & Reichstein, 2018) annual measurements for all years available and for the selected years of 2007 and 2008. Given the large difference in sample means for the CARB-CAR and NEE1 datasets the true population means may also be significantly different. We test the null hypothesis that the two sample populations were drawn from the same underlying population of annual values for forests. The combined data set consists of 340 CARB-CAR and 540 NEE1 data points (“Total”), each reported as representing an annual cycle determined by each protocol. A formula for a large-sample confidence interval is used for the bar labeled “Total”; no assumption on equal standard deviations between the two data sets has been made. Amongst the years with overlapping data (2001–2014), we choose 2007 and 2008, as they have the largest number of combined sample points, 65 in 2007 (23 for ARB and 42 for NEE1) and 65 in 2008 (24 for ARB and 41 for NEE1), excluding initial year values. The null hypothesis that the two data sets come from the same population is rejected implying that CARB-CAR protocols reflect anomalous values and cannot be relied upon to manage forest carbon sequestration. The 5% estimation error does not overlap with the 95% confidence interval demonstrating that the CARB-CAR estimates are more than the allowed 5% from the NEE1 population values. The standard deviation for the CARB-CAR data is very large compared to the NEE1 standard deviation, irrespective of the year. For example, in 2008, the standard deviations for CARB-CAR and NEE1 were respectively, 1,170 and 255 gC m−2yr−1, a ∼5× over-estimation difference for interannual NEE1 values. This leads to a very wide confidence interval that also establishes that the CARB-CAR project data are invalid based on the permitted 5% compliance margin of error. To our knowledge, CARB-CAR compliance testing of project results has not been reported employing directly measured forest CO2.

Time interval plot of CARB-CAR data (open bars) from 2002 to 2015 and NEE1 measurements (filled bars) from 1992 to 2015.

Figure 3: Time interval plot of CARB-CAR data (open bars) from 2002 to 2015 and NEE1 measurements (filled bars) from 1992 to 2015.

Values are plotted as positive numbers representing net sequestration of carbon. The averages for the two data sets are shown by each bar representing forest carbon sequestration calculated annually over all available locations. The error bars represent 5% of the CARB-CAR year for 2006 and applied to all CARB-CAR project annual averages.

Project annual interval plot

Figure 3 shows a time interval plot of CARB-CAR annual data from 2002 to 2015 and NEE1 (note: NEE values are illustrated as positive for the purposes of this graph) annual measurements (Baldocchi, Chu & Reichstein, 2018) from 1992 to 2015 to further test the invalidation of CARB-CAR offsets according to the 5% invalidation compliance rule. The averages for the two data sets are shown by each vertical bar representing forest carbon sequestration calculated annually over all available locations. The selected intervals are absent first year data for the CARB-CAR population to present a conservative case for testing the null hypothesis. The year 2006 was selected, in which the largest average carbon offset by CARB-CAR sites (n = 12) has been recorded, namely −2,038 gC m−2yr−1, and apply the corresponding 5% admissible error of 101.9 gC m−2yr−1 to all CARB-CAR years, shown as error bars for each CARB-CAR year. No intersection between the admissible interval and the actual NEE1 measurements for any of the overlapping years (2001–2015) is observed. The NEE1 averages through the interval are consistent relative to the large year-to-year fluctuations observed for the CARB-CAR dataset. The null hypothesis is rejected; the CARB-CAR data are invalid by exceeding the 5% compliance threshold for the years represented in this analysis. The absence of negative values, in this case representing CO2 emissions to the atmosphere, implies that the CARB-CAR protocol is biased against detection of a switch from net sequestration to net positive emissions, arguably a criterion for invalidation.

p-values table

Table 2 shows the results of a null hypothesis that the difference between the CARB-CAR and the NEE1 annual means is under the allowed 5% threshold. A year-by-year detailed comparison between the population data sets to further test the 5% invalidation threshold for the CARB-CAR data is presented. The test is performed separately for all years between 2002 and 2015; p-values are recorded in the last two rows of Table 2. The p-values range from 0.00 to 0.065. Typically, p-values ≤5% demonstrate a rejection of the null hypothesis. The results reject that the estimation error is within the allowed 5% value, with three exceptions to the 14-year record. In the case of years 2004, 2013 and 2014, the p-values are higher than 5% (i.e., 6.53%, 5.48% and 5.24%). However, in these cases (i.e., 2004; p = 0.065, 2013; p = 0.055, 2014; p = 0.052) the probability that the CARB-CAR data were not out of the norm is only 1.87 × 10−4%, supporting the null hypothesis rejection. Variation in annual p-values is expected according to the number of data points included per year represented as overlapping intervals.

Howland forest survey protocol comparison

The Howland Research Forest Carbon Project (CAR681) represents the only case in which CARB-CAR and Ameriflux (Ho-1) NEE data are available for the same project location, land area (552 acres; 233.4 hectares), and across shared time intervals (2003–2013) allowing direct comparison of results. Comparability of the Ho-1 NEE1 and CAR681 data is established by the location of two eddy covariance towers within the CARB-CAR survey areas. Half-hourly and integrated annual carbon exchange values recorded at the separate Howland towers were similar, with average annual net carbon uptake differing between the two towers by <6% (Hollinger et al., 2004; Hollinger et al., 2013; Richardson et al., 2019) (see Supplemental Information 10 for project area map, sampling locations and CAR681 webpage offset data; Table 1).

Table 1:
CARB-CAR site locations, links to online data source and anomalous features.
Climate action reserve # Successor climate action reserve # ARB project ID # Project name & location Longitude Latitude Functional type Type of protocol Acres** Hectares Project interval (vintage years) Project management Offsets issued with serial numbers Cumulative performance report Anomalous features* Soil carbon status
1 CAR1 01 NA CAFR0049 The Van Eck Forest (Humboldt County, CA) −124.08 40.87 Northern California Coast (Coast Redwood/Douglas Fir Mixed Conifer) & Southern Cascades (Southern Cascade Mixed Conifer) Early Action 2,104 851 2001–2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=101&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%201011 ˆˆ(2008) to 2001; ## (2008) to 2014 %%Reporting Year 2006, Soil Carbon Pool tCO2e = 0
2 CAR1 02 CAR1098 CAFR0040 Garcia River Forest (Mendocino, CA) −123.51 38.91 Temperate coniferous Early Action 23,780 9,623 2005–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=102&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%201023 ˆˆ(2007) 2007–2004; ##(2007) 2008–2014 %%Reporting Year 2010, Soil Carbon Pool Excluded from carbon pool calculations
3 CAR4 08 CAR1100 CAFR0041 Big River/Salmon Creek Forests (Mendocino, CA) −123.67 39.301 Temperate coniferous Early Action 15,911 6,439 2007–2017 IFM https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20408 https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20408 ##(2007) 2007–2014; $2012 %%2007 Project Submittal Form, Soil Carbon Excluded
4 CAR4 30 NA CAFR0073 McCloud River (McCloud, CA) −122 41.2 Temperate broadleaf and mixed Early Action 9,200 3,723 2006–2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=429&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20429 ## (2006) 2006 to 2014 %%2009 Project Submittal Form, Soil Carbon N/A
5 CAR4 30 NA NA RPH Ranch (Comptche, Mendocino County, CA) −123.59 39.26 Temperate coniferous Early Action 106 43 2010 IFM https://thereserve2.apx.com/mymodule/reg/TabDocuments.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=doc&id1=430 https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20430 ##(2002) 2002 to 2010; $$(00.00) 2002 to 2009 %%2008 Project Submittal Form, Soil Carbon N/A
6 CAR4 97 NA CAFR0029 Blue Source—Alligator River (Hyde County, NC) −76.031 35.631 Northern Atlantic Coastal Swamp Hardwoods, Cypress Early Action 2,272 919 2010–2017 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=497&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20497 $(2011) %%Project Design Document, Section 15. Carbon Stock Inventory, Soil Carbon Excluded
7 CAR5 75 NA NA Arcata Sunnybrae Tract (Humbodlt County, CA) −124.05 40.864 Northern California Coast (Coast Redwood/Douglas Fir Mixed Conifer) & Southern Cascades (Southern Cascade Mixed Conifer) Not Eligible 171 69 2006–2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=575&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20575 ˆˆ(2012) 2012 to 2006; $(2006) one month %%Project Design Document, 4. Onsite Carbon Inventory Methodology
8 CAR5 82 CAR1130 CAFR0103 Finite Carbon—MWF Brimstone IFM Project I (Scott County, TN) −84.455 36.272 Mixed Oak Early Action 4,861 1,967 2007–2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=582&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20582 ˆˆ(2013) 2013 to 2007; $(2007) ∼3 months; $(2013) ∼8 months; $(2015); %(2015) %%Project Design Document, Item 23. Soil Carbon Pool Absent; Table 23, soil carbon absent, Table 10, soil carbon absent
9 CAR5 90 NA NA Lompico Forest Carbon Project (Santa Cruz County, CA) −122.04 37.13 Temperate coniferous Temperate broadleaf, mixed Coastal Redwood forest Not Eligible 425 172 2010–2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=590&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20590 $$(1074.00) 2010 to 2012 %%Project Design Document, Section 3. Onsite Carbon Inventory Methodology, soil carbon excluded as an optional carbon pool.
10 CAR6 45 CAR1088 CAFR0080 Finite Carbon—The Forestland Group Champion Property (Franklin, St. Lawrence & Lewis Counties, NY) −75 44.3 Spruce-fir; Pine and hemlock; Northern hardwoods Early Action 100,000 40,469 2009–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=645&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20645 ˆˆ(2012) 2012 to 2009; $(2009) ∼7 months; $(2014, 2015, 2016); %(2014, 2015, 2016); %(2016) 2015, 2016 %%Project Design Document, Table 5. Sources, Sinks, and Reservoirs, IFM-6, Soil Carbon excluded
11 CAR6 46 NA NA Katahdin Iron Works Ecological Reserve (Piscataquis County, ME) −69.17 45.45 Evergreen Needleleaf Forest Not Eligible 10,000 4,047 2007–2012 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=646 https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20646 $(2007) ∼8 months; ˆˆ(2013) 2013 to 2007 %%Project Design Document, Section 3. Inventory Methodology, IFM-4, Soil Carbon excluded
12 CAR6 48 CAR1086 CAFR0047 Finite Carbon—Potlatch Moro Big Pine CE (Calhoun County, AR) −92.54 33.5 Evergreen Needleleaf Forest Early Action 16,000 6,475 2006–2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=648&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20648 $(2006) ∼1 month; ˆˆ(2013) 2013 to 2006; $(2013) ∼7 months; % (2014) 2012, 2013, 2014; $(2014) ∼7 months %%Project t Design Document, Table 7. Sources, Sinks, and Reservoirs, IFM-6, soil carbon excluded.
13 CAR6 55 NA CAFR0105 Alder Stream Preserve (Piscataquis County, ME) −69.015 45.114 Evergreen Needleleaf Forest Early Action 1,460 591 2006–2013 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=655&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20655 $ initial year 1 month, 2006; ##2012, 2014; ##2006 to 2013 %%Project Design Document, Section 3, Inventory Methodology, IFM-6, soil carbon excluded
14 CAR6 57 CAR1063 CAFR0002 Finite Carbon Farm Cove Community Forest Project (Near Grand Lake Stream, Maine) −67.851 45.187 Evergreen Needleleaf Forest Early Action 19,769 8,000 2010–2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=657&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20657 $(2003) <1 month; ˆˆ(2012) 2011 to 2003 %%Project Design Document, Section A13. Carbon Pools,. IFM-6, soil carbon excluded
15 CAR6 58 CAR1134 CAFR0087 Finite Carbon—Brosnan Forest (Near Charleston, SC) −80.45 33.167 Evergreen Needleleaf Forest Early Action 10,209 4,131 2010–2011; 2015–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=658&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20658 #(2010) $(2013) ∼8 months; ˆˆ(2013) 2011 to 2010 %%Project Design Document, Section A13. Carbon Pools,. IFM-6, soil carbon excluded
16 CAR6 59 NA CAFR0026 Blue Source—Pungo River Forest Conservation Project (Washington County, NC) −76.64 35.804 Atlantic Coastal Plain—Swamp Hardwood and Cypress Early Action 704 285 2003–2016 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=659&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20659 $(2003) <1 month; ˆˆ(2012) 2011 to 2003 %%Project Design Document, 11.2.3.4.1 Soil carbon was sampled to establish starting carbon stocks that would be degraded if the baseline scenario was followed, e.g., full conversion to agricultural use. The soil carbon was excluded as source of CO2 over the lifetime of the project (e.g., AC-6).
17 CAR6 60 CAR1099 CAFR0042 Gualala River Forest (Southern Mendocino County, Near Gualala, CA) −123.402 38.796 Coastal Redwood and Douglas Fir Early Action 13,913 5,630 2004–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=660&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20660 ˆˆ(2011) 2010 to 2004; %(2015) 2015, 2016; %(2017) 2016, 2017 %%Project Design Document, Section 7. Summary of the carbon stock inventory for the Forest Project by each pool, soil carbon pool excluded
18 CAR6 61 CAR1140 CAFR0001 Willits Woods (Near Willitis, CA) −123.357 39.411 Coastal Redwood and Douglas Fir Early Action 18,008 7,288 2004–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=661&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20661 ˆˆ(2011) 2010 to 2004 %%Project Design Document, Section 5. Calculation methodologies for determining metric tones per acre for each of the included carbon pools, soil carbon excluded
19 CAR6 72 NA CAFR0116 Hershey Mountain (North of Concord, NH) −71.667 43.567 Adirondacks & Green Mountains Northern Hardwood Early Action 2,141 866 2007–2013 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=672&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20672 #(2007); $(2007) ∼6 months; ˆˆ(2014) 2013 to 2007 %%Project Design Document, Section A13. Carbon Pools, soil carbon FM-6 excluded
20 CAR6 76 NA CAFR0031 Pocosin Lakes Forest Conservation Project (Tyrrell County, NC) −76.209 35.862 Atlantic Coastal Plain, Swamp Hardwood and Cypress Early Action 1,349 546 2003–2012 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=676&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20676 ˆˆ(2012) 2011 to 2003; $(2003) ∼2 months; %%Project Design Document, 11.2.3.4.1 Soil carbon was sampled to establish starting carbon stocks that would be degraded if the baseline scenario was followed, e.g., full conversion to agricultural use. The soil carbon wasexcluded as source of CO2 over the lifetime of the project (e.g., AC-6)
21 CAR6 81 NA CAFR0106 Howland Research Forest (Howland, ME) −68.627 45.246 Red Spruce and Eastern Hemlock Early Action 552 223 2008–2013 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=681&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20681 #(2008); $(2008); ˆˆ(2014) 2013 to 2008; %(2008) 2008, 2008 %%Project Design Document, Section 3 Inventory Methodology, IFM-6, soil carbon excluded
22 CAR6 83 NA CAFR0030 Francis Beidler Project (Berkeley, Dorchester and Orangeburg Counties, SC) −80.358 33.321 Native Hardwoods, Softwoods, Mixed Forest Early Action 5,548 2,245 2007–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=683&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20683 #(2007) ∼5 months; $(2012); ˆˆ(2012) 2012 to 2007; ˆˆ(2015) 2015 to 2012; $(2015); $(2016); $(2017) %%Project Design Document, Section 3, Inventory Methodology, soil carbon excluded
23 CAR6 86 CAR1160 CAFR0058 Virginia Conservation Forestry Program—Clifton Farm (Near Rosedale, VA) −81.86 37.022 Mixed Pine Hardwood, Cove Forests, Oak—Hickory Early Action 4,069 1,647 2004–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=686&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20686 ˆˆ(2012) 2011 to 2014; ˆˆ(2014) (2013 to 2012); $(2016) 2015, 2016 %%Project Submittal Form, Item 10, soil carbon excluded
24 CAR6 88 NA CAFR0028 Blue Source—Noles North Forest Project (Washington and Hyde Counties, NC) −76.548 35.881 Atlantic Coastal Plain, Swamp Hardwood and Cypress Early Action 281 114 2002–2016 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=688&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20688 $(2002) ∼7 months; ˆˆ(2012) 2011 to 2002; $$(6,099.00) 2003 to 2009; $$(5,830.00) 2013 to 2014 %%Project Design Document, 11.2.3.4.1 Soil carbon was sampled to establish starting carbon stocks that would be degraded if the baseline scenario was followed, e.g., full conversion to agricultural use. The soil carbon was excluded as source of CO2 over the lifetime of the project (e.g., AC-6)
25 CAR6 94 NA NA Lucchesi Tract (Humboldt County, CA) −124.064 40.875 Temperate coniferous, Temperate rainforest; Not Eligible 322 130 2010–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=694&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20694 ˆˆ(2012) 2011 to 2001; $$(00.00) 2001 to 2009; $$(2,182.00) 2012 to 2016 %%Project Design Document, Section D. Step 4. Determine the baseline carbon stocks over 100 years for all required and optional carbon pools in the Project Area, soil carbon excluded
26 CAR6 96 CAR1159 CAFR0057 Rich Mountain (Russell & Washington Counties, NW of Saltville, VA) −82.03 36.831 Allegheny & North Cumberland Mountains—Mixed Pine Hardwood, Cove Forests, Northern Hardwoods, Oak—Hickory Early Action 5,750 2,327 2002–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=696&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20696 $(2002) ∼6 months; ˆˆ(2012) 2011 to 2002; ˆˆ(2015) 2014 to 2013; %(2016) 2015, 2016 %%Project Submittal Form, item 10. IFM-6, soil carbon excluded
27 CAR6 97 CAR1147 CAFR0102 Tazewell—Elk Garden (Russell, Washington, and Tazewell Co. near Tazewell, VA) −81.559 37.124 Allegheny & North Cumberland Mountains—Mixed Pine Hardwood, Cove Forests, Northern Hardwoods, Oak—Hickory Early Action 11,697 4,734 2007–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=697&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20697 ˆˆ(2014) 2013 to 2005; %(2014) 2015, 2016; %(2016) 2015, 2016 %%Project Submittal Form, item 10. IFM-6, soil carbon excluded
28 CAR7 30 CAR1139 CAFR0123 Usal Redwood Forest (Mendocino County, CA) −123.847 39.876 Coast Redwood/Douglas-fir Mixed Conifer Early Action 49,000 19,830 2007–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=730&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20730 #(2007); $(2207) ∼6 months; ˆˆ(2015) 2015 to 2007 %%Project Submittal Form, item 10. IFM-6, soil carbon excluded
29 CAR7 49 CAR1109 CAFR0063 Green Assets—Middleton (Charleston, SC) −80.141 32.9 SE Middle Mixed Forest Piedmont Atlantic Coastal Plain & Flatwoods Early Action 3,732 1,510 2007–2017 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=749&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20749 $(2007) <1 month; ˆˆ(2013) 2011 to 2007; $(2011) 2013, 2014; ˆˆ(2014) 2013 to 2011; %(2015) 2014, 2015; %(2016) 2015, 2016; %(2017) 2016, 2017 %%Project Design Document, Section 11.2.3 Data gathering procedures and parameters, AC-6, soil carbon excluded, Table 5, soil carbon emissions excluded
30 CAR7 77 NA CAFR0064 Yurok Tribe Sustainable Forest Project (Northwest Humboldt County, CA) −123.8 41.406 Northern California Coast (Coast Redwood/Douglas Fir Mixed Conifer) & Southern Cascades (Southern Cascade Mixed Conifer) Early Action 21,240 8,596 2011–2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=777&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20777 #(2011) $(2011) ∼8 months; ˆˆ(2014) 2013 to 2012 %%Project Desing Document, Section 3. Inventory Methodology, IFM-6, soil carbon excluded
31 CAR7 80 CAR1062 CAFR0088 Shannondale Tree Farm (Washington County, NC) −91.45 37.367 Atlantic Coastal Plain—Atlantic Coastal Plain Swamp Hardwood and Cypress Early Action 4037 1,634 2010–2013 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=780&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20780 #(2013); $(2013) ∼4 months; ˆˆ(2013) 2011 to 2010; ˆˆ(2015) 2013 to 2012 %%Project Design Document, Section A13. Carbon Pools, IFM-6, soil carbon excluded
32 CAR8 02 NA CAFR0027 Noles South Forest Project (Washington County, NC) −76.548 35.865 Atlantic Coastal Plain—Atlantic Coastal Plain Swamp Hardwood and Cypress Early Action 324 131 2003–2016 AC https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=802&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20802 $(2003) ∼1 month; ˆˆ(2012) 2011 to 2003; $$(5,180.00) 2005 to 2009; $$(5,830.00) 2011, 2012 %%Project Design Document, 11.2.3.4.1 Soil carbon was sampled to establish starting carbon stocks that would be degraded if the baseline scenario was followed, e.g., full conversion to agricultural use. The soil carbon was excluded as source of CO2 over the lifetime of the project (e.g., AC-6)
33 CAR9 35 NA NA Arcata City Barnum Tract (Arcata, CA) −124.049 40.876 Northern California Coast Redwood/Douglas-fir Mixed Conifer Not Eligible 280 113 2003–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=935&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%20935 $(2003) ∼11 months; ˆˆ(2012) 2011 to 2003; $$(2,904.00) 2005 to 2011; $$(2,527.00) 2012 to 2016 %%Project Design Document, Section Step 4. Determine the baseline carbon stocks over 100 years for all required and optional carbon pools in the Project Area, IFM-6. soil carbon excluded
34 CAR1 013 NA CAFR5055 Buckeye Forest Project (Sonoma County, CA) −123.31 38.74 Coast Redwood/Douglas-fir Mixed Conifer and Northern California Coast Mixed Oak Woodland Compliance 19,525 7,901 2014–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1013&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2014); $%(2014); $%(2015); $%(2016); $%(2017); %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
35 CAR1 015 NA CAFR0100 Rips Redwoods (Sonoma County, CA) −123.212 38.711 Coast Redwood/Douglas-fir Mixed Conifer and Northern California Coast Mixed Oak Woodland Early Action 1426 577 2013–2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1015&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%201015 #(2013); $(2013) ∼7 months %%Project Design Document, Section 2.B.B. Carbon Sinks, Sources and Reservoirs, IFM-6, absent
36 CAR1 032 NA CAFR5037 Virginia Highlands I (Russell, Buchanan and Dickenson Counties, VA) −82.347 37.085 oak-hickory, loblolly-shortleaf pine, and mixed oak-pine Compliance 9,753 3,947 2013 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1032&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2013); $(2013) ∼7 months %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
37 CAR1 041 NA CAFR5038 Sacramento Canyon ARB001 (Shasta County, CA) −122.29 41.05 Southern Cascade, Mixed Conifer Compliance 16,941 6,856 2015–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1041&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2015); %(2015) 2013, 2014, 2015; $(2016) 2015, 2016; $(2017) 2016, 2017 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
38 CAR1 046 NA CAFR5076 Trinity Timberlands University Hill Project (Trinity County, CA) −123.5 40.58 ”Northern California Coast (Coast Redwood/Douglas Fir Mixed Conifer) & Southern Cascades (Southern Cascade Mixed Conifer)” Compliance 11,900 4,816 2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1046&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2014); $(2013) ∼10 months; %(2013) 2013, 2014 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable (Attachment E)
39 CAR1 066 NA CAFR5058 Buck Mountain ARB002 (Siskiyou County, CA) −121.85 41.38 Southern Cascade, Mixed Conifer Compliance 12,486 5,053 2015–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1066&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2015); $(2015) ∼9 months; %(2015) 2014, 2015; $(2016) 2015, 2016; $(2017) 2016, 2017 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
40 CAR1 067 NA CAFR5063 Sustainable Mountain (Humboldt County, CA) (near Willow Creek) −123.76 40.91 Douglas Fir Mixed Conifer Compliance 2,112 855 2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1067&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA $(2014) ∼6 months; %(2014) 2013, 2014 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, excluded
41 CAR1 092 NA CAFR5087 Big Valley (Near Aiden, CA) −121.24 41.13 Douglas Fir Mixed Conifer Active 14,622 5,917 2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1092&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2016); %(2016) 2014, 2015, 2016 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
42 CAR1 094 NA CAFR5095 Ashford III (Ashford, WA) −122.04 46.46 Northwest Cascade Mixed Conifer Compliance 5,290 2,141 2014 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1094&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2014); %(2014) 2012, 2013, 2014 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
43 CAR1 095 NA CAFR5096 Brushy Mountain (Mendocino County, CA) −123.26 39.63 Southern Cascade Mixed Conifer Compliance 16,392 6,634 2014-2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1095&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2014); $(2014) ∼8 months; $(2016) 2015, 2016; %(2015) 2014, 2015; %(2016) 2015, 2016; %(2017) 2016, 2017; %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, excluded (Addendum to Listing Application)
44 CAR1 102 NA CAFR5148 Montesol Forest Carbon (Napa and Lake County, CA) −122.564 38.671 Southern Cascade Mixed Conifer Compliance 3,102 1,255 2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1102&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2016); $(2016) ∼7 months %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
45 CAR1 103 NA CAFR5149 Forest Carbon Partners—Glass Ranch Improved Forest Management Project (Humboldt County, CA) −123.644 40.346 Southern Cascade Mixed Conifer Compliance 22,676 9,177 2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1103&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2015); %(2015) 2014, 2015 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
46 CAR1 104 NA CAFR5150 Forest Carbon Partners—Gabrych Ranch Project (Humboldt County and Trinity County, CA) −123.606 40.713 Southern Cascade Mixed Conifer Compliance 4,039 1,635 2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1104&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2015); %(2015) 2014, 2015 %%Application for Listing, Part VII, Carbon Stock Inventory, IMF-6, Not applicable
47 CAR1 114 NA CAFR5114 Crane Valley −123.606 40.713 Southern Cascade Mixed Oak Woodland and Sierra Mixed Oak Woodland Compliance 19,384 7,844 2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=1114 NA #(2016); %(2016) 2014, 2015, 2016 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): N/A
48 CAR1 175 NA CAFR5195 Finite Carbon—Passamaquoddy Tribe (Frankin, Somerset, Penobscot, Hancock, and Washington Counties, ME) −67.63 45.288 New Brunswick Foothills & Lowlands, White Mountains Mixed Hardwoods Compliance 98,492 39,858 2015–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1175&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2015); %(2015) 2014, 2015; %(2016) 2015, 2016; %(2016) 2015, 2016 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
49 CAR1 180 NA CAFR5280 Maillard Ranch (Mendocino County, CA) −123.36 39.92 Temperate coniferous Compliance 12,360 5,002 2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1180&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2016); %(2016) 2015, 2016 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
50 CAR1 183 NA CAFR5283 Forest Carbon Partners-Mescalero Apache Tribe (Otero & Lincoln County, NM) −105.65 33.17 Red Spruce and Eastern Hemlock Compliance 221,822 89,768 2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=1183 NA #(2016); $(2016) ∼10 months; %(2016) 2015, 2016 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable):Not Applicable
51 CAR1 191 NA CAFR5291 Hollow Tree (Mendocino County, CA) −123.782 39.85 Coast Redwood/Douglas-fir Mixed Conifer Compliance 20,295 8,213 2016 IFM https://thereserve2.apx.com/mymodule/reg/TabDocuments.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=doc&id1=1191 NA #(2016); %(2016) 2015, 2016 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Not Applicable
52 CAR1 197 NA CAFR5297 Upper Hudson Woodlands ATP, LP (Warren, Hamilton, Essex, Washington, Saratoga and Fulton, NY) −74.33 43.88 Mixed conifer/mixed hardwood forest Compliance 86,825 35,137 2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1197&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2017); %(2017) 2015, 2016, 2017 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
53 CAR1 204 NA CAFR5304 AMC Silver Lake (Piscataquis & Aroostook Counties, ME) −69.15 45.44 Spruce-Fir and Mixed Hardwood forests Compliance 89,315 36,145 2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1204&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA #(2017); %(2017) 2015, 2016, 2017 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
54 CAR1 209 NA CAFR5309 Wolf River (Antigo, WI) −88.86 45.23 Northern hardwood/mixed conifer forestland Compliance 17,722 7,172 2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=1209 NA #(2017); %(2017) 2015, 2016, 2017 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
55 CAR1 213 NA CAFR5313 MWF Adirondacks (Franklin, St. Lawrence & Lewis Counties, NY) −74.91 44.35 Adirondacks & Green Mountains Northern Hardwood Compliance 100,094 40,507 2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=1213 NA #(2017); %(2017) 2015, 2016, 2017 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
56 CAR1 215 NA CAFR5315 Molpus Ataya (Campbell & Claiborne Counties, TN) −83.89 36.54 Allegheny & North Cumberland Mountains—Mixed Pine Hardwood, Cove Forests, Northern Hardwoods, Oak—Hickory Compliance 26,261 10,627 2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=1215 NA #(2017); %(2017) 2015, 2016, 2017 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
57 CAR1 217 NA CAFR5317 West Grand Lake (Washington County, ME) −67.75 45.23 New Brunswick Foothills & Lowlands, White Mountains Mixed Hardwoods Compliance 19,552 7,912 2015 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=cr&id1=1217 NA #(2015); %(2015) 2013, 2014, 2015 %%PART VII. CARBON STOCK INVENTORY IFM-6 Soil (if applicable): Excluded
58 CAR9 73 NA CAFR5003 Bishop Project (Near Bessemer, MI, and other locations) −87.852 46.5620 Tree cover, broadleaved, deciduous, closed to open (>15%) NA 2,112.86 855.044862 2013–2016 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=973&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub https://thereserve2.apx.com/myModule/rpt/myrpt.asp?r=802&md=Prpt&id1=%201004 $2013 %% Blue Source—Bishop Improved Forest Management Project ARB Project Listing Form Attachments February 4, 2013, Part V.B, Soil carbon excluded
59 CAR1 004 NA NA Berry Summit (Near Eureka, CA) −123.758 40.905 Tree cover, needle leaved, evergreen, closed to open (>15%) NA 2,112.86 2013 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1004&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA $2013 %% Project Description Document, Table 5, IFM-6 not included
60 CAR1 174 NA CAFR5224 Eddie Ranch (Mendocino County, CA) −123.17 39.456 Tree cover, needle leaved, evergreen, closed to open (>15%) NA 2,286 2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1174&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA $2017, %2017 %% Application for Listing, Part VII-A, IFM-6, Soil Carbon, not applicable
61 CAR1 190 NA CAFR5220 Greenwood Creek (Mendocino County, CA) −123.631 39.073 Tree cover, needle leaved, evergreen, closed to open (>15%) NA 8,659 3,594.17 2015–2017 IFM https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1190&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA $2015-2016-2017; %2015, 2016, 2017 %% Application for Listing, Part II-C, IFM-6, Soil Carbon, not applicable
62 CAR1 262 NA NA San Juan Lachao Pueblo Nuevo, Oaxaca, Mexico −97.125 16.158 Tree Cover, broadleaved, deciduous, closed NA 32,840.31 13,290 2014–2016 Forestry https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1262&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA $2014, 2015, 2016; %2014, 2015 %% Carbono en el suelo: No se incluye, REPORTE DE PROYECTO Captura de Carbono en San Juan Lachao, Oaxaca San Juan Lachao Pueblo Nuevo, Oaxaca 11 de octubre de 2017 CAR1262
63 CAR1 306 NA NA Ejido San Nicolás Totolapan, CDMX, Mexico −99.2544 19.2994 Tree cover, broadleaved, deciduous, closed to open (>15%) NA 5,302.83 2,145.98 2017–2018 Forestry https://thereserve2.apx.com/mymodule/reg/TabProjectEmissions.asp?id1=1306&ad=Prpt&act=update&sBtn=&r=111&Type=PRO&tablename=cr&aProj=pub NA $2017, 2018 %% https://thereserve2.apx.com/mymodule/reg/TabDocuments.asp?r=111&ad=Prpt&act=update&type=PRO&aProj=pub&tablename=doc&id1=1306
DOI: 10.7717/peerj.7606/table-1

Notes:

*Anomalous Features

# Vintage year is an outlier defined in Fig. 1A. Issue date may vary.

ˆˆBackward model reporting year run (in parenthesis) for the interval noted. Issue date may vary.

## Forward model reporting year run (in parenthesis) for the interval noted. Issue date may vary.

$ A single vintage year (in parenthesis) is reported as a partial year or a single vintage year is split representing two or more reported carbon sequestration intervals as indicated. Issue date may vary.

$$ Exact values (in parenthesis) for carbon sequestration are repeated over interval as indicated. Issue date may vary.

% A single vintage year (in parenthesis) represents two or more reported years and or multiple net carbon sequestration years as indicated. Issue date may vary.

**Project size ranged from 221,822 to 106 acres with a mean size of 21,256 acres, standard deviation of 37,451 acres. Issue date may vary.

%% Soil carbon pool excluded and not directly measured as specified in project documentation.

IFM, Improved Forest Management. This protocol applies to forest offset projects that involve management activities that maintain or increase carbon stocks on forested land relative to baseline levels of carbon stocks.

AC, Avoided Conversion. This protocol applies to forest offset projects that involve preventing the conversion of forestland to a non-forest land use by dedicating the land to continuous forest cover through a qualified conservation easement or transfer to public ownership, excluding transfer to federal ownership.

Table 2:
Results of a hypothesis test with a null hypothesis that the difference between the CARB-CAR and the NEE1 means is under the allowed 5% threshold.
The test is performed separately for all years between 2002 and 2014, the p-values are recorded in the last two rows.
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
CARB-CAR mean −742.31 −719.94 −539.51 −1021.69 −2038.03 −992.42 −950.47 −593.86 −983.74 −725.04 −682.77 −865.27 −999.38 −1432.69
CARB-CAR SD 928.79 585.94 1049.34 683.21 3433.06 1418.73 1170.20 644.84 1460.80 1190.51 1049.74 1541.08 1590.56 1835.66
CARB-CAR (n) 2.00 5.00 9.00 9.00 12.00 23.00 24.00 25.00 32.00 32.00 31.00 32.00 30.00 23.00
NEE1 mean −190.55 −189.15 −243.45 −267.26 −225.08 −241.51 −217.16 −206.75 −184.51 −92.29 −93.45 −53.87 −2.38 −9.29
NEE1 SD 249.06 266.80 268.25 250.02 243.39 237.56 254.86 275.89 244.93 231.71 161.39 199.85 214.46 207.25
NEE1 (n) 40.00 48.00 45.00 42.00 44.00 42.00 41.00 31.00 24.00 17.00 12.00 11.00 8.00 7.00
p-value 0.01 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.04 0.05 0.05 0.03
p-value in % 0.87 0.05 6.53 0.00 0.07 0.13 0.03 0.36 0.81 2.37 3.88 5.48 5.24 3.25
DOI: 10.7717/peerj.7606/table-2

CAR681 methodology and calculations described below are reported in the Project Design Document (PDD, published 12-03-2014) (see Table 1, link to CAR681 project page). Results for CAR681 include data for the period 2008 to 2013 as hindcasted and reported in 2014; a timber inventory was completed in October 2013. The CAR681 Protocol excludes soil carbon (Table 1, FM-6) (California Air Resources Board, 2015b) (PDD, pp. 10, 19), employs the common practice method to model onsite above ground carbon stocks of 46 tCO2e acre−1 (18.6 tCO2e hectare−1) (PDD, p. 33), and established a timber re-inventory interval of 12 years. Model operation (protocol version CARv3.2) included growing the inventory forward 5 years to 2018, then de-growing the inventory to align with the 10∕8∕2008 start date. Reduced growth rates were selected and employed in model runs to establish the project baseline through a series of estimated growth and harvesting scenarios over 100 years. CAR681 results are available on the CAR website page: Cumulative Performance Report (Table 1, Supplemental Information 10).

Carbon sequestration for the initial CAR681 vintage year, 2008, is reported as −43,687.0 tCO2e, or −5,334.7 gC m−2yr−1, ∼26× in excess of the reported NEE1 population mean (e.g., −198.0 gC m−2 yr−2) and ∼34× the NEE2 population mean (e.g., −156 gC m−2 yr−2) (Supplemental Information 9, filled rectangle symbol). Subsequent vintage years, 2009 to 2013, were invariant with exact values of −1,033.00 tCO2e, or −126.1 gC m−2yr−1. CAR681 data for the interval 2008 to 2013 yield a mean and standard deviation of −994.0 ± 2,126.1 gC m−2yr−1, respectively. The total CAR681 offsets issued equal 48,852 tCO2e (Table 1, Supplemental Information 10, CAR681 Project Page, Cumulative Performance Report). CAR681 results were audited and approved (see link, Table 1). Serial numbers were assigned to offsets on 03∕13∕2015 (Table 1, Supplement S8).

In contrast, Ho-1 NEE for 2008 was reported as −287.1 gC m−2yr−1 (Hollinger et al., 2013; Hollinger et al., 2016), ∼19× smaller compared to the 2008 CAR681 value, representing an over-estimation error of ∼1,757% (e.g., footnote 2). The CAR681 result clearly exceeds the 5% CAR invalidation threshold criteria and is arguably invalid. Ho-1 NEE values for the years 2009 to 2013 ranged from −191.9 to −330.9 gC m−2yr−1 with a mean and standard deviation of −255.02 ± 57.7 gC m−2yr−1, respectively (Hollinger et al., 2016), reflecting typical interannual ecosystem carbon dynamics. In contrast, the CAR681 model values for 2009 to 2013 were invariant, a trend not observed for NEE1 sites. The CAR681 subsequent year data are in error, on average by 50% less, compared to the Ho-1 NEE1 data, an exception for the CARB-CAR population, a trend resulting from selection of model parameters. Ho-1 NEE for the 2008–2013 period totals ∼13,446 tCO2e in contrast to 48,852 tCO2e based on the CARB-CAR protocol. Ho-1 NEE data for the period 1996 to 2002 and 1996 to 2008 (Hollinger et al., 2004; Hollinger et al., 2013) have not been compared to CAR681 data. CAR681 results (−994.0 ± 2,126.1 gC m−2yr−1) do not reflect Ho-1 directly measured mean or interannual values (−255.02 ± 57.7 gC m−2yr−1) and cannot be ignored as criteria for invalidation. However, if CARB-CAR data is interpreted as approximating GPP (e.g., not NEE), and ∼82% of assimilated carbon is lost as ecosystem respiration (Hollinger et al., 2013; Hollinger et al., 2016; NEE1,2), an approximate correction to the CARB-CAR data reduces the mean Howland NEE1 and CARB-CAR population values to ∼−179 and ∼−170 gC m−2yr−1, respectively, similar to the NEE1 (e.g., −198 ±261.6 gC m−2yr−1) and NEE2 (e.g., −156 ± 284 gC m−2yr−1) population means. The high variance for interannual CARB-CAR results (e.g., ± 1504.8 gC m−2yr−1) cannot be adjusted based on the correction, nor is the correction proposed as a universal revision to individual CARB-CAR projects. However, the adjustment generally supports the results presented here recognizing that Reco is absent from CARB-CAR carbon accounting (Supplemental Information 8). This comparison emphasizes the difficulty of determining soil carbon efflux and Reco terms with CARB-CAR protocol tools; the NEE method integrates GPP and Reco fluxes providing NEE (e.g., NEE1,2, Supplemental Information 9).

The financial and carbon market consequences for the CAR681 offset errors are substantial. Assuming a 2015 average price of $9.70 USD per offset credit (tCO2e) (Hammrick & Gallant, 2017), sale of CAR681 offsets equal ∼$473,864 compared to $130,431 for Ho-1 NEE based offsets, a difference of $343,433. Overpayment of ∼4× for Howland Forest offsets establishes carbon asset value loss and error in subsequent financial transactions including a debit to the AB32 compliance carbon ledger. The initial vintage year overcrediting results from selectively establishing the Common Practice above-ground standing live carbon stock (e.g., 46 tCO2e acre−1; 31 tC ha−1) to ∼4× below the independently determined value for the site (119–150 tC ha−1; Hollinger et al., 2013). The CARB-CAR estimated baseline scenario maximizes credits generated in the initial year that cannot reflect known rates of CO2 removal from the atmosphere (e.g., 5,332.3 gC m−2yr−1, ∼26× in excess of the reported population annual mean NEE1 data of e.g., −207.99 gC m−2 yr−2, and ∼34× the population annual mean NEE2 data of −156 gC m−2 yr−2). Lowering the Common Practice value relative to actual standing carbon stock for Howland Forest, incorrectly attributes historic carbon (e.g., unknown years of cumulative forest growth prior to the project) to a single vintage initial year of carbon sequestration. In effect, this results in higher initial year crediting, consistent with independent error analyses of similar CARB-CAR projects (Dunlop, Winner & Smith, 2019; Haya, 2019; Haya et al., 2016).

In contrast, Ho-1 NEE increased by ∼6 gC m−2yr−1 over the last 19 years, representing ∼50% overall increase of forest carbon sequestration; each year represents an annual baseline relative to net negative or net positive emissions to the atmosphere (Hollinger et al., 2013; Richardson et al., 2019). There is no initial-year front loading of historic carbon. The time series trend for Ho-1 would not be detected based on the CARB-CAR invariant annual data (excluding first year data), emphasizing the importance of trend detection sensitivity for carbon sequestering ecosystems. Ho-1 provides extensive data to test CARB-CAR protocols including process-based model development (Sihi et al., 2018), independent direct measurement of soil CO2 efflux and ecosystem respiration (Giasson et al., 2013), contemporaneous NEE for CO2, CH4 and N2O (Richardson et al., 2019), response to shelterwood harvest (Scott et al, 2004) and diverse ecological data (Giasson et al., 2013). In summary, incomplete carbon accounting, model-based overcrediting carbon offsets by >5% relative to actual values, and demonstration that the CARB-CAR results do not agree with actual forest carbon sequestration, invalidate the CARB-CAR protocol for this project. Claimed reductions in net CO2 emissions, carbon offset creation and carbon market transactions cannot be validated with the available data for CAR681 supporting a material misstatement calculation as defined in the AB32 rules (California Air Resources Board, 2013b) (endnote 2).

CARB-CAR project review

Table 1 summarizes the CARB-CAR project sites (n = 63) and attributes considered in this study. Links to serial numbers for specific year vintage offsets issued and to summary pages of the CAR online documentation are provided. Features of the CARB-CAR carbon accounting process are identified and documented: (1) exclusion of the soil carbon pool and soil and ecosystem CO2 emissions as respiration for the 63 CARB-CAR projects (Methods, CARB Protocols) cited in this study, (2) projects that record and assign serial numbers to offsets for single vintage year carbon sequestration but are based on records of partial or multiple years of carbon sequestration (63 instances), (3) arbitrary model operations initiated and executed as forward and/or backward runs relative to the project start date (33 instances), and (4) excess initial year project carbon sequestration values (31 instances). Considerable discretion by CARB-CAR stakeholders appear to explain protocol inconsistencies; documentation for revisions is not provided. Protocol discretion, including increasing acceptance of default values for protocol components have been documented for related protocols (Kollmuss & Fussler, 2015) consistent with CAR -CAR protocol observations reported here.

Survey of CARB-CAR linked protocols

Similar expressions are employed in the ACR, CDM and VCS protocols (Supplemental Information 8) extending the CARB-CAR carbon accounting uncertainty to large-scale forest carbon projects such as the UN-REDD and REDD+ (United Nations Framework Convention on Climate Change (UNFCCC, 2019), herein referred to as REDD+. REDD+ approved projects rely on the VCS (Supplemental Information 8). Methodologies developed under the United Nations CDM accepts projects and programs registered and approved by the VCS such as method VM0007 REDD+ Methodology Framework (REDD-MF), v1.5 (VERRA, 2015). Technical reports for REDD VCS applications categorically exclude forest soil carbon and ecosystem respiration (e.g., AC-6) from carbon accounting (UNFCCC, 2017) (Table 1). The implementation of REDD in Ghana, Africa, for example, is subject to impacts of potential invalidation for projects (Asante et al., 2017; Kagombe et al., 2018; McFarland, 2018) including World Bank sponsored bond programs similar to that operating in Kenya, such as the Kasigau Corridor project (McFarland, 2018), also based on VCS protocols. Results for REDD+ programs appear to be uncertain, in part, due to carbon offset monitoring and economics (Foss, 2018). Incomplete carbon accounting implies that results for REDD+ net forest carbon sequestration may not be verifiable or capable of identifying net annual ecosystem carbon change in response to reduced deforestation, climate and anthropogenic forcing, and may not be well suited for carbon pricing and trading of carbon financial instruments. Despite carbon accounting limitations, compliance carbon trading platforms and pricing initiatives are rapidly expanding (e.g., 45 national, 25 subnational jurisdictions; World Bank and Ecofys, 2018) emphasizing the importance of improved, shared methodology for forest carbon sequestration product offerings. Although it is not clear how REDD+ will be integrated within the Paris Agreement (e.g., Article 6) (Schneider & La Hoz Theuer, 2018) or into existing compliance markets (Hein et al., 2018), improved quantification of forest carbon sequestration would link REDD+ entities and mechanisms together in a harmonized universal science-based transactional framework. Macroeconomic trends consistent with offset invalidation risk for voluntary and carbon trading markets are provided in endnote.5

CARB-CAR CA site data

The CARB-CAR project sites within California, representing 31 of the 63 projects (Table 1), represent a mean and standard deviation of −1,163.2 ± 1,889.8, respectively, more negative and variable than values for the CARB-CAR population data set (63 projects) of −948.8 ± 1,504.8 gC m−2yr−1. The CA sites establish the anomalous nature for this group relative to NEE1 (−198.0 ± 261.6 gC m−2yr−1) (Supplemental Information 9) and NEE2 (−156.0 ± 284.0 gC m−2yr−1) with up to ∼7× the mean and ∼12× the standard deviation (e.g., interannual variance) relative to NEE2 population values. The CARB-CAR sites are located generally within the western mountain biome and the marine coastal zones. The CARB-CAR biomes extend from southern to northern California to Oregon and Washington (Supplemental Informations 1, 9, filled gray rectangle symbol), with similar ecological patterns to support characteristic forest species. The CARB-CAR CA sites report invariant values (i.e., interannual variance of 0.0) for CAR590 (5 of 5 years), CAR694 (5 of 7 years), and CAR935 (12 of 14 years), that do not reflect natural forest ecosystems. Direct overlap of CARB-CAR and NEE methods is not required to establish the anomalous features of forest growth implied by the results for CARB-CAR CA sites relative to known population values. An accurate measurement of net carbon sequestration for any forest location is expected to comply with the GPP vs Reco relationship for NEE1 (Supplemental Information 9, filled black and gray rectangle symbols for west and east coast forest values as described) and NEE2 (e.g., −156.0 ± 284.0 gC m−2yr−1). The CARB-CAR CA project sites according to the statistical analysis of NEE1 sites (e.g., Figs. 13, Table 2) and comparison with NEE1,2 data, indicate that the CARB-CAR CA sites do not represent realistic forest carbon sequestration and are arguably invalid.

Discussion

The offset errors, discretionary protocol inconsistencies, incomplete carbon accounting and statistical differences between the Howland Forest (CAR681) project data and directly measured contemporaneous Ho-1 NEE data pose an insuperable factual challenge to the scientific, regulatory and financial validity of the CARB-CAR protocol process and CARB compliance offsets. The consequences of erroneous financial transactions (i.e., ∼4× overpayment), overcrediting to compliance carbon markets, early project termination, and loss of opportunity to manage net removal of atmospheric CO2 do not satisfy the stated AB32 policy goals to “quantify net carbon sequestration as real, additional, permanent, verifiable, and enforceable” (California Air Resources Board, 2011; California Air Resources Board, 2014; California Air Resources Board, 2015b; Nunez, 2016). Net GHG removal, while stated as the objective of the CARB-CAR protocol, falls short of including established carbon accounting protocol terms for soil and ecosystem carbon efflux to achieve the required result (e.g., RF-6, IFM-6, AC-6, Methods 2.1, Table 1). The CAR681 project offsets exceed the allowable 5% threshold for overcrediting error (i.e., section 95985 of the AB32 regulation) rending buffer pools of limited value in compliance enforcement. The CAR681 data (i.e., anomalous initial year value, invariant subsequent years) has no counterpart in nature and is arguably invalid on this basis. The CARB-CAR Howland Forest project did not result in a long-term comparison between the protocols; 2014 was the last vintage year reported. In contrast, NEE data collection for Howland Forest has continued, comprising one of the longest NEE time series on record (1996-present) (AmeriFlux, 2019) documenting interannual changes in forest carbon storage balance between GPP and Reco (Baldocchi & Penuelas, 2019). The well-established anomalous nature of CARB-CAR CA projects (−1,163.2 ± 1,880, n = 31), do not require site-specific overlapping NEE data for comparison with NEE1,2 population data to conclude that the CA site data do not reflect known ranges for NEE and are arguably invalid, consistent with systemic errors noted for Howland Forest (e.g., 31 cases of anomalous initial year values, Table 1). Cases for invalidation based on problems of additionality and leakage (Dunlop, Winner & Smith, 2019; Haya, 2019) as well as questions raised by extension of CARB-CAR protocols to REDD+ tropical forests (’Survey of CARB-CAR Linked Protocols’, endnote 5) (California Air Resources Board, 2018b), underscore the need and importance of resolving CARB-CAR protocol uncertainties.

Exclusion of the soil carbon and ecosystem respiration fluxes impose additional invalidation uncertainty for CARB-CAR protocols by the requirement of a 100-year invariant project baseline to ensure forest carbon storage permanence (California Air Resources Board, 2011; California Air Resources Board, 2014; California Air Resources Board, 2015b; Climate Action Reserve, 2018b). Soil carbon comprises up to three times the magnitude of above ground carbon composition, contributes up to ∼82% of ecosystem carbon exchange (Baldocchi & Penuelas, 2019; Barba et al., 2018; Giasson et al., 2013; Hollinger et al., 2013) and cannot be excluded from a complete, scientifically valid, carbon sequestration value for a forest project (Comeau et al., 2018; DiRocco et al., 2014; Li et al., 2018a; Li et al., 2018b). Soil warming and related soil CO2 efflux predictions, including feedbacks to the biosphere (Davidson & Janssens, 2006), vary over the coming decades (Bond-Lamberty et al., 2018; Hicks Pries et al., 2017; Melillo et al., 2011; Wang et al., 2014; Yang et al., 2013) but they typically deny the assumption that the soil carbon pool and resulting fluxes will remain invariant over the 100-year required project interval (Bond-Lamberty et al., 2018; Li et al., 2018a; Li et al., 2018b). The global soil-to-atmosphere (e.g., total soil respiration) CO2flux, driven by climate change, is increasing across diverse contemporaneous ecosystems (Bond-Lamberty et al., 2018), a trend supported by a series of NEE observation platforms, inclusive of 19 of the 59 NEE1 sites represented in this study (Baldocchi, 2008; Baldocchi, Chu & Reichstein, 2018; Bond-Lamberty & Thomson, 2010). The CARB-CAR estimated invariant baselines, and the invariant interannual records (Table 1) have no counterpart in nature and should be replaced by direct measurement. The omission of soil-based CO2 efflux for CARB-CAR and related protocols preclude differentiation of net-negative to net-positive CO2 forest emissions, a critical test for forest carbon protocols, and a criterion for invalidation.

The CARB-CAR and NEE1 comparison establish systemic uncertainty and corrective action that apply broadly to the CARB-CAR and linked protocols (e.g., ACR, CDM, VCS) (Supplemental Information 8), including: (1) absence of direct, high frequency molecular CO2 measurement to determine annual NEE, a requirement for project validation compliance testing and enforcement, corrected by inclusion of CO2 flux measurements, (2) incomplete carbon accounting by exclusion of GPP and Reco, corrected by requiring inclusion of soil carbon and ecosystem respiration terms, (3) discretionary adjustment of model growth forecasts (e.g., selection of FVS values) to achieve specific project goals including baseline levels, corrected by regulation of protocol rules, (4) creation of excess initial-year project vintage carbon sequestration values exceeding the known range of NEE values, violating the 5% invalidation threshold, corrected by using actual NEE measurement for year one in the same manner as for all years, and (5) creation of unverified forest carbon financial products for carbon market transactions, corrected by employing third party verifiers with independent CO2 measurements.

The protocol inconsistencies and invalidation criteria noted above (e.g., Howland Forest) support results of statistical analyses for CARB-CAR and NEE1 population comparisons. Mean values, Figs. 1A and 1B, and discrete interannual segments including exclusion of initial year data (e.g., Figs. 2 and 3, low p-values Table 2) were compared with the same result: the CARB-CAR protocol cannot be relied upon to represent valid net annual forest carbon dynamics and will likely exceed the 5% compliance threshold. Results for CARB-CAR net carbon sequestration cannot be argued as exceptions to known variance for forest populations (i.e., mean and standard deviation), but rather stand clearly outside of the known boundaries (Supplemental Information 9). We argue that the diverse analyses reported, considered together, provide robust observational evidence that the CARB-CAR and related protocols do not reliably reflect known values for forest carbon sequestration and are invalid.

The problems with estimation-based protocols can be addressed with existing technology and scientific methods. In contrast to estimation-based protocols, NEE for CO2 determined by eddy covariance integrates vertical carbon fluxes between forest and soil ecosystems and atmosphere (e.g., assimilation and respiration) (Burba, 2013), resulting in the key outcome of net forest carbon sequestration (e.g., NEE as gC m−2yr−1). The eddy covariance method employed at the Howland Forest and NEE1,2 sites has been applied worldwide as standalone field research installations (Burba, 2013; Curtis & Gough, 2018; Aubinet, Vesala & Papale, 2012; Lee, 2017) in combination with remote sensing (Hopkinson et al., 2016; Liu et al., 2016) and as research networks (FLUXNET, 2019; Novick et al., 2018; Ocheltree et al., 2007; Papale & Valentini, 2003) for CO2, CH4 and N2O, capability that cannot be implemented by CARB-CAR and related protocols. Eddy covariance networks are not typically interconnected in real-time or applied across large project areas for creating commercial forest carbon financial products. Commercial engineering development of low-cost eddy covariance networks, to attain state-wide spatial coverage across the US or for specific regions, for example, coupled with innovative features, including unmanned aerial vehicles (Berman et al., 2012; Metzger et al., 2012), shared data networks (Dai et al., 2018) and automated reporting is achievable offering a modernized alternative to estimation protocols. Advancements in blockchain accounting platforms (Düdder & Ross, 2017), artificial intelligence (Reis et al., 2018) and the internet of things (Subashini et al., 2018) can be readily integrated within eddy covariance networks but for the reasons we discuss here cannot be successful without direct measurement of CO2. Eddy covariance as an instrumental method has characteristic limitations and uncertainties (Nicolini et al., 2018) and faces engineering challenges for large-scale deployment as well as upscaling eddy covariance methods (Barba et al., 2018; Kumar et al., 2016; Ran et al., 2016; Warner et al., 2019; Wutzler et al., 2018). NEE platforms and networks are established in numerous countries including those with REDD+ platforms and commitments to the Paris Agreement providing a foundation for expanded NEE observations to support global climate change policies.

Given the feasibility of establishing eddy covariance networks, expanding carbon market exchanges (World Bank and Ecofys, 2018) and the abundance of deforested landscapes, we suggest that standard methods and protocols be adopted for forest carbon and GHG financial products that: (1) are based on direct measurement of molecular CO2 forest flux, (2) employ shared gas standards for CO2 analyzer calibration, performance testing (e.g., World Meteorological Organization; Brailsford, 2012) and global reference frameworks (Andrews et al., 2019), (3) employ standardized protocols, model parametrizations and criteria such as that established by the Integrated Carbon Observation System (Kutsch et al., 2018; Vitale & Papale, 2017; Wutzler et al., 2018) (ICOS) and the Global Atmosphere Watch (Schultz et al., 2015), and (4) establish universal measurement-based criteria for the transformation of NEE to forest carbon offset products suitable for carbon financial transactions establishing equivalence between voluntary and compliance forest carbon products.

We acknowledge the limitations of this study related to the small sample sizes for annual intervals presented and limited overlapping project sites. However, in the absence of CARB-CAR validation and apparent protocol discrepancies reported here, NEE offers the best available data for independent verification. The results presented here form the basis for ongoing comparison between CARB-CAR and NEE1,2 results. CARB-CAR CA sites, likely to increase based on AB398 legislation (Schatzki, Stavins & Hall, 2018), we suggest, should incorporate current and evolving NEE methods, terminology and knowledge of forest carbon sequestration in collaboration with NEE research efforts.

Conclusions

In conclusion, our results, unless proven otherwise, call into question the scientific, regulatory and financial validity of estimation-based protocols for net forest carbon. Diverse criteria demonstrate that CARB-CAR protocols are unreliable and unlikely to represent actual carbon sequestration and are invalid. The importance and urgency of validating forest carbon sequestration cannot be overestimated as forests provide additional ecosystem services of soil carbon sequestration and water conservation (Masciandaro et al., 2018), biodiversity safeguards (Keesstra et al., 2018), and the coupling of avoided forest carbon emissions with Indigenous Peoples habitation (Mesoamerican Alliance of Peoples and Forests, 2015; Ramos-Castillo, Castellanos & Galloway McLean, 2017). Measurement and standardized methods are hallmarks of the Montreal Protocol with 197 national signatories to monitor and enforce the reduction in emission of chlorofluorocarbons demonstrating the success of collective action within a common standardized analytical framework (Bielewski & Śliwka, 2014; Hurst, Bakwin & Elkins, 1998; Newman, 2018). Nothing less is required to advance forest carbon management and carbon market value. The demise of the Chicago Climate Exchange (Sabbaghi & Sabbaghi, 2017), coincident with near zero-dollar value for forest carbon (Peters-Stanley et al., 2012), and false claims of automotive emission avoidance offers a lesson learned that without actual measurement and accountability, institutional GHG emission reduction frameworks are vulnerable to economic and policy failure (Van Renssen, 2018) and fraud (Li et al., 2018a; Li et al., 2018b). Project specific climate finance and monetization mechanisms are a key but unspecified component of the Paris Agreement that if combined with direct measurement of forest CO2 will benefit societies and economies in the coming decades and prove crucial to correcting the imbalance between nature and anthropogenic activity and resulting climate change.

Supplemental Information

Project Locations Interactive Map

The project locations are displayed for the locations of 59 NEE1 and 63CARB-CAR project sites. Information for each site, including the underlying biome and land cover classification. A toggle is provided to move between the screens with biomes, land cover and boundary lines. Base Map: © Mapbox© OpenStreetMap. Data: U.S. EPA Office of Research & Development (ORD) - National Health and Environmental Effects Research Laboratory (NHEERL). Imagery: © DigitalGlobe.

DOI: 10.7717/peerj.7606/supp-1

Statistical Analyses

Statistical methods are described for Figs. 13 and Table 2.

DOI: 10.7717/peerj.7606/supp-2

CARB-CAR site names, website project links and latitude and longitude are provided for each site

DOI: 10.7717/peerj.7606/supp-3

NEE1 Project Information

NEE1 site names, website project links and latitude and longitude are provided for each site.

DOI: 10.7717/peerj.7606/supp-4

CARB-CAR Project Annual Data

CARB-CAR raw data values for annual net forest carbon sequestration are provided for each site as gC m−2 yr−1.

DOI: 10.7717/peerj.7606/supp-5

NEE1 Project Annual Data

NEE1 raw data values for annual net forest carbon sequestration are provided for each site as gC m−2 yr−1.

DOI: 10.7717/peerj.7606/supp-6

Combined CARB-CAR and NEE1 Annual Values

All raw data annual values for CARB-CAR and NEE2 sites are combined in a single file.

DOI: 10.7717/peerj.7606/supp-7

CARB-CAR Net GHG Quantification Equations

The equations for net GHG carbon balance as defined by the CARB-CAR protocols (California Air Resources Board, 2014; California Air Resources Board, 2015a; California Air Resources Board, 2015b) are analyzed considering the simple case of a one-year project. Related forest protocol equations (ACR, CDM, VCS) are also summarized and reviewed. We show that protocol equations exclude efflux of CO2 soil carbon and ecosystem respiration (e.g., Reco) in the CARB-CAR and related forest carbon protocols.

DOI: 10.7717/peerj.7606/supp-8

Plot of GPP vs Reco for NEE1 Data

A plot of the annual sums measured in the field for gross primary productivity (GPP) versus ecosystem respiration (Reco) is shown based on NEE1 data (Baldocchi, Chu & Reichstein, 2018) employed for the statistical analyses reported in this study. The mean and standard deviation of the NEE 1 data are shown on the right axis. The plot represents data for 59 locations with a total of 544 site-years for direct eddy covariance measurement of net carbon fluxes in diverse natural and managed ecosystems. The mean and standard deviation for NEE1 is −198.0 ± 261.6 gC m−2yr−1 (r2 = 0.907).

DOI: 10.7717/peerj.7606/supp-9

Howland Research Forest CAR681 Updated Offset Data Substantiates Invalidation

Howland forest carbon offset data (CAR681), reported in our initial analysis, are updated with new offsets (CAR1161). New data are inconsistent with previously published records. The combined data are shown to be invalid based on comparison with known values for forest carbon sequestration and related criteria.

DOI: 10.7717/peerj.7606/supp-10
The concentration of CO2 that would cause the same amount of radiative forcing as a given mixture of CO2 and other greenhouse gases.
percentageerror = E s t i m a t e d v a l u e N E E 1 v a l u e N E E 1 v a l u e × 100 Where estimated value = CARB-CAR, and NEE1 value = directly measured value. We propose recalculation of material misstatement errors based on NEE values as follows: Percent error determination for offsets based on the material misstatement equation (California Air Resources Board, 2013b) is defined as follows: % e r r o r = D i s c r e p a n c i e s + O m i s s i o n s + M i s r e p o r t i n g T o t a l r e p o r t e d e m i s s i o n s × 100 % . Simplifying, where Discrepancies are defined as the CARB-CAR value, Total reported emissions are defined as NEE1 reported emissions, NEE1 represents the accepted value, and Omissions and Misreporting = 0, the expression simplifies as, p e r c e n t a a g e e r r o r = | C A R B C A R N E E v a l u e | N E E 1 v a l u e x 100
CARB-CAR. The 63 project types include avoided conversion (AC, n = 7), reforestation (RF, n = 2), and improved forest management (IFM, n = 54) projects (Table 1). AC and RF projects involve temperate zone historically forested areas and must maintain forest stocks through CARB-CAR regulatory practices (California Air Resources Board, 2011; California Air Resources Board, 2014; California Air Resources Board, 2015b). Reforestation projects involve land that has been deforested (e.g., canopy cover less than 105) and is actively replanted with trees. Details of management practices for each site vary, however, all sites comply with prohibition of clear-cut (e.g., <40 acres harvested), broadcast fertilization, extensive soil disturbance and extensive short harvest rotations typical of timber plantations, ensuring comparability of baseline management practices and application of the compliance protocol process as regulated by the CARB-CAR protocols (California Air Resources Board, 2011, 2014, 2015b). Aggressive management practices involving timber removal would trend towards more positive CARB-CAR values (e.g., net positive emissions to the atmosphere) rendering the CARB-CAR data base conservative for comparison with NEE values. The IFM and AC projects (n = 61) are subject to similar land management activities, including minimal harvesting, as defined by the protocols. CARB-CAR net forest carbon sequestration for the 63 projects is characterized by a mean and standard deviation of −948.8 ± 1,504.8 (Results section 3.1). Descriptive reports and cumulative emissions data are derived from the CAR website homepage (Climate Action Reserve, 2018a). Forest carbon results for the American Carbon Registry (American Carbon Registry, 2018) and the Verified Carbon Standard (Verified Carbon Standard, 2018), both approved project registries by the ARB, were reviewed but were not included in this study. ACR project data was not available in summary format for carbon offsets presenting a challenge to verify ACR results. A total of 55 ACR forest carbon projects were listed as of 09-01-2018; 18 identify values for registered carbon credits but serial numbers for ARB issued offsets were not available (American Carbon Registry, 2018). Analysis of the ACR Part VII forest project listing applications, identical to those for CARB-CAR applications, verified that soil carbon was not included in the carbon pools employed in net forest carbon estimations (n = 30: 189, 173, 192, 199, 202, 211, 298, 249, 200, 256, 265, 266, 267, 268, 262, 265,255, 276, 282, 282, 273, 274, 284, 288, 292, 303, 324, 360, 277, 278.). The VCS identified 12 proposed forest projects, however, offset credit summaries and serial numbers for ARB issued offsets were not available. Based on the information available, ACR and VCS results were not considered in this study. CARB-CAR defines forests in terms of photosynthesis and carbon stored in soils and litter. A forest project is a planned set of activities designed to increase removals of CO2from the atmosphere, or reduce or prevent emissions of CO2 to the atmosphere, through increasing and/or conserving forest carbon stocks. A net climate benefit, or verification of a net negative CO2 balance for the forest ecosystem must result from the project activities. The Offset Project Boundary defines GHG emission sources, GHG sinks, and GHG reservoirs that must be accounted for and quantified for a Forest Project’s GHG reductions and removal enhancements. Tables in the CARB-CAR protocols provide a comprehensive list of project components that must be included in the Offset Project Boundary for each type of Forest Project. A “reservoir/pool” is accounted for by quantifying changes in carbon stock levels. GHG sources or GHG sinks, GHG reductions and GHG removal enhancements are accounted for by quantifying changes in GHG emission or GHG removal enhancement rates. The CARB protocols and accounting requirements are described in three related primary documents published in 2011 (California Air Resources Board, 2014), 2014 (California Air Resources Board, 2014) and 2015 (California Air Resources Board, 2015b) entitled: “Compliance Offset Protocol U.S. Forest Projects”. The cited protocols identify field methods employing forest mensuration surveys, model simulation requirements and primary sources, sinks and reservoirs in a series of tables for Reforestation Projects (RF), Improved Forest Management Projects (IFM) and Avoided Conversion Projects (AC). The CARB and CAR forest carbon protocols, while published in separate documents (California Air Resources Board, 2015b; Climate Action Reserve, 2018b) rely on shared estimation protocols for net GHG reductions and removals. For example, both protocols employ forest growth simulation models that use the Forest Vegetation Simulator (FVS) and related vegetation proxies for forest project species. FVS features are coupled with identical numerical equations and carbon pool terms for net forest carbon sequestration directly linking the CARB and CAR protocols (e.g., CARB-CAR) (California Air Resources Board, 2011; California Air Resources Board, 2014; California Air Resources Board, 2015b; Climate Action Reserve, 2018b) (Supplemental Information 8). The underlying models and their specific application to the CARB project location are detailed in documents associated with each of the CARB projects as listed on each CAR project page. Table 1 provides features of the CARB-CAR data sets that appear to be discretionary and are applied inconsistently across the CARB-CAR project sites. CARB-CAR cites seven approved forest growth and yield models (California Air Resources Board, n.d.); otherwise, shared standards and references are lacking. Details of the ARB Compliance Offset Program and offset credits issued are provided by the CARB website (California Air Resources Board, 2018a). All approved verification protocols must adhere to CARB standards (California Air Resources Board, 2013a). The CARB-CAR protocol specifies the soil carbon reservoir/pool as item 6 listed as RF-6, IFM-6 and AC-6. The soil carbon information applicable to the protocols are listed in Tables 5.1 (RF-6), 5.2 (IFM-6) and 5.3 (AC-6) for the protocols published in 2011 (California Air Resources Board, 2014) and 2014 (California Air Resources Board, 2014), respectively. The same information is listed in Tables 4.1 (RF-6), 4.2 (IFM-6) and 4.3 (AC-6) in 2015 (California Air Resources Board, 2015b). Reference to inclusion or exclusion of the soil reservoir for each project listed in Table 1 is indicated and linked to one of the above protocols as cited in the summary documents provided for each project. Forest mensuration, or biometric methodology, is intrinsic to the CARB-CAR protocol process and outcomes and is briefly reviewed here. Timber surveys, designed for timber operations, are required every six years or longer (California Air Resources Board, 2014; California Air Resources Board, 2015b), however, simulation models estimate annual incremental change for CARB-CAR net forest carbon sequestration absent annual surveys. CARB-CAR Forest mensuration methods rely primarily on measurement of tree diameter at breast height (DBH)(Gonçalves et al., 2017). The limitation of the biometric approach is that biomass is not directly measured as it is not quantified by harvest and weight of the carbon pools; this approach is not practical or economically feasible resulting in destruction of the forest. Uncertainties of 50%–80% for individual trees and 20+ % for plot level estimation persist for forest mensuration (Gonçalves et al., 2017; Holdaway et al., 2014; Paré et al., 2015). Timber survey errors include: (1) variation in the parameters of allometric equation(s) and natural variability of tree structures, (2) measurement errors (DBH, tree height) and differences in frequency of measurement (e.g., multiple measurements per year), and, (3) selection of tree-specific parameters within allometric equations such as wood density. The uncertainties are compounded when the forest areas have been or are subject to management including timber extraction, thinning and prescribed or natural fire. In many cases diverse sources of uncertainty are not identified, or new sources of uncertainty are introduced due to bias in data collection, limited coverage of representative forest areas, exclusion of selected carbon pools and inconsistent application of standards and calibration of equipment between measurements. An example of a comprehensive forest mensuration protocol is found in Barford et al. (2001) (e.g., weekly measurement of DBH during the growing season, biomass calculation using density data from a study of northern hardwood forests similar in latitude and elevation, weekly collection of leaf litter during the fall months sorted by genus, dried and weighed. CO2 ecosystem and soil respiration cannot be estimated from forest mensuration methods. Examples of forest mensuration, including soil CO2 and ecosystem respiration efflux, in relation to eddy covariance approaches are well represented (Curtis et al., 2002; Luyssaert et al., 2009; Ouimette et al., 2018).
NEE. The NEE1 study reported a mean and standard deviation of approximately -200.00 ± 162 gCm−2 yr−1(Baldocchi, Chu & Reichstein, 2018), compared to a mean of -198.0 and standard deviation of 261.6 gCm−2 yr−1 calculated in this study. The difference in mean and standard deviation result from different approaches to calculation. We calculated the sample mean and sample standard deviation of the pooled annual data of 544 points (Supplemental Information 7). The authors of the NEE1 data calculate the mean by first obtaining the 59 means corresponding to the 59 different locations, and then calculating the mean of means (Baldocchi, Chu & Reichstein, 2018). The NEE1 standard deviation is also based on the deviations of each data point from the corresponding location mean (as opposed to the global mean). The difference in mean and standard deviation noted do not change the conclusions of the summary study (Baldocchi, Chu & Reichstein, 2018) or our use of that data. Additional data for the Howland Forest (CARB-CAR671, NEE1-Ho1) covering the years 2008 to 2013 that were not included in the NEE1 dataset were obtained from an additional publication (Hollinger et al., 2016); these values were not included in summary statistics reported. We note that although 544 site years was reported, four of those site years were absent data; we use the available 540 site year values and identify the four years of missing data in Supplemental Information 7. Data Availability Statement: NEE annual Data cannot be shared publicly because of Fluxnet2015 (https://fluxnet.fluxdata.org/data/data-policy/) and Lathuile (https://fluxnet.fluxdata.org/data/la-thuile-dataset/) data policies. However, one can access these data in the same manner as the authors via the Fluxnet2015 dataset (https://fluxnet.fluxdata.org/ data/fluxnet2015-dataset/) and the LaThuile dataset (https://fluxnet.fluxdata.org/data/la-thuile-dataset/). The NEE method is based on direct and fast measurements (e.g., 10 Hz) of actual gas transport characterized by a three-dimensional wind field in real time. In this study, we did not control for tower height or upscaling results across the diverse site locations, however, all eddy covariance platforms were positioned above the canopy to capture CO2 flux dynamics. The concentration of the gas of interest (e.g., CO2, 13CO2 and 14CO2) is measured concomitantly resulting in flux of the gas. Flux data are first converted to half-hourly mean grams of carbon per square meter (gC m−2s−1) and then summed for each year as the cumulative annual net carbon exchange (gC m−2yr−1). Tower based estimates of net ecosystem exchange are reported in negative units reflecting a micrometeorological sign convention where flux from the atmosphere is negative, unless otherwise noted. The EC method has been applied worldwide under remote and harsh conditions employing solar power often for months without maintenance (Burba, 2013). Open or closed path gas analyzers (e.g., CO2, CH4, N2O) using shared gas standards coupled with automated flux calculation, telemetry and integrated micrometeorological sensors, for example, are typical and deployed across numerous field platforms readily delivered to the project site. EC data are analyzed by a variety of models across small and large scales to calculate NEE (Burba, 2013). The spatial footprint of the NEE observation scales with height of sampling inlet above the canopy, representing from ∼1 km2 to ∼10 km2 for typical single EC platforms. Upscaling of EC data provides up to 100 km2 of carbon sequestration data coverage (Kumar et al., 2016; Palmer et al., 2018; Richardson et al., 2012; Stanley et al., 2018). Annual errors in NEE typically range between 30 and 100 gC m−2 yr−1(Baldocchi, 2008; Burba, Madsen & Feese, 2013; Hagen et al., 2006). Gas analyzers for EC measurements are available from a variety of vendors (e.g., Los Gatos Research, San Jose, CA, USA). The CARB-CAR and NEE project locations are diverse across similar biomes, overlapping in one case of the Howland Forest, Maine, USA, (See Supplemental Information 10 for Howland Forest site information).
Carbon markets ideally conserve natural resources (Osborne & Shapiro-Garza, 2018; Warren-Thomas et al., 2018), limit surface warming to <1.5 °C relative to the pre-industrial period (IPCC, 2018; Mengis et al., 2018; Rogelj et al., 2018), and are commercially viable (Sandor, Walsh & Marques, 2013; Rogelj et al., 2016; Bryant, 2018). However, uncertainty in carbon product asset value (Dhavale & Sarkis, 2018; Foss, 2018; Dunlop, Winner & Smith, 2019) and market function can negatively affect carbon markets and their efficacy to manage climate change (Helleiner & Thistlethwaite, 2013; Mehling, Metcalf & Stavins, 2018; Morrissey, 2018; Zhang, Randhir & Zhang, 2018). For example, global carbon compliance markets have declined from ∼$95B €in 2011 (Thomson Reuters, 2016) to $41B € in 2017 (Thomson Reuters, 2018), a decrease of ∼57%, attributed to the absence of a price for carbon (Johannsdottir & McInerney, 2016), oversupply of offsets (Ervine, 2018; Schatzki, Stavins & Hall, 2018), ambiguity of disparate trading platforms (Green, 2017), and as we argue here for forest carbon, absence of direct and verifiable measurement of CO2 and related carbon storage products (Nisbet & Wiess, 2010; National Research Council, 2010; Peters et al., 2017; Palmer et al., 2018). This uncertainty is reflected in global carbon markets and platforms. Macroeconomic trends for voluntary carbon trading markets are reflected in REDD+ verification programs. For example, 2016 prices for forest carbon were the lowest for REDD+ projects, averaging $4.60 tCO2e on the largest volume of all project types (Hammrick & Gallant, 2017). VCS protocol projects were characterized consistently by the lowest pricing of $4.10 tCO2e on the largest volume of all standards employed for forest projects (Hammrick & Gallant, 2017). Transaction volume (millions tons CO2e) for forest carbon offsets fell ∼40% from 2014 to 2016 (Hammrick & Gallant, 2017). We suggest that the low prices for REDD+ and VCS are, in part, related to the uncertainty and risk of invalidation for net carbon sequestration (Foss, 2018), as demonstrated for Howland Forest. In contrast, CARB-CAR pricing of ∼$9.70USD tCO2e for 2015 compliance offsets (Hammrick & Gallant, 2017), emphasize the asymmetry in carbon pricing; similar uncertainties apply to REDD+ (e.g., voluntary) and to CARB-CAR (e.g., compliance) forest carbon offsets.