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Submitted 1 October 2019
Accepted 11 March 2020
Published 27 April 2020
Corresponding author
Bruno D.V. Marino,
bruno.marino@pem-carbon.com
Academic editor
Paolo Giordani
Additional Information and
Declarations can be found on
page 26
DOI 10.7717/peerj.8891
Copyright
2020 Marino et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Direct measurement forest carbon
protocol: a commercial system-of-systems
to incentivize forest restoration and
management
Bruno D.V. Marino1, Vinh Truong2, J. William Munger3and Richard Gyimah4
1Executive Management, Planetary Emissions Management Inc., Cambridge, MA, United States of America
2Planetary Emissions Management Inc., Cambridge, MA, United States of America
3School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard
University, Cambridge, MA, United States of America
4Forestry Commision of Ghana, Accra, Ghana, Africa
ABSTRACT
Forest carbon sequestration offsets are methodologically uncertain, comprise a minor
component of carbon markets and do not effectively slow deforestation. The objective
of this study is to describe a commercial scale in situ measurement approach for
determination of net forest carbon sequestration projects, the Direct Measurement
Forest Carbon ProtocolTM, to address forest carbon market uncertainties. In contrast
to protocols that rely on limited forest mensuration, growth simulation and exclusion of
CO2data, the Direct Measurement Forest Carbon ProtocolTM is based on standardized
methods for direct determination of net ecosystem exchange (NEE) of CO2employing
eddy covariance, a meteorological approach integrating forest carbon fluxes. NEE is
used here as the basis for quantifying the first of its kind carbon financial products.
The DMFCP differentiates physical, project and financial carbon within a System-
of-SystemsTM (SoS) network architecture. SoS sensor nodes, the Global Monitoring
PlatformTM (GMP), housing analyzers for CO2isotopologues (e.g., 12CO2, 13 CO2,
14CO2) and greenhouse gases are deployed across the project landscape. The SoS
standardizes and automates GMP measurement, uncertainty and reporting functions
creating diverse forest carbon portfolios while reducing cost and investment risk
in alignment with modern portfolio theory. To illustrate SoS field deployment and
operation, published annual NEE data for a tropical (Ankasa Park, Ghana, Africa)
and a deciduous forest (Harvard Forest, Petersham, MA, USA) are used to forecast
carbon revenue. Carbon pricing scenarios are combined with historical in situ NEE
annual time-series to extrapolate pre-tax revenue for each project applied to 100,000
acres (40,469 hectares) of surrounding land. Based on carbon pricing of $5 to $36
per ton CO2equivalent (tCO2eq) and observed NEE sequestration rates of 0.48 to
15.60 tCO2eq acre−1yr−1, pre-tax cash flows ranging from $230,000 to $16,380,000
across project time-series are calculated, up to 5×revenue for contemporary voluntary
offsets, demonstrating new economic incentives to reverse deforestation. The SoS
concept of operation and architecture, with engineering development, can be extended
to diverse gas species across terrestrial, aquatic and oceanic ecosystems, harmonizing
voluntary and compliance market products worldwide to assist in the management
of global warming. The Direct Measurement Forest Carbon Protocol reduces risk of
How to cite this article Marino BDV, Truong V, Munger JW, Gyimah R. 2020. Direct measurement forest carbon protocol: a commer-
cial system-of-systems to incentivize forest restoration and management. PeerJ 8:e8891 http://doi.org/10.7717/peerj.8891
1‘‘Carbon dioxide equivalent’’ or ‘‘CO2eq’’
is a term for describing different
greenhouse gases in a common unit. For
any quantity and type of greenhouse gas,
CO2eq is a term for describing different
greenhouse gases in a common unit. For
any quantity and type of greenhouse gas,
CO2eq signifies the amount of CO2 which
would have the equivalent global warming
impact.
invalidation intrinsic to estimation-based protocols such as the Climate Action Reserve
and the Clean Development Mechanism that do not observe molecular CO2to calibrate
financial products. Multinational policy applications such as the Paris Agreement
and the United Nations Reducing Emissions from Deforestation and Degradation,
constrained by Kyoto Protocol era processes, will benefit from NEE measurement
avoiding unsupported claims of emission reduction, fraud, and forest conservation
policy failure.
Subjects Ecosystem Science, Coupled Natural and Human Systems, Biosphere Interactions,
Climate Change Biology, Forestry
Keywords Harvard forest, Ankasa park ghana, Forest carbon quantification, Forest carbon
trading , Deforestation, Forest net ecosystem exchange, Paris agreement, REDD+, Climate action
reserve, Clean development mechanism
INTRODUCTION
Forest landowners and forest communities typically lack economic incentives and social
benefits to balance deforestation with conservation and preservation (Duguma et al.,
2019). A constellation of factors is responsible for deforestation (Busch & Ferretti-Gallon,
2017), claiming ∼50% of tropical forested landscapes (Brancalion et al., 2019;Rozendaal
et al., 2019), including contested land rights, high carbon project cost and requirements
for landowners (Kerchner & Keeton, 2015), failure of payment for ecosystem services
(Fenichel et al., 2018;Lamb et al., 2019), low or negative payments resulting from the United
Nations Reducing Emissions from Deforestation and Degradation (REDD+) programs
(Köhl, Neupane & Mundhenk, 2020), and as we argue here, uncertainty for forest carbon
sequestration (Engel et al., 2015;Marino, Mincheva & Doucett, 2019;Zhang, 2019). Carbon
markets are primarily driven by reduction/avoidance of emissions to the atmosphere from
energy production and consumption (Liddle, 2018) while investment in removal of CO2
from the atmosphere by reforestation and conservation has not gained carbon market
traction (Gren & Zeleke, 2016;Laurance, 2007) declining by ∼72% from 2011 to 2016
(Hamrick & Gallant, 2017;Molly Peters-Stanley Gonzalez & Yin, 2013). Discount pricing
for forest carbon (e.g., <$5 tCO2eq1, 2017: <1$, 2018) (Hamrick & Gallant, 2018;Hamrick
& Gallant, 2017) results in limited ecological, social and economic benefits of carbon
trading to stakeholders due, in part, from risk of offset invalidation intrinsic to estimation
protocols.
Estimation protocols do not directly observe forest CO2fluxes; terms for ecosystem
photosynthesis and respiration are absent unavoidably introducing uncertainty for
annual net forest carbon determination and monetization to carbon markets (Dunlop,
Winner & Smith, 2019;Haya, 2019;Marino, Mincheva & Doucett, 2019). Invalidation
risk for estimation protocols stems from reliance on forest mensuration (e.g., timber
survey) conducted every 6 or 12 years (California Air Resources Board, 2015a;Forest
Carbon Partners, 2013;Marland et al., 2017) coupled with tree growth simulation models
to infer annual changes in net forest carbon offsets (California Air Resources Board,
2011;California Air Resources Board, 2014;California Air Resources Board, 2015b;Climate
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 2/41
Action Reserve, 2018). Forest mensuration uncertainties up to 80% for individual trees, and
up to 20% for plot level estimation of annual forest carbon have been reported (Gon¸
calves
et al., 2017;Holdaway et al., 2014;Paré et al., 2015) suggesting that such uncertainty is
unaccounted for in estimation based protocols.
Estimation based protocols including the California Air Resources Board (CARB)
(California Air Resources Board, 2014), the Climate Action Reserve (CAR) (Climate
Action Reserve, 2018), the American Carbon Registry (ACR) (Winrock International, 2016),
the Verified Carbon Standard (VERRA), and the Clean Development Mechanism (CDM)
(Zhang et al., 2018) share offset uncertainty (Köhl, Neupane & Mundhenk, 2020;Kollmuss
& Fussler, 2015) in the absence of direct measurement. Approximately 0.9 billion hectares
of forests are available worldwide for large-scale restoration opportunities (Bastin, 2019;
Brancalion et al., 2019), however, in addition to carbon quantification uncertainties,
financing for large-scale projects has proven difficult (Foss, 2018). Complete and direct
carbon accounting of forests is required to track biospheric carbon dynamics given
the limited and impermanent nature of forest and soil carbon (Baldocchi & Penuelas,
2019;Schlesinger, 2003;Schlesinger & Amundson, 2018). Here, we address forest carbon
accounting uncertainties by linking direct measurement of net ecosystem exchange
(NEE) of forest carbon fluxes for a project with carbon market transactions in a Direct
Measurement Forest Carbon Protocol (DMFCP).
The objective of the DMFCP is to efficiently monetize sustainable forest management
and direct revenue to landowners and communities in lieu of deforestation. The DMFCP
commercializes large-scale (e.g., 1 +million hectares), direct, in-situ measurement
of vertical gross forest CO2fluxes (e.g., photosynthesis and ecosystem respiration) to
determine net forest carbon sequestration or net ecosystem exchange (NEE), a universal
feature of NEE research platforms (Baldocchi, 2019;Baldocchi, Chu & Reichstein, 2018;
Baldocchi & Penuelas, 2019;Burba, 2013). The DMFCP, employing a network system
architecture, the SoS, and a sensor platform, the GMP, account for carbon from
measurement-to-monetization of NEE based products as described in Fig. 1 (overview)
and Fig. 2 (annual accounting). NEE has been measured in over 600 locations worldwide
(Fluxdata, 2020a;Novick et al., 2018) but has not been utilized to support commercial SoS
networks for realization of verified forest carbon products and carbon market transactions.
NEE, notwithstanding limitations intrinsic to the methodology, offers a transformative
advancement compared to estimation protocols for annual net forest carbon sequestration
that lack direct CO2measurement (e.g., gC m−2yr−1). The DMFCP commercial platform is
described employing NEE data from two research sites, the Ankasa Park tropical rainforest
located in Ghana, Africa (Nicolini, 2012), and the Harvard Forest deciduous forest site
located in Petersham, MA, USA (Barford et al., 2001;Munger, 2016;Urbanski et al., 2007).
The NEE time series data for each site, in combination with carbon pricing scenarios, is
used to establish revenue projections across an areal expanse of 100,000 acres (404,685.6
hectares). We compare landowner benefits and incentives to restore forests and reverse
deforestation employing the DMFCP and traditional estimation-based protocols as well
as compare uncertainties for each approach and their significance to supporting verifiable
forest carbon financial products.
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 3/41
Figure 1 Showing an overview of the DMFCP structure and process. The Direct Measurement Forest
Carbon Protocol (DMFCP) measures gross vertical fluxes of carbon forest ecosystems important for car-
bon trading shown as: geographical project boundary (dashed line); NEE, net ecosystem exchange of CO2
fluxes; AGC, above ground carbon; BGC, below ground carbon; Photosynthesis, the total carbon uptake
by plants or gross primary productivity (GPP); Respiration of ecosystem (Reco), total sources of CO2re-
leased to the atmosphere from plants (AGC, Ra) and soil microbes (BGC, Rh); SoS sensor network; and,
a Global Monitoring Platform (GMP). The SoS network and GMP’s are deployed across the project land-
scape, according to an engineering plan specifying number, height and placement of sensors, to determine
net ecosystem exchange (NEE) representing net forest carbon sequestration for a project. Forest carbon
gross fluxes (GPP, Reco) measured in situ and resulting in NEE is designated as Physical Carbon, total land
area and time period of project performance are designated as Project Carbon, and annual accounting and
registration of project carbon provides the basis (e.g., quantity of tCO2eq available) and pricing for sale of
Financial Carbon. Multiple projects and resulting forest carbon products are combined in a Pooled Port-
folio and listed in a registry detailing project accounting and verification criteria. Pooled Portfolio carbon
products, based on equivalent carbon accounting, can be sold to voluntary and compliance buyers world-
wide. Pooled Portfolio products may also incorporate additional greenhouse gase fluxes (e.g., CH4, N2O)
and isotopic forms (e.g., isotopologues)2that can be measured with precision in the field and typically re-
ported in the delta notation with per mil unilts. 3The geographical project boundary may be comprised
of local, regional or larger land areas (e.g., state, country). Project types include: R, reforestation refers to
a project that plants trees on a site previously forested; AD, avoided deforestation refers to a project that
prevents deforestation; FM, forest management refers to a project that improves the net carbon sequestra-
tion; AF, afforestation refers to a project that establishes trees on land that otherwise would not be planted;
AG, agroforestry refers to a project that combines forest conservation and or tree planting with agricul-
ture; TM, timber/wood products involves sustainable (continued on next page... )
Full-size DOI: 10.7717/peerj.8891/fig-1
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 4/41
2The term isotopologue refers to chemical
species that differ only in the isotopic
composition of their molecules or ions.
3The term stable isotope has a similar
meaning to stable nuclide but is preferably
used when speaking of nuclides of a
specific element. The expression ‘‘stable
isotope ratio’’ is used to refer to isotopes
whose relative abundances are affected
by isotope fractionation in nature. The
stable isotopic compositions of low-mass
(light) elements such as oxygen, hydrogen,
carbon, nitrogen, and sulfur are normally
reported as ‘‘delta’’ (d) values in parts per
thousand (denoted as h) enrichments or
depletions relative to a standard of known
composition. The symbol his spelled
out in several different ways: permil,
per mil, per mill, or per mille. The term
‘‘per mill’’ is the ISO term, but is not yet
widely used. d values are calculated by:
(in h) = (Rsample/Rstandard - 1)1000
where ‘‘R’’ is the ratio of the heavy to light
isotope in the sample or standard. For
the elements sulfur, carbon, nitrogen,
and oxygen, the average terrestrial
abundance ratio of the heavy to the light
isotope ranges from 1:22 (sulfur) to 1:500
(oxygen); the ratio 2H:1H is 1:6410. A
positive d value means that the sample
contains more of the heavy isotope than
the standard; a negative d value means
that the sample contains less of the heavy
isotope than the standard. A d15N value of
+30 hmeans that there are 30 parts per
thousand or 3 hmore 15N in the sample
relative to the standard.
Figure 1 (...continued)
harvest of timber within the project area resulting in wood products for construction and manufactur-
ing. Traditional protocols do not directly observe CO2but rely on proxies and estimation. The DMFCP is
formalized with standardized intake forms listing a project (e.g., project listing application) and a project
management plan defining terms and conditions for carbon product operations across multiple 10-year
intervals. NEE records reductions in photosynthesis caused by fire and deforestation should these events
occur in the project areas. Standing carbon inventory derived from biometric or remote sensing methods
will be employed to augment and cross-check project NEE data. The SoS and GMPs operate as an inte-
grated autonomous system to monitor, measure and transform GHG flux data relative to local, regional
and global reference materials for bulk and isotopic composition, providing the basis for calculation of
verified tradeable GHG financial products that differentiate biogenic from anthropogenic net GHG fluxes
(Marino, 2013;Marino, 2014a;Marino, 2017b;Marino, 2017a;Marino, 2014b;Marino, 2014c;Marino,
2015b;Marino, 2015a;Marino, 2016b;Marino, 2016d;Marino, 2016c;Marino, 2016a;Marino, 2019).
METHODS
Net ecosystem exchange (NEE) is a measure of the net exchange of carbon fluxes between
an ecosystem and the atmosphere (per unit ground area) and is a universally accepted and
fundamental metric of ecosystem carbon sink strength (Baldocchi, Chu & Reichstein, 2018;
Baldocchi & Penuelas, 2019;Kramer et al., 2002). NEE can be defined as:
NEE =GPP +Reco (1)
and,
Reco =Ra+Rh,(2)
where GPP =gross primary production or photosynthetic assimilation, Reco =ecosystem
respiration, Ra=autotrophic respiration by plants, and Rh=heterotrophic respiration by
soil microbes. NEE can be expressed as Net Ecosystem Productivity (NEP) plus sources and
sinks for CO2that do not involve conversion to or from organic carbon: −NEE =NEP
+ inorganic sinks for CO2−inorganic sources of CO2(Chapin et al., 2006;Lovett, Cole &
Pace, 2006;Luyssaert et al., 2009). NEE measurements integrate (1) and (2) (e.g., Burba,
2013), consistent with the focus presented here on sequestration and monetization of
biospheric carbon where CO2reduction/increase is a credit/debit to forest and biospheric
carbon storage. For example, a negative NEE flux represents a net carbon sink into the
biosphere (e.g., removal or capture of CO2from the atmosphere) and a positive NEE
represents a net carbon source into the atmosphere from the biosphere (e.g., increase of
CO2in the atmosphere). The sign convention accommodates the definition of a carbon
credit as representing 1 tone CO2equivalent (CO2eq)ii sequestered or captured from
the atmosphere (Kollmuss et al., 2010). We assume that loss of carbon due to fire, UV,
removal of biomass and import of biomass is negligible as both project sites are protected
(Ankasa Park) or managed as conserved land (Harvard Forest). NEE potentially records
reductions in photosynthesis caused by fire and deforestation should these events occur
in the project areas (Goulden et al., 2006;Mamkin et al., 2019;Ney et al., 2019;Ueyama et
al., 2019). Standing live carbon inventory derived from biometric and or remote sensing
methods, typically would be employed to augment NEE data (Ouimette et al., 2018;Verma
et al., 2013).
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 5/41
Figure 2 Showing DMFCP components and time series. (A) Features and benefits of the DMFCP com-
prised of the SoS and GMPs include: (1) direct field measurement of NEE (CO2, CH4and N2O) relative to
a zero-emission baseline showing positive, negative or neutral GHG emission employing an SoS and one
or more GMPs; a positive NEE indicates a GHG source or emission to the atmosphere from the biosphere,
whereas a negative NEE indicates a CO2sink or emission reduction from the atmosphere and results in
carbon credits or offsets, (2) ex ante, annual accreditation periods that can be applied to multiple GHG’s,
(3) exits from a landowner agreement after 10 years with a penalty according to a ton-year accounting cal-
culation, (4) landowner benefit from initial upfront payment (t0) and annual royalty on sales payment
(t1). (B) Multiple projects subsequent to data quality checks by a data center can be listed in a registry and
grouped into pooled portfolios; verification of system performance by external third-party verifiers of ref-
erence values and calibration of GHG analyzers is performed according to operation of the SoS. Products
can be purchased by voluntary and compliance buyers worldwide through multiple sales channels. The
hypothetical values shown for CO2, CH4and (continued on next page... )
Full-size DOI: 10.7717/peerj.8891/fig-2
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 6/41
Figure 2 (...continued)
N2O (bars) resulting from a field sensor platform are negative in year one and mixed in years 2 and 10.
Simple addition of the values for each GHG for annual periods result in a positive, negative or neutral
GHG balance. Multiple projects located in specified property boundaries can be grouped to address simple
numerical additionality. The DMFCP process simplifies existing protocols for forest carbon sequestration
(Table 1). Traditional protocols rely on proxies for CO2(i.e., not measured or observed at any time in the
protocol process) to establish a baseline and test for additionality. NEE records reductions in photosyn-
thesis caused by fire and deforestation should these events occur in the project areas. Standing carbon in-
ventory derived from biometric or remote sensing methods will be employed to augment and cross-check
project NEE data. The SoS and GMPs operate as an integrated autonomous system to monitor, measure
and transform GHG flux data relative to local, regional and global reference materials for bulk and iso-
topic composition, providing the basis for calculation of verified tradeable GHG financial products that
differentiate biogenic from anthropogenic net GHG fluxes (Marino, 2013;Marino, 2014a;Marino, 2017b;
Marino, 2017a;Marino, 2014b;Marino, 2014c;Marino, 2015b;Marino, 2015a;Marino, 2016b;Marino,
2016d;Marino, 2016c;Marino, 2016a;Marino, 2019).
A detailed review of the eddy covariance method, parameter values, data processing
codes and uncertainties for determination of NEE are not under study in this work and
have been reported in detail elsewhere (Blackwell, Honaker & King, 2017;Vitale, Bilancia &
Papale, 2019). A data processing flowchart for Fluxnet2015, the source of data used in this
study, is available (https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/).
Uncertainties of up to 20% of annual flux for a single NEE tower have been reported
(Finkelstein & Sims, 2001) but vary according to random and systematic uncertainty terms
and applicable corrections. Annual mean NEE fluxes between co-located towers were found
to be within 5% of each other for the Howland Forest eddy covariance site (Hollinger &
Richardson, 2005). NEE uncertainty can be influenced by the number of towers for a given
project area (Kessomkiat et al., 2013), instrument noise, spectral attenuation, atmospheric
turbulence, and data processing (Foken, Aubinet & Leuning, 2012;Polonik et al., 2019), in
addition to upscaling from tower footprint to larger areas (Hollinger & Richardson, 2005;
Richardson et al., 2006;Mauder et al., 2013). NEE uncertainties for single and multiple
towers are actively under study as are quantitative corrections including those for: (1)
chronic underreporting of nocturnal fluxes due to low friction velocity (Aubinet, Vesla
& Papale, 2012; (Staebler & Fitzjarrald, 2004;Wutzler et al., 2018), (2) filtering of raw EC
data for conversion to half-hourly data commonly reported and as reported in this study
(Fluxnet, 2020b), (3) gap-filling protocols (Reichstein et al., 2012), and, (4) extrapolation
of EC data to areas outside of the EC footprint (e.g., single sites and networks) (Jung et al.,
2019;Wang et al., 2016). The reported corrections and uncertainties are noted for the data
employed in this study.
NEE data for a single tower for each site was accessed from online data sources and
transformed into tones carbon dioxide equivalent per acre per year (e.g., tCO2eq acre−1
yr−1) (Supplemental Information 2). The NEE values for both sites representing footprints
of ∼1–10 km2are used to extrapolate NEE to 100,000 acres (40,469 hectares) to illustrate
potential revenue for large-scale projects. The extrapolation of NEE data is for illustration
purposes only as single tower data for both sites may not be representative of larger
forest areas, discussed below. Extrapolated NEE values were combined with carbon prices
ranging from $5 to $36 tCO2eq to explore pre-tax revenue scenarios including definition
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 7/41
of hypothetical carbon products underlying the projections. Cumulative tCO2eq is based
on summing the annual tCO2eq for each record across the extrapolated area of 100,000
acres (40,469 hectares).
Field Sites
Ankasa Park, Ghana, Africa (Fig. 3A)
The Ankasa Park (AP) eddy covariance tower (5◦1700000 N2◦3900000 W: GH-Ank) is located
in a wet evergreen forest in south-western Ghana (Fig. 3A) (Nicolini, 2012) within the
Ankasa Conservation Area. The 62-meter-high AP tower equipped with an open path
CO2analyzer was developed and operated as part of the CarboAfrica project (Stefani et
al., 2009) and was operational for four years (2011 to 2014) by the University of Tuschia,
Italy (Nicolini, 2012). NEE data used in this analysis are available online from the Fluxnet
(2015) quality-checked database (http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/) as
annual NEE based on the gap filled VUT_NEE_REF values (e.g., Wutzler et al., 2018). The
NEE data are gap-filled, filtered and corrected for low friction velocity periods that likely
underestimate night time respiration (‘‘Data Processing—Fluxdata, 2020a;ORNLDAAC,
0000;Nicolini, 2012). Uncertainty for the corrected AP 30-minute NEE data was reported
as 0.20 µmol m−2s−1or 6.7% of the daily means (Nicolini, 2012). The Ankasa Resource
Reserve, established in 1934 (Hall & Swaine, 1981), lies within the administrative rule of the
Jomoro district in the Western region of Ghana and is under the paramount chief of Beyin
(Bandoh, 2010). The reserve was managed as a protected timber producing area until 1976
at which time it was designated as the Ankasa Resource Reserve (Damnyag et al., 2013).
The forest area is comprised of ∼500 km2surrounded by deforested landscapes; the area is
∼90 m above sea level with mean annual temperature of ∼25 ◦C. According to Hawthorne
& Abu-Juam (1995), the Ankasa Resource Reserve has an average Genetic Heat Index
(GHI) of 301, compared to a maximum of 406 (Janra & Aadrean, 2018;Vanclay, 1998),
designating the reserve as a global priority conservation area that should be permanently
removed from timber production. Hilly portions of the reserve showed the highest GHI
score of 406 (Hawthorne & Abu-Juam, 1995). The high GHI scores in Ghana are amongst
the ‘‘hottest’’ patches of genetic rarity in Africa, many of the species concerned being
found elsewhere only across the border in Southern La Cote D’Ivoire. Official records on
timber logging activities in the Ankasa Resource Reserve are incomplete as the management
objective has been primarily for protection and resource conservation, however, illegal
logging in the reserve may have occurred during the period of observation. Poor soils
of the area have generally discouraged commodities (i.e., cocoa) production and food
farmers. The population around the reserve has been historically low for a forest area
(Hall & Swaine, 1981), but has experienced dramatic population increase. For example, in
1960, the estimated population was 45,162 but declined to 37,685 by 1970. The results of
Ghana’s 1984 population census recorded a jump in population of 70,881, an increase of
88%. According to the 2000 population census, the population of the Jomoro district had
increased significantly to 111,348 an increase of 57% since 1984 (Bandoh, 2010). In the
recent 2010 census, the population recorded for the district was 150,107 (Bandoh, 2010)
representing an increase of 34%. The red color in Fig. 3A, denoting deforestation over
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 8/41
approximately the last decade, illustrates the anthropogenic pressure the reserve faces in
the future.
Harvard Forest, Petersham, MA, USA (Fig. 3B)
The Harvard Forest (HF) Environmental Measurement Site tower (42.537755◦N,
72.171478◦W; US-Ha1) is a component of the Harvard Forest Long Term Ecological
Research (LTER) site in Petersham, Massachusetts (Harvard Forest Long Term Ecological
Research Site, 2019) and a core site in the AmeriFlux network (US-Ha1). The elevation
of the research area of ∼16.18 km2ranges from 320 to 380 m above sea level (Fig.
1A). NEE data used in this analysis are available online from the Fluxnet2015 database
(https://fluxnet.fluxdata.org/doi/FLUXNET2015/US-Ha1). Additionally, current data are
available from AmeriFlux data repository (http://dx.doi.org/10.17190/AMF/1246059)
and LTER data archives (Environmental Data Initiative; https://doi.org/10.6073/pasta/
74fe96d1571db7f15bf6f1c4f53c0c02). The HF tower measurements, initiated in Oct 1991
with closed path CO2analyzers, provide the longest continuous set of flux measurements
in the US (Barford et al., 2001;Urbanski et al., 2007). The mixed deciduous forest stand
surrounding the tower has been regenerating on abandoned agricultural land since the
late 1890’s punctuated by a major hurricane disturbance in 1938. The Harvard Forest,
US-Ha1 data are NEE quality-checked (e.g., Pastorello et al., 2014), gap-filled, filtered and
corrected for low friction velocity periods (e.g., friction velocity (u*) less 0.4 m/s) that
underestimate nighttime respiration flux (e.g., Urbanski 1996). Uncertainty for US-Ha1
gap-filled data for the years 1992–2004 was reported as ±0.03 tCO2yr−1(95% Confidence
Interval) relative to a mean NEE for the period of −2.42 tCO2ha−1yr−1, determined by
random sampling of NEE error populations (Richardson et al., 2006;Urbanski et al., 2007).
US-Ha1 data are corrected for horizontal advection of CO2reported of up to 35% of the
CO2budget during summer intervals from 1999 to 2002 (Staebler & Fitzjarrald, 2004)
and up to 40% loss of daytime CO2flux depending on wind speed (Sakai, Fitzjarrald &
Moore, 2001). Corrections are routinely applied to account for incomplete flux emphasizing
the importance of understanding site conditions at each location to assess optimal and
representative flux results. The dataset was read in line by line and processed using Python
Libraries (Pandas, NumPy) with txt format. Year, month, day, hour and NEE data were
selected for this study. NEE was determined by calculating the mean of 48 half-hour data
for each day as µmol CO2m−2s−1, converting the value to gC m−2d−1, and summing
daily NEE to calculate annual NEE for each year. The US-Ha1 NEE data over the period of
record (e.g., 2000–2019) has been interpreted in the context of historical land use including
agriculture, reforestation and hurricane disturbance, demonstrating the impact of weather,
climate and human activity on NEE (Cogbill, 2000;Compton & Boone, 2000;Barford et al.,
2001;Bellemare, Motzkin & Foster, 2002;Urbanski et al., 2007). The Harvard Forest project
area has been studied using a variety of remote sensing data to assess species composition
across the project area and to understand short and long term responses to climate
change (Kim et al., 2018). Hyperspectral and lidar data (https://glihtdata.gsfc.nasa.gov)
(e.g., (Kampe, 2010), Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 9/41
Figure 3 Locations of the eddy covariance sites analyzed in this study. (A) Location of Ankasa Park
Tower, Ghana, Africa, (B) Location of Harvard Forest Tower, Massachusetts, USA (Image credits: (A)
http://earthenginepartners.appspot.com/science-2013-global-forest, powered by Google Earth (c) 2020;
inset map: https://commons.wikimedia.org/wiki/File:Ghana_(orthographic_projection).svg, License: CC
BY SA 3.0; (B) Map credit: Google, Delta SQ, NOAA, US Navy, GA, CEBCO Landsat/Copernicus US Ge-
ological Survey 2019); inset map: https://commons.wikimedia.org/wiki/File:Massachusetts_in_United_
States_(zoom).svg, License: CC BY SA 3.0).
Full-size DOI: 10.7717/peerj.8891/fig-3
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 10/41
(https://aviris.jpl.nasa.gov/dataportal/), and Landsat data (Melaas et al. 2016) are also
available for the US-Ha1 project area.
DMFCP technical description
The DMFCP is comprised of hardware and software components designed to function as an
automated commercial field sensor network (Figs. 1 and 2), the System of Systems. (Marino,
2011;Marino, 2012;Marino, 2016c;Marino, 2016d;Marino, 2017a;Marino, 2017b;Marino,
2017c;Marino, 2018a;Marino, 2018b;Marino, 2018c;Marino, 2019;Marino, 2013;Marino,
2014a;Marino, 2014b;Marino, 2014c;Marino, 2015a;Marino, 2015b;Marino, 2016a;
Marino, 2016b). An integrated sensor platform, the Global Monitoring Platform, is
positioned at each node of the network. The Software components of the SoS are configured
to interact with all nodes for automated reporting of data and instantaneous third-
party verification of systems, processes, uncertainties and results. The SoS summarizes
measurements of GHG fluxes against local, regional and global reference materials for bulk
and isotopic composition, providing the basis for calculation of verified tradeable GHG
financial products for forests and anthropogenic net carbon fluxes for fossil fuel derived
CO2. The DMFCP provides the operational framework for underlying contract terms
defining project time periods, land area, management objectives, measurements and cases
for intentional and unintentional forest carbon reversals, conditions beyond the scope of
this study. Additional details for the SoS, GMP and related field equipment for NEE flux
determinations, in addition to typical project agreement and contract terms, are described
in Supplemental Information 1. A comparative summary of the features and benefits of
the DMFCP and widely employed estimation protocols (e.g., CARB, CAR, ACR, VERRA,
CDM) is presented in Table 1.
RESULTS
Figure 4 illustrates the annual (tCO2eq) NEE for HF (24 years) and AP (4 years) sites
relative to a zero-reference baseline established by instruments (i.e., open or closed path
CO2analyzers) and standard calibration protocols at both sites and to a zero-emissions
baseline defining negative (e.g., net CO2sequestered), positive (e.g., net CO2emissions
released to the atmosphere) or neutral carbon balance (e.g., 0 sequestration/emissions).
Annual NEE values for HF and AP were negative over the intervals shown resulting from
active forest carbon sequestration and generation of carbon credits (Fig. 4A). Annual NEE
for HF ranged from a minimum of −0.53 (2010) to a maximum of −9.09 (2008) tCO2. The
mean and standard deviation (SD) for the HF site for 24 years was −4.5 tCO2acre-1 yr-1
±2.3 (SD). Annual NEE for AP ranged from a minimum of −6.74 (2013) to a maximum of
−15.2 (2011) tCO2. The mean and standard deviation (SD) for the AP site for 4 years was
−10.2 tCO2acre-1 yr-1 ±3.6 (SD). Pre-tax revenue annual variance and risk are illustrated
in the HF 2010 NEE (Fig. 2A), emphasizing a reversal of +4.79 tCO2eq relative to 2009,
equivalent to a one-year loss of $4,790,00 ($10 tCO2eq), but again reversed the following
two years attaining −5.04 tCO2eq and revenue of $4,510,000 ($10 tCO2eq). Figure 4B
shows the corresponding cumulative NEE across the observational periods recorded for
each site extrapolated to 100,000 acres (40,469 hectares). The HF and AP linear cumulative
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 11/41
Table 1 Comparison of features and benefits for existing forest carbon protocols and the DMFCP. Protocol features such as inclusion/exclusion of soil respiration
CO2flux, measurement time intervals and compatibility with expanded forest carbon flux measurements of CH4and N2O are compared for existing forest carbon proto-
cols and the DMFCP. Each feature is discussed in the text.
Protocol feature Existing protocols aDMFCP Benefit to Landowner
1 Project tradable units Total tCO2equivalent (tCO2eq)
is reported for project area (e.g.,
CO2eq acre-1) without reference
to project area
Tons CO2equivalent (tCO2eq),
gC m−2yr −1(derived from 10 Hz
CO2data, daily, weekly, monthly
and annual sums)
Project reporting on an area ba-
sis is a fundamental metric of for-
est carbon sequestration area, (e.g.,
per acre), a metric not reported in
estimation-based protocols
2 CO2observations by direct mea-
surement
No Yes; infrared and laser-based gas
analyzer methods for CO2(10
Hz)
Direct measurement of GHG’s re-
duces risk of invalidation, increases
quality of forest carbon offsets and
offers management information
3 Monitoring implementation A timber cruise is completed fol-
lowed by forward and/or back-
ward tree growth model simula-
tions across arbitrary time inter-
vals
A network of observation plat-
forms is established across the
project area, the System of Sys-
tems (SoS), with diverse sensors
including high precision gas an-
alyzers for CO2, N2O, CH4and
micrometerological data compris-
ing the Global Monitoring Plat-
form (GMP); the SoS automates
data and reporting for GMP net-
work nodes, including analytical
uncertainty
The SoS and GMP commercial
products are standardized and
designed to deploy as turn-key
engineered operations in the
field, lowering the cost of NEE
measurements and improving
the coverage of NEE across large
landscapes; estimation protocols are
not standardized and do not directly
measure CO2and other gases of
interest
4 Calculation of net forest carbon
sequestration
Plot based timber inventory con-
ducted every 6 or 12 years em-
ploying non-standardized tree
growth simulation models
Use standardized scientifically ac-
cepted equation for NEE (NEE =
GPP + Reco (Reco =Ra + Rh)),
where GPP =gross primary pro-
duction or photosynthetic assim-
ilation, Reco = ecosystem respira-
tion, Ra = autotrophic respiration
by plants, and Rh = heterotrophic
respiration by soil microbes. (see
Methods section, equations (1)
and (2) in the text)
Landowners and stakeholders can
rely upon an accepted universal ap-
proach to quantify net forest car-
bon sequestration (NEE) in contrast
to estimation protocols that do not
employ actual CO2measurement at
any time during the protocol process
5 Vertical gross and net flux obser-
vations
No Yes; eddy covariance methods are
applied resulting in 30" averages
of gross vertical CO2fluxes used
to calculate daily/annual net car-
bon flux (NEE)
Direct measurement reduces risk of
invalidation, increases quality of for-
est carbon offsets and offers man-
agement information
(continued on next page)
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 12/41
Table 1 (continued)
Protocol feature Existing protocols aDMFCP Benefit to Landowner
6 Universal metric for Annual Net
ecosystem exchange (NEE) or net
forest carbon sequestration
No, forest carbon sequestration
is based on regionally applied es-
timation algorithms and growth
models as proxies for annual
changes in net forest carbon
Yes; vertical CO2fluxes are used
to calculate daily, seasonal and
annual NEE reported as ppm CO2
m−2time−1
Net changes in annual forest car-
bon sequestration are based on
30" interval data providing daily,
weekly, monthly and annually re-
solved changes in NEE; in contrast
estimation-based model runs start
and end subject to user discretion
and without validation by direct
measurement of CO2
7 Soil CO2flux No Integrated in vertical fluxes Complete accounting of carbon flux
is required for NEE; the DMFCP
provides data on soil ecosystem dy-
namics
8 Cost to Landowner Substantial fees are incurred from
inception to registry listing of car-
bon credits; fees increase with size
of project
No direct fees from inception to
listing on a registry; upfront pay-
ments and annual royalty pay-
ments may be structured within
a project agreement and contract
between landowner and service
provider
Elimination of direct fees to initiate
a forest carbon project incentivizes
landowners to engage in forest car-
bon programs with economic, eco-
logical and business advantages
9 Time interval to achieve positive
revenue
Years (1–5) Daily to yearly, subject to project
agreement and contract
Revenue to landowner is achievable,
in practice, based on daily NEE but
more typically would be paid annu-
ally, or over multiple years resulting
in long term incentivizes for sustain-
able management; traditional pro-
tocols may require years to receive
initial payment
10 Marketing and sales of GHG off-
sets
Responsibility of landowner (e.g.,
fee-based listing on a registry);
voluntary and compliance offsets
are priced differently based on
discretionary criteria
Projects and products are pooled
into portfolios and listed in a no-
fee registry for sale to voluntary
and compliance buyers world-
wide, subject to project agree-
ment and contract
Relieves landowner from handling
carbon offsets once issued and from
additional cost; direct measurement
creates equivalent voluntary and
compliance offsets
(continued on next page)
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 13/41
Table 1 (continued)
Protocol feature Existing protocols aDMFCP Benefit to Landowner
11 Baseline Estimated and uncertain; based
on counterfactual arguments and
proxy data; positive values are not
permitted, or default value is used
Baseline is the zero-emissions
point from which positive, nega-
tive or neutral emissions of CO2
flux occur; the zero baseline is
shared across analyzers via cal-
ibration with shared standards
and references
All NEE results are instrumentally
and financially comparable provid-
ing improved management of mul-
tiple project landscapes; a measured
zero baseline eliminates estimation
invalidation risk
12 Additionality Based on uncertain counterfac-
tual arguments regarding unob-
served CO2or default values and
other criteria
Simple mass balance of carbon
(e.g., NEE) across designated ar-
eas can be summed to determine
overall carbon balance, or test
for differences between pooled
portfolios offering measured and
numerical tests of additionality
management plans or contracts
Eliminates uncertainty associated
with this factor; provides near real
time data for NEE and forest project
management planning across addi-
tional landscapes and property own-
ership (e.g., municipal, private)
13 Invalidation period and compli-
ance testing
Up to 8 years based on 5% invali-
dation rule
No invalidation period is re-
quired as validation with shared
universal standards is conducted
every 30"; invalidation can be
triggered at any time instrument
performance is reported as faulty
Elimination of an invalidation
period with a near-real time system
check will attract more project
participants and buyers of carbon
project products
14 Third party verification Third party validates calcula-
tions and estimation protocols re-
ported in project documents; it
does not include validation by in-
dependent direct measurement
Third party validation is made
by independent direct measure-
ments by an unaffiliated group as
specified in the governing project
agreement and contract
True independent third-party vali-
dation will support pricing of GHG
products and market transactions as
well as provide strict testing for in-
valid and fraudulent claims of GHG
reductions based on direct measure-
ment records
15 Test for switch to net positive
emissions
No Yes; NEE identifies transitional
net negative (i.e., carbon offset
producing) to net positive forest
carbon dynamics
Switch to positive emissions may
suggest landowner management
practices to attain net neutral or net
negative balance and may indicate
changes in forest ecosystem function
due to climate change
(continued on next page)
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 14/41
Table 1 (continued)
Protocol feature Existing protocols aDMFCP Benefit to Landowner
16 Permanence 100-year requirement Up to 100 years but achievable
in decadal increments; a 100-
year time horizon is an arbitrary
project interval
A 100-year forest carbon perma-
nence requirement is a primary bar-
rier to landowner and investor par-
ticipation; 10-year interval project
planning allows extensions or exit
and is compatible with short term fi-
nancial forecasting
17 Project exit or termination High penalty Ton-year accounting is employed
to adjust exit penalty based on
project impact of atmospheric
emissions sequestered over time
Barriers to forest carbon manage-
ment are lowered when reasonable
exit strategies are available based on
an accepted accounting method
18 Monitoring of CH4, N2O and
other gases
Not applicable as estimation pro-
tocols based on forest mensura-
tion are not intrinsically linked to
CH4and N2O emissions
Eddy covariance can be used to
determine next flux for CH4, N2O
and other gases employing com-
mercially available instrumenta-
tion, similar to the method used
for CO2; eddy covariance pro-
vides a combined three-gas GHG
budget
A three-gas GHG budget offers
landowners more options to man-
age GHG neutral budgets and will
expand areas of project applications
and increase product options
19 Incorporate isotopologues of CO2
and other GHG’s
Not applicable as estimation pro-
tocols based on forest mensura-
tion are not intrinsically linked to
GHG isotopologues
Eddy covariance can be used to
determine isofluxes for any iso-
topologue, similar to the method
used for CO2creating new prod-
uct categories
Isotopologues of CO2, CH4and
N2O, among others, may offer the
landowner additional options to
manage projects for net or negative
GHG impacts and will increase the
diversity of forest carbon product
options
20 Wetland, aquatic and oceanic
emissions
Not applicable as estimation pro-
tocols based on forest mensura-
tion are not intrinsically linked
aquatic/oceanic sources for CO2
and CH4
Eddy covariance can be applied
to wetlands, aquatic and estuarine
features and to oceanic systems
by measurement of CO2and CH2
exchange with the water surface
Landowners with wetland and
aquatic features will benefit from
inclusion of these aquatic sources as
associated forest GHG products; all
stakeholders benefit from expanded
knowledge of Earth system function
including oceanic GHG dynamics
(continued on next page)
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 15/41
Table 1 (continued)
Protocol feature Existing protocols aDMFCP Benefit to Landowner
21 Contribution to forestry & at-
mospheric science and climate
change studies and models
Lack of publications employing
estimation-based protocol data
with numerical climate change
studies and models
All GHG flux data are relevant to
evolving ecosystem function rela-
tive to climate change and human
activity; all data may be incorpo-
rated in climate change research
and atmospheric transport mod-
els based on calibrated and stan-
dardized measurement protocols
All stakeholders benefit from under-
standing the mechanisms affecting
forest GHG dynamics and policies;
landowners may employ model data
to develop climate change mitiga-
tion strategies
22 Uncertainty and errors Uncertainty and error sources
are estimated at up to ∼82%,
representing the contribution of
ecosystem respiration to NEE, a
quantity not measured with esti-
mation protocols; errors for N2O
and CH4are unknown as forest
growth models are not parame-
terized for these gases and direct
measurements are not made
Uncertainties and errors for eddy
covariance methodology and cal-
culation of up to ∼20% annual
NEE have been reported, and in-
clude instrumentation, set up,
data processing, and up-scaling
NEE from a single tower, to yield
30 min CO2flux averages; correc-
tive measures are typically applied
to sources of uncertainty resulting
in errors of ∼±0.03 tCO2yr−1
Up-scaling from eddy covariance
tower data can be addressed by in-
creasing the number of observation
platforms and tower heights within
the SoS sensor architecture; widely
accepted corrections for NEE uncer-
tainties and errors can be uniformly
applied to NEE data across the SoS
harmonizing uncertainty analysis
and corrections that are under active
and evolving study in contrast to es-
timation protocols that, to the best
of our knowledge, have not under-
taken comparison with directly mea-
sured CO2
23 Underlying Financial Terms and
Contract
Estimation Protocols employ typ-
ical contract terms but do not in-
clude standardized performance
metrics based on direct measure-
ment of GHG’s
Project terms and contract lan-
guage will be standardized includ-
ing performance metrics, pricing
metrics and exit strategies (e.g.,
item 17) including force majeure
clauses and technology perfor-
mance specifications (Figs. 1 and
2;Supplemental Information 1)
Standardized measurement perfor-
mance terms and contracts apply to
all projects, voluntary and compli-
ance, harmonizing efforts for forest
conservation and reforestation
Notes.
a(California Air Resources Board, 2011;California Air Resources Board, 2014;Anadiotis et al., 2019;Kollmuss & Fussler, 2015;Winrock International, 2016;Marland et al., 2017;Climate Action Reserve, 2018;
Zhang et al., 2018;Marino, Mincheva & Doucett, 2019)
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 16/41
Figure 4 Net ecosystem exchange for the Harvard and Ankasa times series. (A) Annual NEE observed
at the Harvard Forest, Petersham, MA, USA and at Ankasa Park, Ghana, Africa. (B) Cumulative NEE
records corresponding to annual NEE and extrapolated across 100,000 acres are employed for illustration
of pre-tax cash flows.
Full-size DOI: 10.7717/peerj.8891/fig-4
NEE provides insight into the potential short and long-term sequestration capacity of the
respective forest landscapes. The AP NEE slope of −8.40×is 1.7 times that of the HF
suggesting that in this case, the tropical wet evergreen forest site experienced consistently
greater sequestration of carbon than the temperate deciduous forest. However, caveats
apply in that tropical forests may not result in larger long-term carbon sinks, nor is
continued net negative carbon sequestration guaranteed or required for forest carbon
trading markets. For example, tropical forests typically have larger gross production but a
corresponding larger respiration (Baldocchi, Chu & Reichstein, 2018;Baldocchi & Penuelas,
2019). Additionally, the two forest locations differ in stand age and history of disturbance,
factors that are known to affect NEE (Hollinger et al., 2013;Ouimette et al., 2018;Urbanski
et al., 2007). However, NEE provides a quantitative record of daily and annual sums of
carbon sequestration characterizing the fundamental nature of derivative carbon products
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 17/41
Figure 5 The pre-tax cash flow for two hypothetical cases for landowner revenue associated with for-
est carbon management. The graph depicts projected cash flows for landowners for the two cases de-
scribed for Harvard Forest, USA, and Ankasa Park, Africa. Upfront payments are paid to the landowner
prior to project initiation. Additional cash flows are created by selling carbon products after the initial year
of monitoring (Fig. 1). Case 1 (unfilled bar, Harvard Forest; filled black bar, Ankasa) shows the total pre-
tax cash flow for an upfront payment of 10% of the projected annual revenue. Case 2 (light shaded bar,
Harvard Forest; dark shaded bar, Ankasa) shows the total pre-tax cash flow for an upfront payment of 8%
of the projected annual revenue plus deferred payouts of 6% of the realized revenue from the sale of all
carbon products. The vertical bars represent the impact of a ±20% market variance on realized revenue.
These examples are provided for purposes of illustration and do not represent actual carbon products by
type or cashflow.
Full-size DOI: 10.7717/peerj.8891/fig-5
that cannot be replicated by proxies for forest carbon sequestration (e.g., estimation-based
protocols). Annual NEE trends may also be difficult to characterize for sites with less than
five years of NEE data emphasizing the importance of establishing new and sustained
NEE observation platforms (Baldocchi, 2019); Dennis (Baldocchi, Chu & Reichstein, 2018).
Figure 5 illustrates landowner pre-tax cash flow (millions USD) relative to variable carbon
pricing of tCO2eq ($5, $10, $15, $36) for cumulative NEE consisting of 24 and 4 years for
the HF and AP sites, respectively. The values represent extrapolations of measured local
NEE to 100,000 acres (40,469 hectares) multiplied by the annual NEE record for each site.
Two cases are represented in which the landowner receives a single upfront payment (Case
1) or an upfront payment plus annual royalty on sales (Case 2). Case 1 pre-tax cash flow
estimates range from upfront payments (e.g., 10%) of $230,000 to $1,670,00 and $510,000
to $3,680,000 for HF and AP, respectively, across carbon prices of $5 to $36 tCO2eq. Case 2
pre-tax cash flow estimates range from an upfront payment (e.g., 8%) plus deferred payouts
based on realized revenue from the sale of all carbon products (e.g., 6%) of $3,520,000
to $25,360,000 and $1,640,000 to $11,790,000 for HF and AP, respectively, across carbon
prices of $5 to $36 tCO2eq. Variance for the total pre-tax sales value of ±20% of realized
revenues is indicated by vertical bars to reflect uncertainty in the sale of carbon products
for Case 2.
Figure 6 illustrates cases of pre-tax cash flow change for a decrease/increase in native
carbon sequestration strength based on the minimum, mean and maximum values of NEE
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 18/41
Figure 6 Projected pre-tax cash flows for the Harvard and Ankasa forest over the time series studied.
Landowner pre-tax cash flows are depicted based on a price of 10 per tCO2eq across the minimum, mean
and maximum values recorded for the Harvard Forest, USA (unfilled bars), and the Ankasa Park forest,
Africa (filled bars), extrapolated to 100,000 acres for the historical record of each site. The vertical bars
represent the impact of a ±20% market variance on realized pre-tax revenue. These examples are provided
for purposes of illustration and do not represent actual carbon products by type or cashflow.
Full-size DOI: 10.7717/peerj.8891/fig-6
observed for each site’s historical record (extrapolated to 100,000 acres or 40,469 hectares).
Local sequestration strength is expected to vary annually in response to rainfall and related
ecological factors. We use the minimum, mean and maximum values for NEE recorded at
each site to illustrate the effect of variable annual sequestration rate on pre-tax revenue.
Project value ranges from $760,000 to $13,830,000 and from $2,140,000 to $4,860,000
across the minimum, mean and maximum values for the annual records of the HF and AP
sites, respectively.
Figure 7 illustrates pre-tax cash flows for mixed carbon product types and pricing for
Case 2; example product inventory and pricing for the products is indicated below each
set of bars. Note that the hypothetical carbon products range in price from $12 tCO2eq
for compliance offsets to $50 tCO2eq for carbon products with the additional element
of biodiversity (e.g., Genetic Heat Index and conservation of floral and faunal species).
Total pre-tax cash flow for Case 2 is $16,380,000 and $7,610,000 for the HF and AP sites,
respectively. These data illustrate the higher potential revenue based on sale of mixed
products and pricing for voluntary, compliance and regulatory markets. The vertical bars
for Case 2 represent 20% variance in market uncertainty.
DISCUSSION
The SoS and DMFCP features continuous eddy covariance measurements for determination
of NEE for forest carbon providing standardized commercial methods and operations (Figs.
1and 2) in contrast to estimation based protocols that do not observe CO2assimilation
via photosynthesis or efflux via respiration. Shared calibration of instruments and reliance
on a shared zero-emissions flux baseline (e.g., carbon negative, neutral or positive) ensures
that all analyzers and results (e.g., SoS and GMP sensor nodes) within a network or
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 19/41
Figure 7 Hypothetical mixed carbon product types and projected pre-tax cash flows based on the ex-
ample product inventory noted. Total pre-tax cash flow for the Harvard Forest, USA (light shaded bar),
and the Ankasa Park, Africa (dark shaded bar), is $16,380,000 and $7,610,000, respectively. Both project
projections illustrate the potential value of offering a mix of products and pricing to maximize revenue.
Products may also incorporate additional GHG’s (e.g., CH4, N2O), isotopic species of the GHG’s, aspects
of the project land and cultural features related to landownership and stewardship. These examples are
provided for purposes of illustration and do not represent actual carbon products by type or price.
Full-size DOI: 10.7717/peerj.8891/fig-7
between networks (e.g., SoS) are comparable, inclusive of analytical uncertainties (Table
1). The near real-time data (i.e., 30-minute average of 10 Hz CO2measurements) for
forest NEE achievable with the eddy covariance sensor of the DMFCP offers insights into
forest carbon dynamics and ecosystem function previously unavailable to landowners,
investors and related stakeholders (Baldocchi, 2019). The result is a first of its kind
pooled portfolio of diverse forest projects and harmonized products for sale to voluntary
and compliance buyers worldwide transacted as tCO2eq (Figs. 1 and 2). The DMFCP
incentivizes forest conservation efforts, communities and management of atmospheric
CO2emissions compared to estimation-based protocols (Table 1) and REDD +platforms
that rely on such protocols (Köhl, Neupane & Mundhenk, 2020). NEE uncertainties can
be quantified and corrected for each project (e.g., single, multiple networks) according
to established and evolving methods within the forest carbon research community (e.g.,
Vitale, Bilancia and Papale, 2019b), particularly in conjunction with remotely sensed data.
Commercialization of established forest carbon research methodologies is feasible and
applicable to forest projects worldwide.
The NEE sites described in this work representing tropical and deciduous forests,
when pooled as a portfolio, provide species and ecological diversification with respect to
NEE source strength, vulnerability to climate change, population pressure and external
risks (e.g., currency value, national/sub-national environmental regulation) (Tarnoczi,
2017), a common investment risk reduction approach employed in modern portfolio
theory (Busby, Binkley & Chudy, 2020;Paut, Sabatier & Tchamitchian, 2020). For example,
while HF experienced the lowest NEE during 2010 (−0.59 tCO2acre−1yr−1,−0.4 tC
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 20/41
ha−1yr−1), a period known to be associated with anomalous drought and up to 86%
respiration relative to gross primary productivity (Gonsamo et al., 2015;Munger, Whitby &
Wofsy, 2018), AP experienced the highest NEE of the available record (−15.2 tCO2acre−1
yr−1, 10.2 tC ha−1yr−1), in part offsetting the HF loss. A portfolio with diverse projects
would be similarly buffered from extreme changes. Landowner agreements and contracts
could also specify options for aggregation of annual data intervals to buffer extreme
weather conditions (excluding catastrophic events such as wildfire, multiyear drought, and
hurricane) (see Supplemental Information 1). The upfront and royalty revenue structure
resulting from sale of DMFCP products, proposed in this study, provide financial incentive
for the landowner to rapidly enter into reforestation and forest management projects
in lieu of deforestation (e.g., legal and illegal) and increased anthropogenic disturbance.
Given the high rates of population growth within the AP reserve area (∼298% population
increase from 1970 to 2010), revenue programs may be uniquely suited for preservation and
management of protected areas in conjunction with community based efforts (e.g., Bempah,
Dakwa & Monney, 2019). Long-term forest carbon projects are likely to increase harvest
ages and management of forest stocking for optimal forest growth while promoting carbon
benefits of active sustainable forestry (Bastin, 2019;Chazdon & Brancalion, 2019); Chazdon,
2008). Enhancement of biodiversity, food webs and cultural engagement may also accrue
as forests grow (Bremer et al., 2019;Li et al., 2019;Watson et al., 2018). Conservation and
commercial forestry operations, although likely to have different goals, are accommodated
by the features and benefits of the DMFCP for effective carbon management.
The hypothetical financial structure and cases for pre-tax revenue for landowners
illustrate the potential impact of the DMFCP. The long-term cumulative value of both
sites, shown in Figs. 4B and 7(e.g., Total revenue from mixed products and pricing),
benefit landowner property valuation and reduces cost of delayed reforestation in-line with
indices for value of timber land operations (Ferguson, 2018;Keith et al., 2019;Zhang, 2019).
Figure 7 emphasizes the revenue potential of mixed forest carbon products incorporating
features of project biodiversity, such as noted for AP by high Genetic Heat Indices of
up to 401 (Hawthorne & Abu-Juam, 1995), and allocation of offsets for specific markets.
Pre-tax revenue for mixed carbon products and pricing is projected at up to $16,380,000
for the HF over the 24-year period (Fig. 6), an ∼5×and ∼2×return compared to pricing
of $5 and $10 tCO2eq (Fig. 5, Case 2), respectively, covering voluntary and compliance
carbon pricing levels (Hamrick & Gallant, 2018;World Bank Group, 2015). The two sites,
irrespective of the differing time-series length, actively sequester carbon at different rates;
it is not known if the observed trends will reverse as a result of climate change and/or
anthropogenic activity. The requirement for long term CO2measurement cannot be
understated for determination of variance in annual changes of NEE and for creation of
corresponding annual forest carbon financial products resulting from NEE (Baldocchi &
Penuelas, 2019;Marino, Mincheva & Doucett, 2019;Munger, Whitby & Wofsy, 2018). For
present purposes we assume that 100% of the products are sold in each case covering the
cost of the DMFCP.
There is no single figure of merit for NEE uncertainty. One of the main concerns
with eddy covariance based NEE, applicable to establishing networks of eddy covariance
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 21/41
towers as proposed, is the upscaling of limited footprints for individual EC towers to
surrounding ecosystems (Table 1, #22) Baldocchi, 2003;Kumar et al., 2016;Ran et al., 2016;
Román et al., 2009). Up-scaling is particularly important for mixed forest projects wherein
changing wind direction alters the source weight of heterogeneous land cover (Kim et
al., 2018) and remains a challenge to large-scale NEE determinations including use of
very tall towers (e.g., >300 m) and mesonet configurations to expand eddy covariance
footprints (Andrews et al., 2014;Chi et al., 2019;Goulden et al., 2006). SoS architecture
and sensor placement details will vary for each project addressing sources of uncertainty
established by initial survey and temporary placement of SoS platforms for evaluation.
Scale-up of eddy covariance flux tower data combined with remote sensing data is under
active study and directly relevant to developing approaches for the SoS (Fang et al., 2020;
Kenea et al., 2020;Peltola et al., 2019;Xiao et al., 2008). In actual DMFCP implementation
for HF and AP sites reported here (Fig. 3), remote sensing data would be used to establish
reliability of data extrapolation from a single tower and to guide the placement of additional
towers to fill spatial gaps in NEE measurement. A minimum two-tower configuration or
paired GMP sensor nodes for larger networks, would provide redundancy and cross-
checks to report SoS NEE combined uncertainty (e.g., He et al., 2010;Post et al., 2015;
Griebel et al., 2020). In addition, the use of open source eddy covariance processing
software such as ONEFlux (https://ameriflux.lbl.gov/data/download-data-oneflux-beta/;
see Supplemental Information 1, Eddy Covariance) and commercial software (e.g.,
https://www.licor.com/env/products/eddy_covariance/software.html), applied uniformly
across the SoS would harmonize data treatment including uncertainties for CO2and CH4
flux (e.g., Richardson et al., 2019).
Accepting NEE uncertainties (e.g., ‘Methods’ section), we argue that the approach is a
game-changer for creation and verification of forest carbon financial products compared to
estimation and model simulation-based protocols. For example, terms defined in (1) and
(2) (Methods section) are not defined or measured in estimation protocols (e.g., CARB,
CAR, CDM, ACR, VERRA), unavoidably introducing fundamental uncertainties in NEE
rendering the basis for reporting gC m−2yr−1for NEE as problematic and unverifiable
(Marino, Mincheva & Doucett, 2019). Moreover, interpreting estimation based timber
inventory protocols as representing only above ground carbon (e.g., photosynthetic
assimilation) likely results in over crediting errors given that ecosystem respiration
accounts for up to ∼82% of gross carbon flux from the soil to the atmosphere (Baldocchi
& Penuelas, 2019;Bond-Lamberty et al., 2018;Giasson et al., 2013;Richardson et al., 2013).
The anomalously low NEE for HF year 2010 (−0.53 tCO2eq), associated with drought
demonstrates the requirement for ecosystem respiration measurement for NEE. NEE
establishes 30-minute flux data comprising detailed baseline resolved time-series for
each project yielding annual mean data based on 17,520 such intervals for each CO2
analyzer. To our knowledge, estimation protocols have not been directly compared
with CO2measurements, or peer reviewed, (e.g., California Air Resources Board, 2011;
California Air Resources Board, 2014;California Air Resources Board, 2015a;California
Air Resources Board, 2015b) limiting scientific acceptance and demonstrating a need for
improved and peer reviewed non-NEE based methods.
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 22/41
In addition to potential revenue for landowners, the DMFCP simplifies the forest carbon
protocol process compared to traditional approaches that differ in methods, assumptions
and allowance for discretionary revisions (Kollmuss & Fussler, 2015;Marino, Mincheva &
Doucett, 2019). A summary of DMFCP protocol features and benefits to landowners is
provided in Table 1, with reference to Figs. 1 and 2. Equivalent units of tCO2eq or units as
converted are employed for the DMFCP and traditional protocols (#1), noting (e.g., tCO2eq
acre-1) that estimation protocols do not report carbon sequestration according to project
area, potentially misleading landowners. Items #2 - 7 have been covered above, defining
the insuperable differences between direct measurement of CO2versus the use of proxies
(i.e., CO2is not directly observed at any time in the estimation protocol process) that do
not provide data for quantifying NEE (e.g., ecosystem photosynthesis and respiration)
according to accepted universal scientific practice (i.e., Item 4, Table 1,Eqs. (1) and (2),
Methods section).
Revenue and time-to-revenue are key factors in landowner forest carbon project
participation. Traditional protocols (e.g., CARB, CAR, ACR, VERRA, CDM) require
lengthy periods (e.g., 2–5 years) of fee-based project certification and registration prior
to payment, limiting landowner participation (Kerchner & Keeton, 2015;Köhl, Neupane &
Mundhenk, 2020). In contrast, the DMFCP process can provide an upfront payment and
annualized payment (e.g., case 2, Figs. 5 and 7) in a no-fee agreement (Fig. 2) available
immediately via cell phone payment according to a governing agreement (e.g., contract) that
also includes a no-fee listing in an open source registry (summarized by #8,9,10, Table 1).
The DMFCP embodied in the SoS and GMP obviates three features intrinsic to traditional
protocols including elimination of baseline estimation (#11), tests for additionality (#12),
and a multiyear invalidation period (#13) linked to compliance testing and third-party
verification (#14). Direct measurement establishes forest carbon flux as either negative
(e.g., CO2sequestration), positive (e.g., CO2efflux), or zero (sequestration balances
efflux)—measurements cannot be made retrospectively. It follows that a zero-emissions
baseline is intrinsic to a time-series of positive/negative/zero NEE measurements (Fig. 2A)
integrating forest tree species, vegetation and carbon fluxes across and within the project
area including all above and below ground carbon fluxes (DiRocco et al., 2014;Urbanski
et al., 2007). DMFCP carbon accounting is not subject to uncertainty related to selection
for species distribution and growth simulation models typical of traditional protocols
(Kollmuss & Fussler, 2015). Additionality tests require a counterfactual argument (Ruseva
et al., 2017) that cannot be validated and is subject to discretionary adjustment. A credit
is considered additional if the emissions reduction that underpins the credit would not
have occurred in the absence of the activity that generates the credit (Kollmuss & Fussler,
2015). In contrast, the DMFCP results in near-real time (30-minute average of 10 Hz
measurements) NEE time series and trends (Dou & Yang, 2018), obviating reliance on
uncertain project scenarios and an impractical prediction of future emissions against
possible forest disturbance. Further, tests of net emission reduction across project areas
or jurisdictions for specified periods of time can be readily calculated from DMFCP
results for independent projects, establishing simple numerical additionality (Fig. 2B)
rules for established private and public lands, as could be adopted by municipal and
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 23/41
private entities. The DMFCP does not require an invalidation period (# 13) compared
to estimated forest carbon offsets. In contrast to long inspection intervals for traditional
forest carbon protocols (e.g., 6 or 12 years; California Air Resources Board, 2011, 2015),
the DMFCP results are subject to instantaneous invalidation by third party inspection and
routine flags for anomalous operation within the SoS. The DMFCP is subject to replication
of equipment and system performance standards, precision and accuracy of universal
references and review of NEE from raw data to financial products at any time. The DMFCP
employs a real-time wireless reporting and verification concept of operations architecture
including third-party independent observers of all data developed for each SoS network
(Anadiotis et al., 2019) with invalidation authority (#13). In contrast, third party validation
for CAR projects, for example, is based on desk and paper review of unobserved CO2
(e.g., proxies) and cannot support instantaneous spontaneous invalidation testing and
enforcement.
Once a project is in operation, a switch from carbon negative to carbon positive
ecosystem function is key to project management, revenue projections, accounting and
contract terms and to an understanding of ecosystem function in relation to climate change
and anthropogenic activity. Traditional forest carbon protocols do not appear capable of
determining when a forest project switches to net positive emissions to the atmosphere
on an annual basis; the DMFCP NEE measurements provide this diagnostic (#15). Item
#15 is also linked to demonstration of project permanence (#16) and termination of a
project (#17). Traditional protocols require an arbitrary 100-year period of monitoring
and maintenance for project carbon with a punitive penalty for early termination; lack
of CO2measurement renders both factors indeterminate, impractical and biased against
the landowner. The DMFCP employs ton-year accounting, an IPCC recognized method
that does not impose an artificial time horizon for tree growth (e.g., 100 years) opening
forest carbon sequestration projects to a wider range of forest project types and project
intervals (Cunha-e-Sá, Rosa & Costa-Duarte, 2013;Levasseur et al., 2012). The ton-year
accounting method accommodates combined budgets of CO2, CH4and N2O resulting in
a comprehensive and realistic net GHG project budget (Courtois et al., 2019;Richardson
et al., 2019), an approach that can be applied to the spectrum of projects from pure
conservation to working forests, however, not achievable with estimation-based forest
carbon denominated protocols.
Items 1 to 17 for existing protocols address two key factors favoring deforestation
engagement: transaction requirements and liquidity. Forestland as a timber asset requires
long periods of growth to harvest and is generally financially illiquid until harvested
(Mei, 2015). It is argued here that business development of forest carbon projects, as
practiced according to traditional protocols, is overly cumbersome and lengthy to establish
offset transactions, and financially inviable to compete with the short time intervals of
deforestation often resulting from illicit transactions (Alam et al., 2019;Tacconi et al.,
2019;Tellman, 2016). In addition, with the use of satellite imagery, illegal and non-
conforming deforestation can be detected in near real-time, with spatial resolution of
meters limiting potential gaming of the system and uncertainty in the sources of CO2flux
(Hayek et al., 2018;Tang et al., 2019). Rapid set-up of the SoS direct measurement platform,
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 24/41
no-fee based agreements, upfront and annualized payments, discrete revenue intervals of
10 years, and reasonable exit terms align landowner business operations (private and
commercial) within realistic financial frameworks to potentially deter rapid deforestation
within culturally diverse transactional and transnational frameworks (Fenichel et al.,
2018). Additional points of comparison concern the limitation of traditional protocols
to accommodate the spectrum of relevant GHG’s (e.g., N2O, CH4, PFC’s, HFC’s, SF6,
NF3) (#18), isotopologues of GHG’s (e.g., 13CO2,14 CO2) (#19), the inclusion of aquatic
features (e.g., rivers, lakes ponds, wetlands, oceans) (#20) and the lack of contribution to
ecosystem science and climate change studies and models (#21). Traditional forest carbon
protocols (Kollmuss & Fussler, 2015) were developed for singular application to forests,
incorporating methodology employed for timber management and primarily restricted to
capturing above ground carbon. As a result, algorithms developed for forest CO2are not
readily applicable to other GHG species and diverse biospheric landscapes. Based on the
comparisons, the insuperable shortcomings of traditional protocols do not provide data
that contribute to the evolving science of forest carbon sequestration, climate change studies
and related model development that are well established in the growing NEE methodology
(Baldocchi, 2019). Climate change impacts on forest carbon storage are not included in
project risk for estimation based protocols (California Air Resources Board, 2015b) even
though soil carbon efflux over the 100-year required period is likely to respond to global
warming and changes in precipitation (Amundson & Biardeau, 2018;Bond-Lamberty et al.,
2018;Schlesinger & Amundson, 2018).
The DMFCP can be applied to international emission reduction policies recognizing
scientifically accepted methods, shared NEE data processing algorithms, elucidation of
uncertainties (#22), standardized terms and contracts for voluntary and compliance offsets
(#23, Supplemental Information 1) including clauses for reversal of net forest carbon
sequestration due to intentional or unavoidable natural conditions (e.g., fire, hurricane,
drought). For example, the expansion of measurement networks, data integration and
carbon trading are key but unrecognized components of the Paris Agreement (Clemencon,
2016;Rimmer, 2020), and REDD+ programs (Foss, 2018). For example, Article 10 of the
Paris Agreement, lacks guidance on how pledged and claimed reductions that are non-
binding will be verified and traded (Ollila, 2019;Rimmer, 2020;Spash, 2016), shortcomings
that are mitigated by the DMFCP. The estimation approach remains embedded in the
United Nations Framework Convention on Climate Change (UNFCCC) (UNFCCC,
2013) that promulgated reporting of emissions based on estimation, rather than direct
measurement, an approach constraining advancement of carbon credit trading. According
to the UNFCCC approach, estimates of greenhouse gas emissions are inventoried and
multiplied by an emission factor to yield a national emission rate for each source and
each greenhouse gas (Cheewaphongphan et al., 2019;Van Vuuren et al., 2009). Emissions
of Kyoto gases are multiplied by the Global Warming Potential for each gas specifying
the radiative efficiency as a warming agent for each gas relative to that of carbon dioxide
over a 100-year time horizon (Kollmuss & Fussler, 2015). The resulting estimation for
national emission inventories, used by vendors and policy platforms (e.g., REDD+),
are widely acknowledged as flawed and inaccurate (Jonas et al., 2019;Pacala et al., 2010).
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 25/41
Importantly, the estimation data are not directly comparable across diverse ecosystems
lacking shared standards and universal measurement methodology. The DMFCP updates
the UNFCCC and REDD+ methods to validate and monetize claims of emission reduction
and to determine GHG budgets across diverse ecological landscapes at the national and
sub-national levels fulfilling the Paris Agreement (e.g., Article 10) and REDD goals and
objectives. The DMFCP normalizes forest emission reduction determinations for voluntary
and compliance markets bridging the gap between methods, project types and outcomes
for stakeholders.
CONCLUSIONS
The DMFCP comprises a commercial standardized measurement-to-monetization system
for the determination of NEE. NEE enables creation of verified forest carbon financial
products contributing to the improvement of methods that underpin large-scale forest
conservation and reforestation, a global problem of high importance in the management
of anthropogenic climate change. The SoS and GMP components can be applied to
GHG’s across large-scales and diverse locations, corrects traditional carbon credit gaps
in validation and recalibrates equivalent voluntary and compliance programs that rely
on them such as the CARB, CAR, ACR, VERRA and CDM, as well as the REDD+ and
Paris Agreement platforms. The DMFCP, coupled with contributions of the forest carbon
research community to commercialization efforts, and updated policies, can address the
∼0.9 billion hectares of restorable landscapes, offering a viable approach to retain the
Earth’s natural protective capacity to sequester atmospheric CO2now and for future
generations.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this work.
Competing Interests
Bruno D.V. Marino is an Academic Editor for PeerJ. A competing interest is disclosed by
Bruno D.V Marino as the author of the patents cited. The data and scientific analyses, while
citing the patent literature as supportive of potential real-world applications, was conducted
independently of the work presented. Data presentation, analysis, and interpretation are
based on established scientific principles and reported in an objective manner. Bruno
D.V. Marino and Vinh Truong are unpaid associates of Planetary Emissions Management
Inc. Richard Gyimah is a paid employee of the Forestry Commission of Ghana. J William
Munger is a paid employee of Harvard University.
Author Contributions
•Bruno D.V. Marino conceived and designed the experiments, performed the
experiments, prepared figures and/or tables, authored or reviewed drafts of the paper,
and approved the final draft.
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 26/41
•Vinh Truong analyzed the data, prepared figures and/or tables, authored or reviewed
drafts of the paper, and approved the final draft.
•J. William Munger and Richard Gyimah analyzed the data, authored or reviewed drafts
of the paper, and approved the final draft.
Patent Disclosures
The following patent dependencies were disclosed by the authors:
Marino, B. D. V. (2011) System of systems for monitoring greenhouse gas fluxes,
European Patent Convention patent #2391881.
Marino, B. D. V. (2012) System of systems for monitoring greenhouse gas fluxes, Hong
Kong patent #1165004.
Marino, B. D. V. (2013) System of systems for monitoring greenhouse gas fluxes, United
States of America patent #8595020.
Marino, B. D. V. (2014a) System of systems for monitoring greenhouse gas fluxes, Japan
patent #5587344.
Marino, B. D. V. (2014b) System of systems for monitoring greenhouse gas fluxes,
Mexico patent #319180.
Marino, B. D. V. (2014c) System of systems for monitoring greenhouse gas fluxes,
Mexico patent #326190.
Marino, B. D. V. (2015a) System of systems for monitoring greenhouse gas fluxes,
Australia patent # 2010207964.
Marino, B. D. V. (2015b) System of systems for monitoring greenhouse gas fluxes,
United States of America patent #9152994.
Marino, B. D. V. (2016a) System of systems for monitoring greenhouse gas fluxes, China
(People’s Republic) patent #ZL201080015551.5.
Marino, B. D. V. (2016b) System of systems for monitoring greenhouse gas fluxes, Japan
patent #5908541.
Marino, B. D. V. (2016c) System of systems for monitoring greenhouse gas fluxes, Korea
patent #10-1648731.
Marino, B. D. V. (2016d) System of systems for monitoring greenhouse gas fluxes,
United States of America patent #9514493.
Marino, B. D. V. (2017a) System of systems for monitoring greenhouse gas fluxes,
Australia patent #2015203649.
Marino, B. D. V. (2017b) System of systems for monitoring greenhouse gas fluxes, China
(Peoples Republic) patent #CN102405404.
Marino, B. D. V. (2017c) System of systems for monitoring greenhouse gas fluxes,
Republic of Korea patent #10-1699286.
Marino, B. D. V. (2018a) System and methods for managing global warming, Canada
patent #2813442.
Marino, B. D. V. (2018b) System of systems for monitoring greenhouse gas fluxes,
Canada patent #2751209.
Marino, B. D. V. (2018c) System of systems for monitoring greenhouse gas fluxes, Hong
Kong patent #1242029.
Marino et al. (2020), PeerJ, DOI 10.7717/peerj.8891 27/41
Marino, B. D. V. (2019) System of systems for monitoring greenhouse gas fluxes, India
patent #311228.
Data Availability
The following information was supplied regarding data availability:
The raw data for NEE and CARB-CAR sites are available in the Supplementary File. The
data are also available from FLUXNET2015 (https://fluxnet.fluxdata.org/data/fluxnet2015-
dataset) under Tier One data following the guidelines of the CC-BY-4.0 data usage license
(Attribution 4.0 International (CC BY 4.0); https://creativecommons.org/licenses/by/4.0/).
That license specifies that the data user is free to Share (copy and redistribute the material
in any medium or format) and/or Adapt (remix, transform, and build upon the material)
for any purpose. https://fluxnet.fluxdata.org/data/data-policy/.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.8891#supplemental-information.
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