U.S. patent application number 17/460915 was filed with the patent office on 2022-07-28 for methods and systems for automating carbon footprinting.
This patent application is currently assigned to THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK. The applicant listed for this patent is THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK. Invention is credited to Robert Christian KUILE, Klaus S. LACKNER, Christoph Johannes MEINRENKEN, David Joseph WALKER.
Application Number | 20220237472 17/460915 |
Document ID | / |
Family ID | 1000006243779 |
Filed Date | 2022-07-28 |
United States Patent
Application |
20220237472 |
Kind Code |
A1 |
MEINRENKEN; Christoph Johannes ;
et al. |
July 28, 2022 |
METHODS AND SYSTEMS FOR AUTOMATING CARBON FOOTPRINTING
Abstract
Methods and systems for automating carbon footprinting are
disclosed. In some embodiments, the methods include a plurality of
steps. In some embodiments, related to predetermined resources
associated with an item from predetermined data sources is
obtained. Then, estimated emission factors are calculated for each
of the resources. Next, a contributory uncertainty of the data and
of the emission factors is determined. Then, a user is guided based
on a comparison of the respective contributory uncertainty of data
related to the resources or emission factors. Next, both data
related to the resources and the estimated emission factors of the
resources are utilized to determine a carbon footprint of the
item.
Inventors: |
MEINRENKEN; Christoph Johannes;
(New York, NY) ; LACKNER; Klaus S.; (Dobbs Ferry,
NY) ; WALKER; David Joseph; (Stamford, CT) ;
KUILE; Robert Christian; (Farmers Branch, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW
YORK |
New York |
NY |
US |
|
|
Assignee: |
THE TRUSTEES OF COLUMBIA UNIVERSITY
IN THE CITY OF NEW YORK
New York
NY
|
Family ID: |
1000006243779 |
Appl. No.: |
17/460915 |
Filed: |
August 30, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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17015705 |
Sep 9, 2020 |
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17460915 |
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15385212 |
Dec 20, 2016 |
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17015705 |
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13578297 |
Mar 20, 2013 |
9524463 |
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PCT/US2011/025116 |
Feb 16, 2011 |
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15385212 |
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61304800 |
Feb 16, 2010 |
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61367165 |
Jul 23, 2010 |
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61387218 |
Sep 28, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/00 20130101;
Y02P 90/845 20151101; Y02P 90/84 20151101; G06N 5/02 20130101 |
International
Class: |
G06N 5/02 20060101
G06N005/02; G06Q 10/00 20060101 G06Q010/00 |
Claims
1. (canceled)
2. (canceled)
3. A non-transitory computer readable medium having
computer-executable instructions for calculating overall impact of
an item, the instructions comprising: identifying a number of
predetermined resources; obtaining data related to each of the
predetermined resources from an entity's databases; obtaining an
impact coefficient for those predetermined resources for which the
impact coefficient is known from an entity's database or from
external databases categorizing each of the predetermined resources
in a predetermined cluster of similar resources; obtaining a
statistics-based, estimated impact coefficient for each of the
predetermined clusters; for those predetermined resources for which
the impact coefficient is not known from an entity's databases or
from external databases, calculating an impact coefficient for each
predetermined resource based on the statistics-based, estimated
impact coefficient for the corresponding predetermined cluster;
identifying a number of the predetermined resources that are
associated with an item; and calculating an overall impact of the
item based the data and the estimated impact coefficients for each
predetermined resource associated with the item.
4. The computer readable medium according to claim 1, wherein the
predetermined resources include a material, energy, activity, or
combinations thereof.
5. The computer readable medium according to claim 1, wherein the
item is a product or service.
6. The computer readable medium according to claim 1, wherein the
impact coefficient is a carbon emission factor, water quality
impact factor, toxicity impact factor, biodiversity impact factor,
social impact factor, financial cost and/or benefit impact factor,
value chain risk impact factor, or combinations thereof.
7. The computer readable medium according to claim 4, wherein the
impact coefficient is a carbon emission factor, and further
comprising: calculating a carbon footprint for each of a plurality
of an entity's items; and estimating any portion of the scope 3
emissions for the entity based on the carbon footprint of the
plurality of items.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application is a Continuation of U.S. patent
application Ser. No. 17/015,705, filed Sep. 9, 2020, which is a
Continuation of U.S. patent application Ser. No. 15/385,212, filed
Dec. 20, 2016, which is a Continuation of U.S. patent application
Ser. No. 13/578,297, filed Aug. 10, 2012, issued Dec. 20, 2016 as
U.S. Pat. No. 9,524,463, which was a National Stage Entry of
International Patent Application PCT/US2011/025116, filed Feb. 16,
2011, which claimed the benefit of U.S. Provisional Application
Nos. 61/304,800, filed Feb. 16, 2010, 61/367,165, filed Jul. 23,
2010, and 61/387,218, filed Sep. 28, 2010, each of which is
incorporated by reference as if disclosed herein in their
entirety.
BACKGROUND
[0002] Life cycle analysis (LCA) has been practiced since the
1960s. When in 1969, a well-known beverage company commissioned a
comparative study of traditional, re-usable glass bottles vs.
plastic bottles, this arguably marked the debut of LCA as integral
to product development even for mass market consumer goods and
services companies. More recently, the more widespread public
awareness of the risks of global warming and the role of
anthropogenic greenhouse gas (GHG) emissions has prompted a
renaissance of LCA concepts in the form of standardized carbon
footprinting (CF) of products and services.
[0003] Companies usually seek to quantify CFs for one or more of
the following reasons: (i) internal transparency and identification
of carbon reduction strategies; (ii) communication of results to
external stakeholders such as environmental monitoring groups, or
to apply for certification; or (iii) requests from a company's
corporate customers for scope 3-relevant data, to use in their
corporate carbon accounting.
[0004] While LCA has continuously evolved, prompting both often
re-cited criticism and improvement, the new need for accurate and
comparable CFs has catalyzed efforts to overcome many of LCA's
traditional shortcomings and provided standards for CF. Today,
companies embarking on CF benefit from detailed protocols,
industry/sector specific guidance, software packages, and databases
that provide support with the following: (i) choice of functional
unit; (ii) system boundaries; (iii) emission factors (EFs) of
materials and activities; and (iv) specialty issues such as
recycling and biogenic carbon and storage. Crucially, guidelines
also provide a more head-on approach to materiality and
realistically achievable levels of accuracy. For example, the
rounding rules of the UK Carbon Trust imply that even a
best-practice CF will have a residual uncertainty of 5-10%.
[0005] While the above developments represent tremendous progress
and improvements over the status quo even just a few years ago,
quantifying the CFs for hundreds or thousands of individual
products/services is currently impossible, short of a massive
buildup of a company's dedicated personnel and LCA expertise.
Specifically, practitioners today face two fundamental obstacles
when performing CF at the scale of large companies:
[0006] 1) required time and expertise: collecting, organizing, and
validating LCA inventory (easily hundreds of data items for a
single product/service), as well as mapping to EFs, typically takes
hundreds of man hours and specialized knowledge; and
[0007] 2) lack of uniformity and integrated platform: CF today is
usually performed as a series of one-off efforts, e.g., using
non-interlinked, separate spreadsheets for the CF of each new
product/service; once the practitioner has completed data entry and
calculation for one product, to the desired accuracy, the
practitioner moves on to the next product, often without maximizing
the re-use of any previously collected information.
[0008] The obstacles related to known LCA practices result in
missed opportunities that currently prevent CF from realizing its
full spectrum of possible benefits, which include the
following:
[0009] (1) What-if impacts across products, carbon management, and
cost-benefit evaluations: Arguably, one of the greatest
opportunities of CF is to enable a company to identify and
prioritize reduction strategies. However, because the CFs for a set
of different products are usually calculated in a set of
non-integrated files, it is difficult to quantify the combined
impact of a reduction strategy. For example, counting all impacts
on raw materials, transportation, and disposal, what would be the
total company-wide GHG reductions if all PET packaging were made
15% lighter? What if all factories in a country moved 30% of their
primary energy consumption to hydropower-rich electricity? Which
LCA stages in the supply chain-measured across all products or by
business line-offer the largest reduction potential? Given an
assumed carbon price, would the costs for required upgrades (e.g.,
modified energy mix, packaging, or ingredients) be a worthwhile
investment?
[0010] (2) Flexibility vis-a-vis regulatory change: Standards for
CF are still evolving. With current practice, a future change in
the CF "accounting rules" would mean tremendous time and resource
effort on behalf of a company, to essentially fix the manual CF
calculations for hundreds of products/services. This poses
significant "regulatory" risk.
[0011] (3) Synergy with corporate carbon accounting ("corporate
footprint"): There is a direct relationship between the various LCA
stages that count toward a product/service CF and those that count
towards a corporate footprint. Therefore, there are significant
synergies between the data collection and analyses for
product/service CFs and the scopes 1, 2, and 3 of corporate
footprints. Current CF practice often lacks the coverage,
uniformity, or transparency that would enable the company to make
use of such synergies
SUMMARY
[0012] Methods and systems according to the disclosed subject
matter embody one or more of the following three techniques. First,
each CF is based on a single, uniform data framework that applies
to all products/services. Rather than manually, data is entered,
wherever possible, via auto-feeds from existing enterprise data,
e.g., BOM (bill of materials) and energy usage at
company-controlled factories. This technique minimizes the number
of data items that require manual input. Second, particularly for
remaining data entries, concurrent uncertainty analysis points the
user to those activity data or EFs where additional data or
improved accuracy would most improve the accuracy of the calculated
CFs. This technique uses manual entries more efficiently. Third, a
statistical model approximates EFs, thereby eliminating the manual
mapping of a product/service's inventory to the vast selection of
EF databases.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The drawings show embodiments of the present disclosure for
the purpose of illustrating the invention. However, it should be
understood that the present application is not limited to the
precise arrangements and instrumentalities shown in the drawings,
wherein:
[0014] FIG. 1 is a schematic diagram of a system according to some
embodiments of the disclosed subject matter.
[0015] FIG. 2 is a chart of a method according to some embodiments
of the disclosed subject matter; and
[0016] FIG. 3 is a chart of computer-executable instructions
according to some embodiments of the disclosed subject matter.
DESCRIPTION
[0017] Referring now to FIGS. 1 and 2, some embodiments include
systems and methods for automating carbon footprinting. Some
embodiments include a system 100 for determining the carbon
footprint of an item, such as a product or service. Some
embodiments of system 100 include the following cooperating modules
that transform data to determine the carbon footprint of an item: a
data automation module 102; an emission factor estimator module
104; a calculation module 106; and a user guidance module 108
[0018] Referring now to FIG. 1, data automation module 102 includes
mechanisms for obtaining data 110 related to predetermined
resources 112 associated with an item 114 from predetermined data
sources 116. Resources 112 typically include materials and
activities expended during the manufacture or production of item
114. Predetermined data sources 116 typically, but not always,
include an enterprise resource planning (ERP) program database 118.
Mechanisms for obtaining data 110 include an automated data feed
120 mapped to enterprise resource planning program database 118.
Other mechanisms include manual data entry or automated data feeds
to myriad databases. In some embodiments, data obtained by
automated data feed 120 include data required by emission factor
estimator module 104 for calculating the emission factors of
resources 112.
[0019] Referring now to Table 1, certain data items required for
product CFs are generally readily available from existing ERP
systems or similar data warehouses.
TABLE-US-00001 TABLE 1 Suggested taxonomy of LCA stages and
possible data sources. LCA Stage Data source Purchased goods - ERP
system or similar data warehouses (specifically packaging "bill of
materials"). Purchased goods - ERP system or similar data
warehouses (specifically other "bill of materials"). Transportation
- Usually, manual entries or country-specific default inbound
settings (unless available in ERP). Also note that many EFs of
purchased goods may be inclusive of the inbound transportation ("to
gate"). Production ERP system or similar data warehouses, such as
factory energy consumption monitoring. Transportation - Usually,
manual entries or country-specific default outbound settings
(unless available in ERP). Distribution Usually, manual entries or
country-specific default and retail settings (some of which could
be based on sector- specific guidance). Use phase Same as for
"distribution and retail". Disposal (end Usually, manual entries or
country-specific default of life) settings. Note these are usually
at the level of material classes (such as "glass", "cardboard",
"plastic", etc.).
[0020] In some embodiments of the disclosed subject matter, rather
than manually entering data manually, conduits control automatic
uploads of as many data inputs as possible. This also allows, for
example, quarterly updates, which offers the additional advantage
of keeping footprint results more up to date than with traditional,
labor-intensive models. Where certain purchased goods cannot be
mapped across products and to EFs via unique IDs, in some
embodiments, fuzzy logic-type mapping is used to maximize automatic
entry, e.g., "farm potatoes" used for frozen fries should be mapped
to "farm potato" used for crisps, and both should be mapped to the
country-relevant EF for "potatoes".
[0021] Emission factor estimator module 104 including mechanisms,
e.g., software 122, for calculating estimated emission factors for
each of resources 112. As described below with respect to FIG. 3,
software 122 includes a statistical model that approximates EFs,
thereby eliminating the manual mapping of a product/service's
inventory to the vast selection of EF databases.
[0022] Some embodiments of the disclosed subject matter allow for
overrides of automatically obtained data and automatically
estimated data/factors on a case by case basis. For example,
customized EFs that are based on primary, product-specific data can
be manually entered instead of automatically estimated.
[0023] Calculation module 106 includes mechanisms, e.g., software
124, for utilizing both data related to resources 112 and the
estimated emission factors of the resources to determine a carbon
footprint 126 of item 114.
[0024] Software 124 includes an algorithm for calculating carbon
footprint 126 that interprets table of data related to an item,
uses look-ups from all other tables of data, and calculates the CF
according to a chosen protocol. In almost all cases, the CF can be
represented by a summation over many multiplications, where each
multiplication represents the CF contribution from a particular
item (equation (1)). Equation (1) is linear in Di (and in the
EFs).
[0025] Let CF denote the footprint of an item including a product,
set of products, or a carbon reduction strategy, driven by a set of
input data D.sub.i, e.g., transportation distance, electricity
consumption, EF, each of which varies by a certain coefficient of
variation (CV.sub.i) around its mean. We approximate the resulting
uncertainty of CF via a sum of its partial derivates:
CF .about. := CF _ + i .times. .DELTA. .times. .times. CF i = CF _
+ i .times. ( D i - D i _ ) .differential. CF .differential. D i (
1 ) ##EQU00001## [0026] Where CF denotes the footprint with all
D.sub.i set to a specific value from their respective distribution,
and denotes the approximation of this CF; [0027] CF denotes the
arithmetic mean of CF (if CF is linear in all D.sub.i, then CG
equals CF when all D.sub.i are set to their arithmetic means
D.sub.i); and [0028] .differential.CF/.differential.D.sub.i denotes
the partial derivative of CF by D.sub.i, evaluated at D.sub.i set
to D.sub.i.
[0029] In some embodiments, system 100 includes user guidance
module 108 including mechanisms, e.g., software 130, for
determining a contributory uncertainty of the data and contributory
uncertainty of the emission factors and for prompting a user 132 to
enter data related to resources 112 or emission factors of the
resources based on a comparison 134 of the respective contributory
uncertainty of data related to the resources and emission
factors.
[0030] Software 130, which includes an uncertainty algorithm, uses
the compounded uncertainty of the total CF (of a roll-up or carbon
reduction scenario), which is calculated using Equation (1).
Equation (1) represents a simple sum, where the variance of CF
equals zero and the variance of each .DELTA.CF.sub.i equals the
square of CV.sub.iD.sub.i.differential.CF/.differential.D.sub.i.
Based on standard stochastics, the variance of a sum across
(uncorrelated) components equals the sum of the variances of each
component, whether the distribution of the components are Gaussian
or not. Furthermore, since CF is linear in D.sub.i, we can rewrite
the partial derivatives as finite differences of CF evaluated at
different values of each D.sub.i. Thus we obtain:
C .times. .times. V CF .about. = i .times. [ CF .function. ( D i _
+ D i _ CV Di ) - CF _ ] 2 CF _ ( 2 ) ##EQU00002## [0031] Where C
denotes the CV of the (approximated) CF [0032] CV.sub.Di denotes
the CV of D.sub.i [0033] CF(D.sub.i+D.sub.iCV.sub.Di) denotes CF
evaluated at D.sub.j plus one standard deviation (and all other
D.sub.j at D.sub.j).
[0034] Equation (2) is essentially the sum across the squares of
individual "impacts" (followed by square root and division by CF),
where each such impact is the change in CF if one Di is increased
by one standard deviation (while all others are kept at their mean,
i.e., "ceteris paribus").
[0035] In addition to determining the uncertainty of the CF
calculation. In some embodiments, the uncertainty of each data
input is determined as it is input, which enables the overall
quality of the inputs to be increased in real time. By way of
example, Equation (2) allows one to calculate that the CF of a bag
of potato chips is 110 g CO2e.+-.18%. Assuming that such a CF
calculation is driven by 3 uncertain inputs, e.g., the number of
kWh consumed during production, the EF of the packaging material,
and the (average) transportation distance from factories to stores,
to reduce the CV of the CF to something more accurate, it is
helpful to break the CV down into the contributions from each
driver, i.e., to learn which one of the three inputs contributes
the most to the CV and which one the least. As follows, time/effort
can then be focused on improving the accuracy of the inputs that
have the biggest impact on the accuracy of the overall footprint,
e.g., of a product, set of products, or carbon reduction strategy.
In some embodiments, to allocate the CV of the total CF based on
each inputs (Di's) contribution to the variance, the following
Equation (3) is used:
CV C , Du = .times. CV CF ~ [ CF .function. ( D i _ + D i _ .times.
.times. CV Di ) - CF ] _ 2 j .times. [ CF ( D j _ + D j _ ( CV Dj )
- CF _ ] 2 = .times. [ CF .function. ( D i _ + D i _ .times.
.times. CV Di ) - CF ] _ 2 CV - CF 2 ##EQU00003## [0036] Where
CV.sub.C,Di denotes the contribution of input driver D.sub.i to the
total uncertainty C; and [0037] All others as above.
[0038] Using Equation (3), the contributory CVc, Di sum up to the
CV of CF, which is typically less than 100%, e.g.: the CV of the
electricity consumption contributes 1%, the CV of the
transportation distance 6%, and the CV of the packaging EF 11%. The
CV contribution, as defined above, is sensitive to both the CV of a
driver Di as well as the driver's absolute impact on CF.
[0039] In some embodiments, the data obtained by data automation
module 102 is sorted into predetermined tables of data thereby
defining a uniform data structure. In addition to the overall data
structure described in Table 1, in some embodiments, data is
organized into distinct look-up tables. These look-up tables
reflect redundancy in the data, such that each CF essentially
becomes a permutation of various elements in the tables. Note that
most tables store two sets of data, one for the mean and one for
the associated uncertainty of the respective datapoint.
[0040] In some embodiments, the look-up tables include the
following five distinct look-up tables (A)-(E):
[0041] (A) Products: This table stores inventory for all LCA
stages, for all items, i.e., products/services of a company. It
covers material and activity data such as amounts of purchased
goods, including scrap, spillage, etc., production and
distribution, transportation routes, and use-phase characteristics.
In addition, table A stores product attributes such as country,
brand, business line, and annual production volume (to report
roll-ups and breakdowns of the CFs by various characteristics).
[0042] (B) Assemblies: This table stores information on those
materials and activities that constitute sub-products or
sub-services in themselves. For example, an assembly may specify
that a kg of oranges specified in table A refers to a set of
purchased goods (fertilizers, manure, pesticides) and activities
(fertilizing, pruning, harvesting). Practitioners will have to
maintain the information for such assemblies only once, and then
all products that use oranges from the same supplier are mapped to
the same assembly.
[0043] (C) Purchased goods library: This table stores
material-level meta information to further specify each purchased
good contained in either table A or table B, for example to
determine EFs.
[0044] (D) EFs: This table stores EFs for any purchased good
(ingredients, packaging) and activity occurring in either table A
or table B. Note that some practitioners employ EFs at the level of
assemblies. While this facilitates calculating a CF, e.g., instead
of adding up multiple items in the assembly, simply multiply
Weight.sub.Orange.times.EF.sub.Orange, it obscures granular
reporting of the resulting CF by purchased good, production,
transportation, and thus reduces the usefulness of the resulting
analyses with regards to carbon reduction strategies.
[0045] (E) Standards & defaults: This table stores global and
country-specific default values as described above.
[0046] In some embodiments of system 100, at least a portion of the
data obtained by data automation module 102 and the emission
factors of resources 112 determined by emission factor estimator
module 104 is generated via an estimate based on data and emission
factors related to similar materials and activities. For example,
in some embodiments, the estimate includes averaging data and
emission factors related to similar materials and activities.
[0047] Some embodiments of the disclosed subject matter include
self-learning look-up tables. By quantifying contributory
uncertainties at all stages of the CF calculation, the system can
automatically select the available data entries with the lowest
contrinutory uncertainty without separate user intervention thereby
expediting the calculation. For example, in some embodiments, the
parameters for the CF estimates are automatically updated to always
reflect any recently added bottom-up CFs. Similarly, instead of the
default plant-to-store distance in a given country, in some
embodiments, the system utilizes the average respective distance
from any other products in that country that were already
characterized bottom up, as soon as the associated uncertainty of
that sample falls below the one specified for the default
distance.
[0048] Some embodiments of the disclosed subject matter include the
use of wizards. The uniform data structure facilitates the creation
of assisted-data-entry tools such as wizards to guide users through
quantifying the CF for any product/service. This is especially
helpful for analyzing or developing new products whose data is not
yet available through ERP-systems or similar data warehouses and
therefore must be entered manually.
[0049] Referring now to FIG. 2, some embodiments of the disclosed
subject matter include a method 200 for determining the carbon
footprint of an item such as a product or service. At 202, input
data related to predetermined resources associated with an item is
obtained from predetermined data sources. Resources associated with
an item typically include materials and activities expended or
conducted in the manufacture or generation of the item.
Predetermined data sources include data from an enterprise resource
planning program database, at least a portion of which is provided
via an automated data feed that is mapped to the database. As used
herein, enterprise resource planning program is broadly defined to
include any types of programs and databases that include similar
types of enterprise data. In some embodiments, method 200 includes
the use of fuzzy logic to identify particular data in the
enterprise resource planning program database. At 204, estimated
emission factors are calculated or obtained. For the estimated
emission factors that are calculated, the calculations include data
obtained by the automated data feeds. At 206, the data and/or
estimated emission factors obtained are sorted into predetermined
tables of data according to a uniform data structure. At 208,
utilizing both data related to the resources, the estimated
emission factors calculated, and predetermined emission factors
obtained, a carbon footprint of the item is calculated. At 210, a
contributory uncertainty of each of the input data and a
contributory uncertainty of each of the emission factors is
determined. At 212, a comparison of the respective contributory
uncertainty of each of the input data and each of the emission
factors is performed. In addition to guiding a user to enter data,
in some embodiments, the comparison in 212 is the decision basis to
choose between many different possible candidates/sources for data
inputs, such as data input from another user, data from another
predetermined source, data from another estimating algorithm, and
averages of other already existing data inputs. For example, in
some embodiments, data related to the resources or emission factors
of the resources is automatically obtained via an automated data
feed or automatically generated via calculations based on a
comparison of the respective contributory uncertainty of data
related to the resources or emission factors. Over time method 100
and related systems become self-learning and evolving as their
databases grow. At 214, based on the comparison in 212, a user is
guided and prompted to enter data related to the resources or
emission factors of the resources. Then, at 206, data entered is
sorted into predetermined tables of data according to a uniform
data structure. In some embodiments, the data entered is used to
recalculate estimated emission factors at 204.
[0050] Referring now to FIG. 3, some embodiments of the disclosed
subject matter include computer-executable instructions 300 for
estimating an emission factor for a resource such at a material or
activity expended or undertaken in the manufacture, generation, or
acquisition of an item. Instructions 300 are typically in the form
of a software program provided as a computer-readable medium. At
302, in some embodiments, instructions 300 include categorizing the
resource in a predetermined cluster of similar resources. At 304,
an average estimated emission factor (EF.sub.Cluster, i) for the
cluster is calculated or obtained. At 306, an estimated price
(Price.sub.i) of the resource is obtained. In some embodiments, the
EF.sub.Cluster, i and Price.sub.i are automatically obtained from
an enterprise resource planning program database. At 308, the
estimated emission factor (EF.sub.i, estimated) for the resource is
calculated according to the following equation:
EF.sub.i,estimated=a+bln(EF.sub.cluster,i)+cln(Price.sub.i);
EF.sub.i, estimated denotes the model-generated EF for the material
i (in g CO.sub.2e per g); a, b, and c denote the three coefficients
that are calibrated/optimized, EF.sub.Cluster,i denotes the average
of all known EFs that share the same cluster as material i; and
Price.sub.i denotes the price of material i (in USD per kg).
[0051] Clustering involves using the average EF of a respective
cluster, i.e., sorted group of like data, as an approximate EF for
the material/process in question. In one analysis, when
approximating each of 1758 EFs in with the average EF of the
respective cluster, the CV of the thus estimated EFs ranged from 0%
to 476% (depending on the cluster). The average of the CVs of all
77 clusters was 91%.
[0052] In some embodiments of a software program including
instructions 300, a user manually enters a few characteristics of
the material or process, and the program generates an estimated EF.
In other embodiments, the characteristics required as inputs to the
model are chosen such that they are automatically available through
a company's ERP system or similar data warehouses, e.g., material
type, price, etc. In such embodiments, manual intervention to
determine EFs is longer required. Regardless, a user retains
insight into the accuracy of the fully automatically generated CFs
via a concurrent uncertainty analysis.
[0053] In some embodiments, software programs including
instructions 300 are "trained" using the thousands of known EFs
from LCA studies and public or commercial EF databases. Significant
amounts of meta data such as geography, boundaries, e.g., to farm,
to gate, in/excluding biogenic, etc. are also input to the software
program. In some embodiments, neural network based algorithms are
used to estimate EFs.
[0054] Methods and systems according to the disclosed subject
matter offer benefits and advantages over known technologies.
Methods and systems according to the disclosed subject matter
reduce the count of required manual data entries by as much as a
factor of 1000 vs. current practice, depending on how much data can
be imported from ERP systems or similar data warehouses.
[0055] In comparison with current CF practice, which are usually
manual, product-by-product calculations in multiple,
non-interlinked spreadsheets, methods and systems according to the
disclosed subject matter offer at least the following
advantages:
[0056] (1) Scalability: CF for hundreds or thousands of products is
currently simply impossible short of a massive buildup of a
company's dedicated personnel and LCA expertise. Methods and
systems according to the disclosed subject matter make the process
scalable to a company's global product/service portfolio. Linking
the CF to automatic ERP/data warehouse uploads, with, for example,
quarterly updates, also allow companies to keep footprints current.
Any changes in factory energy usage or raw material consumptions,
e.g., less spill, are captured in the next round of footprints,
without requiring manual updates.
[0057] (2) Transparency: The concurrent uncertainty analysis
assists a user in improving overall speed and accuracy, by
identifying those input data that currently contribute the most to
the uncertainty of the CF result in question. The uniform structure
of drivers and algorithms assist the practitioner in comparing CFs,
including the traditionally difficult analysis of changes in
baseline vs. actual CF and detailed product or process comparisons
of two CFs with overlapping error margins.
[0058] (3) Carbon management and cost/benefit evaluations: Knowing
CFs for all products, including breakdowns by LCA stages, allows
"slicing and dicing" the CFs for the company's global portfolio in
any desired way including national roll-ups, by product type, by
business line and break-downs of the company's total product CF,
e.g., by packaging, transportation, disposal, etc. Carbon reduction
strategies such as light-weighting the packaging, substituting
purchased goods with low-carbon alternatives, improving
distribution and transportation efficiency, etc. can be evaluated
instantly, and the resulting changes in GHG emissions can be
compared with the estimated investment costs of the initiative.
[0059] (4) Certification and communication with eco-labeling
groups: Input data and algorithmic details such as allocation rules
are transparent and such that the resulting CF for individual
products are easily certifiable, based on system-generated,
detailed reports.
[0060] (5) Synergies with corporate GHG reporting, especially scope
3: Product/service CF and corporate GHG reporting (scopes 1, 2, and
3) are often performed as separate efforts, by different teams, and
with different datasets. Methods and systems according to the
disclosed subject matter can unlock important synergies between the
two reporting efforts.
[0061] (6) Low regulatory risk: Because specific CF algorithms
operate in parallel to all inventory data, on an integrated
platform, any changes to the CF "accounting rules", e.g., treatment
of recycling, can easily be implemented by adjusting respective
parts of the software code.
[0062] In typical CF and wider LCA studies, uncertainty analysis,
if included at all, is carried out only after all data have been
collected and the footprint quantified. Methods and systems
according to the disclosed subject matter use analytic error
propagation carried out concurrently with all data entry and CF
calculations. This approach provides, at any point in time, full
transparency into (i) the uncertainty (standard deviation) of the
calculated results and (ii) which input data contributes how much
to the uncertainty of each CF, thus facilitating a more focused and
efficient effort to improve data quality in the overall system.
Concurrent uncertainty analysis points a user to those activity
data or EFs where additional data or improved accuracy would most
improve the accuracy of the calculated footprints. This technique
uses manual entries more efficiently.
[0063] With current CF practice, carbon management analyses only
become possible once all relevant product/service have been
footprinted one by one. Methods and systems according to the
disclosed subject matter accelerates this, by focusing on entering
data that the system can use for many products simultaneously,
e.g., number of days of refrigeration of all beverage products in a
certain country, even if the data may not yet have the desired
accuracy. Hence, additional time spent on data entry, e.g., for
those data items that are not automatically loaded from ERP
systems, is used to increase the accuracy of all CFs and reporting
analyses, rather than the cumulative number of individual products
that have been footprinted. Concurrent uncertainty analyses
quantify the remaining uncertainty, for an individual CF, for a
roll-up of CFs, or for specific reports. For example, a
practitioner may find that the report "what is the relative
contribution of PET to our overall CF across all products" can
already be performed, to sufficient accuracy, before specific
refrigeration times have been entered for every product
individually (because the contribution of the refrigeration
activity to the uncertainty of the overall CF may be small).
[0064] Methods and systems according to the disclosed subject
matter enable multi-user input. For example, one user may improve
the accuracy of a certain EF (by updating from the system-provided
to a more bespoke/primary value) while, at the same time, another
practitioner simultaneously updates the aluminum recycling rate in
China. The system then simultaneously updates the CFs of all
products that require one or both of these inputs, thus minimizing
overall required resources.
[0065] A single, uniform data structure is used for all
products/services. A uniform data structure is particularly
advantageous for at least three reasons. First, comparability is
different from accuracy. Suppose we review the CFs of two
"competing" products, one 10% smaller than the other, but both with
a margin of error (standard deviation) of .+-.20% (often referred
to as "overlapping error margins"). While we may not know the true,
absolute CF of either (only to within .+-.20%), there will still be
many such situations where we can say with certainty that one CF is
definitely smaller than the other (and thus the product or
associated supply chain preferable in this narrow respect). For
example, the 20% margin of error may be driven largely by the
uncertain EF associated with the electricity consumption (CO2e per
kWh); still, if the actual electricity consumption by product A is
significantly (e.g., p<0.05) smaller than that by product B (and
all else being equal), then the total CF of A is significantly
smaller, even though the respective margins of error overlap. A
straight forward comparison, however, is possible only because (i)
focusing on the electricity consumption and (ii) confirming that
"all else is equal" are facilitated by a uniform data
structure.
[0066] Second, data amount and accuracy are balanced. A "one data
structure fits all" framework enables CF for many products/services
virtually simultaneously by looping the same, generic algorithm in
the software. In one example, the data structure is such that it
uniformly quantifies GHG emissions for (road) transportation of a
product from factory to market as a single "leg", e.g., EF
multiplied by transported mass multiplied by distance. This
enables, for example, the use of estimated "distance to market"
parameters that are used as default input data for all products in
a certain region, so that a CF can be estimated even if a
product-specific distance has not yet been entered into the
software. In essence, this enables using a single data entry across
hundreds and thousands of appropriate products, thus reducing the
volume of required data entry. Third, a uniform data structure
provides meaningful reporting and reduction analysis.
[0067] There is a direct relationship and hence synergies between
the various LCA stages that count towards a product/service CF and
corporate carbon accounting. These synergies are readily
exploitable only if the LCA taxonomy in the footprinting model has
been set up accordingly and is applied universally across all
products/services. For example, the energy consumed during the
"production" phase of a product CF also contributes to scopes 1 and
2 of the corporate footprint. Using methods and systems according
to the disclosed subject matter, factory energy consumption data
has to be collected only once, and can then be used both for scope
1 of corporate carbon accounting and for the "production" portion
of the product/service CF (followed by allocation to individual
products). Similarly, the cradle-to gate portion of a full product
CF counts toward the scope 3 emissions of a customer's corporate
footprint. Therefore, any company that has quantified its products'
CFs can report the same results when approached by its corporate
customers for scope 3-relevant information. The company merely has
to exclude the contributions from LCA phases "distribution and
retail", "use phase", and "disposal" to convert the CFs from
cradle-to-grave to cradle-to-gate (and possibly adjust the outbound
transportation phase from "plant-to-retailer" to
"plant-to-customer").
[0068] A uniform data structure enables integrated reporting across
hundreds or thousands of products/services. For example, what is
the GHG contribution of making the purchased goods vs. transporting
them to the company's plants? What is the GHG reduction
potential--cumulative across all affected products--if these goods
were sourced more locally or transported by rail vs. road? Such
analyses are easier to carry out if the CF of every product follows
the same taxonomy of life cycle stages and input data.
[0069] Although the invention has been described and illustrated
with respect to exemplary embodiments thereof, it should be
understood by those skilled in the art that the foregoing and
various other changes, omissions and additions may be made therein
and thereto, without parting from the spirit and scope of the
present invention.
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