SOIL CARBON MYTHS AND BLUNDERS:
8 COMMON MISCONCEPTIONS ABOUT SOIL CARBON
By Michael Kiely BA (Hons) Dip. DM., Dip eComm, A.Dip. A
Convenor, Carbon Coalition
There are many incorrect statements made about soil carbon that betray a dangerous level of ignorance given the importance of the issue to the future of the nation.
Myth #1: “Australian soils are too old and degraded…”
This myth started in the Australian Greenhouse Office. It was established at a 2000 workshop sponsored by the CRC on Greenhouse Accounting on sequestration. The report concluded that:
“Australian climate, soils and agricultural management histories are significantly different to those of developed countries in the northern hemisphere. These differences generally result in considerably less potential for increase in soil carbon stocks associated with changing crop or pasture management practices in Australia compared with northern temperate regions.” .
This conclusion was distilled in an Australian Greenhouse Office policy framework document as: “Typically Australian soils have a poor capacity to store large quantities of carbon."
The Australian Farm Institute was misled into adding its gravitas to the myth: "The bulk of Australian farms may not operate as carbon sinks, due to the age of the soils."
The most definitive version of the myth was announced by the Grains Council: “Given the age and degraded nature of Australian cropping soils and the ‘natural’ low levels of organic carbon, there is no scientific evidence to suggest that there is a real possibility that organic carbon levels can be increased by cropping or farming practices at anything other than slow rates, reaching an equilibrium point well below that of northern hemisphere soils.”
The Grains Council took to the airwaves in an energetic campaign to bury Australian soils: “Our soils are very old, very fragile, very thin, very weathered. Often we are running soils with 1% or less carbon.”
NB. No research scientist made such statements.
NB. No scientific evidence has been given to support such statements.
• Prof. Alex. McBratney, Pro-Dean, Professor of Soil Science, Faculty of Agriculture, Food & Natural Resources, The University of Sydney – “It is misleading to say that because Australia has old soils there isn’t potential for enhanced sequestration of carbon in our soils – there is, and it’s a win win win win. A win for our soils, a win for our environment, a win for our farmers and a win for our industry. While it is true that much of Australia’s soil cover is on old landscapes, this in itself does not preclude reasonable levels of soil carbon.”
• Soils Officer with the Lachlan Catchment Management Authority Ian Packer said: " The people who make these remarks don't get around enough to know what's going on."
• Dr K Yin Chan, Principal Research Scientist (Soils), NSW Department of Primary Industries, believes we can recover the 25 tonnes per Ha of soil carbon lost since 1770. He calls it the "Soil C Sequestration Potential".
• Generalisations about Australians soils are dangerous. Alpine soils can contain around 10% soil carbon, and desert soils around 0.5%. Soils tested for soils workshops with farmers at Mudgee and Rylstone have between 0.9% and 7% Carbon and averaging 2.2% at Mudgee and 2.7% at Rylstone.
• One percent of carbon is a significant amount. It can translate into 42 tonnes of soil carbon which equates to 154 tonnes of CO2e per hectare (at Bulk Density 1.2 and 30cms depth). A 1% increase in soil carbon per hectare – at $25/tonne – in this situation would be worth $3850. Multiply by a thousand hectares and you have a significant figure.
Myth #2: Science proved our soils have little potential to sequester
Myth #1 was based on research done for the National Carbon Accounting System (NCAS).
But analysis of Technical Reports 34 and 43, the core data reports for the construction of the NCAS, reveals that the data sets are incomplete, focusing almost exclusively on conventional rather than regenerative land management techniques.
It studied only soils managed in ways that caused losses of carbon rather than soils managed in ways that capture and store carbon (ie. regenerative land management techniques such as biological farming, time controlled grazing management, pasture cropping, etc.)
Farming has changed in the 20 years since most of the studies reviewed for NCAS were done. The scientific methodology was flawed because it did not choose a representative range of samples.
For this reason, there are gaps in the data sets. Therefore the data cannot support the conclusions being drawn from it. The authors of these reports warned against relying on them for definitive conclusions. (See Appendix 1.)
The consultant hired to assess the data sources was also concerned: “While there are some very useful datasets available, there are also considerable deficiencies in the completeness of the data… In many established agricultural areas, there are practical difficulties in finding true pairs… The approach is limited by gross lack of data…”
The AGO admitted that the data was insufficient. “Development of the NCAS was undertaken with the clear understanding that data would be imperfect, but that the significance of data limitations could be assessed only in a functional integrated system.”
The AGO took a ‘fix it in the mix’ approach: “The tacit acceptance of variability in data provided for a proper focus on matters of accuracy and bias, rather than on potentially unachievable precision.” The Agency believed the sheer weight of data points would carry the day, provided there was no bias in the inputs: “Over a large sample … a national inventory derived from an aggregation of fine-scale events can provide a robust central estimate provided inputs are not biased.”
But the inputs were biased. The data sets were incomplete.
No AGO research has studied the “potential” of Australian soils to take up carbon. Most official studies recorded poor carbon performance because they studied only traditional techniques which are destructive of soil carbon. They did not find sequestration because they weren’t looking for it.
They were looking for declining carbon. They found it. There are several trials underway to fill the gaps. Further evidence that the gaps existed and the conclusions were unsustainable.
• Exhibit 1: The NSW DPI, DECC and CSIRO are currently evaluating an increase in soul carbon recorded on grazing and cropping land from 2% to 4% recorded on “Winona”, Gulgong, between 1995 and 2005.
• Exhibit 2: There was a 0.46% carbon difference between a paddock managed by conservation farming techniques (stubble retained/no-tillage) and a paddock heavily grazed and conventionally tilled over 10 years at Greenethorpe, NSW translated into a difference of 185 tonnes of carbon per hectare (or 675 tonnes of CO2e.)
• Exhibit 3: A CSIRO study (unpublished) in Albany WA found a significant difference in organic matter between two paddocks, one stubble-burned 3 years previous then no-tillage treatment for three years (3.35% OM), the other managed with no-tillage (5% OM).
• Exhibit 4: Dr K Yin Chan, Principal Research Scientist (Soils), NSW Department of Primary Industries, has a research project which has stretched over 20 years. In the soils studied, he found that there was on average 70 tonnes of soil carbon per hectare under undisturbed native vegetation. This fell dramatically to 40 T/ha under conventional tillage by the 1940s. It rose 5T/ha under Reduced Tillage, to 45T/ha. Dr Chan believes we can recover the (25T/ha) balance. He calls it the "Soil C Sequestration Potential".
• Exhibit 5: “Permanent unimproved pastures in moister areas of NSW, SA, WA and Qld, after sowing to introduced grasses and legumes and fertilised with superphosphate have been shown to exhibit linear increases in soil C at a rate of about 0.4 t C ha-1 yr-1 over several decades. (Russell and Williams 1982, Gifford et al 1992).
• Exhibit 6: Barrow (1969) reported a soil C gain of 440 kg/ha/yr in sandy soils under permanent pasture during a period of 30-40 years in Western Australia. The pasture outscored undisturbed native vegetation on soil C by 2.0% to 0.8%.
• Exhibit 7: Senior CSIRO soil scientist Jeff Baldock says there is today no technical barriers to a fully-functioning market in soil carbon, and that such a market could make it ‘more economic to farm for carbon than to farm for yield.’
Myth #3: Soil carbon is hard to measure
Soil carbon is not hard to measure. Scientists and laboratories do it every day, to very exacting standards, including all the different ‘fractions’ of carbon (eg. labile, humic, etc)
The issue is not measurement. It is deciding how to reconcile a variety of results so we can agree on a figure that represents the amount of carbon that a piece of land holds.
Soil carbon is subject to “Flux” (variability throughout the day) and “Spacial Variability” (variability across paddocks).
The lack of a system of Measurement, Monitoring and Verification (MMV) that satisfies the accounting principles of the Kyoto Protocols has been the major barrier to trade in soil carbon for more than 15 years. Despite millions of dollars in research grants, books, papers, conferences, and government enquiries, we are no closer to a solution. Although we know more about soil carbon, the “practical difficulties” remain unresolved.
The Garnaut Enquiry Issues Paper identified two major barriers to trade:
1. “A difficulty in the development of any offset project is the transaction costs in baseline setting, accreditation, monitoring, measurement and reporting, ensuring additionality, and preventing ‘double counting’ with actions covered by the scheme”.
2. “While significant at the national scale, agriculture and forestry emissions sources and sinks are often small, diffuse and difficult to measure and verify at the individual entity level. Sources and sinks are frequently small relative to the measurement effort required.”
Prime Ministerial Task Group on Emissions Trading Report agreed:
‘factors that suggest initial exclusion of the agricultural sector from an emissions trading scheme ... that is, the lack of reliable measurement methodologies at the farm level and the complexity and cost of verifying emissions.’
CRC for Greenhouse Accounting’s John Carter, (program leader on soil carbon) was even more definitive:
“Soil carbon is more difficult and expensive to measure and verify than carbon in tree plantations. …. These types of measurements require expensive mechanical sampling and laboratory measurements. Soil carbon is spatially variable, even at quite fine scales… High spatial variability increases the amount of sampling (and, hence, analysis costs) required to precisely estimate soil carbon stocks. ’ (Australian Farm Institute, Strategic Roundtable Conference, Future Agriculture, 2–3 November 2006, Sydney)
This mercurial nature of soil carbon is at the heart of the problem.
Rebuttal 1: A buyer of offsets is buying an ‘aggregated tonne’ from a large ‘aggregated pool’ of tonnes that have been ‘equalised’ ie., flux is statistically ‘compressed’ (peaks and troughs equalised)? The buyer buys from an aggregated pool of tonnes as part of an aggregated pool of buyers. The significant variations at individual tonne level are eliminated by statistical smoothing.
• This approach was first noted by Sandor and Skees who say that we need not worry about how much carbon is sequestered on an individual paddock, because, while estimates at an individual level may be flawed, the error has ‘typical statistical properties’ and that estimating many individual parcels and aggregating them into a single parcel will improve the estimate significantly. (Sandor, R. L. & Skees, J. 1999. Creating a market for carbon emissions. Choices 3rd Quarter, pp 13-17.)
• A similar note was sounded by the Australian Farm Institute: “if measurement or estimation systems are robust and unbiased… the aggregate result for the combined scheme will be relatively accurate due to the effect of combining many estimates together.” (The New Challenge for Australian Agriculture: How do you muster a paddock of carbon?)
• Wholesale aggregators are already commonly used in carbon markets and the system for aggregation exists. The Australian Greenhouse office already recognizes the benefits of aggregation in forest sinks, called ‘carbon pooling’. Dr Lal also sees the way forward in pooling: “[A protocol to trade C credits] will require development of routinely usable techniques to measure change in soil C pool at landscape level over a time span of 1 to 2 yr.”
• The concept is in tune with the call by Dr John Kimble for a ‘real world’ approach to soil carbon measurement, based on what is known about the behaviour of soil carbon.
The conclusion that soil carbon has attributes which make it unsuitable for inclusion in an accounting scheme would indicate that the accounting scheme was the problem, given the potential benefits of soil carbon. The accounting scheme was established to facilitate a market to encourage widespread adoption of desirable practices.
The IPCC’s purpose is not to produce a perfect accounting model. It’s purpose is to deploy the resources of its member nations to urgently remove CO2e from the atmosphere.
If the world’s 5.5billion hectares of agricultural soil were able to sequester 0.5 tonne of Carbon/hectare/year, it would remove 10billion tonnes of CO2e.
Myth #4: Costs of trading will be too high for individuals to trade
The Garnaut Enquiry Issues Paper identified cost as a major barrier to trade: “A difficulty in the development of any offset project is the transaction costs in baseline setting, accreditation, monitoring, measurement and reporting, ensuring additionality, and preventing ‘double counting’ with actions covered by the scheme”
American farmers have been trading soil carbon on the Chicago Climate Exchange for 3 years. There are 580 enrolled in the State of Illinois alone.
They are receiving only between US$1.50-US$4 per acre, yet they are wiling to trade.
The costs of trading are accounted by the following:
• Aggregation fee (pooling the produce of a large number of farmers to form a minimum order of 25,000 acres).
• Brokerage fee (standard practice)
• Insurance (which can be in the form of buffer stock)
• Administration (overheads)
The cost to the farmer totals 30% of the sale price, at this early stage of the market. AS volumes increase and more aggregators come on to the market, prices should fall.
The CCX example reveals that the cost issue is a furphy.
Myth #5: Costs of measurement (MMV) will be too high for individuals to trade
“The Cost of Measurement Monitoring and Verification may be so high as to make it uneconomic.” This objection is yet another common myth based on ignorance of real world conditions.
Rebuttal 1: Bulk Buying
If Australia’s 130,000 landholders were to bear the cost of soil mapping, baselining, and regular intense surveys and sampling and the price of CO2e remains less than $10tonne/ha/year, then this objection could have some merit.
But the concept of cost per sample must be seen in context of a dynamic equation:
• Bulk buying of soil carbon baselining and monitoring across such numbers would bring the cost per sample down to below $1 (according to Montana State University’s Associate Professor David Brown).
Rebuttal 2: What price carbon?
If the price of CO2e rises to $100/tonne – not unlikely given the shortage of tradable carbon in the world and the absence to date of the three largest emitters, USA, China, and India, whose demand will drive the price of carbon –the cost of measurement becomes trivial.
Rebuttal 3: Which measurement regime?
The key question at the core of MMV is what measurement regime?
The scientists who did much of the research for the NCAS have been in discussions with the Carbon Coalition and have agreed that the degree of exactitude required for scientific certainty is more than is required for the purposes of trading. While scientists take extensive arrays of core samples, other techniques are available including:
• remote sensing
• visual audit
• regional calculator model
The economic gains to be made by the nation in the value inherent in a restored natural resource base should be included in the ROI equation. Researchers in NZ found that, while soil C is valuable for agricultural production, it is between 40 and 70 times more valuable to environmental protection.
The adoption of soil carbon scoring by the Commonwealth and State Governments as the key performance indicator for a range of ecological improvements to be rewarded by stewardship payments is an efficiency practice: one KPI. Used for this function as well, would further offset the cost of measurement.
Myth #6: Trade will be too difficult for individual farmers to manage
The aggregation of growers is an everyday reality in commodity marketing. Marketing Boards, Grain Desks, and Producer Groups are well understood by growers.
Woolgrowers are setting up “demand chains” or “supply chain solutions for retailers”. Wool buyers or growers themselves aggregate the produce from enough growers to amass sufficient volume to meet the demand of a retail chain for wool of a certain specification so that it can be identified to consumers as a differentiated product.
The diffuse sources and sinks argument ignores common practice. As well as the technology solution lurking in Environmental Management System (EMS). Environmental management systems are “that part of the overall management system which includes organizational structure, planning activities, responsibilities, practices, procedures, processes and resources for developing, implementing, achieving, reviewing and maintaining the environmental policy.”– ISO 14001, Environmental Management System Standard
An EMS would enable the landholder to set their own ‘carbon farming’ goals and report progress. It could be linked to Local Catchment Authority for auditing, enabling decentralised reporting without the fear of Government “Big Brother” oversight.
A modified EMS would enable a “pool manager” to amass saleable quantities of produce and/or carbon. As it can be web-based, “buyers” and “sponsors” can log in and see volumes available.
Myth #7: Farms are more likely to be net emitters
John Carter, who was Program leader in the CRC for Greenhouse Accounting for soil carbon, is a soil carbon trading sceptic.
“ In practice, the following attributes of trading schemes make soil carbon accounting a difficult task: inclusion of all gases, all pools, gross-net or net-net accounting…”
The fear among many in positions of influence has been that the methane and nitrous oxide liabilities would far outweigh the soil carbon asset. This fear was and is baseless, based as it was on two myths:
1. soil carbon can only be grown in tiny amounts and over hundreds of years, and
2. methane and N20 levels are very high.
Rebuttal 1: AGO Calculators
Calculators designed to estimate emissions of various types of farm operations reveal that an ‘average’ operation will not necessarily be a net emitter.
A “Sheep Greenhouse Accounting Tool” developed by Dr Peter Eckard of the University of Melbourne and the Victorian Department of Natural Resources & Environment presents a case study for “Average Joe”. He has 2,300Ha and runs 18,000 sheep on 1400 while cropping 700, with 100Ha under offset forest. Rainfall 500ml-700ml. After allowances for credits for the forest, the calculator reveals Joe owes for 2,687 tonnes CO2e. There is no offset for soil carbon provided for in the model.
However, another calculator called “Carbon Sequestration Predictor for Selected Land Use Change In Inland Areas of NSW. Version 2” - developed by NSW Forests, the CRC for Carbon Accounting, NSW Department of Agriculture and the Department of Infrastructure Planning & Natural Resources – does allow Joe to sequester soil carbon, at very conservative rates. In his rainfall zone, if he had changed the 1400Ha from cropping to perennial pastures or a management change of such an order, he could expect to sequester 0.6tonnes Carbon/Ha/year.
Mathematics: 2687 tonnes CO2e is equivalent to 732 tonnes soil carbon. Over Joe’s 2200Ha, that represents 0.33 tonnes carbon/ha/yr. Now Joe can sequester 0.6tonnes/year. Which means that, in this example, the farmer would have enough tonnage to offset his Methane and other Greenhouse gases PLUS 0.27tonnes Carbon/Ha/year to trade (1 tonne CO2e/Ha/yr or 2200tCO2e.) At $5/tonne, this is worth $11,000.
Rebuttal 2: AGO estimates overstated
Australian Nitrous Oxide trials reveal early IPCC estimates dramatically over-stated the seriousness of the problem here.
IPCC default emission factors (used to calculate Australia’s emissions profile) held hat 1.25% of all nitrogen fertiliser applied here was lost as N2O. But this was based on data from the Northern Hemisphere data. The DPI VIC Greenhouse in Agriculture studies found the figures range from 0.05%-0.1% winter wheat to 0.4%-0.55% dairy.
Still the Australian Greenhouse Office Handbook on non-CO2 Greenhouse Gases features Australian cases higher than IPCC default:
• Irrigated maize MIA 1.5%-2.7%
• Flood-irigated perennial dairy pasture
• Sugar cane 22% (18 times higher that IPCC default)
… and only passing reference is made to dryland wheat studies which reveal local N2) emissions ‘as low as one-third of IPCC estimates’ …
even though the area devoted to irrigation vs dryland cropping disproportionately small.
The early research data on Methane was similarly unsound, based on British herds feeding on British pastures.
Free from the AGO’s obvious agenda, the methane emissions levels can be expected to be less dramatic than early indications threatened.
Myth #8: Soil carbon has unmanageable levels of uncertainty
The entire Kyoto Protocol process is entangled in uncertainties, and that these uncertainties and ambiguities have been addressed to achieve practical outcomes. We believe soil has been subject to discrimination and hyper-exactitude.
Uncertainty afflicts forests as tradable sinks:
• “In soils we can go to a 100m2 field and sample every square meter and look at the differences we find. But if you sample every tree in a large area you would see a similar variability,” says Dr Kimble.
• The Australian Academy of Science stated that “accounting for the carbon contained in forests is difficult. The amount of carbon in forest soils, forest litter and the trees themselves needs to be measured. Different types of trees store different amounts of carbon when growing on different types of soils in different climates. In addition, we might expect natural year-to-year variations in carbon stored, related to climate variations.”
More than 50% of the carbon stored in a forest can be found beneath the ground. Yet it is not counted. Changes in below ground C-stocks can be in the opposite direction from the changes above ground upon a change in land use.
• “A decline in soil C has been observed in NSW for pastures planted to radiata pine. There are several other examples in the literature of soil C-content being lower under trees than under matched pasture sites ... Thus if changes in soil C are not included with the estimate of the above ground plantation sink, that plantation sink will be wrongly estimated.”
• “It is possible for a forest to be a source of emissions rather than a sink…. The soil organic pool is a large carbon reservoir: in a mature forest it commonly contains at least 50% of the total forest carbon stock. .. When agricultural land is reforested there may be significant losses from the soil carbon pool…
Uncertainty and the Global Greenhouse Gas Protocol
The unavoidable uncertainty the typifies all Climate Change activities is acknowledged in the Greenhouse Gas Protocol developed by the World Resources Institute and the World Business Council for Sustainable Development.
It identifies two types of uncertainty in estimating emissions or sinks: scientific uncertainty and estimation uncertainty. The latter is further divided into model uncertainty and parameter uncertainty. (See Appendix 2)
Uncertainty and the National Greenhouse Gas Inventory
Uncertainty is a key aspect of greenhouse emission estimates produced for publication in the National Greenhouse Gas Inventory. The Inventory is compiled from data from a range of sources, and in many cases represents a ‘scaling up’ of sample, experimental or case study results. AGO is open about the fact that some estimates are likely to be more reliable than others. For the 2003 NGGI, the AGO estimates an uncertainty band for the estimated national emissions outcome of 550 Mt CO2e ±5.2%.
“Uncertainty over agricultural emissions makes a relatively strong contribution to uncertainty regarding the overall national emissions outcome —particularly with respect to estimates for agricultural soils, savanna burning, forestry and land clearing. However, the reported uncertainties appear to be primarily associated with uncertainty about activity levels rather than the complex and variable biological processes that generate greenhouse gases… The AGO reports that ‘… uncertainty in the reported cattle numbers was the most significant contributor to the overall uncertainty’
The myths surrounding soil carbon are able to flourish only in the absence of the facts. Opinion leaders who circulate these myths do so at risk of being challenged in pubic for a.
The soil carbon issue has finally made its way onto the national agenda and must withstand the scrutiny of the industry, governments, and the community.
Soil carbon advocates seek a level playing field and a fair go… with the evidence inspected objectively, free of the ignorance and prejudice displayed to date.
The decision the nation takes on this issue will affect the lives of millions.
Appendix 1: Methodology Issues in Soil Carbon Accounting
The authors of the Technical Report 34 complained that the study was insufficiently resourced to cover the range of land management styles:
• “As it would have been too time-consuming and expensive to examine land clearing in all parts of the State, the AGO specified that this project should focus on certain clearing hotspots in NSW…” (Ie. only high emissions locations were selected.)
• “However, with resources for ten possible comparative sites, only a limited range of land-use transitions were included in the study… It should be noted that ten paired sites is not a sufficient number to adequately sample all the land use changes that are occurring in the clearing belt in NSW… (Ie. insufficient data sets.)
• “Classifying the paddock histories into particular management practices can be difficult, as a ‘recognised practice’ may have considerable variation in the methods and effectiveness with which it is implemented. The environmental and economic conditions under which practices are implemented can also vary. Both these things can cause variability in any expected changes in soil properties...”
The authors of Technical Report 43 also complained of a lack of data:
“Data from over 50 independent studies across Australia was compiled to create a comprehensive data set of 586 values. Information identifying associated site histories, climate and soil type was recorded.
“Of the trials considered, many had incomplete information, lacking details on:
• soil properties below a depth of 10 cm;
• carbon densities;
• soil bulk densities;
• implements used during tillage operations;
• other issues relating to tillage operations;
• stubble management practices; and
• historical site management data.”
Appendix 2: Uncertainty and the Global Greenhouse Gas Protocol
• Scientific uncertainty arises when the science of the actual emission and/or removal process is not completely understood. For example, many direct and indirect factors associated with global warming potential (GWP) values that are used to combine emission estimates for various GHGs involve significant scientific uncertainty. Analyzing and quantifying such scientific uncertainty is extremely problematic and is likely to be beyond the capacity of most company inventory programs.
• Estimation uncertainty arises any time GHG emissions are quantified. Therefore all emissions or removal estimates are associated with estimation uncertainty. Estimation uncertainty can be further classified into two types: model uncertainty and parameter uncertainty. Model uncertainty refers to the uncertainty associated with the mathematical equations (i.e., models) used to characterize the relationships between various parameters and emission processes. For example, model uncertainty may arise either due to the use of an incorrect mathematical model or inappropriate input into the model. As with scientific uncertainty, estimating model uncertainty is likely to be beyond most company’s inventory efforts;
• “…uncertainty estimates for corporate GHG inventories will, of necessity, be imperfect.”
• “For these reasons, almost all comprehensive estimates of uncertainty for GHG inventories will be not only imperfect but also have a subjective component and, despite the most thorough efforts, are themselves considered highly uncertain.”
• “... a reduction in air travel would reduce a company’s scope 3 emissions. This reduction is usually quantified based on an average emission factor of fuel use per passenger.
• “Generally, as long as the accounting of indirect emissions over time recognizes activities that in aggregate change global emissions, any such concerns over accuracy should not inhibit companies from reporting …
“Preparing a GHG inventory is inherently both an accounting and a scientific exercise. Most application for company-level emissions and removal estimates require that these data be reported in a format similar to financial accounting data. In financial accounting, it is standard practice to report individual point estimates (i.e., single value versus a range of possible values). In contrast, the standard practice for most scientific studies of GHG and other emissions is to report quantitative data with estimated error bounds (i.e., uncertainty).
“Just like financial figures in a profit and loss or bank account statement, point estimates in a corporate emission inventory have obvious uses. However, how would or should the addition of some quantitative measure of uncertainty to an emission inventory be used? In an ideal situation, in which a company had perfect quantitative information on the uncertainty of its emission estimates at all levels, the primary use of this information would almost certainly be comparative.
“Such comparisons might be made across companies, across business units, across source categories, or through time. In this situation, inventory estimates could even be rated or discounted based on their quality quantitative metric for quality. Unfortunately, such objective uncertainty estimates rarely exist.
“Uncertainties associated with GHG inventories can be broadly categorized into scientific uncertainty and estimation uncertainty. Scientific uncertainty arises when the science of the actual emission and/or removal process is not completely understood. For example, many direct and indirect factors associated with global warming potential (GWP) values that are used to combine emission estimates for various GHGs involve significant scientific uncertainty. Analyzing and quantifying such scientific uncertainty is extremely problematic and is likely to be beyond the capacity of most company inventory programs.
Estimation uncertainty arises any time GHG emissions are quantified. Therefore all emissions or removal estimates are associated with estimation uncertainty. Estimation uncertainty can be further classified into two types: model uncertainty and parameter uncertainty.3 Model uncertainty refers to the uncertainty associated with the mathematical equations (i.e., models) used to characterize the relationships between various parameters and emission processes. For example, model uncertainty may arise either due to the use of an incorrect mathematical model or inappropriate input into the model. As with scientific uncertainty, estimating model uncertainty is likely to be beyond most company’s inventory efforts; however, some companies may wish to utilize their unique scientific and engineering expertise to evaluate the uncertainty in their emission estimation models.
Parameter uncertainty refers to the uncertainty associated with quantifying the parameters used as inputs (e.g., activity data and emission factors) into estimation models. Parameter uncertainties can be evaluated through statistical analysis, measurement equipment precision determinations, and expert judgment. Quantifying parameter uncertainties and then estimating source category uncertainties based on these parameter uncertainties will be the primary focus of companies that choose to investigate the uncertainty in their emission inventories.
Given that only parameter uncertainties are within the feasible scope of most companies, uncertainty estimates for corporate GHG inventories will, of necessity, be imperfect. Complete and robust sample data will not always be available to assess the statistical uncertainty4 in every parameter. For most parameters (e.g., liters of gasoline purchased or tonnes of limestone consumed), only a single data point may be available. In some cases, companies can utilize instrument precision or calibration information to inform their assessment of statistical uncertainty. However, to quantify some of the systematic uncertainties5 associated with parameters and to supplement statistical uncertainty estimates, companies will usually have to rely on expert judgment.6 The problem with expert judgment, though, is that it is difficult to obtain in a comparable (i.e., unbiased) and consistent manner across parameters, source categories, or companies. For these reasons, almost all comprehensive estimates of uncertainty for GHG inventories will be not only imperfect but also have a subjective component and, despite the most thorough efforts, are themselves considered highly uncertain. In most cases, uncertainty estimates cannot be interpreted as an objective measure of quality. Nor can they be used to compare the quality of emission estimates between source categories or companies.
Reductions in indirect emissions (changes in scope 2 or 3 emissions over time) may not always capture the actual emissions reduction accurately. This is because there is not always a direct cause-effect relationship between the activity of the reporting company and the resulting GHG emissions. For example, a reduction in air travel would reduce a company’s scope 3 emissions. This reduction is usually quantified based on an average emission factor of fuel use per passenger. However, how this reduction actually translates into a change in GHG emissions to the atmosphere would depend on a number of factors, including whether another person takes the “empty seat” or whether this unused seat contributes to reduced air traffic over the longer term. Similarly, reductions in scope 2 emissions calculated with an average grid emissions factor may over- or underestimate the actual reduction depending on the nature of the grid. Generally, as long as the accounting of indirect emissions over time recognizes activities that in aggregate change global emissions, any such concerns over accuracy should not inhibit companies from reporting their indirect emissions. In cases where accuracy is more important, it may be appropriate to undertake a more detailed assessment of the actual reduction using project quantification methodology.