Eric Marland

The treatment of long-lived, carbon-containing products in inventories of carbon dioxide emissions to the atmosphere

Abstract The United Nations Framework Convention on Climate Change (UNFCCC) requires that all parties to the convention periodically report their emissions of greenhouse gases and the Intergovernmental Panel on Climate Change (IPCC) has published guidelines on how to estimate these emissions. Estimating carbon dioxide (CO 2 ) emissions is complicated by the fact that consumption of fossil-fuels and harvesting of forests do not necessarily mean that the contained C has been released to the atmosphere as CO 2 . Some fractions of fossil-fuels and harvested wood are incorporated into products that have lifetimes ranging from months to centuries. The IPCC methodology addresses durable products by assuming that some prescribed fraction goes to permanent storage while the remainder is oxidized instantly. The question posed here is whether the annual increase in stocks of durable products, i.e. the difference between the rates of production and oxidation, can be reasonably estimated as a simple fraction of their current rate of production. Although the annual stock change can be described as a simple fraction of annual production when production is growing exponentially and oxidation is a first order decay process, a description of annual stock changes needs to consider how both production and oxidation are evolving with time, regardless of the functional forms of these changes with time.

Uncertainty in gridded CO2 emissions estimates

We are interested in the spatial distribution of fossil-fuel-related emissions of CO2 for both geochemical and geopolitical reasons, but it is important to understand the uncertainty that exists in spatially explicit emissions estimates. Working from one of the widely used gridded data sets of CO2 emissions, we examine the elements of uncertainty, focusing on gridded data for the United States at the scale of 1’ latitude by 1’ longitude. Uncertainty is introduced in the magnitude of total United States emissions, the magnitude and location of large point sources, the magnitude and distribution of non-point sources, and from the use of proxy data to characterize emissions. For the United States, we develop estimates of the contribution of each component of uncertainty. At 1 resolution, in most grid cells, the largest contribution to uncertainty comes from how well the distribution of the proxy (in this case population density) represents the distribution of emissions. In other grid cells, the magnitude and location of large point sources make the major contribution to uncertainty. Uncertainty in population density can be important where a large gradient in population density occurs near a grid cell boundary. Uncertainty is strongly scale-dependent with uncertainty increasing as grid size decreases. Uncertainty for our data set with 1 grid cells for the United States is typically on the order of ±150%, but this is perhaps not excessive in a data set where emissions per grid cell vary over 8 orders of magnitude.

A comparison of five high-resolution spatially-explicit, fossil-fuel, carbon dioxide emission inventories for the United States

The quantification of fossil-fuel-related emissions of carbon dioxide to the atmosphere is necessary in order to accurately represent carbon cycle fluxes and to understand and project the details of the global carbon cycle. In addition, the monitoring, reporting, and verification (MRV) of carbon dioxide emissions is necessary for the success of international agreements to reduce emissions. However, existing fossil-fuel carbon dioxide (FFCO2) emissions inventories vary in terms of the data and methods used to estimate and distribute FFCO2. This paper compares how the approaches used to create spatially explicit FFCO2 emissions inventories affect the spatial distribution of emissions estimates and the magnitude of emissions estimates in specific locales. Five spatially explicit FFCO2 emission inventories were compared: Carbon Dioxide Information and Analysis Center (CDIAC), Emission Database for Global Atmospheric Research (EDGAR), Fossil Fuel Data Assimilation System (FFDAS), Open-source Data Inventory for Anthropogenic CO2 (ODIAC), and Vulcan. The effects of using specific data and approaches in the creation of spatially explicit FFCO2 emissions inventories, and the effect of resolution on data representation are analyzed using graphical, numerical, and cartographic approaches. We examined the effect of using top-down versus bottom-up approaches, nightlights versus population proxies, and the inclusion of large point sources. The results indicate that the approach used to distribute emissions in space creates distinct patterns in the distribution of emissions estimates and hence in the estimates of emissions in specific locations. The different datasets serve different purposes but collectively show the key role of large point sources and urban centers and the strong relationship between scale and uncertainty.

Valuing uncertainty part I: the impact of uncertainty in GHG accounting

Background: It has become increasingly evident in the literature that a correlation needs to be made between uncertainty in GHG emissions estimates and the value of emissions. That is, emissions with larger uncertainty are less desirable than those with smaller uncertainty. In fact, concrete advances in trade and reduction agreements depend on finding a set of methodologies for dealing with uncertainty that is acceptable to all parties. Results: Here, we assume that a cost, or value, can be assigned to changes in GHG emissions. As this cost can be assigned to emissions (or sequestrations), then so must a cost be assigned to the associated uncertainty. Standard methods from the actuarial sciences provide an approach to this valuation and we apply these same ideas to dealing to GHG accounting. Conclusion: This framework will allow us to address issues related to agreement structures and motivations for reducing uncertainty, and will enable objective comparisons between options.

A spatial uncertainty metric for anthropogenic CO2 emissions

Large point sources account for as much as 60% of the anthropogenic carbon dioxide emissions for some countries. Because CO2 emissions are seldom measured directly, but are generally estimated from other data, we need to understand the uncertainty of these emissions estimates. Simply stated, for any given geographical and temporal location, we would like to quantify the emissions and the associated uncertainty with as fine a resolution as possible. While US data on point sources are largely assumed to be among the best available globally, the reported locations of these sources, based on the data set used in this analysis, are estimated to differ by 0.84 km on average from their actual locations. This paper presents a metric to quantify spatial uncertainty in point sources and explains why the uncertainty in point source data cannot be described with traditional methods. A Monte Carlo simulation is used to calculate expected emissions values for each point source and the associated spatial uncertainty is derived from these expected values. The uncertainty metric can be used to define and calibrate appropriate levels of resolution for regions with more or less reliable data sets. Gridded data are output to be incorporated into other data products reporting spatially explicit emissions estimates.

Gridded estimates of CO 2 emissions: uncertainty as a function of grid size

A crucial aspect of constructing a gridded model of anthropogenic fossil fuel CO2 (FFCO2) emissions involves careful consideration of uncertainty. Both the spatial resolution of the emissions estimates (grid scale) and the selection of proxy data to represent the spatial distribution of emissions, plus the quality of data on point sources of emissions, have important impacts on uncertainty. In earlier papers, we explored the uncertainties associated with grid selection and the available data on large point sources. In this work CO2 emissions data are spatially distributed using population density as the selected proxy, using three different treatments of large point sources, and with five levels of grid resolution (1o, 2o, 3o, 4o, and 5o). The methods of calculating uncertainty associated with grid size, proxy selection, and reported point-source emissions data are presented, with particular attention being drawn to grid size selection. We find that as the resolution becomes coarser, relative uncertainty (total uncertainty as a percentage of total emissions) at the grid cell level decreases. Relative uncertainty in most grid cells decreases as the portion of emissions attributed to specific point sources increases. Good data on large point sources is very important for spatially explicit emissions inventories.

Additionality and permanence standards in California's Forest Offset Protocol: A review of project and program level implications

A key component of Californias cap-and-trade program is the use of carbon offsets as compliance instruments for reducing statewide GHG emissions. Under this program, offsets are tradable credits representing real, verifiable, quantifiable, enforceable, permanent, and additional reductions or removals of GHG emissions. This paper focuses on the permanence and additionality standards for offset credits as defined and operationalized in Californias Compliance Offset Protocol for U.S. Forest Projects. Drawing on a review of the protocol, interviews, current offset projects, and existing literature, we discuss how additionality and permanence standards relate to project participation and overall program effectiveness. Specifically, we provide an overview of offset credits as compliance instruments in Californias cap-and-trade program, the timeline for a forest offset project, and the factors shaping participation in offset projects. We then discuss the implications of permanence and additionality at both the project and program levels. Largely consistent with previous work, we find that stringent standards for permanent and additional project activities can present barriers to participation, but also, that there may be a trade-off between project quality and quantity (i.e. levels of participation) when considering overall program effectiveness. We summarize what this implies for Californias forest offset program and provide suggestions for improvements in light of potential program diffusion and policy learning.

Valuing uncertainty part II: the impact of risk charges in dealing with time issues in lifecycle analysis and GHG accounting

Background: We have greater certainty for what has happened in the past than for what will happen in the future. Uncertainty on the impact and value of emissions can be very large. Given all of the elements of uncertainty, we are challenged to set global targets for limiting the environmental impact of emissions, to distribute those targets among the many parties responsible for emissions, to evaluate the trajectories toward targets, to understand the risk involved in not meeting targets, to motivate the collective efforts and burden sharing or trading, and to verify that targets have been achieved. We need a clear and consistent framework for dealing with uncertainty and in this article we use the notion of a risk charge on uncertainty to investigate issues of time in GHG and lifecycle analysis accounting. Results: We address critical issues of short-term storage, time horizons, permanence, trading agreements and model error, and explain the consequences of a risk charge on the associated uncertainties. Conclusions: We demonstrate here how the framework we have built naturally extends to address most types of issues that might arise in placing a value on the uncertainty of GHG emissions, and in quantifying management trade-offs and policy strategies for mitigation and adaptation of climate change.

Rounding the Regression

Abstract Rounding is a necessary step in many mathematical processes. We are taught early in our education about significant figures and how to properly round a number. So when we are given a data set and asked to find a regression line, we are inclined to offer the line with rounded coefficients to reflect our model. However, the effects are not as insignificant as they might seem at first. In this paper, we investigate some consequences of rounding the coefficients in a least squares linear regression with respect to the calculated value of R2, and consider ways to minimize the amount of error that can arise.

Adherence to Accounting Principles

With considerable reliance on somewhat qualitative judgments, estimates, models, and assumed factors, the quality of a forest carbon accounting system can be reviewed by examining the extent to which the processes, procedures, and methods prescribed by the offset protocol adhere to its underlying accounting principles. We can examine the extent to which increased adherence to one principle results in a tradeoff of diminished adherence to another principle, and consider how the implementation of these underlying principles impacts achievement of the offset protocol’s stated objective.