Climate Change and Forests: Emerging Policy and Market Opportunities

Chapter 10. Project-Based Mechanisms: Methodological Approaches for Measuring and Monitoring Carbon Credits

Projects based on land use, land-use change, and forestry (LULUCF), such as afforestation and reforestation (AR) projects, have the ability to produce real, significant, positive carbon effects. However, without scientifically tested and rigorously applied methods for measuring these effects, the actual carbon dioxide emission reductions will not be quantifiable or creditable. Fortunately, widely recognized standards exist for the quantification of land-based carbon credits. The responsibility of a project developer is to understand how these methods can be used to design a measurement plan that maximizes the verifiable, conservatively estimated carbon emission credits while minimizing the resources required for project implementation. Robust methodologies for measuring and monitoring carbon stocks and carbon stock changes have been developed over many years of forestry expertise. These methods form the basis of approved methodologies for measuring emission removals under the Kyoto Protocol’s Clean Development Mechanism (CDM). In this chapter we discuss where measurement and monitoring are required in carbon projects in the land-use sector. We also discuss what represents “good practice ” in measurement and outline ways to create measuring and monitoring plans that will inspire confidence. Although other documents exist that detail exactly how to measure the carbon stocks of LULUCF projects,1 we discuss all elements of project measurement and monitoring and how to maximize creditable net carbon Project-Based Mechanisms: Methodological Approaches for Measuring and Monitoring Carbon Credits timothy pearson, sarah walker, and sandra brown 10 135 benefits through good practice. Our aim is to provide project developers and practitioners with the tools to create highly creditable carbon project activities. We discuss measurement and monitoring generally, but where we introduce a focus on a specific offset mechanism, the focus is on the Clean Development Mechanism of the Kyoto Protocol, with mechanisms such as Joint Implementation (JI) and the voluntary market receiving lesser mention. The reasons behind this focus are that the CDM will be the most prescribed, with the highest standards to achieve in measurement, and is the most advanced of the mechanisms in terms of definition of rules, requirements, and regulations. Key Concepts The key concepts under measurement and monitoring are conservatism, accuracy , and precision. Conservatism is an approach to uncertainty. Where a range of possible values exists, a conservative approach to project development and reporting would be to adopt the value that gives the lowest (most conservative) net carbon effect. For example, when 95 percent confidence bounds exist around a value, it is conservative to take the higher bound in the baseline and the lower bound during the project.2 In each case the net carbon benefit will be reduced and will be more conservative . Other conservative approaches are to exclude carbon pools that will increase or remain unchanged during the project and to exclude project emissions that will be higher in the baseline than in the project. Sampling involves examining a subset to understand the whole. Sampling is the basis of all measurement for LULUCF carbon projects, because destructive measurement of all trees is an expensive approach to evaluating carbon stocks and is counterproductive with regard to greenhouse gases (GHGs). For carbon sampling , the two key concepts are accuracy and precision. Accuracy represents how close a measurement is to the true value. When sampling , one cannot know the true mean, but if statistically sound, rigorously tested scientific methods are used and carefully applied, then one can feel confident that the true mean is well represented. Precision represents the range of values between which the true value may lie, or the repeatability of a measure. In other words, accuracy is how well our measurements represent reality, and precision represents how confident we are in the measurements we have taken. To illustrate the concepts of accuracy and precision, we use a hypothetical forest that has a carbon stock of 120 tonnes of carbon per hectare (t C/ha) (table 10-1). In scenario A, the mean of the measurements is identical to that of the known stock, and the 95 percent confidence interval (a measure of the variability of the repeated measurements) is small, even for just five samples. Thus scenario A is accurate and precise. Scenario A should be the desired outcome of a measurement plan. 136 t. pearson, s. walker, and s. brown In contrast, in scenario B the mean is accurate but the range is very large (low...