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Quantifying Uncertainty Estimates and Risk for Carbon Accounting (QUERCA)

Quantifying uncertainty in carbon accounting is essential at scales ranging from individual projects to country-level compensation for reducing emissions from deforestation and forest degradation. Monte Carlo approaches are easy to apply but difficult to implement correctly, with some countries reporting total uncertainties smaller than the largest single source of error, which is clearly incorrect. The goal of this project is to develop and disseminate peer-reviewed tools and approaches for error propagation for use by carbon accounting technicians and researchers, especially those in developing countries.  Ultimately, uncertainty will be needed to evaluate the adequacy of carbon reductions made under the Paris Accord.  

Training Materials

We've gathered various resources to help you dive into these topics.

Activity Data (AD)

Refers to the data on the magnitude of a human activity resulting in emissions or removals taking place during a given period of time (FAO, 2023).

  • Combining Uncertainty with Monte Carlo Simulation in Excel video by Lalita Adhikari and Joe Nash.*
  • Systematic Sampling for Activity Data and Uncertainties Estimations tutorial and simple example by Ana V. Medina.
  • Two-stage Cluster Samplingto Estimate Uncertainties of Activity Data tutorial by Fernanda Gonzalez.
  • Handling partially correlated variables using analytical and Cholesky decomposition method by Sandip Rijal video.
  • Allometric equation and calculating uncertainty in tree by Nathan Tyler video.
  • Good practice for area estimation of land cover by Dingfan Xing video.
  • Combining Uncertainty from Shared and Independent Sources - Monte Carlo by Joe Nash and Lalita Adhikari video.*

Emission factors (EF)

The Greenhouse gas (GHG) emissions or removals per unit of Activity Data (FAO, 2023).

  • Analytical Error Propagation of Excel by Scott Dai video.
  • How to Calculate Uncertainty in Soil Organic Carbon tutorial and simple example by Kelley Gilhooly.
  • Combining and Quantifying Uncertainty in Aboveground and Belowground Biomass Estimates tutorial and simple example by Erin Cornell.*

Note: Documents related to both AD and EF will be marked with a * and listed below each category according to the level of relevance within each topic.

QUERCA related papers and presentations

  • Dingfan Xing's capstone: Uncertainty evaluation of sample-based area estimates in land cover monitoring:
    improved methods for estimating confidence intervals and total variance PDF.
  • Lin, J., J.G.P. Gamarra, J.E. Drake, A. Cuchietti, and R.D. Yanai. 2023. Scaling up uncertainties in allometric models: How to see the forest, not the trees. Forest Ecology Management, 537: 120943. DOI: 10.1016/j.foreco.2023.120943.
  • Yanai, R.D., C. Wayson, D. Lee, A.B. Espejo, J.L Campbell, M.B. Green, J.M. Zukswert, S.B. Yoffe, J.E. Aukema, A.J. Lister, J.W. Kirchner , and J.G.P. Gamarra. 2020. Improving Uncertainty in Forest Carbon Accounting for REDD+ Mitigation Efforts. Environmental Research Letters, 15:24002. DOI:10.1088/1748-9326/abb96f.
  • FCPF. 2021. Guidance Note on estimating the uncertainty of emission reductions using Monte Carlo simulation Version 1.0. Calculating uncertainty of emission reduction using Monte Carlo in Excel: 4 year ultra simple example and R-Code.