Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits
Abstract
:1. Introduction
- historical rates of deforestation, degradation and emission factors, also using adjustment factors to allow inclusion of social and economic variables (named “national circumstances”) [13], and
1.1. State of the Art
1.1.1. Model-Assisted Design-Based AGB Estimation Using Remote Sensing
1.1.2. Cost-Efficiency of Lidar-Based Methods
1.1.3. Addressing Uncertainties in REDD+: the Reliable Minimum Estimate
2. Materials and Methods
2.1. Data Used
2.1.1. Field Data
2.1.2. Lidar Data Extraction
2.1.3. Cost of Carbon Monitoring
2.2. Simulation Approach
- (1)
- We created a series of subsamples from the 223 plots via bootstrapping. We simulated sampling with replacement for each sample size with 1000 iterations, starting from a sample size of 20 plots and increasing the size by one unit at a time, up to 223 plots. This resulted in a total of 204 different sample sizes and 204,000 iterations. Subsequently, the variance and the relative standard error of the estimate of aboveground carbon density (i.e., in Equation (1)) were calculated for each iteration. Finally, the relationship between the relative standard error and the number of field plots was assessed.
- (2)
- We investigated, by a scenario approach, how uncertainties expressed by the relative standard error obtained in step 1 determine the accountable avoided emissions. Each scenario is characterized by a different combination of (i) the accuracy of carbon monitoring (expressed by the relative standard error), (ii) the baseline carbon emissions from deforestation and forest degradation (i.e., RLs), and (iii) target for emission reductions as a result of REDD+ activities. The errors associated with the estimation of carbon stock changes were linked to the potential generation of carbon credits. Table 3 presents details of the scenarios implemented.
- (3)
- Finally, the results of steps 1 and 2 were combined with a set of realistic monitoring costs. For the alternative monitoring systems, as presented in step 2, different levels of uncertainty and cost frameworks were realized and the achievable amounts of accountable avoided emissions calculated. This allows to study the cost-efficiency of alternative MRV-designs.
2.3. Sensitivity Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
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Forest Type | Plot (n) | Mean (tC ha−1) | Standard Error |
---|---|---|---|
Moist forests | 141 | 56.84 | 4.77 |
Wet and rain forests | 82 | 82.35 | 7.52 |
Total | 223 | 66.22 | 4.17 |
Source | Spatial Resolution or Lidar Pulse Density | Coverage or Project Area (ha) | Acquisition and Processing Costs (in US$) |
---|---|---|---|
Hummel et al. [57] | 6.3 points/m2 (mean pulse density) | 12,650 | 5.6–9.3 US$ ha−1 |
Patenaude et al. [58] | - | 2,800,000 | 4.15 US$ ha−1 (only acquisition costs) |
Wulder et al. [59] | 90 cm (average horizontal distance between Lidar returns) | - | 5 CND$ ha−1 |
Böttcher et al. [60] | - | 13,600 | 4–5 US$ ha−1 (plus additional 160 h processing time) |
Asner et al. [20] | 4 points/m2 (mean pulse density) | National-scale (Perù) | 0.01 US$ ha−1 |
Asner et al. 2011 [61] | 50–70 kHz (pulse repetition frequency) | 253,744 | 0.16 US$ ha−1 |
GOFC-GOLD [62] | - | - | 0.5–1 $ ha−1 |
Relative Standard Error (%) | Baseline Emission Rate (or Reference Level) (%) | Emission Reduction Under REDD+ (%) |
---|---|---|
1.2–4 | 1 | 30 |
7–28 | 3 | 50 |
5 | 75 | |
8 | ||
10 | ||
20 |
Emission Rate (%) | Relative Standard Error (%) | Emission Reduction (%) | Cost of Monitoring a Single Ton of Carbon ($): Small Area Monitoring | Cost of Monitoring a Single Ton of Carbon ($): Large Area Monitoring |
---|---|---|---|---|
3 | 1.25 | 50 | 5.6 | 0.56 |
1.25 | 75 | 1.4 | 0.14 | |
2 | 75 | 3.22 | 0.32 | |
5 | 1.25 | 30 | 5.6 | 0.56 |
1.25 | 50 | 1.12 | 0.11 | |
1.25 | 75 | 0.56 | 0.06 | |
2 | 50 | 1.61 | 0.16 | |
2 | 75 | 0.46 | 0.05 | |
3 | 75 | 0.41 | 0.04 | |
8 | 1.25 | 30 | 1.22 | 0.12 |
1.25 | 50 | 0.51 | 0.05 | |
1.25 | 75 | 0.29 | 0.03 | |
2 | 30 | 2.01 | 0.2 | |
2 | 50 | 0.4 | 0.04 | |
2 | 75 | 0.2 | 0.02 | |
3 | 50 | 0.3 | 0.03 | |
3 | 75 | 0.1 | 0.01 | |
4 | 75 | 0.08 | 0.01 | |
10 | 1.25 | 30 | 0.8 | 0.08 |
1.25 | 50 | 0.37 | 0.04 | |
1.25 | 75 | 0.22 | 0.02 | |
2 | 30 | 0.81 | 0.08 | |
2 | 50 | 0.27 | 0.03 | |
2 | 75 | 0.15 | 0.01 | |
3 | 50 | 0.15 | 0.02 | |
3 | 75 | 0.07 | 0.01 | |
4 | 50 | 0.17 | 0.02 | |
4 | 75 | 0.05 | >0.01 | |
20 | 1.25 | 30 | 0.29 | 0.03 |
1.25 | 50 | 0.16 | 0.02 | |
1.25 | 75 | 0.1 | 0.01 | |
2 | 30 | 0.2 | 0.02 | |
2 | 50 | 0.1 | 0.01 | |
2 | 75 | 0.06 | 0.01 | |
3 | 30 | 0.1 | 0.01 | |
3 | 50 | 0.04 | >0.01 | |
3 | 75 | 0.03 | >0.01 | |
4 | 30 | 0.08 | 0.01 | |
4 | 50 | 0.03 | >0.01 | |
4 | 75 | 0.02 | >0.01 |
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Di Lallo, G.; Mundhenk, P.; Marchetti, M.; Köhl, M. Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits. Forests 2017, 8, 271. https://doi.org/10.3390/f8080271
Di Lallo G, Mundhenk P, Marchetti M, Köhl M. Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits. Forests. 2017; 8(8):271. https://doi.org/10.3390/f8080271
Chicago/Turabian StyleDi Lallo, Giulio, Philip Mundhenk, Marco Marchetti, and Michael Köhl. 2017. "Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits" Forests 8, no. 8: 271. https://doi.org/10.3390/f8080271