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Stepwise approach to Reference Levels REDD+

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Stepwise approach to Reference Levels REDD+

  1. 1. A stepwise approach to reference levels Louis Verchot
  2. 2. Business as usual and national capacity• Activity data – types of deforestation and forest degradation• Emission factors – carbon loss per unit area for a specific activity• Drivers – to describe how much DD are caused by each specific change activity THINKING beyond the canopy
  3. 3. Table 9: Activity data on the national level can be estimated from the different approaches as suggested by the IPCC GPG: Activity data Approach 1 Approach 2 Approach 3 Data on forest TOTAL LAND-USE TOTAL LAND-USE AREA, SPATIALLY-EXPLICIT change (or emissions) AREA, NO DATA ON INCLUDING CHANGES LAND-USE following IPCC CONVERSIONS BETWEEN CATEGORIES CONVERSION DATA approaches BETWEEN LAND USES Example: data from Example: National level remote sensing Example: FAO FRA data data on gross forest changes through a change matrix (i.e. deforestation vs. reforestation), ideally disaggregated by administrative regions THINKING beyond the canopy
  4. 4. Three levels of emission factors• Tier 1 methods are designed to be the simplest to use, for which equations and default parameter values (e.g., emission and stock change factors) are provided by IPCC Guidelines.• Tier 2 can use the same methodological approach as Tier 1 but applies emission and stock change factors that are based on country- or region-specific data• Tier 3, higher order methods are used, including models and inventory measurement systems tailored to address national circumstances, repeated over time, and driven by high-resolution activity data and disaggregated at sub- national level. THINKING beyond the canopy
  5. 5. Deforestation/degradation drivers for each continent AMERICA AFRICA ASIA -4% -1% -2% -2% -7% -11% -10% -13% -39% -7% -41%Deforestation -36% -57% -35% -37% 4% 4% 8% 7% 6% 17% 26% 20%Degradation 9% 70% 67% 62% Deforestation driver Forest degradation driver THINKING beyond the canopy
  6. 6. Changes of Deforestation Drivers:Important for assessing historical deforestation Phase1 Phase2 Phase3 Phase4 Pre Early Late Post Transition Transition Transition Transition Forest Cover (%) Time Using national data from 46 countries: REDD-related data and publications THINKING beyond the canopy
  7. 7. Deforestation Drivers Deforested-area ratio of Deforested area deforestation drivers km2 100% 700 Urban expansion 600 80% Infrastructure 500 60% 400 Mining 40% 300 Agriculture 200 (subsistence) (local-slash & burn) 20% Agriculture 100 0% (commercial) 0 pre early late post pre early late post Distribution of 46 countries - Pre: 7, early: 23, late: 12, post: 4 Agriculture (commercial) is 45%, agriculture (local/subsistence) 38%, mining 7%, infrastructure 8%, urban expansion 3% and only agriculture make up 83% of total Ratio of mining is decreasing and urban expansion is relatively increasing over time THINKING beyond the canopy
  8. 8. Criteria for comparing country circumstances and strategies
  9. 9. Criteria for comparing country circumstances and strategies
  10. 10. RLs using regression models – Simple, easy to understand and test new variables – But, data demanding – Predicting deforestation in a period: Pt – Pt+1, based on deforestation in the previous period Pt-1 – Pt and a set of other factors (observed at time t). – Using structure (coefficients) from the estimated regression equation to predict deforestation in period Pt+1 – Pt+2, based on observed values at time t+12000 2004 2005 2009 2010 Historical deforestation Estimated/Predicted deforestation Regression model Predictive model, based on structure from regression model 10 THINKING beyond the canopy
  11. 11. Tier 1 case for 4 countries using FAO FRA data Cameroon Indonesia 3,500 18,000 Forest C stock (Mt)Forest C stock (Mt) 3,000 16,000 14,000 2,500 12,000 2,000 10,000 1,500 8,000 6,000 1,000 4,000 500 2,000 0 0 1985 1990 1995 2000 2005 2010 2015 2020 2025 1985 1990 1995 2000 2005 2010 2015 2020 2025 Year Year Vietnam Brazil 1,500 80,000 Forest C stock (Mt) 70,000Forest C stock (Mt) 1,200 60,000 900 50,000 40,000 600 30,000 20,000 300 10,000 0 0 1985 1990 1995 2000 2005 2010 2015 2020 2025 1985 1990 1995 2000 2005 2010 2015 2020 2025 Year Year
  12. 12. Category Regression coefficient Deforestation rate (2000-2004) 0.395 Trend variable -0.136 -0.145Step 2: Deforestation dummy -0.373 -0.773 Forest stock 2.18 4.756Brazil Forest stock squared -1.8 -3.826 Log per capita GDP -0.034 -0.13Predict Agric GDP (%GDP) 0.28 0.28deforestation rates Population density 0.081 -0.81for legal Amazon Road denisty 0.039 0.0762005- 2009 R2 0.831 0.789 N 3595 3595 THINKING beyond the canopy
  13. 13. Category Regression coefficient Deforestation rate (2000-2004) 01.464 Trend variable -0.006 0.003Step 2: Deforestation dummy -0.011 -0.031 Forest stock 0.067 0.260Vietnam Forest stock squared -0.189 -0.463 Population density -1.177 1.036Predict Road denisty 0.004 -0.001deforestation rates2005- 2009 R2 0.515 0.052 N 301 301 THINKING beyond the canopy
  14. 14. Conclusions• Historical def. is key to predict future deforestation – Coefficients below one simple extrapolation can be misleading• Some evidence of forest transition (FT) hypothesis – Robustness of FT depends on the measure of forest stock • FT supported when forest stock is measured relative to total land area, otherwise mixed results emerge• Other national circumstances have contradictory effects• Contradictory relationships may be linked to data quality and interrelations of econ. & institutions differ THINKING beyond the canopy 14
  15. 15. MRV capacity gap analysis 3000 Net change in forest area since 1990 2000 1000 (1000ha) 0 -1000 -2000 -3000 Very large Large Medium Small Very small Capacity gapMRV capacity gap in relation to the net change in total forest areabetween 2005 and 2010 (FAO FRA)
  16. 16. Thank you

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