A stepwise approach to reference levels
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This presentation was given by CIFOR scientist Louis Verchot on 28 November 2012 at a joint CIFOR and GOFC-GOLD (Global Observation of Forest Cover and Land Dynamics) UNFCCC COP18 side-event in Doha, Qatar.
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A stepwise approach to reference levels
- 1. A stepwise approach to reference levels Louis Verchot
- 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 • Data on existing national monitoring capacities THINKING beyond the canopy
- 3. Table 9: Activity data on the national level can be estimated from the different approaches as suggested by the IPCC GPG: Ac#vity 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: Na:onal level remote sensing Example: FAO FRA data data on gross forest changes through a change matrix (i.e. deforesta:on vs. reforesta:on), ideally disaggregated by administra:ve regions THINKING beyond the canopy
- 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 Guildelines. • 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. Deforestation/degradation drivers for each continent AMERICA AFRICA ASIA -‐1% -‐2% -‐2% -‐4% -‐7% -‐11% -‐10% -‐13% -‐39% -‐7% -‐41% Deforesta#on -‐36% -‐57% -‐35% -‐37% 4% 4% 6% 8% 7% 17% 26% 20% Degrada#on 9% 70% 67% 62% Deforesta#on driver Forest degrada#on driver THINKING beyond the canopy
- 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. Deforestation Drivers Deforested-‐area ra:o of Deforested area deforesta:on drivers km2 100% 700 Urban expansion 600 Infrastructure 80% 500 60% Mining 400 40% 300 Agriculture 200 (local-‐slash & urn) (subsistence) b 20% Agriculture 100 (commercial) 0% 0 pre early late post pre early late post Distribu:on of 46 countries -‐ Pre: 7, early: 23, late: 12, post: 4 n Agriculture (commercial) is 45%, agriculture (local/subsistence) 38%, mining 7%, infrastructure 8%, urban expansion 3% and only agriculture make up 83% of total n Ratio of mining is decreasing and urban expansion is relatively increasing over time THINKING beyond the canopy
- 8. Criteria for comparing country circumstances and strategies
- 9. Criteria for comparing country circumstances and strategies
- 10. RLs using regression models – Simple, easy to understand and test new variables – But, data demanding – Predic:ng deforesta:on in a period: Pt – Pt+1, based on deforesta:on in the previous period Pt-‐1 – Pt and a set of other factors (observed at :me t). – Using structure (coefficients) from the es:mated regression equa:on to predict deforesta:on in period Pt+1 – Pt+2, based on observed values at :me t+1 2000 2004 2005 2009 2010 Historical deforesta:on Es:mated/Predicted deforesta:on Regression model Predic#ve model, based on structure from regression model 10 THINKING beyond the canopy
- 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) 70,000 Forest 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. Category Regression coefficient Deforesta#on rate (2000-‐2004) 0.395 Trend variable -‐0.136 -‐0.145 Step 2: Deforesta#on dummy -‐0.373 -‐0.773 Forest stock 2.18 4.756 Brazil Forest stock squared -‐1.8 -‐3.826 Log per capita GDP -‐0.034 -‐0.13 Predict Agric GDP (%GDP) 0.28 0.28 deforesta#on rates Popula#on density 0.081 -‐0.81 for legal Amazon Road denisty 0.039 0.076 2005-‐ 2009 R2 0.831 0.789 N 3595 3595 THINKING beyond the canopy
- 13. Category Regression coefficient Deforesta#on rate (2000-‐2004) 01.464 Trend variable -‐0.006 0.003 Step 2: Deforesta#on dummy -‐0.011 -‐0.031 Forest stock 0.067 0.260 Vietnam Forest stock squared -‐0.189 -‐0.463 Popula#on density -‐1.177 1.036 Predict Road denisty 0.004 -‐0.001 deforesta#on rates 2005-‐ 2009 R2 0.515 0.052 N 301 301 THINKING beyond the canopy
- 14. Conclusions • Historical def. is key to predict future deforesta:on – Coefficients below one → simple extrapola:on can be misleading • Some evidence of forest transi:on (FT) hypothesis – Robustness of FT depends on the measure of forest stock • FT supported when forest stock is measured rela:ve to total land area, otherwise mixed results emerge • Other na:onal circumstances have contradictory effects • Contradictory rela:onships may be linked to data quality and interrela:ons of econ. & ins:tu:ons differ THINKING beyond the canopy 14
- 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 gap MRV capacity gap in rela:on to the net change in total forest area between 2005 and 2010 (FAO FRA)
- 16. We surveyed 17 REDD+ demonstration projects § 53% use site specific biomass equations § 24% had methods for belowgound C § 41% had methods for dead wood and litter § Most projects will use IPCC defaults for soil-C THINKING beyond the canopy
- 17. Thank you