A stepwise approach to reference levels

1,628 views
1,387 views

Published on

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.

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,628
On SlideShare
0
From Embeds
0
Number of Embeds
284
Actions
Shares
0
Downloads
36
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

A stepwise approach to reference levels

  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•  Data on existing national monitoring capacities 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: 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. 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. 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. 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  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 totaln  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   –  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. 11. Tier  1  case  for  4  countries  using  FAO  FRA  data   Cameroon Indonesia 3,500 18,000Forest 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   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. 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. 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. 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. 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. 17. Thank  you  

×