A stepwise approach to reference levels
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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, ...

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 A stepwise approach to reference levels Presentation Transcript

    • A stepwise approach to reference levels Louis  Verchot  
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Criteria  for  comparing  country  circumstances  and   strategies  
    • Criteria  for  comparing  country  circumstances  and   strategies  
    • 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
    • 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
    • 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
    • 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
    • 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  
    • 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)  
    • 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
    • Thank  you