Regressing REDD Reference Levels Simone Bauch & Arild Angelsen School of Economics and Business Norwegian University of Life Sciences (UMB)

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP UMB projects on REDD+ Ref.Levels  CIFOR + partners Global Comparative Study on REDD+ –Funded by NORAD+ – –C1: National policies and politics –C2: REDD+ projects’ evaluation –C3: Ref.levels and MRV Global Brazil, Indonesia, Vietnam  Other smaller projects –DEEC, UK – input to negotiations –Meridian III report on ref.levels –Advice to Norway’s Climate Forest Initiative, e.g. Indonesia Institutt for økonomi og ressursforvaltning 2

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP REDD Basics  Pay (incentivize and compensate) countries for Reducing Emission from Deforestation and forest Degradation (REDD)  Emission reduction = reference level – actual emissions in a given time period –Main challenge: How to set reference levels?  One of 3 key issues highlighted by the Cancun decision for further consultations this year Institutt for økonomi og ressursforvaltning 3

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP What are reference levels? Several definitions for reference levels, but refer to two very distinct meanings:  BAU: counterfactual, or what would have happened without REDD. The yardstick for measuring the effect of REDD policies or interventions  Crediting levels: The yardstick for payment. Can be seen as a modified BAU, incorporates considerations about e.g. efficient use of limited REDD resources, and higher responsibilities for middle-income than low income countries  The BAU is where research can contribute the most: –how to predict deforestation

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Reference levels Time Past emissions (’historical baseline’) Realised path Crediting baseline BAU baseline Commitment period REDD credits Forest carbon stock

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Contentious but important issue  Changes in reference period and year change payments considerably USD100 million * based on 120 tons C/ha and USD 5/tCO 2

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Reference year: Institutt for økonomi og ressursforvaltning 7 USD250 million

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP In addition to historical deforetstaion, there are other factors that affect deforestation... Forest stock: forest transition curve Source: Angelsen, 2008

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP What else to consider? To know what would happen we need to know what are drivers of deforestation? –Agricultural commodities: soy, cattle, oil palm (prices) –Household consumption: fuelwood, timber, etc –Land tenure: assert ownership of land  National historical deforestation  National circumstances: –Forest cover, reflecting stage in forest transition –GDP/capita  Other factors –War, disasters, …. –Population –Commodity prices  Ex post adjustment: –Brazil being rewarded due to the economic crisis Proposal: regression models

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP How to include these other factors?  Different levels for different types of countries: what defines these types?  How to select variables to include in BAU? –Regression models: allows us to evaluate multiple dimensions simultaneously –Predictive model: what will deforestation be in the near future? Institutt for økonomi og ressursforvaltning 10

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Regression results Institutt for økonomi og ressursforvaltning 11 Bigger coefficients

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Robustness checks: Random Effects model Institutt for økonomi og ressursforvaltning 12

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Robustness check: state level data Institutt for økonomi og ressursforvaltning 13 N= 9 states x 5 years

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Robustness check: each year separately  Model 1: very robust.  Model 2: Institutt for økonomi og ressursforvaltning 14

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Model fit  With deforestation predictions for 2010 and Institutt for økonomi og ressursforvaltning 15

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Some major conclusions  Historical deforestation is key in predicting deforestation  But can improve by including more factors  Poverty cannot be used as argument to adjust BAU in Brazil  Causal factors that drive deforestation, are still important factors after including historical deforestation –continuous pressure, no instant equilibrium of forest stock  The distinction made in the debate between historical and predicted deforestation is artificial  Comparing RL proposals: –Historical deforestation: simplest, assumes linearity –Categories (ad hoc cutoffs): relatively simple, might compromise additionality –Regression: more complex and realistic Institutt for økonomi og ressursforvaltning 16

UNIVERSITETET FOR MILJØ- OG BIOVITENSKAP Thank you!   Institutt for økonomi og ressursforvaltning 17