Presented by Colas Chervier (CIFOR/CIRAD) at Meeting CIFOR-NORAD-NICFI on GCS REDD+ research findings in DRC on 06 September 2022

1. Impact of logging concessions
in DRC
Impact of logging concessions
on deforestation in DRC
Colas CHERVIER (CIFOR/CIRAD)
& Ari Ximenes (CIFOR)
colas.chervier@cirad.fr

2. Policy relevance
• Logging concession is a major policy at the regional level
with a smaller extent and production level in DRC.
• Bigger extent in the future: end of the 2001 moratorium.
• Big debate around the consequences of this decision for
Congolese forests, but no scientific evidence
• Leading country in the region for REDD+, towards
jurisdictional REDD+ programs in which concessions should
key partners
Intro
ToC
Method
Results
Conclusion

3. DRC’s changing legal framework
• 2001- 2002: a key turning point in recent history
• Revocation of a large number of concession contracts, revision
of remaining contracts, moratorium on new concessions, new
forest code.
• A move from a “garantie d’approvisionnement” model
to a logging concession model.
• New features: must have a Forest Management Plan and set up
a local development fund.
Intro
ToC
Method
Results
Conclusion

4. Concession impacts on deforestation: our hypotheses
Low direct impact as many
concessions are reportedly
inactive.
However, the lack of
implementation of FMP and the
use of non-improved techniques
might result in negative impact in
some active concessions.
Increased access is reported
the main mechanism
through which logging
concessions may influence
deforestation in DRC
Intro
ToC
Method
Results
Conclusion

5. Heterogeneity of impact
• At concession level
• FMP (legal requirement)
• OLB certificate (voluntary)
• Active/non active
• Within concessions
• Most valuable commercial species are pioneer species & grow in
forests that have been disturbed in the past
• Flooded forests are de juro and naturally protected.
• Areas near villages/settlements are more likely to be deforested – and
this is planned/legal
Intro
ToC
Method
Results
Conclusion

6. Our overall quasi-experimental approach
Difference-in-Difference estimator
& propensity score matching
• Bias: observable and unobservable time-
invariant factors & time-varying factors
affecting both groups)
Heterogeneity analysis:
• We estimate separately the impact of
concessions that: have a FMP submitted or
approved on time; are engaged in the process
of obtaining a legality certificate
Intro
ToC
Method
Results
Conclusion

7. Treated and control areas
• We randomly pick pixels in…
• Treated:
• The 57 concessions that were
considered as eligible in 2009 and
received a conditional title in 2011
or 2014.
• Control
• The 75 concessions that
submitted an application but were
not considered as eligible in 2009.
Intro
ToC
Method
Results
Conclusion

8. Descriptive statistics (1/2)
Increasing
deforestation
and
differences
between
groups over
time 0,0000
0,5000
1,0000
1,5000
2,0000
2,5000
Control Treatment
Average percentage of loss forest pixels in both control and treated groups per year (before
matching)
Intro
ToC
Method
Results
Conclusion

9. Descriptive statistics (1/2)
• Higher risk of
deforestation
• Matching removes
initial differences
Intro
ToC
Method
Results
Conclusion

10. Preliminary impact results
• Significant &
positive impact
• Magnitude:
OLB>all> FMP
OLB
ALL
FMP
Intro
ToC
Method
Results
Conclusion

11. Next steps
• Still adjusting the models:
• Impact magnitude is likely to change (e.g. use of
generalized linear models)
• But the overall results are quite robust across all the
model specifications I tried.
• Performed a so-called ‘placebo test’ to check if
the ‘parallel trends ’ assumption between
treated and control groups is verified
• Parallel trends assumptions seems to be violated in
years close to 2011 (anticipation effect?)
• Currently trying to find a way to correct this.
Intro
ToC
Method
Results
Conclusion

12. Conclusion / discussion
• Our results support our hypotheses regarding direct
and indirect pathways to impact,
• without allowing to distinguish between both
• without providing sufficient evidence.
• One question that remain to be settled is linked to
the magnitude :
• So far: while impact is relatively small, gross deforestation,
especially in recent years is relatively high
• Key question that necessitate further studies: is
deforestation levels inside concessions still acceptable to
all resource regeneration?
Intro
ToC
Method
Results
Conclusion

13. Beyond the study
• Touchy topic
• need to have a strategy about how this paper could initiate a healthy
debate
• Analyzing the impact of other forest policies in DRC
• What we have built is a GIS project, a database, a method that could be
applied to other policies, including at the jurisdictional redd+
Intro
ToC
Method
Results
Conclusion

14. Thanks for your attention

15. Method (additional information)

16. Unit of analysis
• 30mx30m pixels
• We selected 2000 pixels on average in
each concession.
Intro
ToC
Method
Results
Conclusion

17. Counterfactual (1/2)
Intro
Context
ToC
Design
(i) Pixels with initial tree cover in 2000 below 25% as we our analysis focus on forested areas
(deforestation probability is null if the pixel was already deforested)
(ii) Pixels located both in control and treated concession. There is indeed some overlap between control
and treated concessions in the shape files we use
(iii) Pixels located both inside a mining concession and a treated or control logging concession as it
would be difficult to distinguish the cause of deforestation
(iv) Pixels that are located both inside a protected area and a treated or control logging concession, as it
would be hard to distinguish the effect of protected areas from the effect of logging concession
(v) Pixels that are located inside one of the three types of flooded forest as the risk of deforestation is
close to zero (not exploitable)
We discarded the
following pixels from
our dataset.

18. Database and matching covariates
We ran Propensity
Score Matching using
the following covariates
influencing the
probability to have a
title and the risk of
deforestation:
Variable name Variable description
Covariates at the pixel level
Initial forest cover % of forest cover
Population density Number of people in each cell (1km*1km?)
Slope average slope in the pixel?
Elevation average elevation in the pixel?
Settlements Euclydian distance to closest settlement
Roads including logging roads - OLD OPEN Euclydian distance to an "old" open road
Roads including logging roads - OLD ABANDON Euclydian distance to an "old" abandoned road
Cities (travel time) Travel time to cities
Cities (distance) Euclydian distance to closest city
Distance to capital city Euclydian distance to capital city
Border Euclydian distance to border
River ports Euclydian distance to closest primary or secondary river port
Rivers Euclydian distance to closest primary or secondary river
Precipitation Annual level of precipitation per pixel
Biomass Dummy: pixel has a biomass level above 110 tC/ha or not$
Fragmentation Non-forested area (or nb of pixels) in a 5km buffer around the pixel
Distance to deforestation Distance to closest non-forest pixel
Covariates at the concession level
concession size size of the concession the pixel is in
Intro
ToC
Method
Results
Conclusion

19. Impact estimation
• Difference-in-Difference (DiD) estimator
• Takes into account the hierarchical structure of
data (clustered sampling at the concession
level)
• Takes into account time trends (panel dataset),
• Control for time-varying covariates X (rainfall,
concession title, local fragmentation)
• The model:
𝑦𝑖𝑔𝑡 = 𝛼0 + 𝛼𝑡 + 𝛽1𝐷𝑖𝑡−1 + 𝛽𝑘𝑋𝑖𝑔𝑡−1 + 𝑢𝑖 + 𝑢𝑔 + 𝜀𝑖𝑔𝑡
yigt is the individual level outcome
(whether pixel i in concession g got
cleared in a year t), 𝛼0 is a common
intercept, 𝛼𝑡 is a year fixed effect, 𝛽1𝐷𝑖𝑡 is
the time−varying one−year lagged
concession treatment indicator (which
takes 1 after the concession received a
title either in 2001 or 2014), 𝑋𝑖𝑔𝑡−1 is a
one−year lagged set of pixel−level
time−varying covariates (precipitation,
number of deforested pixels in a 5km
radius of pixel i); 𝑢𝑖 is a pixel−level random
effect ; 𝑢𝑔 is a concession random effect,
and 𝜀𝑖𝑔𝑡 is an idiosyncratic error.
Intro
Context
ToC
Design