o Spatially explicit data from various contexts in Indonesia shows that a very specific understanding of drivers of deforestation and recovery of tree cover is needed as they vary from context to context. Specific leverage points can be identified by understanding this interconnectedness and variation amongst the drivers

1. IUFRO 2014 Salt Lake City: Session C-02 (193) From Understanding
Drivers To Gaining Leverage At The Tropical Forest Margins: 20 Years of
ASB Partnership
Identifying Policy Levers Of Deforestation
and Recovery Of Tree Cover
From The Driver Analyses:
A Case Study From Indonesia
Sonya Dewi, Andree Ekadinata, Asri Joni

2. Outline
Background
Proposed methods
Results
Conclusions

5. Existing driver analysis/studies
 Descriptive: quantification of land use and cover
changes and analysis of association of factors influencing
and patterns of LUCC: what, how much, where, when –
empirical studies
 Explanatory: process of LUCC driven by proximate
causes, underlying causes: who, how and why,
interdependencies – conceptual framework
 Predictive: future LUCC given proximate and underlying
factors – spatial-quantitative up to proximate causes,
econometric with underlying causes
 So-what? Recommendation for intervention: policy
levers to change the course toward promoting what
should happen and avoiding what should not happen –
should be specific and effective

6. Drivers
A1. Land use policies, spatial development planning
A2. LU rights (e.g. community forest mngmnt)
Livelihoods, provisioning &
Conse-quences
&
functions
profitability
Biodiversity, Watershed
functions, GHG emissions,
Landscape beauty
Actors/
agents
Land
use/cover
changes
B2. PES and conditional ES incentives
Response/
feedback
options
B1. Incentive structure through policy change (tax, subsidy etc)
Van Noordwijk, M., B. Lusiana, G. Villamor, H. Purnomo, and S. Dewi. 2011. Feedback loops added to four conceptual models linking land change with driving forces and
actors. Ecology and Society 16(1): r1. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/resp1/

7. Implicit assumptions
 Deforestation is always bad and all has to be
avoided
 Deforestation is the only LUCC that matters
 There is no co-variation between deforestation and
other LUCC (completely independent processes of
decision making)
 There is no co-variation between causes/factors of
LUCC

9. reforestation and regrowth

10. Landuse and cover changes

11. Needs
 Link between descriptive-quantitative pattern and causal
processes and interdependencies among them
 Be consistent and comparable with data and analysis
 Acknowledge legal and customary norms on top of
biophysical characteristics within zonation
 Capture, discuss and use knowledge and perception of
multiple stakeholders
 Synthesize local-specific contexts across heterogeneous
larger landscape to allow upscaling
In participatory processes to identify and
negotiate policy levers from drivers at the
planning stage

13. Local Knowledge
on Land Use/Cover Change
• Historic and future land use change
• Criteria and indicators for ‘legitimate’ and ‘illegitimate’
• Factors and causes; interdependencies
2
Network Analysis of drivers of LUCC
•
•Quantify pattern of agent- and zone-specific changes, configuration,
3
Knowledge
•Structural network model of factors, causes, interdependencies
• Identify policy levers at multiple levels
• Formulate recommendation
• Synthesis and comparative analysis
• Policy Network Analysis or Analytic Network Process
• Dissemination, interpretation, iteration
Action
• Discussion and scenario development
• Scenario simulation through what-if tools
such as LUMENS, LUWES
• Negotiate way forward
5
4
Pattern Analysis
of Land Use/Cover Change
• Quantitative descriptive analysis of both side of
the curves: trajectory
• Local variability/heterogeneity
1

14. Network Analysis
Causes and factors (Nodes):
 Proximate causes
 Underlying causes
 Triggering events
 Capitals
Interdependencies of factors:
 Nature of relationships/interaction: direct
causes, stimulating, deferring, enabling,
prohibiting
 Strength: importance of relationships

18. 1.2
1
0.8
0.6
0.4
0.2
0
Millions
2000-2005
Total defor
Leg_defor
Illeg_defor
Data issues
77% from 10 provinces
1.2
1
0.8
0.6
0.4
0.2
0
Millions
2005-2010
Total defor
Leg_defor
Illeg_defor

19. 2.5
2
1.5
1
0.5
0
Millions
2000-2005
Total_regrowth
Agroforestation
Reforestation
Tree cropping
72% from 10 provinces
2.5
2
1.5
1
0.5
0
Millions
2005-2010
Total_regrowth
Agroforestation
Reforestation
Tree cropping

20. South Sumatra
SOUTH SUMATRA
 Total area of South Sumatra is 9.1 million
hectare
 Permanent in-migration for 2010 is estimated
at 1,01 mil. people while the out-migration is
779,239 people
 The population density in 2014 is estimated at
81 ppl/sq.km. Country average is 124
ppl/sq.km. Rate of population increase in
South Sumatra is 1.85%/years.
 HDI of South Sumatra has increased from 70.2
in 2005 to 74.3 in 2013 which is higher than
average HDI in Indonesia (73.8) (source:
http://www.bps.go.id/)

21. Papua
PAPUA
 Papua is the largest province in Indonesia with
total area of 31.9 million hectares.
 The population density in 2010 is estimated at
8 ppl/sq.km. Rate of population increase in
Papua is the highest in Indonesia, which reach
5.39%/years.
 Permanent in-migration for 2010 is estimated
at 435,773 people while the out-migration is
87,545 people
 Human development index of Papua has
increased from 62.08 in 2005 to 66.25 in 2013
although it is still below average HDI in
Indonesia (73.8)

22. Land Use Changes
SOUTH PAPUA
SUMATRA

23. Land Use Trajectories Maps
Land use trajectories in Papua
within the period of 1990-2010
were dominated by loss of
tree cover/forest to logged
over forest, while in South
Sumatra, the most dominant
land use trajectories were
recovery to tree cropping

24. Land Use Trajectories across LU Zones
Recovery to tree cropping
Recovery to forest
Recovery to agroforest
Other
Loss to logged-over forest
Loss to infrastructure
Loss to cropland
Loss to bare land
SOUTH SUMATRA PAPUA

25. Drivers of Forest and Tree cover
loss to Agriculture
(Sumatra Selatan)
Network Analysis

26. Drivers of Forest and Tree cover
loss to Agriculture
(Papua)
DRIVERS OF FOREST AND TREE COVER LOSS
TO AGRICULTURE (PAPUA)

27. Drivers of Recovery of Tree Cover
(Papua)
DRIVERS OF RECOVERY OF TREE COVER
(PAPUA)

28. Drivers of Recovery of Agroforest
(Papua)
DRIVERS OF RECOVERY OF AGROFOREST
(PAPUA)

29. South Sumatra
Food self sufficiency program
Demand for increasing income
Land suitability for agriculture
Transmigration
Need to increase local revenue
Local customs
Demand for local economic growth
Infrastructure development
Demand for food
Population growth
High market price for commodities
Government program for community…
Lack of law enforcement
Cultural changes
Partnerships with investors
Land availibility
Demand for land for housing
Government program for housing
0.5
0.4
0.3
0.2
0.1
0
Papua
Demand for food
Improvement of local livelihood
Local migration
Transmigration
Demand for land for housing
Demand for jobs/employment
Population growth
People's skill
People's environmental awareness
Land suitability for agriculture
0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160
Drivers of Loss to agriculture
SumSel Papua
People's environmental awareness
0.000 0.020 0.040 0.060 0.080 0.100
Leverage points

30. South Sumatra
Demand for higher hh income
Need to increase local revenue
Rehabilitation program
Land suitability for tree crop
Demand for employment
Partnerships with investors
Easy access to market
Government program for community empowerment
Land availibility
Land grabbing
Cultural changes
People's environmental awareness
Population growth
Information and technology
Wood extraction
Local customs
Demand for wood
Local economic growth
Lack of law enforcement
High market price of tree crop commodities
Demand for NTFP
Demand for improvement of local livelihood
Market demand for tree crop commodity
Privatization
Food self sufficiency program
Transmigration
Forest degradation
Land suitability for agroforest
Conformity to land use plan/ land…
Land suitability for oilpalm
Land titling program
Government program for tree-crop…
Demand for employment
Land availibility
Land procurement by investor
Market demand for tree crop…
Need to increase local revenue
Suitable soil condition
Job shifting
Infrastructure development
Market demand for coffee
Demand for improvement of local…
Local migration
People's environmental awareness
0.000 0.020 0.040 0.060 0.080 0.100
Demand for poverty alleviation
Simple cultivation technique
Local custom
Papua
0.000 0.020 0.040 0.060
0.5
0.4
0.3
0.2
0.1
0
Drivers of Recovery to tree crop
Cap-financial Cap-natural Cap-physical Cap-social Event-pol Prox-infra Prox-wood Und-cult Und-demo Und-eco Und-pol Und-tech
SumSel Papua

31. South Sumatra
Rehabilitation program
Demand for poverty alleviation
Food self sufficiency program
Transmigration
Partnerships with investors
Need to increase local revenue
Easy access to market
Demand for employment
0.5
0.4
0.3
0.2
0.1
0
Population growth
0.000 0.050 0.100 0.150 0.200
Papua
Food self sufficiency program
Land availibility
Demand for improvement of
local livelihood
Climate suitability for coffee
Land suitability for agroforest
Drivers of Recovery to agroforestry
Cap-financial Cap-natural Cap-physical Prox-infra Und-cult Und-demo Und-eco Und-pol
SumSel Papua
Land suitability for agroforest
0.000 0.050 0.100 0.150 0.200 0.250
Infrastructure development

32. Conclusions
CONCLUSIONS
 Drivers are often connected in a graph-like structure rather than
a list or tree or a fish bone
 Locally specific leverage points can be identified through
understanding interconnectedness and covariance across
drivers
 Comparisons between areas are possible; extrapolation domain
can be found
 The process is useful beyond its output; it stimulates multiple
stakeholder to think and discuss the drivers and levers
analytically and iteratively
 Options of leverage points can then be further formulated into
scenarios, taken Zoning into account, and simulated in a tool
that allow ex-ante impacts to be analyzed, such as LUMENS
(Land Use Planning for Environmental Services) tool

33. Thank you