Minimizing Ecological Damage from RoadImprovement in Tropical Forests: The Case ofMyanmar

Susmita DasguptaDavid Wheeler

DECEE, The World Bank2016

Overview

Motivation Research Objective A Composite Biodiversity Indicator Road Improvement and Deforestation Descriptives Model Specification Data Estimation Implications for Forest Clearing

Road Upgrading and Ecological Risk Identifying Critical Road Links Summary

Conservation Management ofTropical Forests

Traditional Measure: Protected Area Strategy Demarcation and protection of large areas deemed

critical for biodiversity conservation. Restrict infrastructure development in “Protected Areas”

that may increase the profitability of forest clearing.

Problems Conflicts arise when forested areas have significant

agricultural potential. Failure of attempts to protect large areas with strong

agricultural potential. Protected areas may not coincide with the areas of

highest ecological value.

Underlying Issues

Lack of understanding: Valuable economic and ecologicalresources have non-uniform and overlapping spatialdistributions.

Lack of understanding: Both development and conservationmay be hindered if a policy regime treats large areas as eithercompletely protected or completely open for development.

Scarcity of information: Assessments of tradeoffs betweendevelopment and conservation objectives is often hindered bylimited information on critical ecosystem and biodiversity.

Research Objective

Developed high-resolution map of potential ecological lossfrom range maps and threat status data for 25,000+ speciesprovided by the IUCN and BirdLife International.

Constructed a spatial panel dataset on tropical forest lossfrom newly-available high-resolution measures of forestcover loss since 2000.

Developed and applied a spatial econometric model thatlinks road upgrading to forest clearing and biodiversity lossin the moist tropical forests of Bolivia, Cameroon andMyanmar.

Provided ecological risk ratings for individual road corridorsto inform environmentally-sensitive infrastructure investmentprograms.

Biodiversity Indicator

Composite Biodiversity Indicator

Composite BiodiversityIndicator

Biodiversity Vulnerability Biome Vulnerability

Incorporating Biodiversity

Species Density(Count of resident animal species for every 250m cell in thestudy region from range maps provided by the IUCN andBirdLife International)

Species Vulnerability Geographic Vulnerability

(Endemicity: Proportion of each species’ range that lies withineach cell of our study region)

Density of endangered and critically endangered species(Count of endangered and critically endangered species in eachcell)

Extinction Risk(Probability of extinction over next 50,100, 500 years usingMooers et al. 2008 and Isaac et al. 2007)

Biome Vulnerability

Use of 825 terrestrial ecoregions* of the world provided bythe WWF.

Computation of percentage of total moist forest area in acountry accounted by each ecoregion.

Use of the inverse of the percentage of total moist forestarea in a country accounted by each ecoregion as thevulnerability index to assign high values to cells in smallerecoregions, where clearing single cells may pose moresignificant threat to biome integrity.

* Ecoregion: a large unit of land or water containing ageographically distinct assemblage of species, naturalcommunities and environmental conditions.

Myanmar: Moist Forest Ecoregions

Myanmar: Biodiversity IndicatorCorrelation Coefficients

Correlations calculated for 250m cells. Biome indicator is a distinct outlier

SpeciesCount

EndangeredSpeciesCount Endemicity

IsaacExtinction

Risk

IUCN50-Yr.

ExtinctionRisk

IUCN 100-Yr.

ExtinctionRisk

IUCN 500-Yr.

ExtinctionRisk

Endangered Species Count 0.42Endemicity 0.97 0.41Isaac Extinction Risk 0.98 0.59 0.94IUCN 50-Yr. Extinction Risk 0.64 0.92 0.61 0.78

IUCN 100-Yr. Extinction Risk 0.62 0.96 0.59 0.77 0.99

IUCN 500-Yr. Extinction Risk 0.71 0.88 0.66 0.83 0.91 0.95

Biome (Ecoregion) Risk -0.26 -0.26 -0.26 -0.31 -0.39 -0.34 -0.27

Myanmar: Composite BiodiversityIndicator

Myanmar

30-4444-5151-5858-6666-7474-8080-8484-8888-9393-100

Road Network and Forest ClearingDescriptives

Myanmar: Road Networks

Secondary roadPrimary road (connector)Primary road (highway)

Myanmar has 2,101 primaryroad and 4,879 secondaryroad links in 2014.

Source: Delorme, Inc.

Forest Clearing

Data Source: High-resolution (30 m) estimates of globalforest clearing from Hansen, et al. 2013 and Hansen 30 mestimates of tree cover in 2000.

Annual files were created with cleared pixels = 1 in the yearwhen most clearing occurred. Uncleared pixels areassigned the value 0.

Hansen estimates are used to compute forest clearing ratesin 250 m (approximately 0.0025 decimal degree in length)cells for expositional convenience, yielding cell areas of7.75 hectares.

Annual cumulative percent forest cleared is computed foreach cell through 2014.

Focus is on areas defined as moist tropical forestecoregions by WWF.

Myanmar: Forest Clearing andRoad Networks, 2000-2014

% Cleared,2000 - 2014

Myanmar: Change in Share of Forest Cleared(2000-2014) vs. Distance from Road Segments

With increase in the distance from the road, there is a steep reduction inmean % forest cleared for the first 5 km, a declining slope through 10 km,and approximate flattening near zero beyond 10 km.

Mean forest clearing within half a km of the road increasedby 15 percent point from 2000 to 2014.

Road Network and Forest ClearingConceptual Framework

Road Construction /Upgrading

Potential Gain Potential Loss

Increase in Trade Increase inAgricultural Income Loss of Biodiversity

Economics of Road Improvement

Road improvement is a problem of competitive selectionamong corridors that traverse the same region.

Corridors differ in length, construction cost conditions,biodiversity value and potential agricultural income.

The optimum corridor choice reflects maximization of a socialutility function that values both income and biodiversity,subject to Fixed budget constraint - Feasible road quality improvement along each

corridor (reflecting the budget constraint)

Expected income growth from agriculture in the corridor

Expected income growth from trade between areas connected by thecorridor

Corridor specific impacts of road quality improvement on biodiversity loss

Economics of Forest Clearing

The proprietor/ occupant of a forested area considers therelative profitability of maintaining/ clearing the area.

In each period, the present-value profitability of sustainablyharvested forest products is compared with the clear-cutvalue of forest products and the cleared land’s present valueprofitability in its best use (e.g., plantation, pasture,smallholder agriculture, settlement).

Generally there is a cost associated with forest clearing.

Determinants of forest clearing highlighted in prior research:Cost of Land, Expected Revenue from Production onCleared Land, Distance from Markets, Quality of TransportInfrastructure, Cost of Capital, Agricultural Input Price,Topography, Soil Quality and Forest Protection Measures.

Model Building Blocks

Trade between areas is encouraged by better road quality.

The proprietor of each road-front parcel confronts

Forest clearing cost, which is constant or increasing with thedistance from the road, depends on elevation and slope of theterrain.

For agricultural production, commodity transportation costs thatincrease with the distance from the road.

Improvement in road quality lowers transport costs and increasespotential profitability of agriculture along the corridor.

Expected profit for each roadside land parcel falls with increase inroad distance.

Expected profitability of agriculture increases with the size of thecleared parcel of land.

Given a fixed road quality budget, road quality declines with roadlength.

Variables of Interest

Distance from the road

Transport cost to the nearest market center

Elevation of land

Terrain slope

Agricultural opportunity value of the land

Legal protection status

Road quality

Model Specification

Road Network and Forest ClearingEconometric Estimation

Data

Variable SourceForest clearing Hansen pixels cleared: per 250 m cell, the

percent of 30 m Hansen pixels cleared.

Distance from road segment Distance from the centroid of each cell to thenearest road segment, calculated in ArcGIS 10.

Distance traveled to the nearesturban center via primary andsecondary road segments

Calculated in ArcGIS 10 from Delorme digitalroad maps.

Elevation Average elevation for a cell, calculated from theCGIAR-SRTM dataset (3 seconds resolution)

Terrain slope Standard deviation of pixel-level elevationmeasures within a cell.

Agricultural opportunity value Mean value for a cell, calculated from the high-resolution global grid developed by Deveny, etal. (2009).

Legal protection status 1 if the cell is in a protected area identified by theWorld Database on Protected Areas (WDPA); 0otherwise.

Travel Time Minimizing Distance

PLOTOF

FOREST

PLOTOF

FOREST

URBAN CENTER

POPULATION:50,000

OR MORE

URBAN CENTER

POPULATION:50,000

OR MOREN

EAR

EST

RO

AD

TRAVEL TIME MINIMIZING DISTANCE

Quality of Road affects Travel Time

Issues Related to Estimation

Simultaneity between forest clearing and to the urbancenter: Problem is addressed via instrumental variables:

geodetic distance from each road increment midpointto the nearest urban center.

Spatial autocorrelation:

Problem is addressed with a spatial econometricestimator using the inverse-distance specification ofthe spatial weights matrix.

Myanmar: Estimation

Sample size: 5.8 million cells.

1. Randomly-drawn sample of 10,000 cells: IV, IV withbootstrapped standard errors calculated from more-dispersed subsamples of 5,000 observations, spatialeconometric estimation using IV.

2. Randomly-drawn sample of 20,000 cells: IV and IV withbootstrapped standard errors.

3. Full sample: IV and IV with bootstrapped standard errors.

Myanmar: Regression Results

Myanmar: Median Parameter Estimates

VariableMedian Parameter

Estimate

Distance from road -0.181Distance to nearest urban center (DU) -0.336DU x Primary road share 0.060Slope (Std. dev. of elevation) -0.467Slope x Elevation 0.044Protected area -0.780Agricultural opportunity value 0.075Constant 1.324

Myanmar: Findings for Distance-Related Variables

1. Forest clearing is inversely related to distance from road.

2. The same pattern holds for distance to the nearest urbancenter.

3. The median elasticity for travel solely on secondary roadsis -0.336.

4. The median elasticity for travel solely on primary roads is

-0.321 (=-0.530+0.209).

In addition, Significant roles for Terrain Slope (-), AgriculturalOpportunity Value (+) and Protected Area Status (-).

Road Upgrading and Ecological Risks

Myanmar: All Secondary RoadsImprovement Impact

Moist Forest Ecoregion

Area Cleared 80-100%(thousand hectares)

2014

Irrawaddy freshwater swamp forests 1.04 2.98 1.95 2.9Irrawaddy moist deciduous forests 55.13 91.78 36.65 1.7Myanmar coastal rain forests 30.16 48.64 18.48 1.6

Chin Hills-Arakan Yoma montane forests 15.28 23.92 8.63 1.6

Northern Indochina subtropical forests 108.61 166.42 57.81 1.5Tenasserim-South Thailand semi-evergreen rain forests 22.43 31.51 9.08 1.4

Mizoram-Manipur-Kachin rain forests 30.85 41.40 10.55 1.3Northern Triangle subtropical forests 26.08 34.81 8.73 1.3Kayah-Karen montane rain forests 38.97 49.90 10.94 1.3Lower Gangetic Plains moist deciduousforests

0.00 0.00 0.00 .

Afterupgrading Change

Impactratio

Myanmar: Predicted Impacts byMoist Forest Ecoregions

The greatest expansion of maximum (80-100%) cleared area isexpected to occur in the: Northern Indochina subtropical forests (eastern Myanmar), from 108,610 to

166,420 hectares; Irrawaddy moist deciduous forests (central Myanmar), from 55,130 to 91,780

hectares; and Myanmar coastal rain forests (western and southern Myanmar), from 30,160 to

48,640 hectares.

Expansions in the range 8,000 - 11,000 hectares will occur in the: Chin Hills-Arakan Yoma montane forests (western Myanmar); Tenasserim-South Thailand semi-evergreen rain forests (southern Myanmar); Mizoram-Manipur-Kachin rain forests (western and northern Myanmar); Northern Triangle subtropical forests (northern Myanmar); and Kayah-Karen montane rain forests (east-central Myanmar).

Indicator: Ecological Risk of RoadUpgrading

Expected biodiversity loss for a cell is estimated as(Biodiversity Indicator Value of the cell) x (Change inthe cleared forest percentage of the cell induced byroad upgrading).

Myanmar: Ecological Risk of RoadUpgrading

Large losses (60-100) areexpected in the far north, aband from the north to eastand scattered areas in thewest and south.

Intermediate expected losses(40-60) are expected in largeareas.

Lowest expected losses are incorridors flanking roads thatalready have primary status.

Identification of Critical Road Links

Road links are graded by expected biodiversity losses whenupgrading occurs (using the forest clearing impacts ofspecific road links).

Mean expected losses in corridors extending 10 km oneither side of the Delrome-identified secondary road links inmoist forest areas is computed.

For ease of comparison, estimates are normalized to therange 0 to 100.

The highest four deciles were color coded for visualcomparison : purple (90-100); red (80-90); orange (70-80);yellow (60-70)] along with primary road links whereupgrading does not occur (blue).

Myanmar: Ecologically High-RiskRoad Corridors

Large clusters of mostcritical(purple) corridors arevisible in the east, with smallerclusters linked to next-priority(red) corridors in the north,west and south.

Clusters in the lower prioritycategories (orange andyellow) are widely scattered.

Summary

This paper develops and applies a spatial econometric modelthat links road upgrading to forest clearing and biodiversity lossin moist tropical forests of Bolivia, Cameroon and Myanmar.

Forest clearing is highly responsive to the distance to thenearest urban market which comprises of the distance of theparcel of land (cell) to the closest point on the nearest road andthe transport-minimizing route to the nearest urban market.

Expected biodiversity loss from upgrading secondary roads toprimary status was estimated using forest clearing responseelasticities and a composite biodiversity indicator.

The research provides ecological risk ratings for individual roadcorridors for environmentally-sensitive infrastructure investmentprograms.

Environmentally-SensitiveInfrastructure Planning

Road upgrading will inevitably accompany rural developmentprograms.

Identification of ecologically-vulnerable areas and roadcorridors can provide two valuable information:

With limited budgets, it can help steer road upgradingprograms toward corridors where expected biodiversitylosses will be minimized.

It can inform adoption of appropriate protection measures invulnerable road corridors and adjacent areas.

Relevance for Other Countries

Use of global database that includes a larger set ofbiodiversity measures.

Use of globally-available road quality estimates.

Use of high-resolution satellite data on forest cover change.

Exclusive use of globally-available databasesensures applicability of this empirical work to allmoist tropical forest countries.

Acknowledgement

Knowledge for Change Trust Fund for funding theresearch.

Ms. Siobhan Murray for GIS support.

Ms. Polly Means for help with graphics.

Mr. Pritthijit (Raja) Kundu for help with thePowerpoint presentation.