REDD Roads? - BREADipl.econ.duke.edu/dthomas/ipopdevo/pfaff.pdf · 2000 roads will permit same...

REDD Roads?

spatial frontier dynamics &

spatial variation in causal impacts

Alexander Pfaff & Juan Robalino(lead authors)

Development / International Population Workshop

Duke University Durham, NC August 28, 2009

Acknowledgements (& related road research)

Brazilian Team (funding: NASA LBA (II & III), Tinker, IAI) Eustaquio Reis, Claudio Bohrer, Robert Walker, Steve Perz, Juan Robalino, James Gibbs, Robert Ewers, Bill Laurance, Steven Aldrich, Eugenio Arima, Marcellus Caldas, others

Mayan Team (funding: Mesoamerican Biological Corridor, Mexico Unidos para la Conservacion, CONABIO, Conservation

International, Conservation Strategy Found (CSF))Dalia Amor (lead), Fernando Colchero, Norman Christensen, with data help from the Mexican Ministry of Transportation, Victor Hugo Ramos (WCS), UNAM, Jaguar Conservancy

Acknowledgements (& related policies research)

Costa Rica Team (NSF-MMIA, NCEAS, Tinker, SSHRC, IAI)Suzi Kerr, Arturo Sanchez, David Schimel, Shuguang Liu, Boone

Kauffman, Flint Hughes, Vicente Watson, Joseph Tosi, Juan Robalino, F. Alpizar, C. Leon, C.M. Rodriguez

InterOceanic Team (funding: Duke University Nicholas Institute)Cesar Delgado (lead), Dalia Amor, Joseph Sexton, Fernando Colchero, with assistance from Juan Robalino, Diego Herrera

Mexico Team (funding: The Tinker Foundation, IAI, RFF)Allen Blackman, Yatziri Zepeda, Juan Robalino, Laura Villalobos

Q: effect of road investments on deforestation?

How can a country lower deforestation (REDD)?

only by not investing in roads and development?

perhaps by altering how development achieved?

How can we explain findings in the literature?

estimated clearing increases less than expected?

in B. Amazon: roads actually lower deforestation?

Challenge: X affects roads and deforestation?

BIAS in the estimate of the average road impact?

roads may go where deforestation high/low anyway

no instrument or experiment but standard matching

X history & spatial dynamics AFFECT road impact?

high prior activity could lower marginal road effect

low prior activity could lower marginal road effect

exact matching for categories of prior development

“Theory”: basic land use choice

Static

land owner chooses land use to maximize returns

factors driving options’ net benefits drive decision

shock occurs -> land use adjusts to new optimum

Limited Dynamics

irreversibility in clearing; choose best time to clear

profitable to clear now but moreso to clear later ?

empirically separate re- & deforestation decisions

Background: land use choice in the Amazon

Producers

for a long time cattle was the story (still 2/3 total)

selective (changing?) timber extraction & frontiers

soy now very profitable in S area (Embrapa) [NYT]

Policies

some initial big federal highways (S & ‘edges’ = ‘arc’)

in spots, free-trade zone & colonization (along roads)

considering infrastructure (soy exports) & parks/ILs

“Theory”: additional relevant dynamics

Endogenous Development [high prior activity / low impact?]

economic activities lead to follow-on investments

followons include new roads, e.g. after old roads

then new roads compete for impact with other X

Partial Adjustment [low prior activity / lower marginal impact?]

lack of activity can mean low inputs to deforestation

labor for clearing primary forest, e.g., is a constraint

large shocks will not yield immediate full adjustment

Spatially Rich Data: using ever smaller units

Census Tracts

6000+ across basin (versus about 300 counties)

avoid measurement errors for (huge!!) counties

better statistical controls, e.g. w/ county effects

Pixels

use more points, here up to 100,000 across Amazon

can create our own relevant-distance neighborhoods

measures drivers even more accurately than in tracts

Spatial Data: roads as assigned to aggregates

Counties Census Tracts

Some Critical Possible Misinterpretations

Andersen et al. 2000, with follow-ups by Weinhold, extrapolate linearly based on an interaction term: new road lowers deforestation if prior clearin.

Claim: impact < 0 applies to the basin on average(!?)

% of forestclearedbeforehand

change in deforestation rate from road investment

100pristine some development lots of development

This is not the situationof the average hectare.

???????????

One Simple Basis For Mis-extrapolations

Spatially Rich Data: road lines, forest points

Temporally Rich Data: Roads & Forest

Roads: 1968 – 1975, 1975 – 1985, 1985 – 1993

from maps, so roads can be mapped to census tracts

can separate Federal vs. State & Paved vs. Unpaved

Forest: 1976 – 1987, 1987 – 1991, 2000 – 2004

in Diagnostico data, little clearing during 1987 – 1991

1991, 2000 sensors differ (1992 – 2000 in TRFIC ??)

2000 roads will permit same analysis on 2000 – 2004

Sample For First Period: 76-87 deforestation

From new park-impact evaluation paper using 2000-04 forest (unfortunately still without the 2000 roads but ideas hold):

We start with a sample of 100,000 pixels (in 5m km2 area).

If the land cover data (with 16 categories) does not clearly indicate that at the start of the period it is in forest cover, then we drop the observation (the categories No Data, Non Forest, Water, Clouds, and Residual). Thus we have a sample clearly in forest in 2000 which can be examined for rates of deforestation.

That sample provides 43,811 observations for our analyses.

Figure 1number of observations for each category of (non-) investment i.e. as a function of the 1968 and 1975 distances to nearest road

Distance to road

1975 (kilometers)

Distance to road

1968 (kilometers)

500 48 0

400

487 185 22

300

1259 746 1363 651

200

2739 1035 2494 2756 3971

100

1024 2941 5276 3619 2926 4780

80

2997 110 1575 1627 834 529 775

40

2374 976 956 4508 2554 1727 1245 1329

20

3472 313 1689 715 2664 973 894 613 581

494 1491 2343 846 2699 943 955 550 595

20 40 80 100 200 300 400 500

Return of the “Theory”: basic land use choice

Static: shock occurs -> land use adjusts to new optimum

Examples:

shock 1 road investments during 0-68 but not 68-75 such that transport cost = Z in 1968 and = Z in 1975

shock 2 road investments during 00-68 and 1968-75 such that transport cost = Z in 1968 but < Z in 1975

shock 3 road investments during 00-68 and 1968-75 such that transport cost > Z in 1968 but = Z in 1975

Figure 2measured 1976-1987 deforestation rates (fraction cleared)

as a function of the 1968 and 1975 distances to nearest roadDistance to road

1975 (kilometers)

Distance to road

1968 (kilometers)

500 0.042 0

400

0.035 0 0

300

0.07 0.003 0.007 0.009

200

0.06 0.015 0.02 0.008 0.01

100

0.10 0.04 0.04 0.04 0.03 0.008

80

0.21 0.03 0.08 0.05 0.06 0.04 0.002

40

0.3 0.16 0.08 0.12 0.09 0.04 0.06 0.02

20

0.4 0.33 0.24 0.19 0.2 0.17 0.11 0.1 0.06

0.44 0.35 0.36 0.3 0.3 0.26 0.3 0.5 0.3

20 40 80 100 200 300 400 500

Covariates: non-road factors in net LU benefits

Urban Distance: distinguish large, medium, small cities

Biophysical Constraints: - amount of rainfall (non-monotonic impact on crops)

- several categories of slope

- soil fertility / “suitability”

Prior Clearing: represents all sorts of possible changes

Census Data: population & output but only for counties

Matching: addressing non-randomness (I…)

Compare treated to similar subset of untreated.

Definition of similarity uses plot characteristics:

- propensity score matching [PSM] compares points in terms of estimated probability of being treated, (Rosenbaum & Rubin 1983) [standard errors ??]

- covariate matching [CM], in contrast, does not use such a prior regression for likelihood of treatment, using instead the multivariate distance in X space (Abadie & Imbens 2006) [standard errors ☺]

Matching: applying standard stuff to this case

Outcome Variables: the deforestation rates during the periods 1976 - 1987, 1987 – 1991, 2000 - 2004

Treatment Variable: created a discrete version of 1968 - 1975 fall in distance to road (=1 if > 5 km)

Observed Covariates: looking for the most similar

untreated observations based on soil fertility index,

rainfall level, vegetation index, slope categories, &

the distances to the small, medium and large cities

Figure 3a (compare in row)measured road-investment impacts, using matching,

explaining 1976-1987deforestation rates (fraction cleared)“by row”, i.e. by end-of-period (1975) distance to the nearest road

Distance to road

1975 (kilometers)

Distance to road

1968 (kilometers)

500 no

400

-0.014

300

-0.002

200

0.002

100

0.006

80

-0.07

40

-0.17***

20

-0.18***

-0.14***

20 40 80 100 200 300 400 500

Figure 3b (compare to diagonal)measured road-investment impacts, using matching,

explaining 1976-1987deforestation rates (fraction cleared)“by row”, i.e. by end-of-period (1975) distance to the nearest road

Distance to road

1975 (kilometers)

Distance to road

1968 (kilometers)

500 no

400

-0.015

300

-0.002

200

-0.001

100

-0.017

80

0.00

40

-0.14***

20

-0.13**

-0.07***

20 40 80 100 200 300 400 500

Matching: exact on ‘history’ (= 1968 distance)

• can’t ignore history, which indicates conditions

• high prior activity, e.g. low prior road distance, indicates and generates conditions for clearing

• low prior activity, e.g. high prior road distance, indicates conditions which discourage clearing

• these unobservables must be controlled for too; here use proxy = historical development activity

Figure 4a (no controls)measured road-investment impacts, exact matching,

explaining 1976-1987deforestation rates (fraction cleared)“by column”, i.e. by start-of-period (1968) distance to the nearest road

Distance to road

1975 (kilometers)

Distance to road

1968 (kilometers)

500

400

300

200

100

80

40

20

0.05**

0.05*** 0.07*** 0.08*** 0.09*** 0.006 0.03** 0.011 nc

20 40 80 100 200 300 400 500

Figure 4b (regression)measured road-investment impacts, exact matching,

explaining 1987-1991 deforestation rates (fraction cleared)“by column”, i.e. by start-of-period (1975) distance to the nearest road

Distance to road

1985 (kilometers)

Distance to road

1975 (kilometers)

500

400

300

200

100

80

40

20

-0.005

-0.03* 0.02* 0.06*** 0.05*** 0.006 0.032*** 0.033 nc

20 40 80 100 200 300 400 500

Figure 5 (matching on all covariates)measured road-investment impacts, exact matching,

explaining 2000-2004 deforestation rates (fraction cleared)“by column”, i.e. by start-of-period (1985) distance to the nearest road

Distance to road

1993 (kilometers)

Distance to road

1985 (kilometers)

500

400

300

200

100

80

40

20

0.012

-0.005 -0.024 0.08*** 0.07*** 0.040*** 0.008 0.002 nc

20 40 80 100 200 300 400 500

Matching: now back to von Thunen / basic LU

Transport cost matters: impact rises with investment

Check on match quality:

- if even the most similar are not in fact very similar then burden on specification remains considerable

- we need to examine match quality (not done yet) and examine all options, including dropping poor

- to start, we present matching means comparisons but also the results from post-matching regression

Figure 8 (matching on all covariates, means & post-regression)measured road-investment impacts, exact matching,

explaining 1976-1987 deforestation rates (fraction cleared)“by cell”, i.e. by start-and-end-of-period (1968 & 1975) distances

Distance to road

1975 (kilometers)

Distance to road

1968 (kilometers)

400-0.00 -0.02

nc

3000.00 0.00

0.02** 0.07* nc

2000.00

0.02**

0.01 0.02*

No Good Matches

nc

1000.01 0.01

0.04 0.01**

0.00 -0.06

0.01* -0.08 nc

800.00

-0.00

0.06*** 0.04***

0.05 0.02

0.05 0.01

No Good Matches

nc

40-0.04 -0.01

0.04* 0.06***

0.12*** 0.06***

0.10 0.06***

0.01 -0.03

No Good Matches nc

20-0.05 -0.03

0.00 -0.02

0.13** 0.07**

0.20*** 0.08***

0.18** 0.09***

0.09 -0.06

No Good Matches nc

0.01 0.01

0.03 0.01

0.009 -0.010

0.31*** 0.08**

0.35*** 0.15***

0.33*** 0.30***

0.06 -0.19**

No Good Matches

nc

20 40 80 100 200 300 400 500

Stepping Back: some perspectives on policy

Should one ever build a road? See any benefits?- long ago not profitable; now cattle & soy & timber- spatial variation too: claims GDP gains bigger in city which means both costs and benefits point to urban

Who makes these decisions and why?

- long ago just the military (borders & guerrillas foci?)

- now both federal and state actors (different choices)

- now also facing ‘carrots’ relative to some baseline??

Avança Brasil – Road Projects in Amazônia Status

Activity Total planned

(km) Finished

(km) In execution

(km)

Paving 6697 1693194

Finish paving 494 0120

Construction 668 4230

Widening and Improvements 1393 No Information No Information

Total (km) 9252 592 3314Total (%) 100 6.40 35.82

Implications for FUTURE New Roads? Avanca Brasil – ex. of existing plans

• toward prior clearing– where paving goes, weighted prior clearing is over 50 %,

but in non-AB census tracts, prior clearing is under 20 %– for AB unpaved, the comparison is roughly 30% to 15 %

• toward prior roads– for AB paved, prior paving is much higher than non-AB– for AB unpaved, recent paving and lagged unpaved higher

• toward cerrado (less forest, more development)– for AB paved, more in cerrado than non-AB, 36% > 18 %– for AB unpaved, more in cerrado than non-AB, 31% > 17 %

Specifically, where does Avanca Brasil go? (if towards prior development, lower impact?)

Corroborating Evidence -- Mayan Forest (D. Amor)

Study area approximately100,000 km2

Second largest forest in the Western Hemisphere after the Amazon.

Biggest patch of continuous forest of the Mesoamerican Hotspot, which holds around 7% of the earth species)

High Biodiversity value -- 375 plant species found nowhere else plus:

>= 18 species of amphibians >= 45 species of reptiles>= 95 species of mammals>= 112 species of fish

All Roads Matter

(the distance to the closest road, of whatever type & whatever time)

Where prior roads ‘far’: ONLY roads matter (creating access)

Where prior roads ‘close’: other factors matter (roads do too)

Mayan Deforestation 1980-1990, 1900-2000using distance to prior roads (pixels, NEW)

SPATIAL issue even for First Decadeconsider deforestation spatial pattern

(makes pristine road impacts look worse)

Habitat loss from 1980-2000

Males: 22%

Females: 36%Dalia Amor’sthesis researchabout habitatfor the jaguar, showing costs of fragmenting.

TEMPORAL issue concerning Total Impact new roads now -> more roads nearby later(again, pristine new road impacts look worse)

Paved building 20 times as likely if past unpaved.

Regression Explaining Investments in Paved Roads Using Prior RoadsObservations: 23,346 (3 periods pooled) Adjusted R-squared: 0.11

Lagged Paved Investment -0.04 (0.00) Second Paved Lag -0.01 (0.24)

Lagged Unpaved Investment 0.07 (0.00) Second Unpaved Lag 0.25 (0.00)

See Caldas et al. on endogeneity micro-processes

Corroborating Evidence – InterOceanic Highway (see presentations here, on Thursday 11/20, by Cesar Delgado)

Study area along the InterOceanic Hwy across Brazil, Bolivia and Peru (consider non-Brazil Amazon)

Early on, InterOceanic created access and significantly shaped clearing, notably in Brazil but also spillover into Bolivia clear and continuing

Peru’s setting retarded past deforestation and may well be shifting over time, meaning a new prediction is needed concerning not-yet-cleared locations

Once highway highly cleared in Brazil, even paving did not shape clearing(indirect effect difficult to separate)

International Scientific Conference.Amazon in Perspective - Integrated Science for a Sustainable Future

Table Nº3: The three logistic regressions were set up to measure the significant difference of the countries, between the included significant categories relative to the excluded categories. Since Brazil unique deforestation dynamics is well known and documented it was treated as the excluded category because is known that Peru and Bolivia are different from Brazil, but not necessarily from each other.

(all distances in km)

Coef Std Err z val. Pr>|z| Sig Coef Std Err z val. Pr>|z| Sig Coef Std Err z val. Pr>|z| SigIntercept 2.363 0.285 8.297 0.000 *** 1.357 0.385 3.522 0.000 *** 0.490 0.356 1.378 0.168

Peru 1.419 0.855 1.660 0.097 1.253 1.215 1.031 0.302 -2.252 1.003 -2.246 0.025 *

Bolivia 1.727 1.745 0.989 0.323 1.452 1.729 0.839 0.401 2.026 1.482 1.367 0.172

Distance to Cities -0.026 0.004 -6.289 0.000 *** 0.009 0.006 1.598 0.110 -0.002 0.005 -0.419 0.675

Distance to Peru Cities 0.022 0.011 2.037 0.042 * -0.016 0.012 -1.279 0.201 0.012 0.013 0.959 0.337

Distance to Bolivia Cities -0.016 0.015 -1.039 0.299 -0.070 0.021 -3.368 0.001 *** -0.018 0.011 -1.611 0.107

Altitude (msl) -12.769 1.376 -9.283 0.000 -9.082 1.886 -4.815 0.000 *** -2.975 1.610 -1.848 0.065 .

Altitude Peru (msl) -13.678 3.655 -3.742 0.000 *** -3.592 5.066 -0.709 0.478 2.099 3.855 0.544 0.586

Altitude Bolivia (msl) -11.574 6.814 -1.699 0.089 . -2.976 7.128 -0.418 0.676 -8.054 5.406 -1.490 0.136

Distance to Rivers 0.022 0.005 4.628 0.000 *** 0.005 0.005 0.877 0.381 0.004 0.004 0.847 0.397 .

Distance to Peru Rivers -0.031 0.019 -1.630 0.103 0.022 0.031 0.694 0.487 -0.032 0.018 -1.795 0.073 .

Distance to Bolivia Rivers -0.069 0.026 -2.684 0.007 ** 0.106 0.033 3.239 0.001 0.016 0.014 1.116 0.264

Distance to prev. Deforestation NA NA NA NA -1.647 0.185 -8.928 0.000 *** -1.848 0.160 -11.562 0.000 ***

Distance to Peru prev. Deforestation NA NA NA NA -0.345 0.473 -0.730 0.466 0.864 0.223 3.878 0.000 ***

Distance to Bolivia prev. Deforestation NA NA NA NA -2.617 0.863 -3.033 0.002 ** 0.503 0.315 1.596 0.111

Distance to IOH -0.030 0.004 -8.380 0.000 *** -0.018 0.004 -4.322 0.000 *** 0.000 0.004 0.122 0.903

Distance to Peru IOH 0.011 0.012 0.909 0.364 -0.062 0.030 -2.058 0.040 * -0.006 0.019 -0.317 0.751

Distance to Bolivia IOH 0.014 0.015 0.995 0.320 0.015 0.019 0.811 0.417 -0.006 0.013 -0.499 0.618

Distance to Secondary Roads NA NA NA NA -0.034 0.009 -3.847 0.000 *** -0.023 0.007 -3.390 0.001 ***

Distance to Peru Sec. Roads NA NA NA NA -0.004 0.018 -0.237 0.813 0.018 0.024 0.736 0.462

Distance to Bolivia Sec. Roads NA NA NA NA -0.040 0.031 -1.287 0.198 -0.171 0.043 -3.935 0.000 ***

n = 19448 / Clearcut = 731 / Forest = 18717 n = 19448 / Clearcut = 420 / Forest = 19028 n = 19448 / Clearcut = 648 / Forest = 18800

SECOND TIME PERIOD MODEL

Forest Change between 2000-2007

BASE MODEL FIRST TIME PERIOD MODEL

DEPENDENT VARIABLE

(Significance codes: 0 =***, 0.001 =**, 0.01=*, 0.05=.)INDEPENDENT VARIABLES

Existing conditions in 1989 Forest Change between 1989-2000


The Chico Mendes Extractive Reserve


2007 Evidence -- Deforestation Pressures Over Time within (& upon?) Chico Mendes Extractive Reserve

Chico Mendes Extractive ReserveChico Mendes Extractive Reserve


Matching Applied To InterOceanic Highway Region PAs(Chico Mendes Extractive Reserve & Acre / InterO PAs)(impacts on 1989 – 2000 & 2000 – 2007 deforestation)

METHODMETHOD

1989 1989 –– 20002000DeforestationDeforestation

2000 2000 –– 20072007DeforestationDeforestation

MendesMendes Rest of PAsRest of PAs MendesMendes Rest of PAsRest of PAs

Average Rates(compare means)

7% 8% 8% 10%

Regression(full data set)

7% 1% (insig) 8% 1.5%

Matching(compare means)

7% 0.2% (insig) 7% 0.2% (insig)

REDD Roads? - BREADipl.econ.duke.edu/dthomas/ipopdevo/pfaff.pdf · 2000 roads will permit same...

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