REDD Roads? spatial frontier dynamics & spatial variation in causal impacts. Alexander Pfaff & Juan Robalino (lead authors)

Transcription

1 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 Page 1

2 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 Page 2

3 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 Page 3

4 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? Page 4

5 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 Page 5

6 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 Page 6

7 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 Page 7

8 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 Page 8

9 Spatially Rich Data: using ever smaller units Census Tracts 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 Page 9

10 Spatial Data: roads as assigned to aggregates Counties Census Tracts Page 10

11 Page 11

12 Some Critical Possible Misinterpretations change in deforestation rate from road investment This is not the situation of the average hectare. pristine some development lots of development??????????? % of forest cleared beforehand 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(!?) 100 Page 12

13 One Simple Basis For Mis-extrapolations Page 13

14 Spatially Rich Data: road lines, forest points Page 14

15 Temporally Rich Data: Roads & Forest Roads: , , from maps, so roads can be mapped to census tracts can separate Federal vs. State & Paved vs. Unpaved Forest: , , in Diagnostico data, little clearing during , 2000 sensors differ ( in TRFIC??) 2000 roads will permit same analysis on Page 15

16 Sample For First Period: deforestation From new park-impact evaluation paper using 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. Page 16

17 Distance to road 1975 (kilometers) Figure 1 number 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 1968 (kilometers) Page 17

18 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 such that transport cost = Z in 1968 and = Z in 1975 shock 2 road investments during and such that transport cost = Z in 1968 but < Z in 1975 shock 3 road investments during and such that transport cost > Z in 1968 but = Z in 1975 Page 18

19 Distance to road 1975 (kilometers) Figure 2 measured deforestation rates (fraction cleared) as a function of the 1968 and 1975 distances to nearest road Distance to road 1968 (kilometers) Page 19

20 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 Page 20

21 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 ] Page 21

22 Matching: applying standard stuff to this case Outcome Variables: the deforestation rates during the periods , , Treatment Variable: created a discrete version of 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 Page 22

23 Distance to road 1975 (kilometers) Figure 3a (compare in row) measured road-investment impacts, using matching, explaining deforestation rates (fraction cleared) by row, i.e. by end-of-period (1975) distance to the nearest road 500 no *** *** -0.14*** Distance to road 1968 (kilometers) Page 23

24 Distance to road 1975 (kilometers) Figure 3b (compare to diagonal) measured road-investment impacts, using matching, explaining deforestation rates (fraction cleared) by row, i.e. by end-of-period (1975) distance to the nearest road 500 no *** ** -0.07*** Distance to road 1968 (kilometers) Page 24

25 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 Page 25

26 Distance to road 1975 (kilometers) Figure 4a (no controls) measured road-investment impacts, exact matching, explaining deforestation rates (fraction cleared) by column, i.e. by start-of-period (1968) distance to the nearest road ** 0.05*** 0.07*** 0.08*** 0.09*** ** nc Distance to road 1968 (kilometers) Page 26

27 Distance to road 1985 (kilometers) Figure 4b (regression) measured road-investment impacts, exact matching, explaining deforestation rates (fraction cleared) by column, i.e. by start-of-period (1975) distance to the nearest road * 0.02* 0.06*** 0.05*** *** nc Distance to road 1975 (kilometers) Page 27

28 Distance to road 1993 (kilometers) Figure 5 (matching on all covariates) measured road-investment impacts, exact matching, explaining deforestation rates (fraction cleared) by column, i.e. by start-of-period (1985) distance to the nearest road *** 0.07*** 0.040*** nc Distance to road 1985 (kilometers) Page 28

29 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 Page 29

30 Distance to road 1975 (kilometers) Figure 8 (matching on all covariates, means & post-regression) measured road-investment impacts, exact matching, explaining deforestation rates (fraction cleared) by cell, i.e. by start-and-end-of-period (1968 & 1975) distances nc ** 0.07* nc ** * No Good Matches nc ** * nc *** 0.04*** No Good Matches nc * 0.06*** 0.12*** 0.06*** *** No Good Matches nc ** 0.07** 0.20*** 0.08*** 0.18** 0.09*** No Good Matches nc *** 0.08** 0.35*** 0.15*** 0.33*** 0.30*** ** No Good Matches nc Distance to road 1968 (kilometers) Page 30

31 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?? Page 31

32 Implications for FUTURE New Roads? Avanca Brasil ex. of existing plans Avança Brasil Road Projects in Amazônia Status Activity Total planned (km) Finished (km) In execution (km) 3194 Paving Finish paving Construction Widening and Improvements 1393 No Information No Information Total (km) Total (%) Page 32

33 Specifically, where does Avanca Brasil go? (if towards prior development, lower impact?) 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 % Page 33

34 Corroborating Evidence -- Mayan Forest (D. Amor) Study area approximately100,000 km 2 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 plant species found nowhere else plus: >= 18 species of amphibians >= 45 species of reptiles >= 95 species of mammals >= 112 species of fish Page 34

35 All Roads Matter (the distance to the closest road, of whatever type & whatever time) Page 35

36 Mayan Deforestation , using distance to prior roads (pixels, NEW) Where prior roads far : ONLY roads matter (creating access) Where prior roads close : other factors matter (roads do too) Page 36

37 SPATIAL issue even for First Decade consider deforestation spatial pattern (makes pristine road impacts look worse) Habitat loss from Males: 22% Females: 36% Dalia Amor s thesis research about habitat for the jaguar, showing costs of fragmenting. Page 37

38 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 Roads Observations: 23,346 (3 periods pooled) Adjusted R-squared: 0.11 Lagged Paved Investment (0.00) Second Paved Lag (0.24) Lagged Unpaved Investment 0.07 (0.00) Second Unpaved Lag 0.25 (0.00) See Caldas et al. on endogeneity micro-processes Page 38

39 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) Page 39

40 International Scientific Conference. Amazon in Perspective - Integrated Science for a Sustainable Future INDEPENDENT VARIABLES (all distances in km) DEPENDENT VARIABLE (Significance codes: 0 =***, =**, 0.01=*, 0.05=.) BASE MODEL FIRST TIME PERIOD MODEL Existing conditions in 1989 Forest Change between SECOND TIME PERIOD MODEL Forest Change between Coef Std Err z val. Pr> z Sig Coef Std Err z val. Pr> z Sig Coef Std Err z val. Pr> z Sig Intercept *** *** Peru * Bolivia Distance to Cities *** Distance to Peru Cities * Distance to Bolivia Cities *** Altitude (msl) *** Altitude Peru (msl) *** Altitude Bolivia (msl) Distance to Rivers *** Distance to Peru Rivers Distance to Bolivia Rivers ** Distance to prev. Deforestation NA NA NA NA *** *** Distance to Peru prev. Deforestation NA NA NA NA *** Distance to Bolivia prev. Deforestation NA NA NA NA ** Distance to IOH *** *** Distance to Peru IOH * Distance to Bolivia IOH Distance to Secondary Roads NA NA NA NA *** *** Distance to Peru Sec. Roads NA NA NA NA Distance to Bolivia Sec. Roads NA NA NA NA *** n = / Clearcut = 731 / Forest = n = / Clearcut = 420 / Forest = n = / Clearcut = 648 / Forest = 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. Page 40

41 International Scientific Conference. Amazon in Perspective - Integrated Science for a Sustainable Future The Chico Mendes Extractive Reserve Page 41

42 International Scientific Conference. Amazon in Perspective - Integrated Science for a Sustainable Future 2007 Evidence -- Deforestation Pressures Over Time within (& upon?) Chico Mendes Extractive Reserve Chico Mendes Extractive Reserve Page 42

43 International Scientific Conference. Amazon in Perspective - Integrated Science for a Sustainable Future Matching Applied To InterOceanic Highway Region PAs (Chico Mendes Extractive Reserve & Acre / InterO PAs) (impacts on & deforestation) Deforestation Deforestation METHOD Mendes Rest of PAs Mendes Rest 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) Page 43