Scholarly article on topic 'Avoided Deforestation Linked to Environmental Registration of Properties in the Brazilian Amazon'

Avoided Deforestation Linked to Environmental Registration of Properties in the Brazilian Amazon Academic research paper on "History and archaeology"

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Academic research paper on topic "Avoided Deforestation Linked to Environmental Registration of Properties in the Brazilian Amazon"

Avoided deforestation linked to environmental registration of properties in the Brazilian Amazon

Alix-Garcia, Jennifer* Lisa Rausch Jessica L'Roe* Holly K. Gibbs§ Jacob Munger^

Running title: Environmental registration in the Amazon

Keywords: Brazil, avoided deforestation, environmental mapping, land registration, impact evaluation

Type of article: Letter Number of words in abstract: 125 Word count manuscript (main text): 2,929 Tables and figures (main text): 4

* Corresponding author: Department of Applied Economics, Oregon State University,, 541-737-2691

Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison,

^Department of Geography, Middlebury College,

§ Department of Geography, Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison,

^Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin-Madison,

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article asdoi: 10.1111/conl.12414

13 Avoided deforestation linked to environmental regis-

14 tration in the Brazilian Amazon

15 Abstract

16 We quantified the avoided deforestation impacts of environmental land registration in

17 Brazil's Amazonian states of Mato Grosso and Para between 2005 and 2014. We find is that the program reduced deforestation on registered lands. The magnitude of the

19 effect implies that deforestation in the two states would have been 10% higher in the

20 absence of the program. The impacts of registration varied over time, likely due to

21 multiple policies linking environmental registration to land use incentives. Our results

22 also reveal that agriculturally suitable lands and those located in regions undergoing

23 the most land-use change were more likely to be registered than those in less suitable,

24 less dynamic regions. We conclude that environmental registration is an important

25 first step to implement avoided deforestation policies targeting private landholders.

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02 Q Q

26 1 Introduction

27 Forest conservation has the potential to provide as much as 40% of the emission reductions

28 needed to mitigate climate change1;2. Brazil houses nearly 13% of the world's remaining

29 forests3, and although its deforestation rate has slowed in recent years, it remains the second

30 highest contributor to global forest loss3. Efforts by private and public sector actors have

31 turned Brazil's Amazon into a laboratory for deforestation policy, especially for conservation

32 on private properties 4;5.

33 A variety of interventions have aimed to control deforestation on private lands in the

34 Brazilian Amazon, including improved satellite monitoring6;7, increased enforcement of the 0) 35 Forest Code8;9, credit restrictions for areas involved in excessive deforestation10;11, and pri-0^)36 vate sector zero-deforestation agreements12;13. Maps of private properties, which facilitate

37 land-use monitoring, link responsibility to a specific producer, and open the path to secure

3s tenure, have the potential to strengthen forest governance efforts14. Brazil has made sub-

39 stantial progress mapping properties for environmental registration, first with a handful of

40 state-level systems in the Amazon, and more recently with a national "SiCAR" system (Sis-t 41 tema Nacional de Cadastro Ambiental Rural15). This paper assesses changes in deforestation

42 behavior associated with environmental registration in the Amazon states of Mato Grosso

| 43 and Para, which had the most developed state-level systems that preceded the SiCAR, and

44 also the highest deforestation rates among states in the Amazon16.

45 The goals of registration programs were similar in the two states: to inform officials

46 and property owners about the degree of Forest Code compliance on each property and to O 47 qualify properties for rural activity licenses. Para's Rural Environmental Registry (CAR by

4s its Portuguese acronym) was introduced in 2004 for rural properties involved in economic

49 enterprises, and then redesigned in 2008, when it became obligatory for all properties17;18.

50 Mato Grosso began offering the LAU (Ligensa Ambiental Unica) for properties compliant

51 with the Forest Code as a prerequisite for certain operational licenses, including those for

52 legal deforestation. In 2009, Mato Grosso introduced the CAR as a voluntary program and

53 a first step toward LAU. Both state governments heavily promoted registration. In Mato

54 Grosso, the MT Legal program protected registrants from fines due to old illegal deforesta-

55 tion as they pursued Forest Code compliance, and in Para municipal governments received

56 incentives to promote enrollment19. In addition, multiple non-governmental organizations

57 launched campaigns to promote registration, sometimes with support from producers' unions

58 and municipal governments20. Many of these programs were still focused on promoting reg-

59 istration during the study period and had not yet used the CAR to sanction non-compliant

60 deforestation, with the exception of the zero-deforestation cattle agreements12. Table S1

61 presents a timeline of policy actions

62 CAR implementation varied between the states. The most important difference was the

63 ease of registration. Para offered registrants a provisional CAR based on self-declared, un-

64 verified information about property location and extent of forest set-asides21. Mato Grosso,

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65 on the other hand, issued a CAR only after an application review process and the signing

66 of legally binding plan to achieve full Forest Code compliance22. Brazil's national SiCAR process parallels that of Para.

68 This paper presents estimates of the impact of CAR registration on deforestation. Specif-

C 3 69 ically, we examined the impacts of registration in Mato Grosso and Para between 2006 and

0) 70 2013 using randomly drawn points from the forested area of the two states. We developed a I ^

71 rigorous counterfactual by limiting our analysis to only areas that eventually enrolled prop-

72 erties, and exploited variation in the timing of enrollment to identify CAR's effect on forest

73 cover. To assess how representative enrolled areas are of the eligible regions of the state,

74 we measured differences in geographic characteristics between areas that enrolled in CAR

75 during the study period and those that did not. Finally, we compared outcomes between the

76 states, and examined how impacts changed over time as complementary policies evolved.

77 2 Methods

7s 2.1 Study area and data

79 We quantified avoided deforestation from property registration across the state of Para and

80 the portion of Mato Grosso in the Amazon biome. We divided the study area into four

81 zones and generated 10,000 random points within each for a total of 40,000 random points.

82 The zones were defined as areas of Paraa with and without CAR, and areas of Mato Grosso

83 with and without CAR or LAU (section S2). Areas not eligible for registration in CAR were

84 excluded.

QJ) 85 Figure 1 shows the points in the sample that still had forest in 2005 and were eligible for

86 CAR registration. It illustrates that large areas of Para are ineligible for CAR, that there

87 are remote eligible areas where points tend not to be registered, and that there are many

88 unregistered points clustered near those that are registered.

89 As points have no area, we assign characteristics of the layers on which they fall to each

90 point. The outcome variable is an indicator for presence of forest cover from 2005 to 2014

91 measured using Brazil's PRODES forest monitoring data16. A key control variable, potential

92 for agricultural productivity, was extracted from a soy production suitability map . For the

0) 93 purposes of assessing selection bias, we also examine other geographic grid point character-


95 risk (see Appendix for further clarification). ^^ 6 FIGURE 1.

2.2 Estimating avoided deforestation on CAR properties

98 We estimated avoided deforestation between 2005 and 2014 at the point level on areas that

99 registered from 2006 to 2013. Because PRODES measures gross deforestation only, we

100 excluded points where forest was already cleared by 2005. In our models that measured

101 treatment effect, we restricted our sample to points falling inside properties that eventually

102 enrolled during our study period. We used a linear probability model with fixed effects at

103 the point level and time effects for each year (formal model in Section S4). We also included

104 interaction terms between year effects and state, as well as between year effects and land

105 productivity. We also tested whether there was heterogeneity in program impacts across

106 states and time periods.

107 Our approach identified the effect of registration by comparing properties that registered

108 earlier to those that register later, rather than comparing areas that register with areas that

109 do not. This method is preferable to matching because it is only possible to match on ob-

110 servable characteristics, while our approach accounted for both observable and unobservable

111 factors that condition registration. The assumption behind this specification was that points 12 that registered later constituted a valid counterfactual for earlier registrants. Just as with

113 a comparison between unenrolled properties, there may have been reasons why landowners

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114 chose to register land early or late. While this assumption is in principle untestable, we found 5—1.15 evidence for its plausibility in summary statistics, and in a test of whether deforestation time

trends for early registrants were similar to those of later registrants in the years prior to the

117 program.

118 In examining the characteristics of different cohorts (Section S3, Figure S1, and Table

0) ii9 S4), we observed that earlier registrants came from larger properties, and that in Pará the

120 earliest cohorts had slightly lower baseline forest cover than later ones. Given the variation

121 used to identify effects, this could have resulted in an overestimate of impacts. However, we ^^122 conducted a robustness check by dropping this group of properties and no found difference in

123 impact (Section S8, Table S8). Furthermore, we found that there were not differences in pre-

124 2005 deforestation trends between early and later registrants (Section S5, Table S5). This

125 suggests that the later registrants provided a reasonable control group. In contrast, we also

126 found pre-program time trends of never versus ever-registered properties to be quite different

127 (Section S5, Table S5, column (1)), suggesting that a comparison with non-registered points

128 would yield biased estimates of impact.

129 3 Results

130 3.1 More productive land enrolled in the CAR

131 The entire sample of points (Table 1) included all those that might possibly have registered

132 for CAR in both states. On average, points on properties enrolled in CAR by 2013 were

133 5.2 percentage points less likely to be forested in 2005 relative to points on properties that

134 did not enroll. Points on enrolled properties were located 10 km farther from the nearest

135 highway, and were nearly twice as likely to be suitable for large-scale agriculture. Points

136 registered in CAR were from municipalities with 27% higher deforestation rates prior to 2005. Q^137 Deforestation risk was higher for registered than for non-registered points, and registered

138 points were more likely to be in pasture or crop land by 2012. Overall, this suggests that

139 registered areas tended to be in more suitable, actively used areas, with a longer history

140 of agricultural use. This implies that using non-CAR land as a counterfactual for CAR

141 registrants would understate the impact of enrolling in the program, since land with higher 1.2 deforestation potential was more likely to register.

143 In Mato Grosso, registered areas were more likely to be forested in 2005 than unregistered

144 areas, while in Para the relationship was reversed. Registered points in Mato Grosso also 0) 145 tended to be in more forested municipalities (in 2001), while the opposite was true in Para.

146 Though registered land in both states had higher scores for agricultural suitability and

147 def°restation ™k thm rtore^ter^ 1^, m M.c Ocsso tlre re« >and ™ «

148 more likely to be forested in 2012. Thus, the bias towards registration of land more actively

149 being brought into production was stronger in Para, where there is a more pronounced

150 forest-agriculture frontier. TABLE 1.

152 3.2 Deforestation in Mato Grosso and Para reduced by 10% fol-

153 lowing property registration

154 Enrollment in CAR reduced deforestation on average by 0.5 percentage points (Table 2,

155 column (3)). This effect gives the average difference in the probability of forest cover before

156 versus after registration. The time trend of deforestation in the sample properties before they

157 are enrolled in CAR is -0.0024 (0.2% per year). This implies that the impact of enrollment

158 in the CAR is, on average, equivalent to about two years of average deforestation. In other

159 words, with each additional year, a point is 0.24% less likely to be forested, but if it is

160 enrolled in CAR, it is 0.50% more likely to be forested on average. For example, across ^■^161 the whole region, land registered for 4 years would have lost approximately .8% of its forest

162 due to background trends, but because it is registered, it only loses .3%. This finding is

163 robust to dropping pre-2010 registrants (Table S8, column (1)), changing the starting year

164 of estimation (Table S8, column (2)), and to an event study specification (Table S7).

165 Given that not all land eventually enrolled in CAR, it is interesting to calculate how

166 much CAR contributed to overall deforestation reduction in CAR-eligible areas in these two

167 states. Using our average effect, we calculated avoided deforestation across all cohorts, which

168 is the product of the forest area registered in each year and the treatment effect. Summing

169 up these areas of avoided deforestation yields 223,768 hectares of forest conserved due to C*~V^170 CAR enrollment during our study period, equivalent to 7.7 million tons of CO2 emissions24.

Between 2006-2013 forest loss in the Amazon biome of the two states was 2,038,900 ha.

172 Without the CAR and the programs affiliated with it, deforestation would have been nearly

173 10% higher (223, 768/(223, 768 + 2, 038, 900) = .098).

Including points that were never registered for CAR as part of the counterfactual renders

175 the effect less clear (columns (5)-(7)). The naive estimation with no controls and including

176 non-CAR points (column (5)) shows a small and marginally significant impact of registration.

177 Even with unit and state-time fixed effects, the estimation including non-CAR points showed

178 lower average impact (column (6)). This is consistent with our observation that areas enrolled

179 in the CAR had a higher deforestation risk than the areas that never registered, making those

180 areas unsuitable as counterfactuals, and resulting in an underestimate of program impact.

181 In both the naive and the preferred specification, when impacts were allowed to vary by

182 state (columns (2), (4), and (7)), there was no observable difference in impact across states.

183 TABLE 2.

184 3.3 CAR effectiveness varied over time

185 We also tested to see if impacts varied by year of registration and by study year. Simple

186 interaction terms showed that CAR had a greater impact after 2011, and a higher impact for

187 early relative to later registrants ( Section S6; Table S6, columns (3)-(5)). Because impacts ' ^J 88 on registered properties might change over time depending on changing linked incentives, we

189 examined effectiveness each year. We generated dummy variables turning to one for years

190 2010, 2011, and 2012, and remaining one thereafter (column (6), Table S6). These terms,

191 interacted with the CAR registration variable, capture effects of both newly registering and

192 already-registered properties in these years, in addition to the baseline effect for years prior

193 to 2010 measured by the CAR variable alone Estimated effects indicated high avoided

194 deforestation in 2009 and earlier (Figure 2). Effectiveness decreased in 2010, increased again ^^195 in 2011, and became much larger and statistically significant from 2012 onwards.

196 FIGURE 2.

197 4 Discussion and conclusion

198 Our results show that CAR enrollment from 2006 and 2013 in Mato Grosso and Para was

199 associated with avoided deforestation of over 220,000 hectares. While CAR effectiveness was

200 not different across states, it varied over time. This may be because of private and public

1We cannot include additional effects for 2013 and 2014, since these variables are already included as year dummies, and all of the points in our samples have CAR equal to 1 from 2013 onwards. This generates collinearity for 2013 and 2014.

201 sector efforts that pressured land users to both reduce deforestation and register in the CAR,

202 which intensified over time (Table S1). Early adopters had the fewest incentives to enroll.

203 This indicates a predisposition toward forest conservation and/or legal compliance; thus, it

204 is not surprising to find substantial deforestation avoidance effect on these properties. This

205 observation also calls into question our counterfactual - if early registrants had stronger

206 conservation tendencies, the impact of registration could be over-estimated. However, we

207 find no evidence of differences in deforestation behavior in our pre-trend analysis.

208 We also found evidence of greater avoided deforestation on properties after 2011, as

209 the set of incentives for enrollment was growing, although the effect was smaller for those

210 properties that registered later. We cannot definitively separate the effects of policies rolled

211 out during the study period because properties were frequently subject to various policies

212 at the same time. However, these policy overlaps may have increased their effectiveness25'26.

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213 The potential of these synergistic effects is indicated by the variation in avoided deforestation

214 by year rather than state, implying that state differences were less important.

<£t Previous studies have suggested that CAR registration and land-use behavior are related,

216 though our assessment shows the strongest evidence for positive forest impacts at an appre-

217 ciable scale. Earlier work estimated that property registration in Mato Grosso had little

218 impact on avoided deforestation14;27. Later work reported small but significant reductions

219 in deforestation among small properties in Para28. Most recently, other researchers have

0 measured small effects that depended on property size and location29.

1 Our results suggest that the initial impacts associated with CAR may be more promising and widespread than previous estimates. Our approach provides a more suitable counter-

223 factual because we did not use unenrolled areas as counterfactuals, because we included

224 suitability-conditioned time trends, and because our estimates are representative of forest

225 cover rather than properties. Our results also suggest that there are incentives beyond land

226 tenure concerns for small properties28. Dropping points on smallholder properties from our

227 regressions did not change the estimated impact. We conclude that the effects of other CAR-

228 associated environmental policies, like threats to market access and expected command and

229 control enforcement, were more important on the larger properties that dominate our sample.

230 We showed that there were significant differences between enrolled and unenrolled land

231 across the two states, and the selection effect was larger in Para. This finding is instructive

232 to those conducting impact evaluations on conservation policy, where it is common to use

233 "untreated" areas as "controls" whether these be non-parks, non-payments for ecosystem

234 services, or other unaffected pieces of land30. Land which is unaffected by a policy is generally

235 unaffected for a reason, particularly if the policy is voluntary, and these reasons are often

236 correlated with the outcomes of interest. Because of this, using unaffected land to proxy for

237 a "control" often leads to mis-estimated impacts31. In our case, the use of unenrolled land

238 for counterfactual comparison attenuated estimates; registration occurred first in areas with

239 higher deforestation risk. However, it is also the case that limiting the comparison to only

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240 those properties that already registered narrows the group of landowners analyzed, and the

241 registration effect estimated from these individuals could differ from the effect in the overall ^^^^242 population.

243 Remarkably, CAR registration within the states of Mato Grosso and Para is now at 93

244 and 100% respectively32. Additional validation work is needed, but this level of registration

245 is a huge achievement and provides a scaffolding for comprehensive enforcement of forest

246 policies. Despite the fact that the CAR was not used systematically for enforcement against

7 illegal deforestation during the study period, it was still associated with avoided deforesta-248 tion. We conclude that investment in property registration even before robust monitoring 49 systems are in place may pay dividends for forest conservation. Furthermore, we expect the 250 conservation outcomes linked to CAR will be more substantial once the registry is used for

251 Forest Code enforcement. However, the temporal heterogeneity in CAR effectiveness sug-

252 gests that there is nothing inherent about registration that automatically protects forests.

253 Continued avoided deforestation depends on maintaining and expanding the complementary

254 policies that discourage deforestation.

255 Acknowledgements: We are grateful for feedback from Paulo Barreto (Imazon), and seminar partic-

256 ipants at the Economics Department of the University of Bordeaux and the Department of Agricul-

257 tural and Applied Economics at the University of Wisconsin - Madison. Funding provided by the

258 Gordon and Betty Moore Foundation and the Norwegian Agency for Development Cooperation's

259 Department for Civil Society under the Norwegian Forest and Climate Initiative.

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356 5 Tables and figures

Figure 1: Sample points

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Estimation sample. Excludes points without forest in 2005, as well as points falling in various types of conservation areas, indigenous reserves, and INCRA settlement zones.

Figure 2: Effect of CAR in different years

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Estimated impacts from Section S6, Table S6, column (6). Bars show the sum of coefficients and 95% confidence intervals for baseline plus all years up to year of effect. For example, the first bar shows the baseline effect of registration, 0.004. The second bar, shows this baseline effect plus the additional effect in 2010: 0.004 — 0.002 = 0.002. The remaining bars show additional sums of coefficients for remaining years.

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Table 1: Summary statistics by CAR enrollment

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■CD in

(1) (2) (3) (4) (5) (6) (7)

CAR by 2014 Never CAR Diff CAR/MT Non-CAR/MT CAR/Para Non-CAR/Para

Proportion forested points, 2005 0.530 0.582 -0.087 0.568 0.517 0.508 0.640

Municipal defor 2001-2004 0.019 0.015 0.158 0.022 0.022 0.017 0.010

% municipality forested, 2001 0.487 0.480 0.011 0.506 0.457 0.478 0.495

Normalized defor risk -0.097 -0.218 0.115 0.223 0.489 -0.280 -0.696

Km to nearest highway 83.272 73.675 0.109 98.056 89.134 74.037 63.988

Km to nearest city 49.474 47.661 0.038 51.086 50.835 48.249 45.788

Agriculturally apt (0/1) 0.275 0.190 0.148 0.499 0.409 0.147 0.045

Slope in degrees 9.854 9.495 0.034 7.068 8.101 11.413 10.328

Elevation in m 205.180 172.888 0.191 284.187 270.729 159.696 107.417

Point in INCRA settlement 0.146 0.148 0.012 0.018 0.125 0.220 0.156

Point in indigenous territory 0.008 0.033 -0.127 0.014 0.042 0.005 0.027

Point in conservation area 0.060 0.120 -0.156 0.000 0.000 0.093 0.205

Pasture, 2012 0.275 0.194 0.153 0.241 0.288 0.294 0.121

Secondary vegetation, 2012 0.087 0.076 0.030 0.055 0.060 0.105 0.084

Forest, 2012 0.458 0.443 0.013 0.455 0.372 0.457 0.494

Crop, 2012 0.035 0.017 0.077 0.085 0.042 0.007 0.001

Observations 13721 16263 29984 4358 5644 9995 9987

Statistics weighted to account for sampling design. Column (3) lists the normalized differences in means across the GAR and non-C'AR properties. These can be interpreted as the differences in means relative to the standard deviations in the data. A normalized difference of 0.10 indicates that the two means are 0.10 standard deviations apart.

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Table 2: CAR registration increases the probability of conserving forest

Dependent variable: Forested at end of year Sample of points that eventually registered Sample of all points

(1) (2) (3) ' (4) (5) (6) (7)

Change in probability 0.008*** 0.005* 0.005*** 0.004 0.003* 0.003** 0.006***

of forest after (0.002) (0.003) (0.002) (0.003) (0.002) (0.002) (0.002)

CAR. registration

Additional CAR. effect 0.005 0.002 -0.004

on forest in Para (0.004) (0.004) (0.003)

Forest -0.004*** -0.003*** -0.002*** -0.002*** -0.002*** -0.002***

cover trend (0.001) (0.000) (0.000) (0.001) (0.000) (0.000)

Additional trend -0.003*** -0.004*** -0.002***

in Para (0.001) (0.001) (0.001)

Time effects yes yes yes yes no yes yes

Time x Para no no yes yes no yes yes

Time x soy apt no no yes yes no yes yes

Observations 58977 58977 58977 58977 126275 126275 126275

Adjusted R'2 0.016 0.017 0.019 0.019 0.000 0.015 0.015

Unit of observation is the point. Standard errors are in parentheses and are clustered at the municipal level.* p< 0.10, ** p<0.05, *** p < 0.01. Observations weighted to reflect sample design. Columns (1) - (4) only include points that registered for the CAR between 2006 and 2013, inclusive. Columns (5) - (7) also include points that did not register in these years, but were in land that would have been eligible for the program.