metadata: authors: 0: author: Chomitz,Kenneth M.
1: author: Wertz-Kanounnikoff, Sheila
count: Brazil
docna: 0: docna: Measuring the initial impacts on
            deforestation of Mato Grosso's program for
            environmental control
docty: Policy Research Working Paper
subtopic: Forests and Forestry,Biodiversity,Environmental Disasters & Degradation,Global Environment Facility,Forestry,Coastal and Marine Resources,Energy and Natural Resources,Global Environment,Crime and Society
teratopic: Environment,Rural Development,Energy
abstracts: cdata!: Although private forest use in Brazil
            has been regulated at least since the Forest Code of 1965,
            cumulative deforestation in the Brazilian Amazon reached
            653,000 k
geo_reg_key: 517191,119228
repnme: repnme: Measuring the initial impacts on
            deforestation of Mato Grosso's program for
            environmental control
display_title: Measuring the initial impacts on
            deforestation of Mato Grosso's program for
            environmental control
listing_relative_url: /research/2005/11/6380768/measuring-initial-impacts-deforestation-mato-grossos-program-environmental-control
topicv3: Biodiversity m1326214 2084,Forestry m1326646 436,Criminal Justice m1326993 1014,Environment, Natural Resources and Blue
            Economy m1327391 566,Deforestation m1503714 3386
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Rural Development,Energy
no_of_pages: 58
author_keys: 0: author_key: 92703
model_extractions: WPS3762

# **Measuring the initial impacts on deforestation of Mato Grosso’s program for environmental control**

## Kenneth M. Chomitz<sup>1</sup> and Sheila Wertz-Kanounnikoff<sup>2</sup>

Rev 3.01

# **Abstract**

Although private forest use in Brazil has been regulated at least since the Forest Code of 1965, cumulative deforestation in the Brazilian Amazon reached 653,000 km<sup>2</sup> by 2003 (INPE 2004). Much of this deforestation is illegal. In 1999, the State Foundation of the Environment (FEMA) in Mato Grosso introduced an innovative licensing and enforcement system to increase compliance with land use regulations. If successful, the program would deter deforestation that contravenes those regulations, including deforestation of riverine and hillside forest (permanent preservation areas), and reduction of a property’s forest cover below a specified limit (the legal forest reserve requirement).

This study seeks to assess whether introduction of the program affected landholder behavior in the desired direction. Simple before/after comparisons are not suitable for this purpose, because there is considerable year to year variation in deforestation due to climatic and economic conditions. Nor is it valid to assess program impacts by comparing licensed and unlicensed landholders, even though the program focused its enforcement efforts on the former. This is because, first, landholders with no intention of deforesting may choose to become licensed; and second, unlicensed landholders may be deterred from deforestation by the mere existence of a serious program that aims for universal licensing.

To meet these challenges, the study applies a difference-in-difference approach to geographically explicit data. It looks for, and confirms, post-program declines in deforestation in high-priority enforcement areas relative to other areas; in more easily observed areas relative to less easily observed areas; in areas of low remaining forest cover (where further deforestation is probably illegal) relative to high remaining forest cover. Thus, even against a backdrop of higher aggregate deforestation (driven in part by higher agricultural prices), there is evidence that the program in its early stages (before 2002) did shift landholder behavior in a direction consistent with reduced illegal deforestation. (The legality of deforestation was not however directly observed). We hypothesize that this behavioral change resulted from an initial perception of increased likelihood of the detection and prosecution of illegal deforestation, following announcement of the program. The study does not assess SLAPR impacts following the change of state administration in 2003.

World Bank Policy Research Working Paper 3762, November 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org. The boundaries, colors, denominations, and any other information shown on maps herein do not imply on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries._

> 1 Development Research Group, World Bank. Email: kchomitz@worldbank.org.

> 2 Interdisciplinary Institute for Environmental Economics, University of Heidelberg, Germany Email: wertz@eco.uni-heidelberg.de

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# **Table of Contents**

|1<br>Introduction......................................................................................................................................4|
|---|
|2<br>FEMA’s Environmental Control system......................................................................................6|
|3<br>Assessing program impacts on deforestation: methodological issues......................................9|
|3.1 A typology of deforestation...................................................................................................9|
|3.2 Defining and detecting program impact on deforestation..............................................10|
|3.3 A difference-in-difference approach..................................................................................12|
|4<br>Methodology...................................................................................................................................15|
|4.4 Study area and time period..................................................................................................15|
|4.5 Data.........................................................................................................................................16|
|4.6 Data sampling........................................................................................................................19|
|5<br>Deforestation trends......................................................................................................................20|
|5.7 Deforestation in protected areas.........................................................................................20|
|5.8 Deforestation trends by biome...........................................................................................20|
|5.9 Trends inside and outside APPs.........................................................................................21|
|5.10 Spatial effects related to the RL requirement ...................................................................24|
|5.11 Trends in deforestation relative to road proximity..........................................................27|
|5.12 Effects on size of deforestation patches ...........................................................................28|
|5.13 Summary.................................................................................................................................29|
|6<br>Statistical analysis of program effectiveness in Mato Grosso..................................................30|
|7<br>Land value and deforestation in Mato Grosso..........................................................................34|
|7.14 Statistical analysis of land values and deforestation in Mato Grosso............................34|
|7.15 Spatial characteristics of land values in Mato Grosso .....................................................36|
|7.16 Pattern of land values and deforestation in Mato Grosso..............................................37|
|8<br>Conclusions.....................................................................................................................................40|
|References ................................................................................................................................................42|
|Glossary....................................................................................................................................................44|
|Tables........................................................................................................................................................45|
|Maps..........................................................................................................................................................53|

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# **Acknowledgments**

We are grateful to the Mato Grosso State Foundation of the Environment (FEMA) for sharing the deforestation data used here and to Dr. Rodrigo Justus de Brito, for advice and assistance. Gabriel Azevedo, Garo Batmanian and Daniel Nepstad provided helpful comments. This work was supported in part by a German Consultant Trust Fund and by the German Research Association (DFG), Graduate Program “Environmental & Resource Economics”, University of Heidelberg.

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Introduction Brazilian federal policy emphasizes the importance of restraining illegal deforestation, as part of the overall goal of encouraging sustainable development in the Brazilian Amazon (Presidência da República 2004). Over the past 15 years, federal and state agencies have put in place a variety of legal and administrative instruments to regulate land and forest use in Amazônia. Yet deforestation of the tropical forest has continued, more or less unabated (see Figure 1), and an even greater proportion of the cerrado (savanna woodland) has been cleared. Much of this deforestation contravened two important requirements of the 1965 Forest Code. The Code requires each landowner to maintain _reserva legal_ (RL: legal forest reserve) of 50% (later 80%) of each property under natural vegetation (in Amazônian forest areas), or 20% (later 35%) in Amazônian cerrado (savanna) areas. The Code also requires maintenance of _areas de preservação permanente_ (APPs: permanent preservation areas) near rivers, on slopes and on hilltops. It places limits on the sum of APP and RL areas; in Amazônian cerrado, for instance, the sum cannot exceed 50% of the property.

The difficulty in enforcing regulations reflects several challenges. First, there is a strong economic incentive driving much deforestation (Margulis 2004). Second, there is a huge area to monitor. Third, while penalties for illegal deforestation were substantially increased in 1998, landholders may perceive a low probability that the long chain of detection, arraignment, prosecution, and judgment will result in an effective penalty (Akella and Cannon 2004; Hirakuri 2003). In other words, the perceived deterrent may be low.

For these reasons, there has been great interest in Mato Grosso’s new system for environmental regulation (SLAPR), which deploys technological and institutional innovations to address these challenges.

On its introduction, the SLAPR was greeted enthusiastically and was credited with contributing to a reduction in Mato Grosso’s state-wide deforestation rate. However, there has subsequently been a dramatic rise in the Mato Grosso deforestation rate, in absolute terms and relative to 4
other Amazônian states. What do these aggregate deforestation rates tell us about the system’s overall effectiveness?

This paper argues that simple before-program/after-program comparisons do not provide a measure of program effectiveness, because commodity prices, weather, and other factors strongly influence year-to-year variation in deforestation rates. Comparisons between deforestation inside vs. outside licensed properties are also problematic guides to effectiveness, because early volunteers for licensing may be systematically different in characteristics or motivation from their more reticent neighbors. Moreover, while we recognize that the program prioritized its enforcement efforts on licensed properties, we hypothesize that it may also have affected behavior in the unlicensed areas. This is because credible operation of the program within the licensed properties might increase the perceived probability of eventual detection and punishment of unlicensed activities.

The paper therefore reviews the methodological challenges in assessing program impact. It then proposes a solution, based on an innovative application of the difference-in-difference approach to geographically explicit data. We hypothesize that the program, if successful, will have a _differentially large_ impact on three types of areas:

- those explicitly targeted for higher enforcement effort

- areas that are more easily observed, e.g. on very large properties

- areas where most deforestation is likely to be unauthorizable, e.g. in contravention of the

- forest reserve requirement.

Using multivariate analysis that holds constant a variety of factors, we find that deforestation in each of these areas declined, _relative to other areas_ , after the program was instituted. The period of analysis however extends only through 2002, and cannot be generalized to the subsequent period. Starting in 2003 there was a change in state administration, with the governorship passing from an environmental advocate to one of the world’s largest soybean farmers.

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The plan of the paper is as follows. The next section describes the Environmental Control System and reviews the broad deforestation trends since its introduction. Section 3 discusses methodological difficulties in assessing program impact and proposes a conceptual framework and econometric test for addressing these issues, looking at post program changes in deforestation differentials across the landscape. The following section describes the data. The ‘difference-in-difference’ predictions are then examined via simple crosstabulations in section 5 and subjected to formal multivariate statistical tests in section 6. Section 7, a digression, looks at spatial variation in land values as an important factor in understanding the pressures for deforestation. The paper concludes with a brief summary.

# **1 FEMA’s Environmental Control system**

In 2000, FEMA (the Mato Grosso state environmental agency) implemented an improved rural environmental control system (State of Mato Grosso 2001), sometimes called SLAPR (an acronym for Rural Property Environmental Licensing System). Licensing, although prominent, is only one component in an integrated system.

The goal of the system is to “enforce current environmental legislation as simply and as inexpensively as possible” with particular focus on enforcing RL and APP requirements, enforcing rules on forest and field burning, and ensuring that all land use activities are appropriately licensed.<sup>3</sup> Cost-effectiveness results from the use of remote sensing (satellite imagery) to monitor large areas for deforestation and illegal forest burning; the use of GIS (geographic information systems) to manage data on licensed properties; and a focus on properties of greater than 1000 hectares, since these are relatively few in number but control most privately owned land and are presumed responsible for the bulk of deforestation.

> 3 The following description of the system is based largely on State of Mato Grosso (2001), from which the quote was taken.

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FEMA has a multipronged approach to encouraging compliance with land use regulations: deterrence of deforestation on nonlicensed properties; encouragement of landowners to license their properties; and enforcement of regulations on licensed properties. In the first prong, satellite and field inspections of high deforestation areas identifies landowners who are out of compliance with regulations. Many of these, especially in the early phases of implementation, will have unlicensed properties. These violators are subject to severe civil and criminal penalties. However, FEMA’s preferred approach is offer a reduction of up to 90% in fines if they repair the damage done to RL or APP areas. This is done through the licensing process. Landowners may also voluntarily opt to license their properties, motivated in some cases by lenders’ requirements to show compliance with regulations.

In either case, recruitment of a property into licensed status facilitates enforcement of regulations. A first step in the licensing process involves detailed mapping of the property, and determination of compliance with RL and APP requirements. Landowners who are out of compliance are given an opportunity to redeem themselves by reestablishing native vegetation, by purchasing a compensating area of RL from another private property, or by purchasing for the state a private property within an already-gazetted conservation unit. Landowners who are already in compliance may apply for an authorization for legally compliant deforestation. Because the maps of licensed properties are stored in a GIS, subsequent violation of license terms can in principle be easily and unambiguously detected. Moreover, the owners of licensed properties are legally bound to observe the licensing terms, so that violation of these terms exposes them in principle to swift and severe penalties.

Initial studies of the environmental control program suggested that it was contributing to an observed reduction in aggregate deforestation. FEMA credited the control system with the decrease in deforestation rates (per total area) from 2% in the biennium 1998-99<sup>4</sup> to 1.35% in the biennium 2000-01 (FEMA 2002, 19). Fearnside’s municipal-level study of FEMA’s data for

> 4 That is, the two year period ending in April 1999.

7
# Deforestation in the Legal Amazon 1988-2003

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accidental (escaped) forest fires (Nepstad _et al._ 1999). Hence there is a strong relation between the El Niño cycle and deforestation. There is also a strong presumption that deforestation responds to the prices of agricultural commodities associated with forest and savanna conversion, especially beef and soybeans. The nominal price of beef approximately doubled between 1998 and 2003 due largely to an abrupt decline in the exchange rate; at the same time, Mato Grosso was declared free of aftosa (food and mouth disease) opening up export options (Kaimowitz _et al._ 2003). This combination of factors could well account for the sharp rise in Mato Grosso deforestation.

In sum, simple before versus after assessments of deforestation do not provide insight into program effectiveness – a common difficulty in the evaluation of any public policy that affects behavior over a large region. The next section considers this problem and suggests a solution.

# **2 Assessing program impacts on deforestation: methodological issues**

## **2.1 A typology of deforestation**

How might the Environmental Control System affect landholder behavior and deforestation? To think about this, it is helpful to distinguish, conceptually, three types of deforestation:

1. <u>non-authorized, non-authorizable (NN) deforestation.  This comprises actions, such as</u> deforestation of APPs, or reduction of RL below the legal limit, which would ordinarily not be licensed.

2. <u>non-authorized, authorizable deforestation (NA). This class of deforestation includes all</u> deforestation which meets the criteria for a deforestation authorization, but was illegal because the landowner did not in fact apply for or receive a license.

9
# 3. <u>authorized, authorizable deforestation (AA).  This includes all deforestation for which an</u>

official authorization was granted. (We assume that all authorized deforestation is also authorizable, ie that authorities correctly interpret and apply existing regulations.) If successful, the SLAPR would be expected to reduce NN-type deforestation, in absolute terms. It would not necessarily reduce the total amount of authorizable deforestation (the sum NA+AA) – it might even increase it, as NN-deforesters shifted to AA. However, the program would be expected to shift NA-type to AA-type. Hence we predict that a successful program would reduce the ratio NN/(NA+AA) and the ratio NA/(NA+AA).

How might the program accomplish this? There are two avenues. First, the agency might directly target some landowners for recruitment into the system, i.e. licensing. Once licensed, these landowners will in principle face large, immediate penalties for noncompliant (NN) deforestation, but no penalties for AA deforestation. Second, program implementation may also affect the behavior of unlicensed landholders. These landholders must weigh the costs and benefits of undertaking unlicensed deforestation. Even though the program is less able to detect and punish illegal behavior outside licensed properties, unlicensed landholders may decide that NN-type deforestation has become riskier now that the program is in operation. They may respond either by entering the system; shifting to NA-type deforestation; or trying to conceal NN deforestation by undertaking it in less detectable locations.

# **2.2 Defining and detecting program impact on deforestation**

This typology suggests that the program could be evaluated for its impact on three different measures of deforestation: total deforestation (NN+NA+AA), illegal deforestation (NN+NA), or unauthorizable deforestation (NN). While all are of potential interest, in this paper we focus on NN. In our view, this is the most policy-relevant measure of environmental impact, since NN-type deforestation is by definition the least desirable. The NA-to-AA shift, in contrast, is essentially a relabeling of deforestation. If successful, the relabeling would reflect a 10
successful extension of the rule of law with important long-run impacts (in reducing future NN-type deforestation) but would not have any immediate environmental implications.

A second and more profound problem is controlling for other factors, besides the introduction of the program, which may affect deforestation. The problem here is lack of a good control area against which to compare the outcome of the state-wide experiment. As noted above, it is uninformative to compare pre-program and post-program deforestation rates, because of confounding factors. It is tempting to propose a comparison between the deforestation rate on licensed vs. unlicensed properties. But this comparison falls far short of the ideal of comparing a treatment vs. a control group. The problem is that licensed properties may differ from unlicensed properties in many ways – some unobservable. In particular, it is possible that the earliest volunteers for licensing were those with no plans for deforestation. This includes properties with no pressure or plans for forest conversion. It also includes properties where most forest was long ago converted, and whose prosperous owners now seek rapidly to achieve compliance status through compensation.<sup>6</sup> So, to take some extreme but illustrative scenarios:

- Suppose licensed properties consist only of those who had no plans for deforestation.  In this case, the measured rate of deforestation rate on the licensed properties would be much lower than that of nonlicensed properties – but licensing (by assumption) had no behavioral effect.

- Suppose that all landowners who planned NA (non-authorized but authorizable)

deforestation obtain licenses and carry out AA deforestation instead; meanwhile surveillance discourages most NN deforestation outside licensed properties. In this case, the program in fact successfully discouraged illegal deforestation, but measured rate of deforestation on licensed properties is much higher than on unlicensed properties.

> 6 The state offered these property owners incentives to exercise the compensation option as soon as possible. The ratio of required compensation area to past RL loss was scheduled to rise from 1 in 2001 to 5 in 2005.

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These problems are, in theory, surmountable. The econometric solution requires:

1. data on the licensing status and deforestation behavior of individual landholders

2. data on variables that influence the landholder’s decision on whether or not to license, but arguably have _no effect_ on the decision whether or not to deforest. This would allow us to analyze natural experiments in the effect of licensing on deforestation. In other words, it would allow us to match ‘treatment’ and ‘control’ cases.<sup>7</sup> In general, it is difficult to find variables that meet criterion 2. Even without them, however, we could look at the pre-program determinants of deforestation, and the post-program determinants of licensing, and thus determine whether licensees are mostly those with low or high deforestation risk.

# **2.3 A difference-in-difference approach**

Unfortunately, property level data on licensing is not available to us, and indeed we do not even have maps of property boundaries. So we propose a different approach to detecting overall program impacts on deforestation behavior. Let us distinguish between ‘high cover’ and ‘lowcover’ areas. High-cover areas are lands (outside protected areas) in which local forest cover (computed as a moving average) exceeds the applicable RL limit, and low-cover areas constitute the remainder on non-protected lands. Deforestation rates may systematically differ between high-cover and low-cover plots of land, even after controlling for observable determinants of deforestation risk, such as road proximity and slope. There are a variety of reasons for this differential, which could work in either direction. Low cover areas may be more attractive for deforestation -- that is why they have already lost their cover. On the other hand, it may be

> 7 For instance, suppose we thought that there was no difference between landholders with pre-program loans from banks that enforce the Protocolo Verde, vs. landholders with pre-program loans from other banks.  But maybe those with loans from Protocolo Verde banks have a greater incentive to get licensed.  So we could match treatment-control pairs of landholders between PV and non PV banks.  We would hypothesize no difference in deforestation before SLAPR, but a large difference afterwards<sup>.</sup> Similarly, we may compare adjacent properties on different sides of the Mato Gross/Pará state border, hypothesizing that they are identical save for the state laws they face.

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that in low cover areas, the only remaining forest patches occupy poor soils unattractive to farmers or ranchers (Chomitz _et al_ , 2005 _)._ How will this differential between high and low cover areas change with the advent of the Environmental Control System? The low cover areas are much more likely to be in violation of the RL requirement. (This is a statistical assertion, since we do not observe the actual compliance status of any point.) If the program is effective in deterring type NN deforestation, then we would expect a decline in the deforestation of low cover areas _relative_ to high cover. In other words, we expect the differential to change after the program is introduced. This is a variant of the difference-in-difference approach widely used in program analysis. However, this is the first application of which we are aware for geographically explicit data.

Formally, let yit= deforestation rate, plot i, time t Xit= characteristics of plot i at time t LOWCOVERit = 1 if plot is low cover at time t, =0 else POSTPROGRAMt=1 if program is operational at time 1, =0 else μt be a year-specific effect We can model the deforestation rate as: (1) yit= Xitβ + γ LOWCOVERit+δ(POSTPROGRAMt* LOWCOVERit) + μt This equation is consistent with a standard spatial model of deforestation (see e.g. Chomitz and Gray 1996). Then the hypothesis that the program reduces NN deforestation of legal reserve implies that: δ<0 13
given the assumed high correlation between LOWCOVER and noncompliance with RL.

We use this general framework to test several hypotheses about program impacts. For instance, we can examine the relative impact of the program in areas targeted for more intensive enforcement, using an ENFORCEMENT dummy variable in place of the LOWCOVER:

- (2)  yit= Xitβ + γ ENFORCEMENTit+δ(POSTPROGRAMt* ENFORCEMENTit) + μt

Here we predict γ>0 (because priority enforcement areas were chosen on the basis of high observed deforestation rates, but again expect δ<0.

We assert that, if successful, the program will be observed to reduce deforestation in:

- low-cover areas relative to high-cover areas

- areas prioritized for enforcement relative to others

- APP areas relative to non APP areas

- areas close to roads (these being more observable and targeted for greater levels of

enforcement) relative to far from roads.

We stress again that these comparisons are not simple differences, but rather, _differences in_

# _differences._

We also hypothesize that the program will have differentially greater impact on large-patch deforestation relative to small-patch deforestation, for two reasons. First, large patches are more easily detectable by remote sensing and might be supposed to attract greater attention from the authorities. Second, large-patch deforestation characterizes large properties, which were the explicit focus of the Environmental Control System. Next we describe our study area, 14
our approach to analyze program effectiveness, the available data sets and our data sampling technique.

# **3 Methodology**

## **3.4 Study area and time period**

With about 906,000 square kilometers, Mato Grosso is the third largest federal state of the Legal Amazon and Brazil. It is located in the center-west region of the country and has parts of three major ecoregions: Amazon moist forests (52%), “ _Cerrado”_ savanna woodlands (41%) and “ _Pantanal”_ wetlands (7%) (FEMA 2002, 6). All of them are considered to be globally outstanding and of high conservation priority. Yet they are threatened by agricultural expansion, road construction, water projects and pollution (Dinerstein _et al._ 1995).

Colonization of Mato Grosso started already in the 1950s and 1960s when extensive tracts of land were sold for large-scale cattle-ranching projects to entrepreneurs from southern Brazil. Induced by the growth-oriented policies of the 1970s and the export-oriented policies of the 1980s, soybean production expanded rapidly throughout the cerrado of Mato Grosso, making the state Brazilian’s largest soybean producer in the 1990s. In 1995, 24.9% of Mato Grosso was used for agricultural purposes, primarily for large-scale pasture (57.7%) and large-scale annual crop cultivation (18.8%). Less than a quarter of the agricultural land was used for subsistence agriculture (13.9%) in 1995 (SEPLAN 2002). In contrast to some other parts of the Amazon, the colonization process of the Mato Grosso was dominated by private large-scale entrepreneurs who are perceived to be the greatest contributors to deforestation in Mato Grosso. In 1996/97, 62.4% of the total deforestation patches monitored by FEMA were greater than 200 hectares (FEMA 2002).

Although the program was announced in 1999, licensing activities began in 2000, accelerating in 2001 and especially 2002. We expect therefore increased program credibility and deterrent 15
effect after 1999. Hence we compare FEMA deforestation rates from periods prior to program implementation (1996-97; 1998-99) with post-program FEMA data (2000-01; 2002). Since FEMA enforcement targets private rural properties, our analysis extent was limited primarily to non-protected areas of Mato Grosso. Because the program prioritized enforcement on large landholders (FEMA 2001:21), we compare the change of large-scale deforestation (deforestation clearings greater 200 hectares) versus small-scale deforestation (clearing below 200 hectares)<sup>8</sup> .

# **3.5 Data**

The original and derived data sets (digitally coded maps) that are used in the analysis are summarized in Table 1. All data is projected to Universal Transversal Mercator (UTM), Zone 21 South, South American Datum 1969.

An important variable in our study distinguishes high/low natural vegetation cover. Ideally, we would like to distinguish between deforestation potentially consistent with the RL requirement versus deforestation on properties that are already out of compliance with the requirement. Lacking a map of property boundaries, we constructed a proxy. Using the SEPLAN land cover map, the proportion of natural vegetation cover within a radius of about 1,250 meters – an area of 625 hectares that corresponds to the mean property size in Mato Grosso (IBGE 1998) – was computed for each 100 meter pixel. Based on the biome specific legal reserve requirement, the mean natural vegetation cover in non-protected areas in Mato Grosso was classified into areas of “high” (in concordance with the RL-definition) and “low” (below the RL-requirement) proportion of natural vegetation cover. We defined high versus low cover based on the legal reserve requirement of the revised Forest Code 1965, i.e. 35% in the Cerrado, 80% in Amazon

> 8 Our criterion based on size of _incremental_ clearing corresponds to the criterion of 200 ha _incremental deforestation size_ which distinguishes FEMA oversight (large-scale deforestation) from IBAMA oversight (Mato Grosso – Ordem Serviçio No. 26/00). However, it differs from the criterion of 300 ha _property size_ which distinguishes FEMA oversight (larger properties) from IBAMA oversight (MMA/FEMA Pacto Federativo 2003-2004).

16
(Provisional Measure 2001) and 50% in the Transition biome (Mato Grosso Complementary State Law No. 38/1995).<sup>9</sup> Data inconsistencies between FEMA and SEPLAN datasets present a challenge to accurate deforestation assessment in Mato Grosso. The deforestation data available to us identified areas on which forest cover was lost, but did not identify pre-existing forest cover. The SEPLAN land use/ land cover map is based primarily on 1995 satellite imagery with some additional images from 1996 and fieldwork conducted in 1997 with updates from 1999 (SEPLAN 2004). As the SEPLAN land use/land cover map integrates information from different years, there are inconsistencies between the SEPLAN map and FEMA deforestation data. The proportion of

> 9 The legal reserve requirement was revised since 1996 (Provisional Presidential Decree No. 1.511/96). Prior to 1996, RL requirement was 50% in forest areas and 20% in the Cerrado (Forest Code 1965).

17
<!-- Start of picture text --> Name Date of Date of ae<br>map content ublicationneers Scale Source Description of the map content Map<br>Municipalities with increased Mato Grosso State Foundation of Municipalitieswoe with. targeted .increased enforcement activitya upon the .implementation. of Map 1<br>FEMA enforcement 2000-01 2001 1:250,000 the Environmental (FEMA) the FEMA environmental control system in 1999, The municipalities were identified from<br>FEMA documents, notably FEMA (2002),<br>Incremental deforestation in 1996-97, 2004 1: 250,000 Mato Grosso State Foundation of Human induced loss of any natural vegetation in Mato Grosso (unlike INPE, FEMA also Map 2<br>Mato Grosso 1998-99, the Environmental (FEMA) considers deforestation in the Cerrado biome), Until 2001, deforestation was measured bi-<br>2000-01, annually (two years), Since 2002, deforestation is measured annually.<br>2002<br>Ecoregions of Mato Groso 1977-1981 2001 1; 1,000,000 Derived from the vegetation map Vegetation map with three biomes (i.e. Amazon, Transition, Cerrado) as classified by FEMA Map 3<br>of the RadamBrasil Project by (Instrugdo Normativata No. 2/2001) identify the required legal reserve proportion for a<br>FEMA given property in Mato Grosso.<br>Permanent preservation unknown 2003 1: 250,000 Mato Grosso State Foundation of APP around waterbodies (rivers and lakes), The width of the waterbody defines the width Map 4<br>areas (APP) of Mato Grosso the Environmental (FEMA) of the APP buffer (Forest Code 1965).<br>Protected areas of Mato 2003 1: 250,000 Mato Grosso State Foundation of Indigenous territories and conservation units Map 5<br>Grosso the Environmental (FEMA)<br>Land use/land cover of Mato 1995 2002 1; 250,000 State Secretary of Planing and Actual land use and land cover bases on satellite imagery primarily of 1995 and some of Map 6<br>Grosso Coordination (SEPLAN) 1996, and fieldwork conducted in 1997 and some updates in 1999, The map was<br>elaborated as part of the socio-economic ecological zoning exercise (ZSEE) in Mato<br>Grosso.<br><!-- End of picture text -->

|Name|Date of<br>map<br>content|Date of<br>neers<br>ublication|Scale|Source|ae<br>Description ofthemap content|Map|
|---|---|---|---|---|---|---|
|Municipalities withincreased<br>FEMAenforcement|2000-01|2001|1:250,000|Mato Grosso State Foundation of<br>the Environmental (FEMA)|woe<br>.<br>.<br>a<br>.<br>.<br>Municipalitieswith targeted increased enforcement activity upon the implementation of<br>the FEMA environmental control system in 1999, The municipalities were identified from<br>FEMA documents, notably FEMA (2002),|Map 1|
|Incremental deforestation in<br>Mato Grosso|1996-97,<br>1998-99,<br>2000-01,<br>2002|2004|1: 250,000|Mato Grosso State Foundation of<br>the Environmental (FEMA)|Human induced loss of any natural vegetation in Mato Grosso (unlike INPE, FEMA also<br>considers deforestation in the Cerrado biome), Until 2001, deforestation was measured bi-<br>annually (two years), Since 2002, deforestation is measured annually.|Map 2|
|Ecoregions of Mato Groso|1977-1981|2001|1; 1,000,000|Derived from the vegetation map<br>of the RadamBrasil Project by<br>FEMA|Vegetation map with three biomes (i.e. Amazon, Transition, Cerrado) as classified by FEMA<br>(Instrugdo Normativata No. 2/2001) identify the required legal reserve proportion for a<br>given property in Mato Grosso.|Map 3|
|Permanent preservation<br>areas (APP) of Mato Grosso|unknown|2003|1: 250,000|Mato Grosso State Foundation of<br>the Environmental (FEMA)|APP around waterbodies (rivers and lakes), The width of the waterbody defines the width<br>of the APP buffer (Forest Code 1965).|Map 4|
|Protected areas of Mato<br>Grosso||2003|1: 250,000|Mato Grosso State Foundation of<br>the Environmental (FEMA)|Indigenous territories and conservation units|Map 5|
|Land use/land cover of Mato<br>Grosso|1995|2002|1; 250,000|State Secretary of Planing and<br>Coordination (SEPLAN)|Actual land use and land cover bases on satellite imagery primarily of 1995 and some of<br>1996, and fieldwork conducted in 1997 and some updates in 1999, The map was<br>elaborated as part of the socio-economic ecological zoning exercise (ZSEE) in Mato<br>Grosso.|Map 6|
|Natural vegetation cover of<br>Mato Grosso|1995|=|1; 250,000|Derived from land use/<br>land cover map (own calculation)|Natural vegetation cover corresponds to the land cover (without any human activity as of<br>1995) of the SEPLAN land use/land cover map.|Map 7|
|High/low natural vegetation<br>cover in Mato Grosso|1995|=|1; 250,000|Derived from natural vegetation<br>cover map (own calculation)|Mean natural vegetation cover on a 25x25 pixel neighborhood (corresponding to a mean<br>property size in Mato Grosso of 625 hectareas) was calculated for each pixel on the 100<br>meter rasterized natural vegetation cover 1995. The resulting mean values were ove|Map 8|
|Elevation of Mato Grosso|2002-2003|2003|90 meter|US Geological Survey (USGS)|Digital elevation model for Mato Grosso from the Shuttle Radar Topography Mission (SRTM)|=|
|Slope of Mato Grosso|2002-2003|o|90 meter|Derived from elevation grid (own<br>calculation)|Slope was calculated from elevation grid.|Map 9|
|Roads of Mato Grosso|unknown|2003|1; 250,000|Mato Grosso State Foundation of<br>the Environmental (FEMA)|Primary (paved) and secondary (unpaved) roads in Mato Grosso|=|
|Distance to roads in Mato<br>Grosso|unknown|=|1: 250,000|Derived from road map (own<br>calculation)|Bird distance to the nearest road calculated for each pixel of the 100 meter rasterized<br>road map.|Map 10|
|Agricultural suitability of<br>Mato Grosso|1995|2002|1; 250,000|State Secretary of Planing and<br>Coordination (SEPLAN)|Agricultural suitability was defined by SEPLAN as part of the socio-economic ecological<br>zoning exercise (ZSEE) in Mato Grosso.|Map 11|
|LandvalueofMatoGrosso|2003|2003|1:250,000|derivedfromINCRA2003|Geo-referenciationoflandvaluestomunicipalboundariesofMatoGrosso|-|

<!-- Start of picture text --> Natural vegetation cover of 1995 = 1; 250,000 Derived from land use/ Natural vegetation cover corresponds to the land cover (without any human activity as of Map 7<br>Mato Grosso land cover map (own calculation) 1995) of the SEPLAN land use/land cover map.<br>High/low natural vegetation 1995 = 1; 250,000 Derived from natural vegetation Mean natural vegetation cover on a 25x25 pixel neighborhood (corresponding to a mean Map 8<br>cover in Mato Grosso cover map (own calculation) property size in Mato Grosso of 625 hectareas) was calculated for each pixel on the 100<br>meter rasterized natural vegetation cover 1995. The resulting mean values were ove<br>Elevation of Mato Grosso 2002-2003 2003 90 meter US Geological Survey (USGS) Digital elevation model for Mato Grosso from the Shuttle Radar Topography Mission (SRTM) =<br>Slope of Mato Grosso 2002-2003 o 90 meter Derived from elevation grid (own Slope was calculated from elevation grid. Map 9<br>calculation)<br>Roads of Mato Grosso unknown 2003 1; 250,000 Mato Grosso State Foundation of Primary (paved) and secondary (unpaved) roads in Mato Grosso =<br>the Environmental (FEMA)<br>Distance to roads in Mato unknown = 1: 250,000 Derived from road map (own Bird distance to the nearest road calculated for each pixel of the 100 meter rasterized Map 10<br>Grosso calculation) road map.<br>Agricultural suitability of 1995 2002 1; 250,000 State Secretary of Planing and Agricultural suitability was defined by SEPLAN as part of the socio-economic ecological Map 11<br>Mato Grosso Coordination (SEPLAN) zoning exercise (ZSEE) in Mato Grosso.<br>Land value of Mato Grosso 2003 2003 1: 250,000 derived from INCRA 2003 Geo-referenciation of land values to municipal boundaries of Mato Grosso -<br><!-- End of picture text -->
|Duplicate deforestation [sqkrm]|1998-99|2000-01|2002|
|---|---|---|---|
|1996-97|60|381|148|
|1998-99|-|a+?|134|
|2000-01|-|-|4|
905,995 sample points. The resulting sample point layer was overlaid on each of the assembled digital maps (Table 1) to extract location-specific characteristics for each point (e.g. deforested or non-deforested in year x) surrendering categorical variable information. After assessing deforestation in protected areas, they were excluded from the subsequent analysis. Water and urban points were also excluded, leaving 745,718 sample points for analysis. To reduce spatial autocorrelation in the econometric analysis, we created sub-samples from the original set of sample points by selecting sets of every ninth point (ie. every third x-coordinate and every third y-coordinate). Several combinations of ninth-point samples were extracted using the modulofunction on the xy-coordinates of the original samples points.

# **4 Deforestation trends**

## **4.7 Deforestation in protected areas**

Before turning to deforestation in private lands, the focus of this study, we briefly assess deforestation in protected areas. For each of the study periods under question we found a deforestation rate (with forest cover as the denominator) of less than 0.08% per year within protected areas – an order of magnitude lower than the deforestation rate outside protected areas. There was no clear time trend in this already-low rate. Although we did not examine the role of remoteness and soil conditions in explaining the extremely low deforestation rate in protected areas, it seems reasonable to suppose that protected area demarcation serves as a reasonably effective deterrent to deforestation.

## **4.8 Deforestation trends by biome**

We turn now to deforestation rates in non-protected areas. _Relative_ annual deforestation rates refer to the geometric average yearly forest cover change with adjusted SEPLAN natural vegetation cover as denominator (Figure 2). _Absolute_ annual deforestation refers to the average yearly deforestation rate with total biome land area as denominator (Figure 3).

20
<!-- Start of picture text --> Relative rate of deforestation 1996-2002<br>= -2.00% 1<br>7a@&4 500% 4----------- geRagen nnn<br>0<br>6 |<br>Ps C1 aa EOS<br>> RE<br>%93 2oisndy eeeeee eee EeeiEeS |<br>f=)*L 9.00% |<br>1996-97 1998-99 2000-01 2002<br>—¢- Amazon -® Transition —*& Cerrado<br><!-- End of picture text -->

<!-- Start of picture text --> Absolute rate of deforestation 1996-2002<br>2.00% 7- _<br>—z®0Es 1.50% +------ gancccneeeeeecccceceseeececcceeeeecececeeseeeeed<br>c |<br>©a 1.00% f---2--esrecseseseccs ego acc are]<br>2 |<br>5a 0.50% fon----nneeneeeceeee eee MTreeceee eeeeeed<br>5a |<br>0.00%<br>1996-97 1998-99 = 2000-01 2002<br>—@ Amazon -®Transition —*& Cerrado<br><!-- End of picture text -->
<!-- Start of picture text --> Amazon<br>Average yearly forest cover change<br>£ -2.50%<br>2 -2.00% ee See ee ee<br>=o -1.50% |-----g.----------2-eo ene<br>oy<br>8<br>pel tNeS Mere hetasereee sarees teeemene tees ate seme teens<br>5<br>ueS0.00% 1996-97 1998-99 2000-01 2002<br>in APP within 300m buffer outside<br><!-- End of picture text -->

<!-- Start of picture text --> Transition<br>Average yearly forest cover change<br>§ -2.50%<br>>oa 2.00%<br>5oo LSOM prsens es rssh sierra558 aT ngs Saag RST<br>oa“<br>o<br>h O.SO% p--- === 2 one nenn aa<br>2<br>ir 0.00% 1996-97 1998-99 2000-01 2002<br>—@in APP = —#-within 300m buffer -—#- outside<br><!-- End of picture text --> Cerrado Average yearly forest cover change <!-- Start of picture text --> =<br>£ -2.50% 32—AYJTADY<br>=<br>Bri= -2.00% +-----gugn------2n ee eee eee eee eee eee<br>SBoo -L5O% f----- penn2 genceccceeeccc<br>2i=] -1.00% +----- meuee SECS TROLsaree eee SSE<br>w»awa -O.50% +-----------------7 weeSe aE<br>2<br>a 0.00%<br>1996-97 1993-99 2000-01 2002<br>—~indPP | = —@ within 300m buffer —*—outside<br><!-- End of picture text -->
<!-- Start of picture text --> Amazon Transition Cerrado<br>a _ 3<br>" R -2.00% Ro. 00% @ 2.00%<br>4 5 -1.50% Ee = -1.50% we eee eee ee ee ee eee eee ee eee eee ener eee eeee] £ 1.5096<br>© 6 5 1 -1,00% p=-=-+--22-222--ene neeen en nen eeeeeeen enone<br>= i GL 0096 cpararararscas<br>ra > seanNaTeATeSIMSIN SIM ATSINE 721,009 f-----222 ence eeee eee ee cee ec eee ee cee ee cece 2-0.50%g +----Pptangc---2-------2n? neeeeee eeeeenone<br>—E 7O -0,50% +----ye-----5=2 ee— irSette ----- rdOo -0.50% +---@— ay——Sgzrirrt ee rt ro ron zs--- 4 0.00% ——_ ——<br>= o.00% 2 0.00% & 1996-97 1998-99 2000-01 2002<br>nS 1996-972 1998-99o 2000-0125 2002 ire 1 exonnkg seonohig ':<br>—®< 200ha —e> 200ha<br>—@—< 200ha —e—> 200ha<br>o ie zg2 -2,00% %-2,00%R-<br>= & -2.009% @ o<br>5 oa © -1,50% 4-----------------222-22 eeeee eee eeeee a<br>a & -1,5096 +---------- 222222222 e eee ee cece eee eee 5oO c5<br><!-- End of picture text --> <!-- Start of picture text --> < 5© -1,0096 4----g0-------22-- ASOR6 ope eee ace ect cate py 10096 “p--- 5B -ene noon neeecccsnecccocececcceceees<br>8 oa2 22 nee eens eeeerences $8 -0,5096 ee SO2 USO cpracsscses Sn<br>% 8 -0.50% po ooo * a<br>3 = 0.00% = 0.00%<br>2 0,009 c He 1996-97 1998-99 2000-01 2002<br>a Peace SGAe 1996-97 1998-99 2000-01 2002<br>—@—< 200ha —e> 200ha —w—<200ha —e=> 200ha SSSi, re Sees‘.<br>o z 3 -2.00% #-2,00%<br>3 7 -2.00% & ° —<br>Pa]w Da© 1.50% 1.---g eee eee eeeeee o- om ee a ee ee ee ee ee nnCHAS O96 sts Gl aer tan stan ean ean Ea Ete<br>° tM E009G) jess ssnsss s . Neeen See BE 92 SSi ee aree EESRe Cee eR<br>PaO SUM cesiees eesepa se es nae ee ees esas Get ge eee ee Ere ASME A 8<br>&rs)5w@ 0.00% 5 5 : iooa5 0.50%0.00% pO-9,50%wi5 0.00%: +--- pie reas ss<br>& es A ar 2002 1996-97 1998-99 2000-01 2002 ira AOERMGRoaeacxoAne=A) anna<br>~i—< 200ha —e> 200ha<br>—®< 200ha —e> 200ha —&< 200ha —e—> 200ha<br><!-- End of picture text -->
|||High natural <br>cov|vegetation<br>er|Low natural <br>cov|vegetation<br>er|Tot|al|
|---|---|---|---|---|---|---|---|
|Biome|Unit|Land area_|Forest area|Land area_|Forest area|Land Area|Forest area|
|Amazon|[krn?]|231,006|230,533|117,103|43,415|348,109|273,948|
||[%]|66.4|84.2|33.6|15.8|100.0|100.0|
||[%]|45.6|48.2|49.5|84.5|46.8|51.7|
|Transition|[kmn?]|87,472|82,938|31,627|4,569|119,099|87,507|
||[%]|73.4|94.8|26.6|5.2|100.0|100.0|
||[%]|17.3|17.3|13.4|8.9|16.0|16.5|
|Cerrado|[krn?]|188,187|164,927|87,871|3,369|276,058|168,296|
||[%]|68.2|98.0|31.8|2.0|100.0|100.0|
||[%]|37.1|34.5|37.1|6.6|37.1|31.8|
|Total|[krn?]|506,665|478,398|236,601|51,353|743,266|529,751|
||[%]|68.2|90.3|31.8|9.7|100.0|100.0|
||%|100.0|100.0|100.0|100.0|100.0|100.0|
<!-- Start of picture text --> Amazon<br>Forest cover change 1996-27002<br>ee<br>F ‘<br>id .<br>e 72.00% STRSTaESELS eararea<br>Fys 72,009 f---- 22-22-222-222 Tbe pete]<br>B -1,0096 }----"--------+—-—__»——* |<br>Ww 72-22 TTS. -----------)<br>5<br>uz 0.00%<br>1996-97 1998-99 2000-01 2002<br>—e—high (> 20%) @—low (= 9096)<br><!-- End of picture text -->

<!-- Start of picture text --> Transition<br>Forest cover change 1996-2002<br>O_o<br>at<br>o<br>£i<br>Ey +o<br>Boy oom suesyune yume sued ce eeE ee Me wus<br>paa ;<br>GC o.0o%<br>1996-97 1998-99 2000-01 2002<br>—w—high (> 50%) low f= 50%)<br><!-- End of picture text --> Cerrado Forest cover change 1996-2002 <!-- Start of picture text --> spo 40096<br>a |<br>Os O09 f----- 2c ence ence ence ence e reece ee<br>ct)<br>uw1.a2,00% +------7e,.------------------------4~ tg ,<br>=<br>: -1,00% cae cee oe i, - = hy ao —— oe<br>2 ro hg<br>2 o,00%<br>1996-97 1999-99 2000-01 2002<br>—#—high (> 3553) S—low (* 35%)<br><!-- End of picture text -->
<!-- Start of picture text --> Amazon<br><!-- End of picture text -->

# Transition

<!-- Start of picture text --> Cerrado<br><!-- End of picture text --> <!-- Start of picture text --> 2 2.00% | 2 2 0% | 5 2.00%<br>o £2| A BOG schewssrasansearsearaceraesnaearaearaearsarees 2 L500 fo e--eeceeee cece eee eee ee cee ce eeeeeeeeeeceered fe 1.50% 4.---------- eee cence eee ee eee<br>> 5Sc 000g abn gueseer sees seoe sees eee eeeeee | S= 1,00% 4---- gedegicensmensceneesnemenemeneeen cess| B®= PuMOggeeces gees sees ree cree cman sensessseesees<br>4 raa —— | va)ay __ — | 5Bis<br>=3] oro : | ors5 | pri5 —<br>= = 0.00% py 000% A 0.0096<br>1996-97 1998-99 2000-01 2002 1996-97 1990-99 2000-01 2002 1996-97 1998-99 2000-01 2002<br>=—§=< 200ha ==> 200ha =< 200ha => 200ha =< 200ha ==> 200ha<br>‘e 2,00%6 _ =<br>& @ 2.00% R 2.00%<br>o peer ray<br>c rae —<br>jm 2.509 iS. eee * 1.5096 ———— oe<br>5 21,0096 +---- agree sone e eens tees e eee e eet ec eens & 5<br>4 IRISes eee — Ce ee<br>o3 5a 0.00% NaS, 2 ees Sl = TTC ne ee EME@o ce0.5096 +----S etapaa cnn— eee= — ErdB 0.50%0.00% --+-WhnasasZ: gesaeaoeocesc ecc e eccose; s<br>6 1996-97 1998-99 2000-01 2002 ioe-97 1ege-99 2ann-n1 zane oes doCkGe Scsnep S85S<br>suea0i aun OnE<br>Ge 200ha == 200ha ? < 200ha —e> 200ha<br><!-- End of picture text -->
<!-- Start of picture text --> Forest cover change in<br>0-2 km road distance<br>ig<br>si - 2.00% a _,<br>imi] |<br>a<br>& -1.50% 1------.--TM 3. ----------------------<br>5|<br>a ei<br>a “Hi |<br>“ -0,50% 4----------2---2---<br>Fi 2-2-2 e eee eee ee<br>5<br>uw O05<br>1996-97 1996-99 2000-01 2002<br><200ha —e—> 200ha<br><!-- End of picture text -->

<!-- Start of picture text --> Forest cover change in<br>5-10 km road distance<br>|<br>ri<br>Dad,<br>a 509 panne nen e ee nee ene eee eee eee<br>4<br>L008 penne nen nee eee<br>u<br>FIuw<br>tl : a<br>td<br>c ooo0%8<br>1996-97 1998-99 2000-01 2002<br>= 200ha —-> 200ha<br><!-- End of picture text -->

<!-- Start of picture text --> Forest cover change in<br>2-5 km road distance<br>ae eot 1<br>zeCcGedSO Shot coene ee eeeeneeeaeeetee eeneed|<br>e |<br>Seo OO Sah ee gee eee ee ae paki eee atin l<br>a _<br>¥ =O. 5096 + ~~ pene ad<br>5in<br>WL O,00%6<br>1996-37 1999-99 2000-01 2002<br><= 200ha —e—> 200hs<br><!-- End of picture text -->

<!-- Start of picture text --> Forest cover change in<br>> 10 km road distance<br>#- 2.00% |<br>Diy i eee<br>es<br>mises<br>iA eee<br>u<br>a<br>PE OPROGi opemiinenicieinn cteinieinnaininnicieiendieenie<br>5 0.00%<br>Fi<br>1996-97 1998-99 2000-01 2002<br>= 200ha —e—> 200ha<br><!-- End of picture text -->
There is some suggestion of program impacts on large-scale deforestation. Based on the graphs, large-scale deforestation declines more rapidly, especially in 0-2 kilometer road distance, than small-scale deforestation. This pattern disappears when moving further than 5 kilometers from roads. Again, fragmentation of the forest within 2 km of roads will eventually result in a decline of large-patch deforestation relative to small. However, overall near-road deforestation appears to decrease relative to deforestation distant from roads.

# **4.12 Effects on size of deforestation patches**

The Environmental Control Program explicitly focused on large landholdings. The argument was that large-holders contribute relatively more to deforestation in Mato Grosso than smallholders. Thus we would expect the program to depress large-scale deforestation (i.e. related to large individual clearings) relative to small-scale deforestation. Indeed, the proportion of largescale deforestation, especially above 1000 hectares, decreased drastically in 2000-01 according to FEMA’s statewide figures (Figure 16).<sup>13</sup> There was an increase in the proportion of deforestation occurring in smaller patches (50-200 has). This suggests an effect on large-scale deforestation which might be connected to the advent of the control system. But arguably it could be connected with changes in the nature or motivation of forest conversion.

> 13 In Figure 16, the value on each bar expresses the per-cent proportion of deforestation per class of clearing size for a given year.

28
Distribution of gross deforestation areas by classes of clearing size in Mato Grosso 1996-2002 <!-- Start of picture text --> 30.00<br>m 1996-97<br>:[i] =m 1998-99<br>gee 2000-01<br>£ ba<br>pet Oo 2002 x<br>3 20.00 ©d Finn oel =zsy<br>woot eee 2<br>o Nie ia] a<br>es w TM N<br>o =<br>‘a co i Bio<br>= 15.00 jh tp —_iis = ——<br>oO ms - a as ‘o 3 fey CN<br>® ts a) 7 re 4 uy co<br>ao ay ot | 7 i = a] in<br>- on ae oe - oa)<br>o 10.00 aa wom — — an wis<br>5 yy 4 4<br>£a<br>oo Ncw i! ae ca —_ _<br>a 5.00 oea<br>1<br>oy<br>0.00<br><15ha 15-50ha 50-100ha 100-200ha 200-500 ha 500-1000ha > 1000ha<br><!-- End of picture text -->
# **5 Statistical analysis of program effectiveness in Mato Grosso**

We now apply multivariate methods to the ‘difference in difference’ tests suggested in section 3. Multivariate analysis allows us simultaneously to test the predicted impacts. It also allows us to correct for the change in sample characteristics over time, as forest areas more amenable to deforestation are converted.

We modify equation (1) in two ways. First, since our data is based on forested points rather than areas, we treat y as a latent variable, where we observe deforestation if y>0, and continued forest if y≤0. This permits the equation to be estimated as a logit or probit. Second, we need to account for the fact that our final period of observation is a single year, while the other periods are each two years in length. This complicates the interpretation of the coefficient δ, which we would like to interpret as a change in the annual deforestation rate. We adopt two approaches. First, we estimate a separate deforestation model for each observation period, eg: y*it = Xitβt+ δ tLOWCOVERit+uit observe deforestation if y*it>0 Here our prediction is that δ t , expressed on an annual basis, should decrease in the postprogram years.

Second, we estimate a multiperiod logit model of the form y*it = Xitβt+ Dtγt +LOWCOVERit(δ0+Dtδt )+ uit

[where a category is omitted from each set of dummy variables] This implies that: ln [(prob deforestation/(1-prob deforestation)]= y*it = Xitβt+ Dtγt +LOWCOVERit(δ0+Dtδt )+ uit 30
In this formulation, the coefficients γt adjust for the difference in the ln odds of deforestation between two-year and one year observation periods. The coefficients δt measure the differential impact of LOWCOVER (or other variable of interest) in preprogram vs. post program years.

The sample consists only of points having forest cover at the beginning of the observation period t, where t is in the set (1996-97, 1998-99, 2000-01, 2002). To reduce spatial and temporal autocorrelation, we chose a different subsample of the 1 km gridded points for each time period, and merged them into a single multiperiod data set. The sample for each time period consists of one out of nine of the original sample points, chosen as points on a 3 km by 3 km grid.

Table 8 contains the summary statistics of the regressors used in the single-period regressions, where deforestation was estimated separately for each observation period; the regressions themselves are in Tables 9-12. Instead of coefficients, the tables show ∆F/∆X, the effect of each categorical variable X on the probability F of deforestation rate, holding other variables at sample means. For instance, in Table 9, location in high cover areas is associated with a 2.76 percentage point lower probability of deforestation, other things constant, as compared to low cover areas.

To facilitate a comparison of the single-period estimations, the effects and their statistical significance are summarized in Table 13. (All deforestation rates are true rates, e.g. expressed as proportions of the standing forest.) Specifically, the single-period estimations find:

- (i) Compared to areas outside the initial enforcement target zones, the probability of deforestation within the initial (2000-2001) enforcement target zones was strongly significantly higher by 1.04% in 1996-97, significantly higher by 0.40% in 1998-99, insignificantly higher by 0.25%, and insignificantly lower by 0.1% in 2002.  In other words, areas with high deforestation rates in 1996-99 were designated as priority enforcement zones for 2000-01.  After that designation, observed deforestation rates

31
inside the enforcement zone dropped to the same level as outside the zone (other things constant. This strongly declining _differential_ in deforestation rates is consistent with a post-program response to increased (real or perceived) enforcement in the initial target zone.

- (ii) Compared to low cover areas, the probability of deforestation in high cover areas is highly significantly lower by 2.76% and 1.87% in 1996-97 and 1998-99, significantly lower by 0.98% in 2000-01, yet insignificantly lower by 0.29% in 2002. The declining differential of deforestation rates in high and low cover areas is consistent with a postprogram response to increased enforcement of the RL-requirement.  (We stress that “response to increased enforcement” is here a shorthand for “a deterrent effect due to increased actual or perceived or feared enforcement”. )

- (iii) For all periods, there is a very strong and highly significant relationship between deforestation road distance. Deforestation rates decrease as distance to roads increases, for example areas in more than 10 km from roads have a lower deforestation rate between 3.01% in 1996-97 and 1.11% in 2002 compared areas less than 2 km from roads.   However, the effect of distance decreases over time.   In 1996-97, the deforestation rate is 1.5% lower in the 2-5 km distance bracket, compared to locations < 2 km from the road, and this effect is significant at the .00001 level.   By 2002 the differential is just 0.2%, and the significance level is 0.2.    The declining differential in deforestation rates in areas close vs far from roads is consistent with a post-program response to a perceived increase in enforcement near roads.

- (iv) The interaction effect between low cover and road proximity is not however, fully consistent with the hypothesis.   If there was a strong deterrent effect associated with the combination of low cover and road proximity, we would expect this interaction variable to become more negative after program implementation.  Instead, it is

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approximately constant over the period 1996-97, 1998-99 and 2000-01, but increases (from a well-determined -0.01 to a poorly determined -0.003) in 2002.

- (v) Compared to areas outside permanent preservation areas (APP), the probability of deforestation for both within APPs and just outside them (within a 300 meter buffer) is strongly significantly lower across all periods.   However, this differential narrows over time, contrary to our hypothesis of relatively increased enforcement or deterrent  in or near APPs.

- (vi) For all periods, a very strong and highly significant relationship between deforestation and soil quality. Areas with soil quality rated “no agricultural value” have deforestation rates of about 2.1% lower than areas rated “good for annuals and perennials” in 199697.  This differential narrows to about 1.0% in 2002, suggesting some possible displacement from more favorable to less favorable areas – possibly consistent with a shift by landowners from more observable to less observable deforestation.

We turn now to the multi-period logit regressions; Table 14 contains summary statistics of the regressors used in the estimation of the multi-period logit model and the regression results are reported in Table 15. (Table 16 reports the antilog of the coefficients: the impact of each variable on the odds in favor of deforestation.) The period dummies in this regression control for the difference in the length of the observation periods. Relative to 1996-97, the odds of deforestation were 14% lower in 199899, 6% lower in 2000-01, but 34% lower in the single year period 2002. Controlling for this, we see the hypothesized effect of road proximity and of low cover. In both cases, these variables are not significantly related to a reduction in deforestation in 1998-99 compared to 1996-97. But in both cases, these variables are associated with large and significant postprogram (2000-1 and 2002) reductions in the odds of deforestation– 40% to 45% reductions in the case of road proximity, 25% to 33% in the case of low cover. The effects of high 33
enforcement are less clearly in accord with the hypothesis. Before allowing for year-specific interactions, deforestation rates are 46% higher in the areas chosen for high enforcement in 2000-01. Compared to 1996-97, however, the odds of deforestation in these priority enforcement areas dropped by 17% in 1998-99, 20% in 2000-01, and 39% in 2002.

In general, the multiperiod regression reproduces the main findings of the individual period regressions. Deforestation odds are substantially lower on poor soils, far from roads, on high slopes, and in APPs.

# **6 Land value and deforestation in Mato Grosso**

Since enforcement against deforestation in Mato Grosso (and elsewhere) appears to have had limited success, the question arises whether this is due to the strength of economic pressure for deforestation, and whether policies could focus more successfully on deterring deforestation that yields only low economic returns. Hence, the objective is to assess the magnitude of economic pressure for deforestation in Mato Grosso by looking at regional differences in environmental costs (loss of biodiversity and carbon sequestration functionality) versus net economic gain (value of land less cost of conversion) from deforestation. This results in two hypotheses:

- (i) land value is closely related to road access, soil quality and rainfall (biome);

- (ii) deforestation rates are closely related to land values.

## **6.14 Statistical analysis of land values and deforestation in Mato Grosso**

To obtain first insight into the hypotheses, mean municipal-level land values for 2003, resulting from subjective estimation by INCRA technicians were assembled to be tabulated across road distance, soil quality and biome (INCRA 2004). However, municipios exhibit considerable internal variation in soil characteristics and road access. INCRA only considers land value 34
variation for some municipalities, notably Cáceres, Campo Verde, Pedra Preta and Primavera do Leste, where land in agricultural use is far more valuable than pasture land. In these cases, INCRA assigns two values. To adjust for further intra-municipal variation in land value, we regressed mean municipal land values on agricultural suitability category, biome and roaddistance category. With the objective to assess the role of land value in private deforestation decisions, only sample points of nonprotected areas of Mato Grosso were used (i.e. where deforestation can be authorized). Based on the SEPLAN land use information, municipalities with two land values became separate observations.<sup>14</sup> We then used the parameters of the municipal level regression to impute land values for each sample point.

In order to spatially impute land values, we assume that plot-level land value is a linear function of land characteristics. Then, by linear aggregation, the INCRA mean municipal land value is a linear function of the proportion of municipio land with each relevant characteristic. We estimated this function by linear regression. Six observations with mean land value above 1700 Reais/hectare presented outliers in our dataset. These were the agricultural land areas in the municipalities of Alto Garças, Alto Taquari, Campo Verde, Itiquira, Pedra Preta and Primavera do Leste, all situated in the old agricultural frontier, mainly in south-east Mato Grosso. The outliers were extracted and the sample used for estimation was limited to observations with mean land value below R$1700/hectare. Table 17 depicts the summary statistics and Table 18 the outcome of the robust regression model. A simple exploratory functional form (chosen for ease of imputation) found that land values reflect locational rents from road proximity and soil quality, confirming the first hypothesis. For subsequent imputation purposes, the omitted observations with INCRA land values greater than R$1700 hectares were left with their original land value; we do not believe that these values are in error but rather reflect characteristics we are unable to measure.

> 14 In fact, the municipio-level data set was derived from aggregating point level information. Hence, additional municipio observations were created by assigning all points of municipio x in agricultural land (pasture land) the INCRA land value for agricultural land (pasture land).

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|Road distance|Unadjusted mean<br>land value [R¢/sqkm]|adjusted mean land<br>value [R¢/sqkm]|
|---|---|---|
|Q-2 km|46,378|66,176|
|2-5km|44,327|13,638|
|5-10km|38,552|11,252|
|>idkm|25,411|9,249|
<!-- Start of picture text --> Agricultural suitabilit Unadjusted mean adjusted mean land<br>g ¥ land value [R$/sqkm] value [R$/sqkm]<br>good, annual & perennial 40,833 52,489<br>regular, annual & perennial 37,974 26,714<br>restricted, annual & perennial 35,838 10,848<br>possible for planted pasture 53,091 49,594<br>possible for natural pasture 36,794 9,216<br>no agricultural value 41 774 5,819<br><!-- End of picture text -->

|Agricultural suitabilit<br>g<br>¥|Unadjusted mean<br>land value [R$/sqkm]|adjusted mean land<br>value [R$/sqkm]|
|---|---|---|
|good, annual & perennial|40,833|52,489|
|regular, annual & perennial|37,974|26,714|
|restricted, annual & perennial|35,838|10,848|
|possible for planted pasture|53,091|49,594|
|possible for natural pasture|36,794|9,216|
|noagriculturalvalue|41774|5,819|

|Biome|Unadjusted mean<br>land value [R¢/sqkm]|adjusted mean land<br>value [R$/sqkm]|
|---|---|---|
|Amazon|35,3829|30,104|
|Transitian|44.112|32,606|
|Cerrada|50,784|40,001|
SEPLAN natural vegetation cover was used. These tabulations implicitly assume that there is no compliance with the RL requirement.

Table 7 contains the mean and total land value per road distance categories and deforested areas in 1996-2002 in Mato Grosso. This tabulation finds:

- (i) Most remaining forest and most deforestation is located in “high cover” areas (and would presumably be legal if properly permitted). However, deforestation rates are higher in “low-cover” areas (presumed illegal) areas although low cover deforestation rates decrease, especially in 2000-01. Contrary to expectation, the value of deforested land is not much higher in low cover areas (perhaps because remaining forest in these heavily exploited areas is on poorer soils), although the difference increases after 2000.

- (ii) In absolute terms, most deforestation occurs within 2 kilometers of the existing road network. Deforestation rates are far higher here than in more remote locations.  And land values are much higher.  The causal connections here could be debated – does road access confer value, or does high-quality land attract roads?  However, there is a clear association between high-value land, road access, and deforestation.

- (iii) There is a strong association between land quality, land value, and deforestation rates. Deforestation rates and land values are highest in the land rated as being best for annuals and perennials.  Deforestation rates and land values are the lowest in land rated as unsuitable or suitable only for natural pasture.  Land rated as ‘restricted’ for crops has low imputed value but rather high deforestation rates.  In 2002, only about 8.6% of deforestation took place on these three classes of low-suitability soils, but this represented a total of 608 km<sup>2</sup> .

- (iv) In 2002, Amazonian forest had lower land values and a higher deforestation than the

Cerrado. (However, effective land values would be higher in the Amazon if RL requirements were enforced.) 38
<!-- Start of picture text --> Deforested in 1996-1997 (2 years) Deforested in 1998-1999 (2 years) Deforested in 2000-01 (2 year) Deforested in 2002 (1 year)<br>Natural<br>vegetation Area Proportion unadjusted adjusted Area Proportion unadjusted adjusted Area Proportion unadjusted adjusted Area Proportion unadjusted adjusted<br>cover 1995, deforested of nat.-veg mean value mean value deforested of nat.-veg mean value mean value deforested of nat.-veg mean value mean value deforested of nat.-veg mean value mean value<br>Geographic variables SEPLAN [sqkm] [sqkm] cover [%] [R$/sqkm] [R$/sqkm] [sqkm] cover [%] [R$/sqkm] [R$/sqkm] [sqkm] cover [9%] [R$/sqkm] [R$/sqkm] [sqkm] cover [%] [R¢/sqkm] [R$/sqkm]<br>Land value of deforested areas in high/low natural vegetation cover areas (own calculation: 625ha properties)<br>high cover 480,294 14,454 3.01% 47,838 52,629 11,865 2.55% 46,995 49,438 9,364 2.0696 43,249 43,544 6,029 1.3696 42,098 41,879<br>low cover 57,277 3,745 6.54% 43,797 50,114 2,747 5.13% 43,453 49,543 1,688 3.3296 42,031 46,323 1,041 2.12% 41,622 48,624<br><!-- End of picture text -->

|Natural<br>vegetation|Defores<br>Area|ted in 199<br>Proportion|6-1997 (2 ye<br> unadjusted|ars)<br>adjusted|Deforest<br>Area<br>P|ed in 1998<br>roportion|-1999 (2 y<br>unadjusted|ears)<br>adjusted|Defore<br>Area<br>|sted in 200<br>Proportion u|0-01 (2 ye<br>nadjusted|ar)<br>adjusted|Def<br>Area|orested i<br>Proportion|n 2002 (1 ye<br> unadjusted|ar)<br>adjusted|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|cover 1995,|deforested o|f nat.-veg|mean value|mean value|deforested of|nat.-veg|mean value|mean value|deforested of|nat.-veg m|ean value|mean value|deforested o|f nat.-veg|mean value|mean value|
|Geographic variables<br>SEPLAN [sqkm]|[sqkm]|cover [%]|[R$/sqkm]|[R$/sqkm]|[sqkm]<br>c|over [%]|[R$/sqkm]|[R$/sqkm]|[sqkm]<br>|cover [9%]|[R$/sqkm]|[R$/sqkm]|[sqkm]|cover [%]|[R¢/sqkm]|[R$/sqkm]|
|Land value ofdeforestedareas inhigh/lown<br><br>|atural vegeta<br>|tion cover<br>|areas (own<br>|calculation: <br>|625hapropert<br>|ies)<br>|||||||||||
|highcover<br>480,294|14,454|3.01%|47,838|52,629|11,865|2.55%|46,995|49,438|9,364|2.0696|43,249|43,544|6,029|1.3696|42,098|41,879|
|<br><br>low cover<br>57,277|3,745|6.54%|43,797|50,114|2,747|5.13%|43,453|49,543|1,688|3.3296|42,031|46,323|1,041|2.12%|41,622|48,624|
|Land value ofdeforestedareas inroaddista<br><br>|nce categorie<br>|s (own caic<br>|ulation bas<br>|edon roadd<br>|ata by FEMA20<br>|03)<br>|||||||||||
|0-2km<br>214,076|11,956|5.58%|47,325|69,476|9,300|4.60%|46,587|67,856|6,012|3.12%|44,048|67,369|3,600|1.9396|44,177|68,240|
|<br><br>2-5km<br>172,930|4,799|2.78%|47,413|18,609|4,107|2.44%|46,375|17,633|3,601|2.20%|43,214|16,317|2,413|1.5096|41,477|16,778|
|5-10km<br>85,963|1,225|1.43%|43,928|20,292|1,051|1.24%|45,536|16,796|1,226|1.46%|39,955|15,209|872|1.06%|37,150|15,723|
|>i0km<br>64,647|219|0.3496|37,587|15,305|154|0.2496|34,835|17,006|213|0.3396|30,277|12,844|185|0.29%|30,426|16,017|
|Land value ofdeforestedareas in agricultura|lsuitability a|reas (SEPL|AN 1995)||||||||||||||
|good,annual&perennial<br>114,218|5,644|4.9496|50,000|68,459|4,913|4.53%|49,426|62,103|3,735|3.6096|42,157|55,076|2,688|2.69%|41,259|54,476|
|<br><br>regular,annual&perennial<br>188,424|7,045|3.74%|41,701|42,270|5,538|3.05%|42,005|41,092|4,267|2.43%|39,558|35,647|2,728|1.5996|39,173|32,760|
|<br><br>restricted,annual&perenn,<br>11,247|404|3.59%|33,276|26,966|343|3.16%|32,246|20,417|195|1.8696|37,554|21,364|165|1.6096|33,643|18,920|
|<br><br>possible forplantedpasture<br>122,061<br> <br>|3,719<br>|3.05%<br>|57,766<br>|60,369<br>|2,552<br>|2.16%<br>|56,297<br>|60,480<br>|2,040<br>|1.76%<br>|54,945<br>|55,106<br>|1,046<br>|0.92%<br>|55,810<br>|54,675<br>|
|possiblefornaturalpasture<br>69,082|1,074|1.55%.|34,299|23,452|1,021|1.5096|36,126|22,725|562|0.8496|36,653|14,390|345|0.52%|35,437|18,322|
|<br><br>no agricultural value<br>32,247|309|0.96%|45,804|18,834|243|0.76%|40,256|22,797|251|0.7996|38,325|13,993|98|0.31%|32,960|7,093|
|Land value ofdeforestedareas across biome<br><br>|s (RadamBras<br>|il 1972-1<br>|980)<br>||||||||||||||
|Amazon<br>274,369<br><br>|9,221<br>|3.36%<br>|40,178<br>|46,312<br>|7,992<br>|3.01%<br>|41,278<br>|45,766<br>|6,598<br>|2.57%<br>|38,223<br>|40,841<br>|4,483<br>|1.7996<br>|37,687<br>|39,329<br>|
|Transition<br>88,464<br><br>|2,812<br>|3.18%<br>|45,415<br>|48,366<br>|2,640<br>|3.08%<br>|44,069<br>|45,981<br>|1,963<br>|2.36%<br>|43,290<br>|42,407<br>|1,002<br>|1.2496<br>|40,981<br>|39,855<br>|
|Cerrado<br>174,738|6,166|3.53%|58,003|63,571|3,980|2.36%|57,984|59,903|2,491|1.51%|55,755|54,198|1,585|0.9896|55,018|55,684|
# **7 Conclusions**

Against a backdrop of rising aggregate deforestation rates, we have attempted to assess whether the SLAPR program altered deforestation behavior, relative to a hypothetical without-program baseline. Our results suggest that announcement and early implementation (to 2002) of SLAPR affected landholder behavior. Landholders appear to have reduced deforestation in more observable areas, areas prioritized for enforcement, and areas with low remaining forest cover, relative to other areas. Although our data do not distinguish legal from illegal deforestation, the results are consistent with a reduction in deforestation that contravenes the legal reserve regulations. The results do not necessarily indicate that the program reduced _aggregate_ deforestation against the hypothetical baseline.

What accounted for the behavioral change? In part, it may reflect direct enforcement activities by FEMA. Once landowners are licensed, they face heightened scrutiny by FEMA, and swifter and more certain penalties for noncompliance with regulations. We suspect, however, that SLAPR’s deterrent effect was the more powerful influence on landholder behavior. With the transition from a lax enforcement regime to a well-organized and well-publicized one, landholders may have perceived an increased likelihood of eventual detection and prosecution for illegal deforestation.

Prospects for future program impact depend on the balance between economic pressures for deforestation and political will in expanding the scope and effectiveness of SLAPR. Our cursory examination of the economics of deforestation suggests that there are powerful economic incentives driving deforestation. In 2002, deforestation created farmland with an imputed gross value on the order of R$300 million. (Net profitability of forest conversion could be lower if average clearing costs exceeded average revenues from sale of timber or charcoal. On the other hand, in a land-surplus market, land prices may be less than the expected present 40
value of profits.) To put this in perspective, agricultural GDP for Mato Grosso was about R$3.3 billion in 2000.

In general, high deforestation rates, and high absolute amounts of deforestation, are associated with areas of high market value for land. This may limit the scope for using economic instruments to discourage deforestation of areas with high environmental value but low agricultural value. Nonetheless, some deforestation is taking place in areas with very low land values (below US$100/ha). Here, modest taxes or fees for deforestation might deter forest clearance, with significant environmental gains. Instituting a trade in legal reserve obligations (Chomitz 2004; Chomitz, Thomas and Brandão 2004) could have the effect of protecting areas far from roads, with current low deforestation rates and land values but at risk of future deforestation as the road network expands.

The continued effectiveness of SLAPR depends on its success in expanding coverage to include all large landholders, and its success in deterring deforestation on areas not yet enrolled in the system. We have hypothesized that the apparent post-program shift in deforestation patterns was due to landholder perceptions of increased probability of law enforcement. Those perceptions – and their effect on landholder behavior – are likely to change as landholders observe actual government behavior in detecting and prosecuting illegal deforestation. In this paper we have analyzed deforestation only up to the final year of a state administration widely viewed as putting a high priority on environmental issues. Future work should examine the post-2002 evolution of FEMA operations and landholder behavior under a different state administration. As a coda we note that in mid-2005, the director of FEMA was arrested and charged with contributing to illegal deforestation – a reminder of the difficulties involved in rationalizing land use on the forest frontier.

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