jose guzman possible causes and reducers of … causes...subsidies for amazonian development,...

Jose Guzman_Possible Causes and Reducers of Deforestation in the Brazilian Legal Amazon:

The Role of Environmental Policies from 2005 to 2012_ PA 388K-59935_Fall_2015

Possible Causes and Reducers of Deforestation in the Brazilian Legal Amazon: The Role of

Environmental Policies from 2005 to 2012

Jose Guzman

University of Texas at Austin

December 9, 2015

Introductory GIS



Executive Summary

Deforestation in the Brazilian Legal Amazon has declined by up to 82 percent in the last decade,

compared to previous years and the 2004 peak in deforestation levels.i The report will spatially

analyze the possible causes of this reversal. In general, the report expands on previous studies

analyzing the effect of large scale cattle ranching, population change and density on increased

deforestation; and the effects of environmental policies, such as priority municipalities, increased

monitoring mechanisms, protected areas and indigenous lands on decreasing deforestation rates

from 205 to 2012.

The report provides descriptive maps on deforestation for the years 2005, 2007, 2010, and 2012;

population and cattle density for the year 2012; population and number of cattle changes from

2005 to 2012; and a map of priority municipalities, and embargoes/fines from 2005 to 2012. The

report also makes use og spatial analysis methods such as Ordinary Least Squares (OLS), and

Geographically Weighted Regression (GWR), which use variables that cause (cattle, roads, and

population variables), and reduce deforestation (embargoes/fines, protected areas, and

indigenous lands).

The spatial analysis results show a positive correlation between cattle density for 2012,

population changes from 2005 to 2012, and priority areas, and aggregate deforestation

percentages for the years studied. The results also show a negative correlation between protected

areas and indigenous lands, and aggregate deforestation percentage levels, indicating that these

areas significantly reduce deforestation in the Amazon. Recommendations for further policy

based on the results include further analysis on the effectiveness of illegal deforestation

monitoring mechanisms, and enhancement or decentralization of management and administration

efforts for protected areas in indigenous lands.



I. Introduction and Literature Review

The Amazon biome occupies almost 50 percent of the Brazil distributed over the Acre, Amapá,

Amazonas, Pará, and Roraima States, 34 percent of the Maranhão and 9 percent Tocantins

States. The biome is known for its hot and humid climates, the predominance of tropical forests,

and its biological diversity. The biome harbors more than 30 thousand species of plants, 1.8

thousand species of continental fish, 1.3 thousand species of birds, 311 species of mammals, and

164 species of amphibians. The Brazilian government created the Amazonia Legal, one of the

three socio-geographic administrative units in the country through Federal Law No. 5.173 and

later alterations that integrated Mato Grosso with Art. 45 of the Complementary Federal Law No.

3111 of 1977, and the state of Tocantins and Transitory Dispositions of the Federal Constitution

(Art. 13)12 of 1988. The Amazonia Legal region is made up of about 5,016,136.3 square

kilometers and encompasses the states of Acre, Amapá, Amazonas, Pará, Rondônia, Roraima,

Tocantins, Mato Grosso, and part of Maranhão. The area represents 59 percent of the Brazilian

territory and has more than 24 million inhabitants. More than 300 thousand Brazilian Indians

who belong to 170 indigenous groups inhabit an area of 1,085,890 square kilometers, or 21.7

percent of Amazonia Legal (Socioenvironmental Institute).

Deforestation of the Brazilian Amazon began in the 1960s through government incentives to

expand and develop in the Amazon area, as well as the creation transportation projects such as

the Trans-Amazonian Highwayii. During the 1970s and 1980s the government increased

subsidies for Amazonian development, invested in road infrastructure, created unclear land

tenure, and created policies that promoted speculation by rewarding deforesters with land titles.iii

As of 2013, 3,341,908 square kilometers of the Brazilian Amazon forest covers remain out of the

4,100,000 square kilometers estimated in the 1970s, representing 81.5 percent of the remaining

forest cover and a total of 758,092 square miles of forest loss since 1970. Deforestation rates

have fluctuated since the 1970s and annual forest loss reached a peak of 27,772 square miles of

forest loss in 2004.iv

The causes of deforestation in the Amazon vary according to different studies, but most point out

to both small and large cattle ranching and farming as the main drivers of deforestation.

Fearnside states, for example, that the number of properties censused in each size class explains

74% of the variation in deforestation rate among the nine Amazonian states, further indicating

through multiple regressions that 30% of the clearing in 1991 can be attributed to small farmers

(properties < 100 hectares in area), and the remaining 70% to either medium or large ranchers.v

Bouchardet and Porsse use spatial analysis to study the spatial pattern of deforestation in a cross-

section and longitudinal perspective using global and local analysis estimating Moran’s I statistic

and Local Indicator of Spatial Association clusters maps. The authors applied statistics to three

outcome variables: absolute deforestation, fraction of the total area deforested in each year and

the cumulative sum of the second variable. The studied resulted in a presence of high spatial

correlation dependency, and persistent spatial concentration of deforestation. The authors

conclude that policy control mechanisms have achieved relative success to reduce the average

deforestation but have not influenced its spatial dispersion.vi



Sydenstricker-Neto utilizes a mixed methodology to investigate the effect of population on

deforestation in the Brazilian Amazon. The GIS analysis section of the study uses among

participatory mapping methods, analysis of land use change. The final land-cover classification

scheme of the analysis included seven categories: primary forest, secondary forest, transition

(i.e., areas recently cleared burned or not, and not in use), pasture, crops, bare soil, and water.

The author concludes that population does affect deforestation. However, population alone is not

the main factor; it has to be conceptualized as being mediated by economic, social, cultural, and

institutional factors.

Other factors contributing to deforestation include soil quality and vegetation density, and factors

affecting transport costs (such as distance to major markets and both own- and neighboring-

county roads) and development projects.vii Kaimowitz et al drew similar conclusions by applying

a spatial regression model to analyze deforestation in Santa Cruz, Bolivia. The authors used as

the dependent variable the probability that a given location covered with forest in 1989 still had

forest in 1994; distance to roads, railroads, trails, and markets, “land use potential” (soils and

topography), rainfall, among others were used as independent variables. The study found that the

forests in Santa Cruz that existed in 1989 were more likely to be deforested over the next five

years if they were closer to roads, trails, railways, markets, and areas that had already been

deforested in 1989.viii

Logging is another factor that contributes to deforestation in the region by increasing

susceptibility to forest fires. Climatic changes or events such as dry seasons or events such as El

Niño also compound to deforestation rates by increasing the chances of forest fires.ix

Deforestation is associated to different impacts in the Brazilian Amazon. Deforestation affects

soil erosion, nutrient depletion, and soil compaction, which in turn affects land productivity.

Deforestation also leads to changes in hydrological patterns, such as watershed functions and

precipitation patterns. Biodiversity loss is also a negative outcome of deforestation by destroying

the natural habitats of highly endemic species. Furthermore, deforestation caused by fires

releases trapped carbon dioxide in trees, with negative implications for world climate change.x

Although Brazil has had one of the fastest rates of deforestation in the world (Indonesia

surpassed Brazil in 2014), the country has experienced a decrease in deforestation rates. When

looking at deforestation data from Brazil's National Institute for Space Research (INPE), which

has monitored deforestation rates since the 1970s, deforestation rates have decreased by more

than 70 percent since 2004.

Similar to the causes of deforestation, several factors appear influence current trends towards

decreased deforestation levels, protected areas and indigenous territories being some of the main

factors.xi Article 68 of the new Brazilian Constitution assured lands to indigenous Brazilians,

recognizing land claims in forested areas and ratifying traditional forms of natural resource

management and ownership. The constitutional article also expanded concepts of inhabited

conservation areas. This concept can be contrasted to the notion of uninhabited, pristine

landscapes and protected areas in the United States. These changes have resulted in an increase



in conservation areas such as protected areas and recognized indigenous areas, which now

account for more than 50 percent of the remaining forests of the Brazilian Amazon.

Apart from increases in protected areas, Nepstad et al developed and examined other

hypothetical causes for the decline in deforestation rates. The authors include higher risks for

landowners of reduced access to markets and finance, increased and harsher legal restrictions and

fines on illegal activities, benefits through payments for ecosystem services, price premiums

from certification, and access to new credit lines by avoiding deforestation, stalled highway

projects, decline of profitability of soy production, increased beef production on already cleared

lands, and a reduction in cattle herd sizes.xii Macedo et al provide further evidence that policies

promoting efficient production of already cleared lands while restricting deforestation can result

in increased soy productivity that does not affect deforestation.xiii

Assunção and Gandour analyzed possible policies that led to the reduction of annual

deforestation levels starting in 2004. The authors single out Presidential Decree 6.321/2007 as

one of the policies that has helped reduce deforestation levels. The decree established

municipalities in the Legal Amazon classified as needing priority action to prevent, monitor, and

combat illegal deforestation. Priority municipalities were chosen following three criteria: (i) total

deforested area; (ii) total area deforested in the past three years; and (iii) increase in deforestation

rate in at least three of the past five years. Priority municipalities have experienced stricter

command and control policies and monitoring of irregular activities. Monitoring measures

include harsher registration, licensing requirements, and the revision of private land titles to

avoid fraudulent documents and illegal occupation. Assunção and Gandour concluded that

“changes to conservation policies implemented beginning in 2004 and 2008 significantly

contributed to the curbing of deforestation rates, even after controlling for different sorts of price

effects”.xiv

II. Research Questions

This report will address the following questions:

What factors help explain the decrease in deforestation rates in the Brazilian Amazon in the past

decade? How do demographic trends, herd sizes, and environmental policies affect

deforestation?

Are monitoring efforts effectively allocated in the Brazilian Amazon’s municipalities?

This report is intended to demonstrate the positive impact that environmental policies have in

reducing deforestation rates in the Brazilian Amazon.

III. Methods and Data collection

1. PRODES –

The PRODES project, operated by Brazil’s National Institute for Space Research (INPE),

monitors annual rates of deforestation in the Brazilian Amazon through satellite imagery. The

project measures annual rates based on increments in deforestation using Landsat satellite images

(20 to 30 meter spatial resolution available every 16 days) in a combination that seeks to



minimize the problem of cloud cover. Environmental organizations, such as Imazon, publish

alternative results through their Deforestation Alert System (SAD), based on MODIS and

Landsat imagery. National and international scientists nevertheless consider PRODES estimates

as reliable for analysis.xvDeforestation analysis for the report will thus be based only on

PRODES deforestation data to retain consistency in the deforestation rates analyzed in each year

of study.

2. The Brazilian Institute on Geography and Statistics (IBGE) –

IBGE provides a rich data set, including the Legal Amazon’s municipality shapefiles, and

population data for each municipality, which this report will include to calculate population

density per municipality for the years 2005 and 2012 (measured in squared kilometers), and

population percent change from 2005 to 2012. IBGE also provides cattle numbers per

municipality over the years 2005, 2007, 2010, and 2012, which the analysis will include to

measure number of cattle density per municipality, and number of cattle percentage change from

2005 to 2012. This data on cattle numbers will be used as a proxy measurement to cattle

production intensity for the years studied. Other data used in the report are Brazil vegetation

types, and data on urban centers.

3. Brazil Environmental Ministry (MMA)

MMA provides the list of municipalities that the Federal government established as priority

municipalities in the Legal Amazon for increased monitoring. This data will be analyzed to see

whether deforestation prevention efforts are effectively allocated in the most deforested

municipalities.

4. Brazilian Institute of Environment and Renewable Natural Resources (IBAMA)

IBAMA provides data on legal enforcement mechanisms such as number of land embargoes per

municipalities, and fines related to illegal deforestation. The report will classify this data

according to the years studied and will be transformed into point shapefiles indicating a count of

an instance of a land embargo or fine.

5. World Database on Protected Areas (WDPA)

WDPA provides shapefile data on Brazil’s protected areas. The shapefiles include protected

areas by categories laid out by the International Union for Conservation of Nature (IUNC). The

categories classified for the descriptive and analysis maps are the following:

A. Ia – Strict Nature Reserves:

Strictly protected areas set aside to protect biodiversity and also possibly

geological/geomorphological features, where human visitation, use and impacts are strictly

controlled and limited to ensure protection of the conservation values.

B. II – National Park

Protected areas are large natural or near natural areas set aside to protect large-scale

ecological processes, along with the complement of species and ecosystems characteristic of



the area, which also provide a foundation for environmentally and culturally compatible,

spiritual, scientific, educational, recreational, and visitor opportunities.

C. V – Protected Landscape

To maintain a balanced interaction of nature and culture through the protection of landscape

and/or seascape and associated traditional management approaches, societies, cultures and

spiritual values.

The categories above where chosen apart from other categories, such as sustainable

development, or indigenous areas, in order to narrow down the scope of protected areas into ones

with strict conservation criteria and ecological value, which the report assumes would be areas

where deforestation will be better managed or prevented.

6. World Resources Institute (WRI)

As part of WRI’s Global Forest Watch forest monitoring alert system, the organization provides

data on indigenous lands in Brazil. The data includes the tribe names of each land/area, type of

indigenous land (Reserve/Territory), and land area.

IV. Methods and Process

All gathered shapefile data was projected to the South America Albers Equal Area Conic

projected coordinate system.

1. Context Maps – Four descriptive maps showing annual deforestation for the years 2005,

2007, 2010, and 2012.

a. Reference Map

- Added Projected country borders of South America shapefile SouthAmerica_Project.shp

- Added Brazil states shapefile Brazil_admin_project.

- Added state municipality shapefile for the Legal Amazon states of Acre, Amapá,

Amazonas, Pará, Rondônia, Roraima, Tocantins, Mato Grosso, and Maranhão. Merged

shapeflies.

- Selected all municipalities in the merged file except those in the state of Maranhão that

are not part of the Legal Amazon. Created new shapefile from the selected features,

Legal_Amazon_Muni.shp.

- Added field to attribute table shape_area. Calculated geometry to measure area of

municipalities in Km Sq

b. Annual Deforestation Maps

- Added SouthAmerica_Project.shp, Brazil_admin_Project, and Legal_Amazon_Muni,

shapefile.



- Added Brazil river network shapefile. Clipped it to the Legal_Amazon_Muni.shp to

create Amazon_River_Network.shp file.

- Added Shapefile of Brazil Vegetation cover types Radam_Vegetacao_SIRGAS.shp.

Clipped it to Legal_Amazon_Muni to create Vegetation_Legal_Amazon.shp.

- Based on IBGE data, selected by attributes types of vegetation that falls under dense

rainforest vegetation. Created DenseOmbophylousForest_1.shp.

- Based on IBGE data, selected by attributes types of vegetation that falls under open

rainforest vegetation. Created OpenOmbrophylousForest_1.shp.

- Based on IBGE data, selected by attributes types of vegetation that falls under mixed

forest.

- Added annual deforestation shapefile data for years 2005, 2007, 2010, and 2012 into

separate maps.

2. Descriptive Maps

a. 2012 number of cattle and population density per municipality (Km Sq)

- Added Legal_Amazon_Muni.shp.

- Added Amazon_River_Network.shp.

- Added shapefile for urban centers for with populations above 100 thousand.

- Joined population data for Legal Amazon municipalities for the years 2005 and 2012.

- Joined number of cattle data for the years 2005, 2007, 2010, and 2012.

- Added field PopDen2012. Used field calculator to divide Pop2012 field by the

shape_area field to calculate population density for 2012.

- Added field Cowden2012. Used field calculator to divide Cow2012 (Number of cattle

for the year 2012) field by the shape_area field to calculate number of cattle density for

the year 2012.

- Classified population density for 2012 into four classes. First, classified by Jenks

(Natural Breaks) method and then manually classified density ranges based on Jenks for

easier interpretation.

- Classified number of cattle density for 2012 into four classes. First, classified by Jenks

method and then manually classified density ranges based on Jenks for easier

interpretation.

- Labeled some of the major municipalities (capitals) for each of the Legal Amazon states.

b. Number of cattle percentage change and population percentage change from 2005 to

2012.



- Added Legal_Amazon_Muni.shp with joined tables.


- Added shapefile for urban centers for with populations above 100 thousand.

- Added field PopChange. Used field calculator to calculate population percent change for

each municipality from 2005 to 2012.

- Added field CowChange. Used field calculator to calculate number of cattle percent

change for each municipality from 2005 to 2012.

- Classified PopChange into three classes. First, classified by Jenks method and then

manually classified percent change ranges based on Jenks for easier interpretation.

- Classified CowChange into three classes. First, classified by Jenks method and then

manually classified percent change ranges based on Jenks for easier interpretation.

c. Deforestation Monitoring Mechanisms in the Legal Amazon Map



- Selected municipalities that MMA established as priority municipalities. Added

PRIORITY field the attribute table. Started a new editing session, classifying priority

municipalities as 1 and non-priority municipalities as 0.

- Added shapefile for embargoes and fines related to illegal deforestation

embargo_IBAMA.shp

- Selected features by year 2005, 2007, 2010, and 2012. Omitted features without

description of fine or embargo. Created separate shapefiles by years. Merged shapefiles.

Used the feature to point operation to turn the polygon shapefiles into points. Points

were used instead of polygons because not all features were embargoes that confiscated

a certain land area.

d. Indigenous Lands and Protected Areas Map


- Added Brazil_indigenous_lands.shp

- Added Brazil_Protected_areas.shp

- Clipped Brazil_indigenous_lands.shp into Legal_Amazon_Muni.shp. Created

Legal_Amazon_Ind_Lands.shp.

- Clipped Brazil_Protected_areas.shp into Legal_Amazon_Muni.shp. Created

Amazon_Protected_Areas.shp.



- Selected clipped protected area shapefile by attributes, choosing categories la, II, and V,

and those designated as World Heritage Sites. Created Amazon_Protect_select.shp.

3. Spatial Analysis Maps

a. Ordinary Least Squares (OLS) – First ran OLS regression using variables from the

descriptive map section to see which values were statistically significant, and which

could be omitted in the second step of the analysis. The unit of analysis selected was Km

Sq per municipality in the Legal Amazon.



- Added 2005_Deforest_PRODES.shp, 2007_Deforest_PRODES.shp,

2010_Deforest_PRODES.shp, and 2012_Deforest_PRODES.shp

- Added 2005_embargoes.shp, 2007_embargoes.shp, 2010_embargoes.shp, and

2012_embargoes.shp.

- Spatially joined 2005_Deforest_PRODES.shp, 2007_Deforest_PRODES.shp,

2010_Deforest_PRODES.shp, and 2012_Deforest_PRODES.shp, summarizing by sum

attributes. This process allowed having a sum of all the deforestation that falls within

each municipality for the joined year. To normalize the sum results, a field was added

for each year, and field calculator was used to divide the sum of deforestation for each

year by the municipality shape_area attribute table. Added field named AGG_Def and

used filed calculator to find the aggregate percentage area deforested for the four years

analyzed. Named final joined shapefile RegressionMap_1.shp

- Spatially joined 2005_embargoes.shp, 2007_embargoes.shp, 2010_embargoes.shp, and

2012_embargoes.shp, and the merged shapefile of all the embargoes/fines, summarizing

by sum attributes. This process allowed having a sum of all the counts of

fines/embargoes that falls within each municipality for the joined year. Named final

Joined shapefile RegressionMap_2.shp.

- Added Amazon_Protect_Select.shp. Performed intersect operation with

Legal_Amazon_Muni.shp, to separate/split protected areas that overlapped multiple

municipalities. The created intersected polygon shapefile was turned into a point

shapfile using the feature to point operation. This was done to avoid extra addition of

protected areas that fell outside municipalities, in order to have only the area summed

that falls within each municipality once it was joined into the regression shapefile.

Spatially Joined Amazon_Protected_Area_point.shp with RegressionMap_2.shp,

summarizing by sum attributes. This process allowed having a sum of the area in each

municipality that falls under a protected area. Added attribute field PROPERAR, and

used field calculator to divide the sum of protected areas in each municipality by the

shape_area attribute to normalize the percentage area of municipality that falls under a

designated protected area. Named final joined shapefile RegressionMap_3.shp.



- Added Legal_Amazon_Ind_Lands.shp. Performed intersect operation with

Legal_Amazon_Muni.shp to separate/split indigenous lands that overlapped multiple

municipalities. The created polygon intersected polygon shapefile was turned into a

point shapefile using the feature to point operation. This was done to avoid extra

addition of indigenous lands that fell outside municipalities, in order to have only the

summed area that falls within once each municipality it was joined into the regression

shapefile. Spatially Joined Legal_Amazon_Ind_Lands_point.shp with

RegressionMap_2.shp, summarizing by sum attributes. This process allowed having a

sum land area in each municipality that falls under an indigenous land. Added attribute

field INDPERAR, and used field calculator to divide the sum of indigenous lands in

each municipality by the shape_area attribute to normalize the percentage area of

municipality that falls under an indigenous land. Named final joined shapefile

RegressionMap_4.shp.

- Added Brazil_Road_network.shp, clipped it to Legal_Amazon_Muni.shp. Intersected

resulting shapefile to Legal_Amazon_Muni to separate road lines into each

municipality. Turned line feature into points using the feature to point operation. Joined

road_point shapefile, summarizing by sum attributes. Added attribute field Road_Den

and used field calculator to measure road density (sum road length divided by

shape_area).

- Ran OLS regression.

Dependent Variable

AGG_DeF – Aggregate percentage area deforested per munitipality

Independent Variables

PopChange – Population percent change from 2005 to 2012

Count_1 – Embargo/Fine instances

CowChange – Number of cattle percentage change from 2005 to 2012

COWDEN12 – Cattle Density 2012

PRIORITY – Whether deforestation falls within priority municipalities

IND_PER_AR – Percentage area of municipality that falls under indigenous lands

PROPERAR – Percentage area of municipality that falls under a designated protected area

b. Geographically Weighted Regression (GWR) – After running the OLS regression,

performed a GWR regression with only the statistically significant result variables from

the OLS Regression. While OLS regression is based on one single equation that assumes

that the relationship between variables is exactly the same everywhere in the legal

Amazon, the GWR regression constructs a separate equation for every feature

(municipalities) in the dataset.



Dependent Variable

AGG_deF – Aggregate percentage area deforested per municipality

Independent Variables

PRIORITY – Whether deforestation falls within priority municipalities

COWDEN12 – Cattle Density 2012

IND_PER_AR – Percentage area of municipality that falls under indigenous lands.

PROPERAR – Percentage area of the municipality that falls under a designated protected area

V. Map Findings

1. Context Maps –

Maps 1.1 through 1.4 demonstrate the deforestation decrease levels for the years selected. The

progression shows an overall decrease in deforestation from the years 2005 to 20012.

Deforestation kilometers for the year 2005 totaled 23,815 square kilometers. Deforestation for

the year 2007 totaled 11,451 square kilometers. Deforestation for the year 2010 totaled 6,735

square kilometers. Finally, deforestation for the year 2012 totaled 4,435 square kilometers.

An important note regarding annual deforestation figures acquired and used for subsequent

analysis. These figures are based on the statistics gathered from the attribute tables the the

polygon shapefiles. These statistics numbers resulted to be higher than official figures and tables

in Brazilian government estimates.



Map 1.1



Map 1.2



Map 1.3



Map 1.4



2. Descriptive Maps

a. 2012 number of cattle and population density per municipality (Sq Kms)

- 670 of the 772 municipalities had population densities ranging from 1 to 20 people per

square kilometer. 80 municipalities had population densities ranging from more than 20

to a 100 people per square kilometer. 13 municipalities had population densities ranging

from more than 100 to 400 people per square kilometer. 7 municipalities had

municipality densities ranging from 400 to 3000 people per square kilometers (Map 2.1)

- 381 of the 772 municipalities had cattle densities ranging from 1 to 20 cattle per square

kilometer. 231 municipalities had cattle densities ranging from more than 20 to 60 cattle

per square kilometers. 111 municipalities had cattle densities ranging from more than 60

to 100 cattle per square kilometers. 49 of the municipalities had cattle densities ranging

from more than 100 to 200 cattle per square kilometers. (Map 2.2)

b. Cattle and Population Percent Change from 2005 to 2012

- 293 of the 772 municipalities in the Legal Amazon experienced negative percent change

in number of cattle, but no more than 1 percent change. 478 municipalities experienced

up to 10 percent number of cattle change. Only 1 municipality experienced a 47 percent

change in the number of cattle from 2005 to 2012 (Map 2.3)

- 237 of the 772 municipalities in the Legal Amazon experienced negative percent change

in population, but no more than 1 percent change. 533 of the municipalities experienced

up to 10 percent number of cattle change. Only 1 municipality experienced a 46 percent

change in population from 2005 to 2012 (Map 2.4)

c. Deforestation Monitoring Mechanisms in the Legal Amazon

- Fines/embargoes totaled 2225 when adding the four years under study (41 in 2005, 531

in 2007, 519 in 2010, and 1134 in 2012). The highest number of fines/embargoes in a

municipality was 305 (Map 2.5)

d. Protected Areas and Indigenous Lands

- 141 out of the designated Amazon protected areas are located within the Legal Amazon,

including 45 natural protected areas (IUNC category V), 54 national parks, 33 Strict

Nature Reserves, and 2 World Heritage Sites.

- The indigenous lands are composed of 377 different tribes for each area. The summed

area represents around 22 percent of the Legal Amazon area (Map 2.6)



Map 2.1



Map 2.2



Map 2.3



Map 2.4



Map 2.5



Map 2.6



3. Spatial Analysis (Map 3.1)

a. Ordinary Least Squares (OLS) - Table 1 shows the results of the regression based on

the variables explained above.

Table 1

- The variables for priority municipality, population change, and cattle density for 2012,

showed a significant positive correlation to aggregate percentage area deforested.

- The variables for indigenous lands, protected areas, and road density show a negative

significant correlation to aggregate percentage area deforested.

- The variables above are statistically significant because the t-statistics are large and the

p-values are below .05. This means that we are 95% confident that these coefficients are

statistically different from zero, or that relationship between aggregate deforestation and

the variables is caused by something other than mere random chance.

- The OLS regression resulted in an R² value of 0.167077 and an adjusted R² value of

0.158344. The variables used in the regression thus underperform in explaining

aggregate percent deforestation.

- The OLS regression appears show clustering along the deforestation corridor noticed in

the context maps as well as the priority municipalities. The spatial cluster of standard

residuals means that the model is missing one or more key explanatory variables.

- Results with Standard Deviation > 1.5 indicate that 51 municipalities experience higher

deforestation higher than the model predicts. Results with Standard Deviation -1.5

indicate that 2 municipalities experience less deforestation than the model predicts.



b. Geographically Weighted Regression (GWR)

- The GWR regression resulted in an R² value of 0.371 and an adjusted R² value of 0.317.

Using GWR increases the goodness of fit compared to OLS regression, showing that

more than 30 percent of the variation is explained by the significant variables used in the

regression.

- Results with Standard Deviation > 1.5 indicate that 47 municipalities experience higher

deforestation higher than the model predicts. Results with Standard Deviation -1.5

indicate that 4 municipalities experience less deforestation than the model predicts.

Map 3.1



VI. Discussion

1. Context Maps

The context maps show a clear decrease in annual deforestation for the years observed. The maps

exhibit a pattern of deforestation along mixed forested lands. This is likely due, among other

factors, to the border of such forest types with savannas and pastured land used for cattle grazing

and soy production for cattle feed.

2. Descriptive Maps

a. Population density for 2012 was not a significant factor when compared to the degree of

deforestation in the Brazilian Legal Amazon. Although some urban centers and the most

densely populated areas are present in small municipalities along the western coastal region,

they do not appear to be correlated to increased deforestation. Furthermore, densely

populated municipalities are spatially spread across the region.

b. Visually, cattle density appears to be correlated to increased deforestation levels, since the

municipalities with the highest cattle density (70 to 200 cattle per square kilometer) are

located along the same municipalities that experience high percentages of annual

deforestation.

c. The majority of municipalities in the Brazilian Legal Amazon have experienced a positive

percentage change in both in the number of cattle and populations. This could be due to the

profitability in cattle ranching, increased development infrastructure, and population

migration towards the Amazon.

d. The priority municipalities appear to be located along the areas most affected by

deforestation. Most counts of embargoes and fines fall within the 41 priorities designated by

decree, indicating increased monitoring efforts in the areas selected.

e. Protected areas and indigenous lands seem to be spread across the Legal Amazon, perhaps

showing some cluster in the northern section bordering Colombia, Venezuela, Guyana,

Suriname, and French Guiana, as well as the Peruvian border. Other clusters appear to be

located centrally. Spatial analysis methods, such as hot spot analysis could help clarify where

the clusters of indigenous lands and protected areas are located.

3. Spatial Analysis

With the OLS regression results, there is 95% confidence that the significant coefficients are

statistically different from zero, or that relationship between aggregate deforestation and the

variables is caused by something other than mere random chance. The presence of priority

municipalities is positively correlated to deforestation levels, indicating that the Brazilian federal

government has effectively designated municipalities that are most likely to experience the

highest levels of deforestation. However, the presence of priority municipalities does not seem to

disperse or reduce deforestation.



There is a negative correlation between fines/embargo instances and deforestation levels,

meaning that these monitoring measures can decrease deforestation levels. However, the

association is not statistically significant. Separating fines by count, and embargoes by

percentage of area embargoed might lead to more significant results than just counting all the

instances of embargoes and fines together. Furthermore, adding continuous data on

embargoes/fines over the 8-year period instead of 4 years might have yielded more significant

results

As expected, protected areas and indigenous lands show a significant negative correlation with

deforestation levels. This means that the higher percentage area of a municipality that falls under

a protected area or indigenous land, the lower the level of percentage area deforested per

municipality.

An interesting finding is the negative correlation between road density and deforestation. This is

likely the case because the OLS correlation measures all the road networks in the Legal Amazon.

Road networks in the Legal Amazon, such as those in the south of Mato Grosso state, are located

far away from forested areas. Hence, the results are likely to show a negative correlation. Better

classification of roads close to deforested areas, or close to the forested Legal Amazon region

would likely result in a significant positive correlation between roads and deforestation.

The GWR improved the R² value because it ran regressions with the variables specified for each

municipality, instead of a global regression, which assumes no variation of the variables in each

municipality. Vegetation, road networks, population densities, and cattle densities differ

depending on the region of the Legal Amazon. It therefore made sense to run a GWR, which

takes these differences into account.

An issue associated with highly deforested municipalities is that they are highly clustered,

resulting in issues of auto correlation. The highest (> 1.5) and lowest (< -1.5) standard residual

results appear to cluster, meaning that the variables included in the regressions do not result in a

proper model, which might be better adjusted with other key explanatory variables.

The OLS and GWR regressions only use four years as samples from the eight-year period.

Continuous aggregate percentage deforestation levels from the years 2005 to 2012, as well as

more robust datasets for the independent variables might result in more robust and significant

models.

VII. Conclusion

Although the variables used for the OLS and GWR regressions do not create a proper model for

analysis (the model is missing other key variables), the purpose of the report was not to include

the best explanatory variables of deforestation, which has been extensively analyzed, but rather

to analyze specific environmental policies, such as the establishment protected areas, the role

indigenous land management and administration of their own lands, and monitoring mechanisms

in priority municipalities. Number of cattle, which was included as a proxy of cattle ranching,

demonstrated to be the leading factor in the increase of deforestation in the Legal Amazon.

Population was also included as a possible variable. However, as the literature points out,



demographic trends should also include other sociodemographic variables, such as wage/income

earnings, and education levels. Further analysis on such variables could better explain the effect

of population changes in the Amazon on deforestation rates. Nonetheless, most of the variables

used for the regression turned out as significant values in the regression, meaning that they do

explain increases or decreases in deforestation levels.

Other questions of interest for further analysis are the following: (1) How much of the land is

going being reforested rather than continuing being used for cattle ranching or soy production?

(2) How would the OLS or GWR differ if deforested area was not aggregated and analyzed by

year? Do previous years of deforestation future deforestation?

Although the Brazilian Legal Amazon experienced a decrease in annual deforestation from 2005

to 2012, the rates have picked up since then, even though monitoring efforts have increased.xvi

As the report hints, the creation of priority municipalities might not be enough to curb

deforestation rates. Policy recommendations based on the findings and current trends in

deforestation include further analysis on the effectiveness of illegal deforestation monitoring

mechanisms, and enhancement or decentralization of management and administration efforts for

protected areas in indigenous lands.

Footnotes

i Brasileiros, “Desmatamento Na Amazônia Legal Diminuiu 82% Na última Década.” ii Kirby et al., “The Future of Deforestation in the Brazilian Amazon,” 434. iii Fearnside, “Deforestation in Brazilian Amazonia,” June 1, 2005. iv “Calculating Deforestation in the Amazon.” v Fearnside, “Deforestation in Brazilian Amazonia,” December 1, 1993. vi Bouchardet and Porsse, “An Exploratory Spatial Data Analysis for Deforestation in Brazilian Amazon.” vii Pfaff, “What Drives Deforestation in the Brazilian Amazon?” viii Kaimowitz et al., “Spatial Regression Analysis of Deforestation in Santa Cruz, Bolivia.” ix Fearnside, “Deforestation in Brazilian Amazonia,” June 1, 2005, 683. x Ibid., 683–684. xi Kaimowitz et al., “Spatial Regression Analysis of Deforestation in Santa Cruz, Bolivia.” xii Nepstad et al., “Slowing Amazon Deforestation through Public Policy and Interventions in Beef and Soy Supply Chains.” xiii Macedo et al., “Decoupling of Deforestation and Soy Production in the Southern Amazon during the Late 2000s.”

xiv Assunção and Gandour, “Deforestation Slowdown in the Brazilian Amazon,” 35. xv Kintisch, “Improved Monitoring of Rainforests Helps Pierce Haze of Deforestation.” xvi Watts, “Amazon Deforestation Report Is Major Setback for Brazil ahead of Climate Talks.”



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