University of Florida Thesis Or Dissertation

AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION

ROBERTA MENDONÇA DE CARVALHO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2019

To my father Roberto (in memorian), whom I lost during the PhD way

ACKNOWLEDGMENTS

I had to step away to see it better. The wonders of Geography, with so many dimensions of space, and the dance of scales before and beyond our eyes has always amazed me. If I was born a geographer and my study proves that.

I write these words still shaken by the final dot of my dissertation. My hands are trembling, my heart racing. It is done. I breathe yoga breaths. I stretch yoga stretches.

And I look back. Yes, I did it, but not alone. I identify countless DNAs of help. No words will ever fully express my gratitude for a helping hand when I was on the verge of drowning. And oh boy, I have drowned countless times. Each time, I was rescued by one or many of the people in my supporting system. I am grateful for all the smiles I encountered on a rainy day, guidance unasked for, and models of scholarly and professional behavior.

I thank my fellow PhD students. Alexandra Sabo was my favorite reviewer (to say the least). Aghane Antunes was my oracle. Aline Carrara was Aline. Gabriel Carrero was my neighbor in Gainesville and in Geography. Caroline Huguenin offered hugs, wise words of encouragement and map-rescue magics. Angelica Nunes gave kind ears and great reviews. Isabel, Felipe, and Cloê’s Friday visits gave me reasons to smile.

Carmem Beatriz offered hugs and ears. Joe Lacey spent his valuable hours revising my work, and others more encouraging me to move on. Simone Athayde shared academic support and yoga; David Kaplan was an inspiration in scholarship; Jane Southworth wisely leads the Geography Department.

I give huge thanks to my adviser in the last round, Timothy Fik; and committee member Steven Perz. I appreciate all of my Geography Department colleagues, students, faculty, and staff. I especially thank fellow graduate students Audrey Smith

and Mehedy Hussain; and Administrative Assistant Alex Henao. Claudio Szlafzstein was my former adviser, co-author, friend and now colleague. Ritaumaria Pereira spread the news that led me here. Evandro Moretto shared a great dataset that made possible most analysis in my study. I thank the University of Florida; the Water Institute Fellow

(WIGF) 2015 cohort and faculty; and the Tropical Conservation Development (TCD)

Program, including professors, staff and students. Cynthia Simmons and Robert Walker offered support from early days, nearly to the end.

I am also extremely grateful to those I love and have neglected. I thank Nazaré

Imbiriba (Chuca) for teaching me to see beyond what is in my eyes. I thank my family and friends for understanding of the long retreat, especially the ones in Brazil. In particular but not limited to my stepson Rafael, aunt Ana Claudia; baby sister Edra and baby brother Lucas. I thank my sister Kedyma for not having me there but having me here. I thank my friend-for-life, Danielle Redig. Thank you to my brother, Ygor Carvalho, for his surprise visits and unconditional support. I thank brothers-in-law Justin

Schweitzer and Arlindo Siqueira, family I did not chose, but love.

For my daughter is the true reason I want to become a better person and a scholar. I thank her for spending her teenager years with a mostly unavailable PhD mom.

I thank my mother, for being there more than ever. Sempre. She was a rock: a rock that kept me rolling, pushing me, guiding me, and sometimes, even dragging me to the right path. She has always defied barriers and set a high example. I could never have done it without the privilege of being her daughter.

I wish I had the chance to thank my father. We lost him unexpectedly in my first month of this doctoral program.

Above all, I thank my beloved husband and life-long accomplice. He said yes when most said no. He said yes to me, and to the adventure of our lives. For courageously stepped completely outside his comfort zone. He was my comfort zone w whether I had none I had plenty. He encouraged me to have wings and fly, even if that meant cutting up his own arms wings.

This is not an end. Instead, I see this as a point along my journey. I am eager to turn the page and move to the next chapter. Before I move on, I pause to look back fondly at what was accomplished.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 10

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 12

CHAPTER

1 AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION ...... 14

2 URBANIZATION IN THE BRAZILIAN AMAZON: HOMOGENOUS OR HETEROGENOUS? ...... 17

Study Area ...... 20 Process of Amazon Occupation ...... 20 Far from Pristine: Uncovering the Colonial Narrative ...... 20 Historical Background: State Development Programs ...... 22 Population: Reconfiguring the Amazon ...... 25 Spatial Reconfiguration: Theoretical Background for Drivers of Urbanization and Municipal Division ...... 27 Drivers of Urbanization in the Amazonian Context ...... 27 Cities of the Forest or Forest of the Cities ...... 29 Heterogeneity in Unplanned Urban Occupation in the Amazon ...... 30 Hydropower as a Driver of Urbanization ...... 33 Methods and Data ...... 36 Data ...... 36 Variables of the Model ...... 37 Outcome variable ...... 37 Dummy variable ...... 37 Independent variables ...... 38 Latitude and longitude ...... 38 Quadrants ...... 38 Results and Discussion...... 39 Model Fitness ...... 39 Concluding Remarks...... 46

3 HYDROPOWER INFRASTRUCTURE AND STATE-SCALE URBANIZATION ...... 55

Theoretical Framework ...... 58

Study Area ...... 60 Pará in Amazon Context ...... 63 Historic Overview of Municipality Formation ...... 66 Attempts to Understand Urbanization in Pará ...... 73 Urbanization and Hydropower Systems ...... 74 Hydropower in Pará ...... 74 Tucuruí Hydropower Plant: Some Historical Background ...... 76 A Broader Horizon for a Dam's Impacts: Connection to Urbanization ...... 77 Methods ...... 79 Data Analysis ...... 79 Data Set ...... 80 Outcome variable – urban population percentage ...... 80 Proposed versus conventional “hydro” variable ...... 81 Categorical variable across time ...... 82 Models ...... 82 Results and Discussion...... 83 New Municipalities, Urbanization and Hydropower Grid ...... 85 Concluding Remarks...... 86

4 URBAN EXPANSION IN A RESOURCE FRONTIER: A MUNICIPAL LEVEL APPROACH ...... 94

Study Area ...... 96 Theoretical Ground for Urbanization, Urban Morphology, and Urban Land Change ...... 97 Historical and Population Context ...... 101 Urban Versus Rural Population: A Dichotomy ...... 102 Urban Land Expansion ...... 105 Data and Methods ...... 106 Data ...... 106 Land Cover Classification ...... 107 Results and Discussion...... 108 Barcarena Land Cover Classes ...... 108 Forest ...... 108 Agriculture ...... 108 Bare Soil ...... 109 Urban ...... 110 Water ...... 110 Accuracy Assessment ...... 110 Land Cover Change and Morphology ...... 111 Concluding Remarks...... 113

CONCLUSION ...... 119

LIST OF REFERENCES ...... 122

BIOGRAPHICAL SKETCH ...... 144

LIST OF TABLES

Table page

2-1 Variables in the models ...... 51

2-2 Model selection sample equations ...... 52

2-3 Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 ...... 53

2-4 Marginal prediction of interaction term hydropower and quadrant ...... 54

2-5 Margins coefficient, municipalities and quadrants ...... 54

3-1 Variables used in the models...... 89

3-2 Margins for urban population regression for 1980, 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 83 municipalities affected (yes) and not affected (no) by hydropower ...... 90

3-3 Urban population regression for 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 143 municipalities affected (yes) and not affected (no) by hydropower (confidence interval 95%) ...... 90

3-4 Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 ...... 93

4-1 Land cover classification ...... 117

4-2 Accuracy assessment for 1984, 1999, and 2017 with users’ accuracy (UA), producers’ accuracy (PA), overall accuracy (OA) and Kappa accuracy (KAPPA) ...... 118

LIST OF FIGURES

Figure page

1-1 Multiscale approach and hydropower as common threads ...... 16

2-1 Brazilian Legal Amazon and Western and Eastern Amazon ...... 48

2-2 New municipalities created in the Brazilian Amazon per Decade 1950-1990 ..... 49

2-3 Total of Municipalities in Brazilian Amazon per decade 1980-2010...... 49

2-4 Brazilian Amazon by quadrants ...... 50

2-5 Urbanization and hydropower impact by quadrant in BLA ...... 51

3-1 Study area: State of Pará ...... 88

3-2 Municipalities considered impacted by hydropower: conventional and proposed classification ...... 89

3-3 Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1991-2010 ...... 90

3-4 Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1980-2010 ...... 91

3-5 Urbanization change in Pará and hydropower infrastructure 1980-2010 ...... 91

3-6 Foundation timeline of municipalities in Pará and hydropower system. Source: IBGE 1980, 1991, 2010 ...... 92

4-1 Study area: Barcarena and State of Pará and the Brazilian Amazon ...... 115

4-2 Total population increase percentage by decade in Brazil, Amazon, Pará and Barcarena (Source: IBGE, 1980, 1991, 2010) ...... 116

4-3 Barcarena urban, rural and total population 1980-2018 (Source: IBGE 1980, 1991, 2010) ...... 116

4-4 Barcarena land cover change from 1984 to 2017 per class ...... 117

4-5 Prediction of urban and agriculture land cover change ...... 118

LIST OF ABBREVIATIONS

ADA Agency of Development of the Amazon

ALBRAS Alumínio Brasileiro (Brazilian Aluminum)

BLA Brazilian Legal Amazon

BMR Belém Metropolitan Region

COSIPLAN Committee of South American Infrastructure and Planning of UNASUL

EIA Environmental Impact Assessment

ENIDS National Integration and Development Axis

GIS Geographical Information System

IBGE Brazilian Institute of Geography and Statistics

IIRSA Initiative to Integrate the Regional Infrastructure of South America

INCRA National Institute for Colonization and Agrarian Reform

INPE National Institute for Space Research

IS Index of Services

LULC Land Use and Land Change

PCA Principal Component Analysis

SPVEA Superintendence of Economic Recovery Plan of the Amazon

SUDAM Superintendence of Development of the Amazon

UNASUL Union of South American Nations

USIPAR Pará Steel Mill Company

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION By

Roberta Mendonça De Carvalho

December 2019

Chair: Timothy Fik Major: Geography

Urbanization in the Brazilian Amazon has intensified in recent decades, with urban population rates surpassing 70% and over 400 new municipalities created in the past forty years. While acknowledging that urbanization has numerous drivers, this study analyzed the impact of hydropower on population growth in Amazonian municipalities as a multiscale process, considering regional, state and municipal frames.

The region’s abundant hydropower-generation potential, along with public policies oriented towards regional integration and energy generation, are bringing about significant change as hundreds of new dams are planned and constructed. This study seeks to understand the diversity and spatial organization of urbanization drivers across the region, with a particular focus on the role of hydropower development in spurring urban growth relative to other drivers. To do so, we used spatial socioeconomic and hydropower development data from 1980 to 2010 to assess similarities and differences in the urbanization process across the Amazon, using quantitative analysis and remote sensing. Results showed that, overall, urbanization was higher in regions impacted by hydropower development in regional and state scales. Land use and land cover change points at urban land expansion, and loss of forest as the main changes, indicating the

undercounted impact of the hydropower system. Although urbanization is a widespread process across the Amazon, it is happening with different intensities in different regions.

Urbanization is manifesting as a function of multiple drivers, including investments in hydropower systems, altering the spatial configuration to debunk different outcomes.

CHAPTER 1 AMAZONS WITHIN THE AMAZON: A MULTISCALE ASSESSMENT OF URBANIZATION

Urbanization in the Brazilian Amazon has intensified in recent decades. Urban population has exceeded 70% and more than 400 municipalities were created in the past 40 years. The Amazon’s intense spatial reconfiguration in the past few decades involves but is not limited to the loss of forest cover. Shifts in national geopolitics led to development initiatives that resulted in a significant population increase. In the process, governments sought to reorganize how space and thus natural resources in the

Amazon were to be exploited and used. Urbanization was the dominant factor in reshaping the region’s spatial configuration.

Urbanization as a means of reconfiguring space in the Amazon challenges treatment of the region as a single homogeneous space. The rainforest is often viewed as a homogenous space, but this obscures the various local outcomes of urbanization.

Recent spatial and social changes belie the assumption of homogeneity. The existence of a dynamic network of urban centers in the Amazon is closely related to the dynamics of deforestation and corresponding economic changes.

This research aimed to assess urbanization in the Amazon on three different scales. Different scales allow us to observe spatial differences from local to regional in the context of the Brazilian portion of the Amazon basin. This allowed us to examine different manifestations of urban expansion and other dynamics in the broader context of the region. In this work, urbanization is used as a synonym for increased urban population.

While urbanization reflects multiple driving forces, this study examines the urbanization process through the role of investment in hydropower. This allow us to

evaluate the role of hydropower as a driver of urbanization. A nested cross-scale approach allowed investigation of the role of hydropower projects at different scales

(Figure 1-1)

The analysis is divided across scales by investigation of a specific scale in chapters 2, 3 and 4. In each study, I pursued an historical approach. I related the main historical facts to increased urbanization. In Chapter 2 discussed urbanization in the

Brazilian Amazon at the regional level, focusing on historical increases in urban population since 1980. This study contributes to our understanding of the spatially heterogenous urbanization process that is reshaping the Amazon and provides an assessment of how infrastructure investments affect urban dynamics.

In Chapter 3, this research assessed the urbanization process at the state scale, considering a more refined reconfiguration of municipalities affected by dams, urbanization and the creation of new municipalities. State territorial configuration and hydropower system development are strongly connected. Urbanization rates are higher in areas with more elements of hydroelectric system. Also, new territorial divisions are spatially related to the elements of the system. The state of Pará, particularly important as a frontier state, with high deforestation and urbanization rates, was affected early by enlargement of the hydropower energy system.

In Chapter 4, this study focused on the single municipality as a unit of analysis, showing the process of land cover change and urban land expansion. Spatial changes within a single municipality show the expansion of urban areas and related covers, with a slower increase of agriculture, and significant loss of natural cover. Loss of forest is the biggest conversion to other uses.

In Chapter 5, provides provide the concluding remarks for all studies, as well as the next steps of research.

Figure 1-1. Multiscale approach and hydropower as common threads

CHAPTER 2 URBANIZATION IN THE BRAZILIAN AMAZON: HOMOGENOUS OR HETEROGENOUS?

At first glance, it would seem obvious to label the Amazon1 as a rainforest biome.

However, this gigantic region, spanning 5,016,136 km2, is actually a heterogeneous space in several respects. The Amazon has undergone intensive urbanization since the mid-20th century, resulting in a network of towns and cities. Calling the Amazon a forest biome is misleading, as the basin is now home to more than 22 million people. More than 74% of that population lives in urban areas (IBGE, 2012). Since the 1950s, populations of some of these cities and states have increased by as much as 500% (da

Trindade Jr, 2005; IBGE, 1970, 2012). Brazil’s population, in contrast, grew by approximately 254% (Sawyer, 2015). Despite high urbanization, some localities remain largely rural. Thus, the region is heterogeneous in terms of its human occupation and spatial processes.

The increase of Amazonians living in urban areas from 36 to 74% highlights a trend toward overall urbanization (Barbieri, Monte-mór, & Barbieri, 2007). Such percentage include urbanization in the forest-centered concerns. Amazon’s new configuration confronts paradigms and leads to the question of how urban is “the forest” and how forest is the urban among. Usually, urban expansion proceeds alongside forest decline. Once the urbanization-deforestation dynamic of the Amazon is recognized, the next logical step is to understand how and where these processes are taking place and what forces are driving them.

1 Amazon hereof refers to Brazilian Amazon.

Amazon’s rapid change are by multiple drivers (Costa & Pires, 2015; da

Trindade Jr, 2005; Parry et al., 2018; Sawyer, 2015). Urbanization is among the outcomes of these drivers. In most cases, urbanization changes rural land uses and land cover. As such, Amazon’s the developing urban network constitutes a key change dynamic responsible for the basin’s recent spatial reconfiguration (Becker, 2005, p. 31).

Whether considering the “Cities2 of the Forest” or the “Cities2 in the Forest,” as highlighted by several authors (Browder & Godfrey, 1997; Garcia, Soares-Filho, &

Sawyer, 2007; Júnior, 2013a; M Santos & Silveira, 2001), urban reconfiguration of the

Amazon, is still an oft-ignored reality in relevant literature.

The new scenario points at an inverse relationship between forests and urban population in the Amazon. The triggers leading the forest into an urbanization path must be identified and comprehended. Infrastructure investments are among the main drivers of changes. Links between infrastructure and urbanization have been long established

(Guedes, Costa, & Brondízio, 2009; Jones, 2016; Taubenböck, Wegmann, Roth, Mehl,

& Dech, 2009; United Nations, 2014). Physical infrastructure is a well-established driver of urbanization worldwide (L. A. Brown, Sierra, Digiacinto, & Smith, 1994; Parry, Day,

Amaral, & Peres, 2010; Thypin-Bermeo & Godfrey, 2012). Hydropower infrastructure are among the drivers of change and urbanization in the Amazon.

Past hydropower investments have stimulated urban growth by attracting population to construction sites. Dam construction creates jobs. The infrastructure required for construction camps stimulates urban buildup. The result is migration, and

2 In some cases, this expression is translated from Portuguese as towns. I choose to use cities as a broader term to identify urban settings, without distinction in size of hierarchical levels.

concentrated urban population. Hydropower thus constitutes a source of urban spatial reconfiguration in the Amazon, given past investments and governmental siting of new dam projects. The region has broadly distributed but locally specific hydropower potential. Federal policies underscore hydropower as the national energy strategy

(Fearnside & Millikan, 2015; Pinto, 2012; Silva et al., 2012; Tundisi, Goldemberg,

Matsumura-Tundisi, & Saraiva, 2014; World Commission on Dams, 2000).

Given the hydropower potential of the Amazon Basin, the largest freshwater reserve in the world, this study focused on infrastructure in the form of the region’s hydropower system. This includes dams, transmission lines, and substations. More than

100 large dams are in operation in the Amazon: 137 planned, and 78 inventoried

(Fearnside & Millikan, 2015; Simmons, 2018). The number of hydropower dams in the

Amazon is thus likely to increase in the future. The government stated goal is to integrate the Amazon region and northern Latin America via a network of energy infrastructure projects (Eduardo & Costa, 2000; IIRSA, 2011; J. C. Tavares, 2016).

This study examined the impacts of existing hydropower projects on nearby urban growth as compared to urban areas without hydropower projects. This will thus help to fill a gap in scientific research by considering an infrastructural driver of urbanization beyond previous scientific studies on the road infrastructure (Barber,

Cochrane, Souza, & Laurance, 2014; Southworth, Munroe, & Nagendra, 2004; Suárez,

Zapata-Ríos, Utreras, Strindberg, & Vargas, 2013; Walker et al., 2017).

This study addressed urbanization at a regional scale in the Amazon with municipal units of observation. This allow us to evaluate local variation and thus heterogeneity within the basin. Considering the municipal division as unit provides

ground to build a profile of local urbanization clusters across the Amazon. That allow us to ask where urbanization happened in the Amazon, and whether hydropower projects led to greater urbanization in nearby municipalities.

In addition, this study provides a historical context for occupation, concentrating on development policies of the Amazon and an overview of its population growth. Next,

I offer theorical background for spatial reconfiguration, including discussions on urbanization, territorial and heterogeneity and hydropower. I then proceed with quantitative analysis, performing a spatial model to investigate the correlations of urbanization and hydropower infrastructure along the region. Lastly, I provide concluding remarks.

Study Area

The Brazilian Legal Amazon accounts for nine states. The scale in this study asses the municipal units of all states. Currently, the BLA consists of 807 municipalities, encompassing more than 5 million km2, or 60% of the country’s territory. Another spatial scale assessed is the division of the Amazon into Easter and Western (Figure 3-1).

Process of Amazon Occupation

Far from Pristine: Uncovering the Colonial Narrative

Ana Roosevelt did not discover the Monte Alegre cave paintings, nor did the botanist who saw the paintings before her, as described in the book The Last Frontier

(London & Kelly, 2007). Local people had long known about the paintings and were the ones who guided the foreign expedition to uncover the secrets of the Amazon. What the scientific community mistakenly calls discovery relies on inhabitants of the ancient

Amazon. Denevan (1992) showed that the Amazon was far from pristine, and Roosevelt showed that old settlements in the Amazon were much more ancient than previously

thought. People have settled in the region for more than 10,000 years and with advanced culture of land use. If we disregard the 11 million indigenous peoples from

2,000 tribes in the Amazon at the time of European contact, we ignore most of the region’s history of occupation (Heckenberger, 2003). Notions of the Amazon as an empty space derive from the colonial history of rapid population decline, caused by the genocide of indigenous peoples of that period (Heck, Loebens, & Carvalho, 2005). For centuries, the Amazon was portraited as exotic and distant from Brazil and the world.

Archaeological findings show that the Amazon was not a pristine region

(Denevan, 1992; Heckenberger, 2003). With exceptions such as the Rubber Boom, the region and its scattered communities were portrayed as secluded and characterized by the slowness of economic activity and longstanding traditions (Browder & Godfrey,

1990). Despite the archaeological work that demystified the Amazon, terms such as

“untouched,” “isolated,” “unproductive,” and “uninhabited” were still used to describe the region throughout the 20th century (Becker, Costa, & Costa, 2009; Bertha Becker,

1990). This served the political purposes of the Brazilian state as it sought to integrate the Amazon. In the process, the state portrayed the Amazon as a single unified whole.

In so doing, the Brazilian State failed to consider the various heterogeneities among localities and peoples (Becker, 1989, p.31).

A few state capitals that emerged from the colonial period concentrated most of the urban population. Rural peoples, whether communities of indigenous people, ribeirinhos, or quilombolas, were scattered inside the forest walls and thus invisible

(Glielmo, 2010). National efforts for Brazil to modernize created a neo-colonial narrative that treated the Amazon as an empty space encouraging immigration from Europe. This

was paired with an open policy to miscegenate the country’s population with the goal of creating a whiter phenotype of descendants. Such early policies foreshadowed later efforts to colonize the Amazon by recruiting migrants from other regions of Brazil.

Historical Background: State Development Programs

As early as the 1930s, the Brazilian state encouraged migration to frontier areas in the country’s interior, to promote national integration and modernization (Backhouse,

2013; Bertha Becker, 1989; Denevan, 1992). The state saw an abundance of land and other natural resources in the Brazilian interior and viewed colonization as a mechanism to promote exploitation of those resources, to impel economic growth. Thus, the

Brazilian state promoted various initiatives to impel land occupation, based on a development model of intensive and exponential natural resource exploitation (Becker,

2001, 2005; Magalhães, 2007; Schmink & Wood, 1992; Tavares, 2001). Development was perceived in its economic dimension, and mainly stood for industrialization and commodity-based activities at the considerable expense of environmental degradation.

By pursuing that path, development was highly extractive and thus environmentally unsustainable (Sawyer, 2015). The extractive model resulted in abrupt socioeconomic, demographic, and environment changes, generating negative outcomes for both natural ecosystems and local livelihoods.

Although national development plans date from the 1930s, it was during Brazil’s

Military Regime, initiated with a coup in 1964, that aggressive development policies were established. The military initiated a fast-paced development trajectory followed by aggravated socioeconomic inequalities and environmental degradation (D. Santos,

Celentano, Garcia, Aranibar, & Veríssimo, 2014). Massive changes during the second half of the last century include significant population increase and rapid urbanization.

In the late 1960s, the military created the Brazilian Legal Amazon (BLA) as a state planning region. The BLA was a symbol of the national occupation policies devoted to integrating the Amazon into the national economy. Geographically, the BLA encompasses a geopolitical and administrative area that includes all seven states in the

North region (Acre, Amapá, Amazonas, Pará, Roraima, Rondônia, and Tocantins); and parts of states from the Northeast region (Maranhão) and the Central-West region (Mato

Grosso).

In the 1960s and early 1970s, the Superintendency for Amazonian Development

(SUDAM) emerged as an executive agency to advance national policies in the BLA.

Under SUDAM, the state offered fiscal incentives to attract capital investments for regional development. The SUDAM agency promoted cattle ranching and large-scale agriculture as prominent economic activities. Massive subsidies such as tax exemptions and access to low-interest credit were used to incentivize agroindustry ventures, making investments very lucrative. Large-scale agriculture and cattle ranching were soon responsible for much of the economic growth in the BLA (Gomes & Vergolino, 1997).

Large-scale agriculture and cattle ranching also became largely responsible for the Amazon’s most significant concern: deforestation (Fonseca et al., 2017; Godar,

Tizado, & Pokorny, 2012; P. D. Richards, 2012). On a global scale, the tropical rainforest was the site of accelerated losses of natural cover. Eighty percent of deforestation in the transnational Amazon occurs within its Brazilian territory. So far,

BLA has lost around 800,000 km2 of forest (INPE, 2018). If the deforested areas were merged to form a country, it would be the 33rd largest country in the world: larger than most countries in Latin America, Asia, Africa, and Europe. Since deforestation

monitoring begun in the 1970s, annual deforestation rates have oscillated to record highs of almost 30,000 km2 in 1995 and 2003 and to record lows of 4,600 km2 in 2012

(INPE, 2018). After a tendency to decrease until 2012, numbers are on the rise again.

The SUDAM agency and other agencies also pursued other development policies in the 1970s. In addition to capital investment, the state promoted colonization.

This was driven by geopolitical concern about other countries occupying the Amazon and also driven by economic development. Slogans such as "integrar para não entregar" (integrate in order to not give it away) and "terra sem homens para homens sem terras" (land for men without land) were popularized to promote colonization. This was a nationalist campaign to connect land to people without land. At the time, people in the Northeast faced harsh socio-economic conditions. To someone living with dry land and desertification, scarce access to water, and indications of severe social misery, a piece of land in the forest must have seemed like a promise of eternal paradise

(Paviani, 2007; Rego, 2016; Tavares, 2011).

Energy infrastructure also became a priority. The national power company

Eletrobrás, created in the 1950s, became an essential instrument for research and development of the hydropower system via new projects (Bertha Becker, 1989, 2005;

Schmink & Wood, 1992; M. G. da C. Tavares, 2011). The Amazon’s enormous hydropower resources became a target for new energy projects. Such projects were politically and economically supported. Enterprises in the region demanded infrastructure to support the growth of agricultural and industrial activities. Investments and colonization also stimulated demand for new roads and improved highways.

Transport infrastructure in turn facilitated spontaneous migration into the Amazon, resulting in rapid population growth.

Population: Reconfiguring the Amazon

According to the 1960 Brazilian census, the total population for the North Region was around 2.9 million, of which 35% was urban, versus the national urbanization rate of 44%. By the 1970 census, the North region had grown by 1.3 million, reaching 4.2 million people, and the urban population increased to 43% versus national urbanization rate of 55% (da Trindade Jr, 2005).

Interregional migrants were attracted to the Amazon by the government’s vision of jobs. Labor would be in demand by major infrastructure ventures and opportunities for land settlement in colonization projects. Megaprojects included both roads and hydropower projects. Construction of the Tucuruí Hydropower Plant began in the late

1970s and transpired alongside the construction and extension of federal highways

(BRs). Both contributed to rapid in-migration, which triggered the expansion of urban centers old and new. In many cases, urban growth proceeded spontaneously along the roads, as seen frequently in settlements in northeastern Pará.

Severe economic crises of the 1980s led to decreased industrial production and access to credit for agricultural projects. The crisis weakened the military regime’s legitimacy and led to the withdrawal of fiscal incentives and support for directed colonization. However, the economic recession affected populations in many parts of

Brazil and attracted a second wave of migrants to the Amazon, drawn by prospects of improving their quality of life. In many instances, labor opportunities proved limited in rural areas of the Amazon, pushing people to make localized moves to urban areas in the region. Guedes, Costa, and Brondízio (Guedes et al., 2009) said "urbanization in the

region has been interpreted as a [deliberated] strategy to stimulate regional economic development and alleviate demographic pressures in other parts of Brazil.” By 1991, the population of the BLA surpassed 17 million, with over 50% concentrated in urban areas

(IBGE, 1991).

Between 1980 and 1991, the western Amazon’s urban areas experienced large population increases. This included state capitals such as São Luís and Belém, to small settlements and villages along the Carajás mining corridor in the Marabá region

(Amaral, 2004; Cabral, 1995; Miranda Rocha, 2011; Thypin-Bermeo & Godfrey, 2012).

Agricultural activities that attracted people to the region, should have resulted in increased rural population. Instead, the urban population grew faster during the 1980s.

For instance, Parauapebas, in the Carajás corridor, grew an average of 19% annually.

Marabá grew 8% annually; while Imperatriz, in Maranhão, nearly doubled from 294,000 in 1980 to 456,000 in 1991. In 2000, the BLA population reached 20 million, with 57% urban. In 2010, the census indicated that the population had surpassed 25 million people. The latest estimate indicated 27 million people in 2013 (SUDAM, 2016).

While the population rose, especially in urban areas, deforestation proceeded.

Forest cover disappeared around urban areas. This suggests that land economy is a big influence on forest loss. Deforestation is more likely to occur on high-value land near urban areas (Browder, 2002; Lambin, Geist, & Lepers, 2003; Parry, Day, et al., 2010;

Parry, Peres, Day, & Amaral, 2010; K. C. Seto, Guneralp, & Hutyra, 2012; Karen C Seto

& Shepherd, 2009). This calls for more attention to spatial distribution of the urban population over time.

Spatial Reconfiguration: Theoretical Background for Drivers of Urbanization and Municipal Division

Urbanization is a historical process with many drivers (Kotkin, 2006; Wranghan,

2009). One indication of the rise of new cities is a shift from nomadism to sedentary settlement. The social and physical reconfiguration of space is impelled by the acceleration of population growth and stemmed from the cumulative concentration of people in certain areas. Agglomerations led to a concentration of economic activities and services that characterize cities. In Brazil, a core administrative element of the urbanization process that reflects and reinforces the reorganization of population in space is the creation of new municipalities.

The creation of new municipalities in the Amazon has surged since 1950. In

Brazil, a municipality by definition has one urban center, the administrative seat. If new urban centers emerge, local populations may petition the federal government to be independently recognized as a new municipality. This is how the emergence of new urban centers in the Amazon led to the creation of new municipalities. Since 1950, nearly 500 new municipalities were recognized in the Amazon. In the 1980s alone, 143 new municipalities were created. The urbanization process continued in the 1990s, with

243 additional municipalities recognized. The urban reconfiguration process slowed in the 2000s: only three new municipalities were created in the first decade of the millennium. Overall, in the three decades encompassing 1980-2010, the total number of municipalities increased from 338 to 807 (Figure 2-2; Figure 2-3)..

Drivers of Urbanization in the Amazonian Context

Bertha (2013, p35.) said if cities are dynamic and also represent the core of economic expansion in the region, they have failed to promote development in the

Amazon. For Becker, the dynamic role of cities implies that different economic activities generate positive externalities due to agglomeration and spatial efficiencies, improving the economic and social as well as environmental aspects of the system. However, urban areas in the Amazon are often islands of economic activities that, instead, foster a distorted spatial model of regional development that increases socioeconomic inequalities and reflects unsustainable extraction of natural resources (Becker, 2013).

The detachment from the concept of dynamism by Becker stems from the fact that development in the Amazon is based on an extractive rather than industrial or service economy. Another explanation, as indicated in the previous section, is that many urban centers in the Amazon are young and small and still developing.

Whatever the reasons for new urban centers, their sustainability is directly linked to their capacity to generate jobs and incomes. Industrialization, as a key element of the urbanization process, fulfills those demands in the assumptions of many development theories. Nevertheless, urbanization in the Amazon deviates from the urban-industrial model since the urbanization process is not connected to industrial investments. Even when industrial investments are made, it does not necessarily translate into local jobs.

“Unfortunately, urbanization and industrialization are not occurring in unison in the

Brazilian Amazon” (Simmons et al. 2002).

Recent urbanization in the Amazon is often framed in terms of boomtown models

(Dufour & Piperata, 2004; Isserman, Merrifield, Geography, & Jan, 2016; Miranda

Rocha, 2011; Perz, 2002; Simmons, Perz, Pedlowsky, & Silva, 2002; Thypin-Bermeo &

Godfrey, 2012; Walker & Homma, 1996). Boomtowns arise in areas with new extractive activities, which permit the rapid emergence of new urban populations. The economic

possibilities of exploiting natural resources attract population to less occupied or unoccupied areas. The overall increase in the percentage of urban population escalated from 37% in 1970 to 74% in 2010 (IBGE, 2012), reflecting spontaneous urbanization rather than planned urban growth. Rapid and frequently unplanned urbanization often spawns environmental and socio-economic imbalances. Short-term economic prosperity attracts waves of migrants, transforms areas into urban boomtowns, often generating an environment of contradictions. Mitschein, Miranda, & Paraense (1989) called this

“savage urbanization”.

Spontaneous urbanization has produced unexpected physical and socioeconomic outcomes. Prior studies have considered the matter and analyzed it mostly as an aggregation phenomenon in a regional context. Such analyses assess either isolated cases of population increase in individual cities or regional approaches assuming a homogeneity (Bertha Becker, 2005, 2013; Browder, 2002; Browder &

Godfrey, 1997; Eloy & Brondizio, 2015b; Godfrey & Browder, 1996; Guedes et al., 2009;

Thypin-Bermeo & Godfrey, 2012). Such approaches often fail to account for heterogeneities across the region caused by distinct local drivers.

Cities of the Forest or Forest of the Cities

Because of urbanization, the Amazon can no longer be characterized entirely as forest ecosystems. The Amazon is much more than forest. Now, cities have become a key element of the Amazonian landscape. The forest is now accompanied by urban landscapes, resulting in a more complex system of elements and interactions.

Urbanization necessarily implies a spatial reconfiguration that includes the loss of natural habitats. In the Amazon, “natural” areas mean forest: deforestation is its antithesis. With the current political shifts toward a predatory far-right government

retreating from environmental policies, recent deforestation rates have risen (INPE,

2018). Physical changes in the Amazon threaten the future of the Amazon forest

(Lovejoy & Nobre, 2018). Deforestation is as much a global as a local concern.

Cities in the Amazon are not isolated areas under a dome: they are part of the whole, part of the regional system. Because urban areas reflect extractive economies that damage local ecosystems, urbanization is an environmental concern in the

Amazon. Livelihoods of people in the Amazon affect the forest and are cause for global concern. This makes the future of Amazonian cities locally and globally relevant

(Robertson, 2005).

Heterogeneity in Unplanned Urban Occupation in the Amazon

Inadequate planning and accelerated urbanization affect much of the Amazon.

Becker (Bertha Becker, 2013) called it unhealthy urbanization. Such processes are often presented as a uniform or homogeneous. Creation of new urban areas and rapid urban growth, though common, are not ubiquitous.

Heterogeneity is present in the urbanization of the Amazon because of different intensities of triggers. Location, infrastructure investments and migration are among the key drivers that produce differences in the urbanization process and how the space is occupied. Perceiving such different outcomes allows for comprehending urbanization as reconfiguring the Amazon at different intensities. This also allows us to trace a more accurate profile of triggers and consequences of urbanization.

Shifting from the assumption that the Amazon is homogenous socially and spatially processes and patterns to a heterogenous approach, allows to identifying degrees of interactions and interdependence among cities under sub-regional geopolitical divisions. When Godfrey and Browder (Browder & Godfrey, 1997; Godfrey,

1990; Godfrey & Browder, 1996) focused on heterogeneity as one of the most important characteristics of urban Amazon regional disaggregation, their work still focused on processes at an aggregated level without considering the interactions among cities in networks. Bertha Becker also pursues analysis at an aggregated scale when regarding the Amazon as an urban forest (Bertha Becker, 1990, 2005, 2013). Furthermore, in most cases, when a system of Amazon cities is considered, they are often framed as isolated spatial entities rather than interdependent elements (Bertha Becker, 1990,

2013; Browder & Godfrey, 1997; Mitschein et al., 1989).

Urbanization at a local scale in an individual city involves the in-migration of people in response to urban-based job growth. The classic pattern of rural-urban migration has long affected countries in the global South. This pattern reflects the desire of individuals to leave lower-paying jobs in agriculture to pursue higher-paying jobs in manufacturing. This also reflected in the lower availability of rural jobs with the increase in mechanization in the agriculture chain (Perz, 2000; Vining Jr., 1986). In the case of

Brazil, from 1980 to 2010, the rural population decreased from 32 to15% of the total.

The rural exodus amounted to some 47% of the rural population, despite a 70 million person increase in the country’s population during the same period (Censo Demográfico

2010, 2012). Present global dynamics show the same spatial pattern: increasing urbanization, with urban populations of between 75 to 90% in coming decades (DESA,

2018). Less-developed regions will undergo more urbanization than developed nations

(United Nations, 2014). The rural exodus from agriculture is well documented for the case of Brazil (Barbieri et al., 2007; Carr, 2009; Perz, 2000; Simmons et al., 2002).

Historically, governments considered relationships among cities in planning for occupation of the Amazon. The military’s development plans prioritized the opening of road to foster the creation of networks of towns along those routes. Such plans only transpired in the States of Pará and Rondônia. But the plans were based on

Christaller’s Central Place theory (Christaller, 1966). His theory viewed systems of cities connected in a network by roads and organized in an urban hierarchy. On that theoretical account, and in the military’s plans, towns would be interdependent along roads like the Transamazon Highway.

On Christaller’s account, the system of cities comprised a three-level hierarchy.

Cities and towns were organized in a uniform spatial pattern described as hexagonal

(Rego, 2016). In Brazil’s military plan for the Amazon, the rurópolis was to be the top of the pyramid and the dominant development pole. This major urban center had satellite settlements and a greater concentration of public services and permanent housing for approximately 1000 families. The next level was the agrópolis, smaller towns of around

300 families, allowing for agricultural production, markets, and storage. The “satellites of the satellites,” the agrovilas, were the smallest units of the system, each with roughly 50 families. Both agrovilas and agrópolis were entirely dependent on their rurópolis

(Brondizio, Moran, Mausel, & Wu, 1994; Moran, 1981; Rego, 2015; Smith, 1982;

Walker, Perz, Arima, & Simmons, 2011).

Although incompletely executed, the plan helped define urban centers along the

Transamazon Highway. Those towns ultimately transformed the spatial configuration of the Transamazon corridor and thus a portion of the Amazon region. The Amazon

experienced notable population and landscape changes related to implementation of these Christaller-style settlements (Walker et al., 2011).

The static Christaller concept, as applied in the Amazon, must be made dynamic to address the growth of urban towns in networks. Despite the creation of new urban clusters, military planning failed partly because it did not address the dynamics of urban network evolution. Urban centers grew at different rates and resulted in a different network of towns than originally planned.

To help understand the Amazon urban system, this study examines whether there is a correlation between the hydropower network and urbanization rates among nearby urban centers in the Amazon. The focus is to overcome the limitations of previous research that tended to consider urban centers in the Amazon as independent and static entities.

Hydropower as a Driver of Urbanization

The Amazon’s current development model relied on neoliberal policies using infrastructure projects and market forces driving development. The rise in large-scale energy production from hydropower dams was based on the abundant hydrological capacity of the region. In conjunction, the government framework linked the notions of development and exploration of the Amazon to increased economic activity based on exploitation of natural resources. Despite a recent decrease in Brazil’s hydropower energy use (to 62% from over 80%) in past decades, the energy matrix is still heavily supported by hydropower. The rising of hydropower infrastructure to support this matrix, has rarely been studied as a complete system as analyses are usually confined to directly impacted areas, and rarely broadened to include indirect impacts of the energy system such as the consequences of transmission lines and distribution nodes (Little,

2014). Indirect impacts also extend to the influence of hydropower infrastructure on urban growth. I therefore raise the question of the impact of hydropower infrastructure on urban growth among localities across the Amazon as a region. I thus consider hydropower infrastructure as a driver of urbanization in the form of increased urban population in existing urban centers.

Scholars have long pointed to infrastructure investment as essential to regional development, with implications for urbanization (Spit 1999; Geist and Lambin 2002;

WCD 2000; Becker 1989; Barber et al. 2014; Walker et al. 2017; Southworth, Munroe, and Nagendra 2004; Richards and VanWey 2015; Brondizio and Moran 2012; Browder

2002). However, for the Amazon, infrastructure has been addressed primarily in the form of roads as a driver of land cover change and linked to deforestation (Aldrich,

Walker, Simmons, Caldas, & Perz, 2012; Barber et al., 2014). Roads make the forest accessible to migrant farmers, who follow the roads to occupy land and clear the forest

(Barber et al., 2014; Brondizio & Moran, 2012; Garcia et al., 2007; Geist & Lambin,

2002; Godar et al., 2012; Rudel, Defries, Asner, & Laurance, 2009; Walker, 2004;

Walker et al., 2017; Wood & Porro, 2002).

The industrial revolution could have happened without an increase in the energy supply (Jones, 2016; Wrigley, 2010). Urbanization, development, and energy access are thus a triad in development planning. Access to electricity allows the process of industrialization to accelerate. Developed countries were successful in promoting industrialization as the base for their economic growth. There is an on-going debate about the mix of energy sources, and whether less-developed nations should follow the current model in order to achieve economic and social progress (Almeida Prado et al.,

2016; Moore, Dore, & Gyawali, 2010; Pinto, 2012). That implies a vital link between electricity and the capacity to expand industry as development. In the Amazon context, energy for development implies the expansion of hydropower infrastructure.

Meeting energy demand serves as a starting point for massive infrastructure projects across the Amazon. Finding alternatives to fossil fuel energy after the petroleum crisis in 1973 became part of the Brazilian government’s plan for developing the Amazon. The military and governments since have generally viewed the Amazon as a vast fount of raw materials, including the giant hydrological potential of its abundant rivers (WCD, 2000).

Infrastructure projects have promoted deforestation and changes in land use

(Walker, Perz, Caldas, & Silva, 2002). In the Amazon, roads, hydropower dams, and other infrastructure projects have attracted people from rural areas into urban areas.

Linking public investment to demographic impact is increasingly important with the emergence of the Initiative to Integrate the Regional Infrastructure of South America

(IIRSA) in the early 2000s. The IIRSA gave rise to COSIPLAN (the South American

Infrastructure and Planning Council), part of the intergovernmental continental union of twelve South American Nations (UNASUL) (Castelar, 2015; Timothy J. Killeen, 2007;

Verdum & Carvalho, 2006). One of IIRSA’s main objectives for the Amazon is to create an integrate the regional transportation system by integrating waterways, railroads, roads, ports, and hydropower plants. For the entirety of South America, there are 177 dams planned, 241 sited, and 210 inventoried (IIRSA, 2011). In the Amazon, major basins have already been dammed in the past decade as a result of totals (Fearnside &

Millikan, 2015; Verdum & Carvalho, 2006; Wanderley et al., 2007). Investments in the infrastructure system under IIRSA imply a significant impact on urban growth.

This Chapter 2 aims at investigating answering the questions of whether impacts of hydropower on urbanization are homogenous or heterogenous.

Methods and Data

Data

This study utilized a regional database for the BLA with socioeconomic (Moretto,

2016) and spatial data. All socioeconomic data were drawn from the dataset of the project ‘Performances de Desenvolvimento dos Municípios Brasileiros Afetados por

Usinas Hidrelétricas’ (Development performance of Brazilian municipalities affected by hydropower plants) (Moretto, 2016). The dataset used in the statistical regression contains total and urban populations for 771 municipalities in BLA for the years 1991,

2000, and 2010. This data set also provided the categorical variable used in the model to differentiate municipalities affected by dams. This classification from the Aneel

(ANEEL, 2001) guides environmental compensation for the municipalities, in the form of royalties. Aneel sets physical connection to the hydropower reservoir as the condition for a municipality to be considered affected by dams.

Spatial data consists of shapefiles for Amazon divisions: municipalities shapefiles; Eastern and Western Amazon; latitude and longitude for municipal headquarters; and layers for the hydropower system with dams, transmission lines and substations (IBGE, 2019b). Data for latitude and longitude for each municipality was grouped regional quadrants. The distinction permitted a comparative analysis of urban growth rates among distinct regions.

A map with the statistical results and geographical spatial data provided for the visual interpretation of results.

Variables of the Model

Variables used in the analysis are listed in Table 2-1.

Outcome variable

Based on the adopted definition of urbanization as the percentage of urban population, I selected it as the dependent variable. Models were tested with urban population percentage for 1991, 2000, and 2010. I explored spatial models considering as dependent variable the increase of urban percentage, the absolute urban percentage, the percentage of urban versus total population, and the urban population growth rate from previous years. The outcome variable in the final model corresponds to urban population by total population in 2010, divided by 1,000.

Dummy variable

The dummy variable is static for all periods because of the original dataset. It indicates connection to the hydropower system through the reservoir area and it is represented by “hydro”. However, I acknowledge that there were changes across time, as new infrastructure such as substation and transmission lines were built since 1991.

Small- and large-scale hydropower dams were also built, such as the Belo Monte, in the state of Pará. The constant value of the hydro variable presents a limitation to designing an accurate spatiotemporal profile of changes. Conversely, because of limited spatial data, I opted for considering the dummy variable as a static component along with the analysis.

Independent variables

The independent variables used in the final model consist of total and urban population in 2000, and the spatial interaction between quadrants and categorical variable “hydro”. The final model stands for Urban Population 2010 = Hydro*Quadrant +

Total Population 2000 + Urban Population 2000.

Latitude and longitude

Latitude and longitude for each municipality corresponds to the location of the municipal urban center, according to the Brazilian Institute of Geography and Statistics

(IBGE, 2012). Despite the large range of municipalities areas, coordinates for urban headquarters tends to concentrate urban population, serving as a reasonable proxy for its location. Latitude and longitude were used as the spatial component for the explanatories and final models. Initially “hydro” was used to generate a spatial.

However, possibly because of the refined of the scale and large number of municipalities, results were not satisfactory. Segmenting municipalities in based on latitude and longitude quadrants improved the model.

Quadrants

I divided the BLA into four quadrants considering Latitude -5.354386 and

Longitude -56.092721. Such quadrants were established based on official division of

Amazon Oriental and Occidental, or Eastern and Western (Brasil, 1967, 1968). West includes the states of Amazonas, Acre, Rondônia, and Roraima. East includes the states of Pará, Maranhão, Amapá, Tocantins, and Mato Grosso. Given the physical impossibility of defining quadrants by state boundaries, I aimed at a more refined scale to identify impacts in different subregions. I visually delimited the longitude from north to

south guided by the one point that correspond to state of Pará’s central-west point

(Figure 3-4).

Results and Discussion

Model Fitness

The dummy variable “hydro” is exogenous, not changed by other variables. The total population and urban populations for the previous year are also endogenous.

However, because the units of observation are spatial units, spatial autocorrelation is likely among values of the observations. Because of that, OLS is not efficient to provide consistent parameter estimators (Wooldridge, 2015). Assuming spatial correlation, I started by exploring simple and spatial regression in two different sets, considering all

771 units of analysis. Exploratory models were divided into two sets of dependent variables: urban population increase and urban population percentage. Models were tested using three time periods, for 1991-2000, 2000-2010, and 1991-2010. Both sets of models showed better results for the overall period 1991 to 2010 and using urban population percentage as the outcome variable. During the exploratory process, I also performed a time-series analysis considering the years as 1, 2, 3. The model using the panel data could not be improved because of inflated autocorrelation in the error terms. Autocorrelation was most likely due to the static component “hydro.”

The second stage consisted of taking a step back to reassess the available data.

Because there were missing data for some municipalities (mostly in 1991), the data set was reconfigured to eliminate 189 municipalities. Data tested positive for autocorrelation using Moran’s I. Given the inconclusive results of latitude and longitude from previous models, I divided the area into quadrants: Northeast, Southeast, Southwest, and

Northwest. Urban and total population data for all years were divided by 1,000 to

facilitate the reading of impact. Throughout the model, an increase or decrease in population data correspond to 1,000 thousand people as a baseline. I checked for extreme outliers and used a histogram test with the outcome variable to confirm a normal data distribution, with no need for additional data normalization.

The characteristic of data with three distinct periods implied autocorrelation among population variables. Given the spatial component of municipal headquarters and the categorical variable for the hydropower system, I chose to run a spatial autoregressive model (SAR). SAR provides methods for fitting models using spatial data. It fits linear models with autoregressive errors and spatial lags of the dependent and independent variables. Thus, SAR makes it possible to determine effects of the values of neighboring spatial units on outcomes in a given spatial unit (StataCorp,

2017). The goal is to test for the spillover effects of hydropower across municipalities in the Amazon and how they behave spatially, through a criterion of proximity (Álvarez,

Barbero, & Zofío, 2016).

The SAR models use autoregressive panel data analysis to include spatially lagged dependent and independent variables to capture spatial interdependence, following the Le Sage approach (LeSage & Pace, 2009). The model was derived from

SAR 푦 = 푊푦 +  +  (LeSage & Pace, 2009). (2-1)

Parameters for estimating effects of the independent variables in the model are

β. W is the spatial weighting matrix and the spatial analog of Ly (Stata, 2017). Ly measures possible increase in urban population from time t-1 to t, whereas Wi1 and i2 measure possible increase in urban population from area i2 to i1 (Stata, 2017). In the mathematics of SAR models, Wy is the lag of the dependent variable, whereas Wxj is

the lag of the independent variable (Stata, 2017, pg.). In this study, Wy was added to the model: βW measured how much the outcomes are affected by nearby outcomes

(Stata, 2017).

According to in spatial statistics, data should be normally distributed, so the assumption of normality was tested by measuring kurtosis and skewness. Kurtosis and skewness for "latitude," "longitude," and “hydropower” exhibit low values, and are thus normally distributed. Eliminating units with zero values for urban population caused distribution of urban populations to also show as normal.

Model fitness was evaluated using indicators of maximum likelihood (ML).

Results for Akaike information Criterion to estimate the predictor error and improve the model, and Bayesian information criterion (BIC) helped to move towards the best models as AIC and BIC gradually improving. The model worked at a 95% confidence interval. A sample of interactions and the improvements in outcomes are shown in

(Table 2-2). Final model consisted of the geographically weighted two-stage least square of urban percentage population in 2010 as a function of the interaction between hydropower and quadrant, total population in 2000 and urban population in 2000 (Table

2-3).

In exploratory analysis considering all years, I had to drop units with empty values. This resulted in a sample of 582 municipalities. However, the best model pointed to the timeframe of data from 2000 and 2010, when most municipalities had values for. I then rerun the model, excluding only municipalities with empty values for these years. The best model sample totals 762 municipal units.

Spatial regression model allows for the assessment of direct and indirect effects.

Indirect affects stand for spillovers. Northeast was set up the base category, as the comparison analysis requires a starting point.

The analysis presented a limitation of insignificant p-values the explanatory variables for direct and total impact in Southwest and Northwest. All variables were nonsignificant for the spillover effect. This means that for the municipalities with hydropower, proximity to other municipalities does not affect the urbanization percentage. Direct impacts of hydropower projects point to a 4.8% increase in urban population percentage for the year 2010 for every 1,000 people. This means that the urban population percentages in municipalities considered affected by dams were higher when compared to municipalities considered to be unaffected. Considering the direct impacts by quadrant, and Northeast as the reference category, Southeast shows

9% increase. The insignificant p-values for Southwest and Northwest overall show these variables cannot be explained by the current model. An insignificant result might be because this region presents the smaller number of municipalities Northwest corresponds to 8% of the sample and Southwest to 19%. In the case of Northwest, the hydropower elements are visually less than the other regions, and since spillover is not registered, it is not possible to measure the effect of proximity to other municipalities affected by hydropower. In Northwest, only nine municipalities are considered affected. by a dam.

Direct impact shows that there is a 9% increase in urban population percentage in the Southwest if compared to the Northeast. In terms of population units, it means 90 more people for each 1,000 people. The impact of hydropower over total population

shows a decrease of 3% per each 1,000 people. Meaning the higher total population in

2000, the smaller the increase in urban population in 2010. With the general high percentage of urbanization in the Amazon, one could consider the urban population percentage tend to present smaller increase rates in areas with a higher urban population. And municipalities with a lower total population tend to present higher increase in 2010. Urban population in 2010 is 0.4% higher as a function of urban population in 2000.

As previously stated, the indirect impacts were non-significant. Therefore, there is no substantial change in the total impact. Total impact reproduces the direct overall impact of 4,8%; and urban population in Southeast is also the same as direct impact

(9%). Similar results are seen for impacts of urban and total population in 2000.

The most significant direct impact occurred in the Southwest region, with the largest percentage of municipalities considered as affected by a dam (52%). Finally, the model indicates a negative impact of the total population, which exhibits a small coefficient for the urban population in 2000.

Indirect effects are the spillover. Overall, they were not significant, so the model suggests no spatial spillover effects. The model indicates that the presence of hydropower projects results in a 5.63% higher urban population percentage. Again, p value for Northwest was nonsignificant. The Southeast presented the highest total impact. Again, northwest had nonsignificant p-values. There was a negative impact of

“hydro” over Total Population in 2000 and a positive, nonetheless low value of (4%) for population in 2000 (Table 2-3). This explains a possible decrease in the total population versus an increase in the urban percentage.

The marginal coefficients provide the margins of response for specified values of covariate. Marginal coefficients for quadrants without considering the hydropower variable placed the Southeast as the higher value (63%), followed by Southwest (56%),

Northeast (54%), and Northwest (51%). Again, the Southeast presents the most significant effect on urban population in 2010. This region concentrates large mining ventures, agricultural and cattle industry, and, the highest deforestation areas.

Based on the marginal prediction, the unit of analysis is 1,000 people. For every

1,000 people increase, the average impact on urban population is 56% on municipalities not affected by hydropower, and 60% on municipalities affected. A difference of less than 1 percent. Predictions for the interaction between hydropower and quadrants show that Northeast has the highest difference. In the Northeast, hydropower impact on urban population for municipalities affected by hydropower is 59%, while 51% in those not affected. model, regarding the overall impact of hydropower by the quadrant, hydropower had a 64% impact on the urban population in 2010 on municipalities considered affected; and 58.8% in municipalities not affected by the hydropower system. The presence of hydropower infrastructure, placing a municipality under the category of affected, and, therefore, exhibiting a positive value of 1 as a binary categorical variable, explains the 5.2% increase of urban population in 2010 if compared to a zero value “hydro."

Combining the interaction of categorical variable and quadrant, when comparing the marginal coefficients of municipalities not affected versus affected the Northeast presents the larger difference (8%), although not the higher percentage of impact on urban population. Southeast presents the larger impacts, but the lower difference

between them (3,5%). Difference in impact on urban population decreases from 8 to 6% in the Southeast. Southwest presents the second-high difference in marginal coefficients (6%), meaning there is less difference on urban population impact between municipalities considered affected or not. This still means the effect of hydropower infrastructure in this region is felt stronger on areas considered affected by the dams.

The Northeast presented a stronger significance in hydropower impacts. Conversely, it also presents the following smaller percentage (the same as the least significant region of Northwest) of municipalities considered affected by the hydropower system: 15%.

One explanation could be the influence of the state capital of Pará, Belém, as a node for

Amazon activities. The main destination of energy produced in other regions.

The Southeast region that, with some of the most relevant results in previous models, was interesting because of municipalities affected or not affected by hydropower. The difference of impact on the urban population was less than 1%.

Nonetheless, 40% of municipalities (84 of 210) are considered impacted by the dams.

The Southwest region showed the highest impact of hydropower on urban populations (68%), with 5.4% difference in the coefficient of impact. It also has the highest percentage of municipalities considered impacted (59 of 113). The large-scale dams of Jirau and Santo Antonio started to operate in 2013 in the city of Porto Velho, in the State of Rondônia in 2014. A few miles away, however, construction began in 2008 and 2009, which might explain the highest impact on urban population in 2010. This area also has a higher percentage of municipalities affected by the hydropower system

(52%).

The Northwest is still the most isolated and preserved area in the BLA. It also has the lowest number of municipalities (61) and the smallest percentage of impacted municipalities. The Northwest presented the only negative impact of hydro if considered municipalities not affected by the system. This means that urban population tend to decrease in municipalities that are not affected by dams. Urbanization impact is higher in municipalities not affected (56%) versus affected (53%). The proximity of the two mega-dams probably attracted people to other urban centers, serving as a driver of intra and intermigration.

Concluding Remarks

Overall, there is broad agreement on the importance of increasing in urbanization in the Amazon. That said, we do not yet fully understand the drivers of urbanization in the Amazon. We tend to view it as a homogeneous process occurring the same way throughout the region (Guedes et al., 2009; Thypin-Bermeo & Godfrey, 2012; Trindade,

2013). Alternatively, previous work examined urbanization in single towns, which fails to scale up or consider interactions with drivers or among urban centers (Cardoso, Negão,

& Pereira, 2012; Carmo & Costa, 2016; Mitschein et al., 1989; Monte-Mór, 2015). This study presented a regional analysis considering the municipal level and the impacts of a key driver as a stimulus of the urbanization process. This approach permits observation of local heterogeneity within the regional context while examining the effect of hydropower systems as a driver of urbanization.

The foregoing analysis showed a strong relationship between the hydropower system and urbanization. In municipalities where a hydropower project is near the urban center, urban population percentages are higher. Further, the analysis confirmed sub- regional spatial variations in urbanization, such that urbanization is more accentuated in

some quadrants than others. These findings expand on understanding of urbanization in the Amazon in terms of the impact of hydropower production across the region (Júnior,

2013a). These findings expand on the call for more discussion of impacts of the hydropower system on urbanization, including areas that are not directly affected by the reservoir.

This study indicates an overall direct impact of hydropower on urban population percentages. Municipalities with nearby hydropower facilities have an urban population percentage 5.2% higher than those without a nearby hydropower facility. In this sense, infrastructure development impels urbanization.

Most notably, this is the first study to investigate how urbanization is taking place in the Amazon, at the municipal level, and identifying cross-scale urbanization as a possible outcome of the hydropower system. Such a scenario is especially worrisome in light of setbacks in environmental policies and loosening of licensing process recently put in practice by Brazil’s extreme right-wing government.

At the same time, Brazil seems to be on the verge of another economic crisis.

The past shows that the last severe economic recession, which occurred in the 1980s, caused a steep population influx into urban areas. If that pattern persists under the possible future scenario of another economic crisis, urbanization in the Amazon will continue to increase. This raises the question of whether hydropower projects will continue to go forward. If they do, they could assume a yet stronger role as a driver of where urbanization occurs in the Amazon. Under this scenario, urbanization in the

Amazon tends to be the outcome of some key drivers like infrastructure, and the urbanization-forestation symbiosis will most likely continue to diminish.

However, some limitations are worth noting The sample size had to be reduced due to the lack of data on urban population for municipalities in 1991. In addition, the static treatment of the hydropower variable may create bias in the observed effects, since hydropower projects are implemented over time. Future work should aim for a more comprehensive time series analysis, also considering the year of municipal creation, the year of new hydropower infrastructure and other important spatial variables such as the municipalities’ area.

Figure 2-1. Brazilian Legal Amazon and Western and Eastern Amazon

Figure 2-2. New municipalities created in the Brazilian Amazon per Decade 1950-1990

Figure 2-3. Total of Municipalities in Brazilian Amazon per decade 1980-2010

Figure 2-4. Brazilian Amazon by quadrants

Figure 2-5. Urbanization and hydropower impact by quadrant in BLA

Table 2-1. Variables in the models Variables Descriptive Independent Quadrant Latitude -5.354386, Longitude -56.092721 Hydro Categorical variable to indicate the hydropower system Total population 1991th Total population by thousand 1991 Total population 2000th Total population by thousand 2000 Total population 2010th Total population by thousand 2010 Urban population 1991th Urban population by thousand 1991 Urban population 2000th Urban population by thousand 2000 Urban population 2010th Urban population by thousand 2010 Dependent Urban Population 2010 Percentage of urban population in 2010

Table 2-2. Model selection sample equations Model Independent variable Independent Dependent AIC BIC Impact spillover spillover of Hydro variable variable % 1 Hydro -279.1606 -266.0612 9.43 2 Hydro Hydro Yes -308.4768 -286.6444 3.23 3 Hydro*Quadrant Hydro Yes -330.8325 -278.4348 6.20 Quadrant 4 Hydro*Quadrant Yes -392.9729 -344.9417 5.87 Urban population 1991th 5 Hydro*Quadrant Yes -400.7731 -352.009 5.17 Total Population 1991th Urban population 1991th Urban Population 2000th

6 Hydro*Quadrant Yes -410.0561 -348.9256 5.29 Total Population 1991th Total Population 2000th Urban population 1991th Urban population 2000th

7 Hydro*Quadrant Yes -411.8314 -355.0673 5.37 Total Population 1991th Total Population 2000th Urban population 2000th

8 Hydro*Quadrant Yes -411.6904 -359.2928 5.64 Total Population 2000th Urban population 2000th

9 Hydro*Quadrant Yes -459.3711 -380.7746 5.21 Total Population 2000th *Quadrant Urban population 2000th Urban population 2000th *Quadrant

Table 2-3. Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 Variables Coef. Std. Err. z P>|z| [95% Conf Interval] Urban Population 2010 Hydro 1 . 0477288 .0153119 3.12 0.002* . 017718 . 0777395 Quadrant Southeast .093591 . 0170557 5.49 0.000* . 0601625 . 1270195 Southwest .0257534 . 0233308 1.10 0.270 -.0199742 .071481 Northwest -.0244451 . 0308642 -0.79 0.428 -.0849378 .0360475 Totpop_2000_th -.0034745 .0007841 -4.43 0.000* -.0050113 -.0019376 Urbpop_2000_th .0042423 .0008001 5.30 0.000* .0026741 .0058104 Indirect Hydro 1 .0005233 .0024966 0.21 0.834 -.0043699 .0054165 Quadrant Southeast .0009241 .0043774 0.21 0.833 -.0076555 .0095037 Southwest .000256 .0013437 0.19 0.833 -.0023776 .0028895 Northwest -.0002156 .0009085 -0.24 0.812 -.0019962 .001565 Totpop_2000_th -.000034 .0001599 -0.21 0.832 -.0003474 .0002794 Urbpop_2000_th .0000415 .0001954 0.21 0.832 -.0003414 .0004244 Total Hydro 1 .0482521 .0157963 3.05 0.002* .0172919 .0792122 Quadrant Southeast .0945151 .0178287 5.30 0.000* .0595715 .1294587 Southwest .0260094 .0242013 1.07 0.283 -.0214243 .073443 Northwest -.0246607 .0306825 -0.80 0.422 -.0847974 .0354759 Total Population 2000th -.0035085 .0007862 -4.46 0.000* -.0050494 -.0019675 Urban Population 2000th .0042838 .0008052 5.32 0.000* .0027055 .005862 Notes. * p <0.05.

Table 2-4. Marginal prediction of interaction term hydropower and quadrant Margin Std. Err. z P>|z| [95% Conf Interval] Hydro 0 .5584817 .0079011 70.68 0.000* .5429957 .5739676 1 .6067337 .0136306 44.51 0.000* .5800184 .6334491

Quadrant Northeast .5355096 .0133355 40.16 0.000* .5093725 .5616467 Southeast .6300247 .0109683 57.44 0.000* .6085272 .6515223 Southwest .561519 .0179661 31.25 0.000* .5263062 .5967318 Northwest .5108489 .0261645 19.52 0.000* .4595674 .5621303

Hydro#quadrant 0#Northeast .5093633 .0147536 34.52 0.000* .4804467 .5382798 0#Southeast .6192508 .0136978 45.21 0.000* .5924036 .6460981 0#Southwest .5427162 .0229726 23.62 0.000* .4976908 .5877415 0#Northwest .5280425 .0252604 20.90 0.000* .4785329 .577552 1#Northeast .592641 .0287839 20.59 0.000* .5362256 .6490565 1#Southeast .6535664 .0169134 38.64 0.000* .6204168 .6867161 1#Southwest .6026043 .021337 28.24 0.000* .5607845 .6444242 1#Northwest .4732797 .0601274 7.87 0.000* .3554322 .5911272 Notes. * p <0.05.

Table 2-5. Margins coefficient, municipalities and quadrants Hydro and Hydro (1) Hydro (0) Difference (%) Total Positive Hydro/ Municipalities Quadrant Total Municipalities Affected (%) Northeast .592641 .5093633 8 39/263 15 Southeast .6535664 .6192508 3.5 113/285 40 Southwest .6026043 .5427162 6 79/148 53 Northwest .4732797 .5280425 -5.5 9/59 15

CHAPTER 3 HYDROPOWER INFRASTRUCTURE AND STATE-SCALE URBANIZATION

There is a growing consensus that understanding the urban phenomena in the

Amazon is vital to tracing its physical and social changing (Bertha Becker, 2013;

Brondızio,́ 2016; Eloy & Brondizio, 2015a; Trindade, 2013). Within a state boundary, the creation of new municipalities poses as a significant indicator of the urbanization process in practice and acts as driver and echo of such changes. A lack of planning and an accelerated urbanization process combine to drive the complex expansion of the urbanization of the Brazilian Amazon. Accentuated urbanization that includes over 70 percent of the population and the creation of new, largely unplanned, and in most cases, spontaneous urban areas, have propelled the emergence of an intricate system of cities that defies typical hierarchical patterns (Sathler, Monte-Mór, Carvalho, & Costa,

2010), establishing additional layers of interaction within sub-state divisions. In addition, this urban network is deeply related to physical and environmental changes at scales from the local to global. In the state of Pará, the rise of urban clusters has contributed to reshaping the geography of the Amazon region, expanding urban areas while webbing their complex interaction, shrinking the forest frontier, where 60 new municipalities were created since 1980.

Not coincidently, the rise in new municipalities happens in parallel with major infrastructure projects aiming to bring economic development that include, but are not limited to, dams and their network elements. The elements of the energy grid system, in particular hydropower stations, substations and transmission lines have been shown to be major sources of environmental, socio and economic changes (de Sousa Júnior &

Reid, 2010; Fearnside, 1999; Latrubesse et al., 2017). However, the impacts of

hydropower production and transmission on urbanization seem to be limited by their direct proximity to reservoirs, as in Tucuruí Hydropower Plant and surrounding areas

(Castro, 2009; Jatobá Caramelo & Faria Cidade, 2012; Miranda Rocha, 2011). That direct connection between urbanization and the proximity of reservoirs and hydropower plants provides a stronger and well-established impacts makes direct impact a fact. Yet, as a state comprehends a system of interactions among its elements, municipalities in this particular case, chances are that impacts of dams’ infrastructure are probably perceived as spillovers on the entire system, rather than restricted to direct connection.

This Chapter 3 thus aims to extend the conceptualization of impacts to investigate the links between the hydropower system and increased urbanization increase across the municipalities of Pará by considering all elements of the hydropower system as drivers of change, from 1980 to 2010. The main premise of this argument is that if a state can be considered as a system of municipalities, then the elements of the hydropower network will have an impact beyond direct spatial connections.

Roads are linked to deforestation and urbanization (Barber et al., 2014; Browder

& Godfrey, 1990; Heitor Pellegrina, 2014; Kaimowit, 2002; Perz, 2002). There is a consensus that hydropower systems create environmental disturbances that contribute to the loss and degradation of natural ecosystems (Finer & Jenkins, 2012; Latrubesse et al., 2017; Nowak et al., 2000). However, the impact of these hydropower systems is neither defined nor quantified (Almeida Prado et al., 2016; de Sousa Júnior & Reid,

2010; Hyde, Bohlman, & Valle, 2018; Lees, Peres, Fearnside, Schneider, & Zuanon,

2016). Many of these impacts are undercounted even in official reports such as the

Environmental Impact Assessment (EIA) (Almeida Prado et al., 2016; Fearnside, 2005;

Laurance, 2018; Little, 2014; Moore et al., 2010).

Among the impacts of dams, urbanization is recognized, but linked almost exclusively to direct impacts in the areas surrounding the reservoir (UNPD, 2012). More recently, teleconnections between dams and urbanization were identified, showing distal connections and relationships are not limited by spatial contiguity (Karen C Seto et al., 2012). Despite these findings, much of the urbanization impact of dams is limited to areas surrounding the hydropower plant, as in the Tucuruí and Belo Monte dams in

Pará (Costa & Pires, 2015; Danilo, Montoya, Maria, Lima, & Adami, 2018; Miranda

Rocha, 2011; Richards & VanWey, 2015; Santos, 2017). Recent studies on Tucuruí

Dam’s impact in Barcarena, a municipality 300 kilometers from the hydro plant, suggest revising the hydropower impacts’ framework (da Trindade Jr, 2005; Fearnside, 2001,

2016; Monteiro & Monteiro, 2007). Notably, Tucuruí Dam is the main source of energy for the aluminum production industry in Barcarena.

Focusing on hydropower system impacts on urbanization in the area directly affected by the reservoir prevents a holistic assessment of regional effects. Despite increased interest in urban phenomena and their drivers, in particular the social and physical impacts linking dams to the urbanization (Caravaggio, Costantini, Iorio, Monni,

& Paglialunga, 2017; Machado, 1999; Roscoche & Vallerius, 2014), no study has examined urbanization and hydropower systems at a subnational scale in Pará, or any other Brazilian Amazon state. Thus, the purpose of this research is to: 1) provide an account for the main factors that led the overall urbanization process in the state of

Pará, accounting for the main social, political, and historical trajectories that led to the

present urban setting; and 2) investigate the relation of increased urbanization from

1980 to 2010 through the lens of the hydropower system.

I begin by providing the theoretical ground for addressing urbanization in the

Amazon. Next, I introduce the study area of Pará and provided an historical account of the urbanization process there. Afterward, I introduced the context of the Tucuruí hydropower plant, and provide a scientific literature review to support a broader understanding of the impacts of hydropower on urbanization, across municipal units.

These steps contribute the main establish a profile background to address urbanization and hydropower impacts in Pará, that are analyzed with statistical and spatial analysis relating the hydropower infrastructure and urbanization in a spatial-temporal assessment.

Theoretical Framework

Urbanization at the city level is generally assumed to result from an influx of population and an increase in economic activity (Alonso, 1977; Ana Claudia Duarte

Cardoso & Miranda, 2018; Lefebvre, Bononno, & Smith, 2003). The recent Amazon urbanization is commonly described as the “boomtown phenomenon” (Browder &

Godfrey, 1997; Thypin-Bermeo & Godfrey, 2012); a steep increase in economic activity, usually linked to access to natural resources. Economic possibilities of exploiting these resources attracts an intense influx of population to less occupied or unoccupied areas.

Considering the population increase overall, with an even more significant increase across urban populations and the creation of new urban areas, urban expansion in the

Amazon has proceeded at a rapid pace, producing unexpected outcomes and often resulting in social and environmental vulnerability (Mansur et al., 2016; Mitschein et al.,

1989; Parry, Day, et al., 2010; Trindade, 2013). Previous studies aiming to explain

urbanization in the Amazon, usually from a regional approach, often analyze a group of towns within and across states, rather than assessing urbanization in individual cities

(Bertha Becker, 2005, 2013; Browder & Godfrey, 1997; Thypin-Bermeo & Godfrey,

2012).

Recent theories, however, have focused on cumulative causation leading to urban agglomerates. Urbanization can be a product of economic relationships such as cumulative causations and their self-reinforcing collective performance and linkages from agglomeration and labor migration (Godfrey, 1990; Krugman, 1990). Assuming the territory is an open system, receiving and sending inputs, I consider the state division to be the system and the municipalities in it, the elements. Infrastructure investment generally has been linked to increased urbanization (Spit, 1999). It has also been linked to changes in Amazon land cover, primarily deforestation (Aldrich et al., 2012;

Barber et al., 2014). Planners and other scholars have identified this role of infrastructure investments as key drivers of urbanization across aggregated and disaggregated scales (Barber et al., 2014; Eloy & Brondizio, 2015a; Godfrey & Browder,

1996). Nonetheless, there are still gaps in assessing urbanization as a function of hydropower inputs in an aggregated form.

Using an aggregated scale, I aim to help fill the gap in aggregated urbanization assessment, adding to the investigation the inductor role of the hydropower networks’ physical components, including transmission lines and substations in the state of Pará. I then aim to expand the notion of hydropower network impacts on urbanization to the entire state system of cities. The current model defines municipalities as affected by dams based on physical proximity to the hydropower plant and reservoir. By moving

beyond this conceptualization, I argue that these impacts can be discerned assessing the impacts not necessarily linked to the system by a physical thread of elements.

Conceptualizing a cumulative causation framework, I assume that a new element in the system will affect all other elements. This is not to limit the urbanization to a single driver. Urbanization is therefore stimulated by different elements, including natural population increase. This study framed hydropower network infrastructure as the element triggering changes that lead to increase in urbanization.

Seeing space as socially produced (Lefebvre, 1991) the Amazon is reconfigured as urban: a product of the complex interaction of public policies designed for economic development. In this scenario, the State is the main driver of this social reconfiguration, pushing changes in spatial practices, perceptions, and values. The global phenomena must, therefore, be translated into a local context to be understood.

As the Amazon space is reconfigured as urban, space is socially produced. As a social product, space assumes the complexities of interactions that shape and categorize its form. The Government acts as the main inductor of spatial reconfiguration

(Lefebvre, 1991). Through public policies, the Government invests in reconfiguring the space as a social product or as a complex social construction which affects spatial practices and perceptions, and, in the Amazon case, drivers toward urbanization.

Study Area

Pará is one of the 27 federative units (states) of Brazil. Along with eight other states, it constitutes the Brazilian Legal Amazon (BLA). Pará shares physical borders with six other Amazon states to the north, west, south, and east; and with the countries of Surinam and Guyana to the far north (Figure 3-1). This high number of geopolitical borders further increases the state’s geographic importance in the region and country.

The state of Pará accounts for an area of 1,247,954 km², larger than most countries in

Latin America. Pará is the second largest subnational division in the Amazon, and 13th largest subnational entity in the world. The territory is divided into 144 diverse municipalities. Within the territorial divisions are Altamira, the fifth largest municipality in the world with 160,000 km², and Marituba, with 103 km². Pará is the most populous

Amazonian state at 8,513,497 million people (IBGE, 2018), one third of the Amazon’s population.

While Brazil’s population doubled between 1970 and 2000, the state of Pará more than tripled (Azzoni et al., 2016). Pará’s significant geographical importance to

Brazil comprises abundant natural resources, such as mineral and freshwater deposits.

Two of the six largest Brazilian mining projects are in Pará (Little, 2014). Large-scale mining reshapes the region and requires high amounts of energy. Because of Pará’s rich hydrological potential hydropower serves as the main source of energy for highly energy demanding mining extraction and industrial activities. Pará has two large-scale dams in operation: Tucuruí and Belo Monte (Figure 3-1).

The state of Pará has attracted massive investment in infrastructure. The resulting influx in population exploits natural resources and causes loss of forest cover.

The east and south Pará form much of the area known as the Arc of Deforestation, comprising 500 km2 with the highest deforestation rates in the Amazon (Marques,

2016).

Deforestation was first measured in the Amazon in 1980 (INPE, 2018). Since then, Pará (along with the state of Mato Grosso) has led the country’s annual deforestation rates. At present date, around 48% of its area is deforested (Fonseca et

al., 2017). With new waves of deforestation, cleared forest areas increased 278% more than in 2018 (G1, 2019; Phillips, 2019). The state made headlines as 893 km2 was deforested in July 2019 (Maisonnave, 2019; Phillips, 2019). Shifts in spatial, social, political, and economic spheres often place Pará as the protagonist of change in the

Amazon. As a process, the accentuated urbanization is driven by all above cited spheres and beyond.

Urbanization can be perceived by the increases in urban population and the creation of new municipalities. This is either planned or spontaneous geopolitical reconfiguration of space. Thus, Pará is a challenging case study to understand Amazon urbanization. Over 60 new municipalities have been created since the 1980s, reflecting the state’s influence in reshaping space (Lefebvre, 1991). New subnational divisions imply a rearrangement of spatial configuration as a response to external drivers and lie among the unfolds of the Amazon development1 and colonization plans embodied in the

1960s. National policies aimed to colonize2 the Amazon, integrating the region to the rest of the country, while stimulating the extraction of natural resources, turn the land productive in terms of agriculture and cattle ranching, and serve as a settlement site for destination population surplus and unemployed from the rest of the country (Bertha

Becker, 1990; Bunker, 1988; Smith, 1982).

1 The term development is herein applied mimics the then government approach to attach it to mere economic processes, not reflecting my personal view of the further dimensions the process of development should be contemplated with, that includes, however not limited to environmental and socio sustainability.

2 The term “colonizing the Amazon” does not reflect my personal believes that the Amazon was already occupied.

Pará in Amazon Context

Applying the classic land theory of Central Place (Alonso, 1977; King, 1984) to

Pará allows for identification of major hubs. Central Place theory hierarchy usually comes with age and political importance. The state capital often sits atop the urbanization pyramid. Nonetheless, urban clusters in Pará have been reconfigured by adding new municipalities (Pinheiro, Pena, Amaral, & Amin, 2011). The rise of new municipalities was mostly unplanned. Public and private investment in strategic sectors such as hydropower infrastructure drive urbanization of space, but also of the territory and of the society, inducting a desmetropolization (Milton Santos, 1996, 2008). This unbalanced outcome in Pará and the Amazon brings the need to understand the nature of these new territorialities.

The region’s rivers and waterways historically play a major role in shaping the locations of towns and cities and the connections among them. According to Garcia,

Soares-Filho, and Sawyer (2007), as well as to city primacy law (Jefferson, 2017), such connections show that Belém and Manaus are both major hubs or macro-poles in the

Brazilian Legal Amazon. Classified by the index of services (IS), this is the ratio between the service economy domestic product and the gross domestic product (Garcia et al., 2007). The index of services indicates the “potential of a municipality to act as a regional center, influencing surrounding municipalities” and is also related to anthropic pressure (Garcia et al., 2007, p. 721).

Garcia et al. (2007) applied an urban hierarchy framework to the Amazon, classifying its municipalities into nine macro-poles, 29 meso-poles, and 48 micro-poles using the index of services (IS). Macro-poles for IS are above 20%, mostly corresponding to state capitals. Meso-poles IS are between 3.2 and 20%; and micro-

poles, are between 0.5 and 3.2%. As urbanization is a spatial process, there is a linear connection between instances of hierarchies. Each instance of hierarchy contains smaller layers. For example, macro-poles contain meso-poles, meso-poles contain micro-poles, and so on. In this case, macro-poles correspond to Brazilian Legal Amazon state capitals. Using the index of services, more interaction also translates to more anthropic pressure. Of the eleven cities in Pará that conform to the 48 micro-poles the highest IS is in the capital, Belém. Some of the lowest are Breves and Almeirin, with municipalities at both ends of the spectrum. This means a high heterogeneity of socioeconomic indexes even among the most prominent urban center in the state.

Pará shows a reality of contradictions. Some municipalities are larger than the intra-state subdivision, called microregions or integration regions, equivalent to counties. A study by the Brazilian Ministry of Planning (Brasil, 2008a) identifies six major territorial areas based on overlapping socioeconomic indexes, population density, physical biomes, and occupational profiles. In the Amazon, Pará is the only state that contains three levels of subdivisions. Despite its attempt to homogenize processes and regions, this state stands out for diversity.

Subdividing the Amazon into eastern and western regions creates two poles represented by the capitals Belém and Manaus. These two cities exhibit commonalities and differences. Both are old settlements in privileged locations on main waterways of the Amazon River. Since the opening of the Amazon frontier in the 1960s, Pará has witnessed urban sprawl well beyond its capital Belém: spreading across its territory and reaching its 144 municipal divisions. On the other hand, most of the state’s population is concentrated west in Manaus, as is most of Pará’s economic activity. Belém in the east

is also of vital importance to the region’s dynamic. The eastern Amazon received massive population migration, and changes in land use and cover, with the upsurge of planned and spontaneous urban settlements. Largescale construction of main and back roads along with agricultural expansion, logging, cattle ranching, and large-scale mining projects contributed to the spatial and social reshaping of the Eastern Amazon

(Simmons et al. 2002; Guedes, Costa, and Brondízio 2009).

From a geopolitical perspective, the state is strongly connected to global commodity chains. Major development projects in the Amazon concentrate on extracting natural resources to supply the growing global market for minerals. Pará is the export leader among Amazon states. This translates into infrastructure projects directly linked to the urbanization process.

Although urbanization has numerous drivers, the main driver in Pará are mining exports, energy production, and ranching (P. Richards & VanWey, 2015). Parauapebas and Oriximiná are two exemplars of rapid urban growth in the state of Pará, in this case largely resulting from mineral extraction and export. They show the importance of smaller cities as sites of rapid economic growth and development. Pará’s changing urban dynamics stem from infrastructure development such as dams, transmission lines, and substations, as well as roads and ports. These smaller but rapidly urbanizing nodes are impacted by the development of the aforementioned infrastructure, whether directly touched by them or not.

Amazonian boomtowns reflect the economic boom cycles that triggered the spatial and economic reconfiguration of Pará. Cities and towns were not to shelter such large population. The result is an unpredictably complex urban network, with the rise of

medium-sized towns across the territory. In most cases, these towns are tied to major natural-resource export ventures.

Historic Overview of Municipality Formation

In the 1600s, the Dutch, French, and English converged to invade Northeast

Brazil. Pioneer military occupation were verified at the mouth of the Amazon River. The future state capital of Pará, Belém, was founded in 1616, by raising a fortress able to contain the foreign invasion. Portuguese geopolitical moves secured this entrance of the

Amazonian territory. In 1621, Portugal created a political-administrative congregation in what are today’s Amazon states of Pará and Maranhão, to continue the national strategy of securing access to the Amazon Region (M. G. da C. Tavares, 2008).

For Machado, cited by Tavares (1989: 28), the first system of territorial control of the Amazon occurred with the introduction of Religious Companies. Carmelites,

Mercedarians, Franciscans, and Jesuits, received select territories as mission areas, and began to dominate the vast area of the region. Thus, indigenous populations were prevented from forming alliances with other European Courts.

The spatial division was based on religious domain. This gave the Jesuits control over the entire southern part of the Amazon river, which involved the tributaries of rivers

Tocantins, Xingu, Tapajós, and Madeira. Mercedarians and Carmelites assumed the

Urubu Valley, the Negro River, the rivers Branco, and Solimões. The Piedade and

Santo Antônio Franciscans were able to assume control of the left bank of the lower

Amazon, Gurupá; as well as Cabo do Norte and Marajó. By 1730, Portugal seemingly had no policy of valuing the land, the people, or the resources of the Amazon. In contrast, the Jesuits handled their activities in harmony with the natural space (Reis,

1956: 53).

In the mid-seventeenth century, Portugal relinquished religious control of the region and instead began controlling the occupation and economic activities of the

Jesuits. The 62 villages established by the missionaries corresponded to the 18th- century parishes founded by the Portuguese in the Amazon region. These parishes were soon elevated to the status of villages, named after cities in Portugal (Fernando &

Rodrigues, 2011; B. M. dos Santos, 2013). Expulsion of the Jesuits from the

Portuguese colonies, including Africa, first happened in Pará. This made Pará the protagonist for territorial and geopolitical changes in the Amazon from Brazil’s early days. Thus, when new administrative units formed, the villages began to function with an elected Municipal Council. Their task was to stimulate local development and to control indigenous labor.

Before being expelled, the Jesuits converted indigenous people to Catholicism and established domain beyond religious valves. Their global scope allowed the Jesuits to obtain trade advantages, including exemption from taxation, and developed a monopoly on much of the wealth produced in the state. They ranched more than

100,000 head of cattle on Marajó island, and held sugar mills in Maranhão, and Grão-

Pará (Glielmo, 2007).

What can be seen at this point are the first concrete attempts of the state to reshape the space (Lefebvre, 1991). Marques de Pombal, who led the Jesuit expulsion, also catapulted the Amazon’s strategic importance. He formalized an agreement with

Spain to trade the Patra Basin for the entire Amazon Region. The Spanish were no longer a threat as foreign invaders anymore, at least not in Brazil.

What follows is the first explicit attempt to set boundaries in the Amazon territory.

On 6 July 1752 Pombal told brother Francisco Xavier de Mendonca Furtado, then

Governor of the Grand-Pará, his mission to demarcate the boarders of the Amazon basin. Pombal’s acknowledged the population vacuum. This led to active immigration of citizens from Madeira and Açoures islands, interracial marriage between Portuguese and indigenous, and the creation of an enslaved workforce. There was also a clear strategy to occupy remote areas in the extreme north (Glielmo, 2007).

The Council’s objective was to stimulate local development, to exercise control, and to continue to exploit indigenous labor. This system of occupation and territorial control the first steps in creating the municipalities that form most of today’s Brazilian

Amazon. The riverine areas of Pará, in particular, were marked by nuclear settlement and large administrative units that shaped the geographical mapping of the territory

(Machado, op cit, p.104).

During the nineteenth century, ancient villages disbanded and were reduced to hamlets. Some became extinct and those that survived became Municipal headquarters. Reconfiguration of the territory was constant and unstable. The Pará geopolitical divisions recognized today were created in 1889. Nonetheless, boundaries within Pará continue to blur.

The geopolitical instability of Pará’s territorial demarcation began a long time ago. Even before 1891, when the Imperial Constitution granted municipal autonomy, the governor of Pará Justo Chermont spoke of the incompatibility of municipal autonomy and the absence of clear municipal boundaries. Three years later, Law number 226 of 6

July 1894, provided regulations to create new municipalities. The territory would

continue to be divided into municipalities, then subdivided into districts (article 55). Law number 226 also guaranteed municipal autonomy and independence, barring violation of federal or state laws (article 56). However, public resources were lacking, and interest in land demarcation was limited. Therefore, delimiting the area of municipalities was not a priority of the State Government (1913, p.253)

Even the first rubber cycle (1879-1912), which allowed colonial expansion and attracted economic, social, and cultural transformations, was not enough to widely establish clear municipal boundaries throughout the state. The exception was the municipalities of São João do Araguaia (1908), Conceição do Araguaia (1909), Altamira

(1911), and Marabá (1913) in Southern Pará. Most of the economic revenues were then concentrated in the state capitals of Belém and Manaus (M. G. da C. Tavares, 2008).

However, the rubber boom significantly influenced migration. The rubber boom attracted people and formed a new spatial cluster. Thus, the occupation of the Amazon focused economic interests mainly on natural resources.

Understanding the process of municipal creation in the state of Pará in the twentieth century requires attention to one essential element: the construction of terrestrial communication networks. Before roads and railroads, waterways were the main networks for connection, communication, circulation, and economic activity.

A public police arrangement to settle 10,000 colonist families in Bragança in

1874 and the construction of the Belém-Bragança railway in 1875 show an attempt at terrestrial occupation and colonization. In the next decades, 10 new villages and towns were established along the railroad. They later became municipalities. By 1938, Pará had 53 municipalities.

With the advent of the 1943 Constitution, came a municipalization wave. For the first time, states created certain requirements for population, housing structure, and minimum income for the new municipalities. This was a clear attempt to prevent further divisions of the territory into autonomous units (Tavares, 2008). In 1948, Law number

158 described the competencies and responsibilities of municipal layers. Tax collection was described as municipal competence, and so was the organization of local public services.

The first project aiming to integrate the Amazon was in the mid-20th century. In

1953, after creating the Superintendence for Economic Valorization of Amazonia

(SPVEA), later organized as Superintendence of Development of the Amazon

(SUDAM), a highway linked the capital Belém to the rest of Brazil through the national capital, Brasília. A boom of cities followed along BR 163, known as Belém-Brasilia.

The Military Regime in Brazil (1964 to 1985) then began an aggressive policy to further occupy the Amazon, on the assumption of wasted space. The policy aimed to exploit natural resources (Da Silva et al., 2015). Part of the plan was to urbanize the region. That was abandoned early. The urbanization plan revolved around the new road

Transamazon: an iconic venture that continues to symbolize the exposure of the

Amazon and its interior and remote lands. The plan, based on Christaller’s (1966) central place theory, envisioned a network of small towns with different functions and weights, planned along the Transamazon portion of Pará. This new urban environment that would allow agrarian reform and income equality. The network of small towns would receive guidance on group conduct, morality, community, and religious spirit, in an attempt to create a kind of utopic society that would inhibit segregation by any creed,

past ties, or tendency. This proposal by urbanist José Geraldo da Cunha Camargo, intended to “start a new civilization in the region” (Rego, 2016).

The idea was to create rural urbanism. Distinct hierarchical levels called agrovilas, agrópolis, rurópolis, and cities would form a network. These nodes would secure rural populations. The city-countryside link mimics the integration of the Garden

City (Camargo apud Rego in the article: Planned Communities in the Amazon). In

February 1974, the first rurópolis was inaugurated in Pará, named after President

Médici. The plan was soon abandoned as unviable, but it left it fingerprints on the spatial configuration of that portion of Pará. Despite its utopian patent, the Transamazon attracted a massive influx of people and allowed deforestation to spread (Katzman, apud Rego, 2016). Transamazon, a controversial road that once showed the world how impassable and inaccessible the Amazon is, remains unfinished and symbolizes danger instead of integration.

Common sense suggests that planned urbanization was destined to fail.

However, what happened along part of the Transamazon highway had a successful precedent north of Paraná. In the late 1920s, the English company Parana Plantations assumed responsibility for colonization endeavors in north Paraná. Once more, the

Amazon brought a reality of its own, defying labels, and requiring a local approach to adapt.

Numerous records confirm that occupation of the Amazon was disordered and predatory. Under leadership of the military regime, the motto "integrate to not give it up" and "men without land to land without men" elevated the idea of developing and occupying the region to a national security strategy. A lack of adequate planning

unfolded in the next decades reducing forest cover, losing biodiversity, and altering ecosystems.

Amazon occupation was founded on the urge to exploit and populate the area at all costs. It collaterally granted protection from future internationalization efforts. The idea was to expand agricultural frontiers, and to install industries with public and private capital (Cavalcante, 2012). To populate the Amazon, occupation plans were created to attract colonists.

Becker (2001) said the occupation and development policies for the region greatly increased immigration. Its origins is traced to remedy severe socio- environmental disturbances, such as the uncontrolled use of land, the aggravation of social conflicts, the advent of epidemics, and impacts on local biodiversity in other parts of the country (Becker, 2001). Development of the Amazon unfolded in two distinct periods. In the first period, starting in the 1960s, urban and rural areas were severely distressed. The second period began with the Federal Constitution of 1988, when a new perspective began to incorporate public policies aimed at sustainable development. This new perspective also recognized the environmental jeopardy the initial occupation created. Both waves of development greatly changed the spatial configuration in the state of Pará.

In 1970, the National Integration Plan (PIN) and the National Integration and

Development Axis (ENIDS) were developed (Serra & Fernández, 2004). These programs were prompted by the "Avança Brasil" Project (Forward Brazil) (Becker, 2004; p.76). Construction, expansion, and opening of roads such as the Cuiabá-Santarém

Highway, the Perimetral Norte, and the Transamazon Highway (Br-230) were part of a

plan to occupy and interconnect the Amazon region to other economic centers in Brazil.

The Transamazon Highway especially reshaped territoriality in Pará, contributing to deforestation and the rise of new urban cores.

In previous centuries, Amazon cities faced the local rivers, the main conduit for people and goods. As new roads shifted transportation networks, cities instead faced roadways. The fringes of such roads gave rise to cities.

Outcomes of this occupation also surprised the government. Brazil created the

Agrarian Reform Institute (INCRA) to promote the regularization of land throughout the

Amazonian territory. What unfolded in Pará during the 1980s, 1990s, and 2000s, and beyond was a series of the most violent conflicts involving land access in the Amazon

(Aldrich et al., 2012; Simmons et al., 2002). Marabá developed under a converging of new roads, mining and hydropower projects; and became known as “Marabala”

(Marabá plus bala, “bullet” in Portuguese). Many new cities were named after violent conflicts, explaining why there is a Tailândia (Thailand), and a Palestina (Palestine) in

Pará.

Attempts to Understand Urbanization in Pará

Urbanization in the Brazilian Amazon fits into three major phases. The first phase relied on the rubber boom exporting latex to external markets, from 1879-1912, and short recovery time during World War II. The second phase started in the 1930s and proliferated in the 1960s. It aimed to occupy the land and make it productive through extractive economic activities focused on exporting the region’s abundant natural resources, such as minerals and logging, and later agricultural commodities such as cattle and soy. It encompassed social, political and economic investment to attract migrants by promising land, or jobs. The third phase took hold in 1980s (P. Richards &

VanWey, 2015). Urbanization of the Amazon continues to prevail. Almost 80% of current urbanization was not forecasted by in the occupation plans and development policies.

Urbanization is reshaping the Amazon. The attempts to identify applicable framework share the search for incorporating local contexts and complexities to universal phenomena. Trindade Júnior (2013) compiled example frameworks to describe Amazon’s urbanization. Browder & Godfrey (1997) said urbanization is polymorphous and disarticulated. They cited different socio-spatial interactions, with hybrid microsocial elements as strong points of regional urbanization. Machado (1999) said heterogeneity in the hierarchical levels of urban areas indicates new urban clusters and municipalities are also created tended to have rural activities. De Oliveira (2000) highlights urban society dissipated not necessarily just over the urban landscape;

Hurtienne (2001) evidenced an intricate urbanization and established a population threshold to indicate urbanization. Monte-Mór (2015) said Amazon’s extensive urbanization extended urban livelihood beyond city walls, promoting an urban lifestyle.

Becker (1985) in 1985 coined the expression ‘the Amazon is an urban forest’. In

2004, she said the Amazon was an urbanized jungle, directly linked to the expansion of economic activities, whereas the frontier is already born urbanized, preceding the rural momentum that usually unfolds into urban areas.

Urbanization and Hydropower Systems

Hydropower in Pará

Demand for energy is growing worldwide (da Silva Veras, Mozer, da Costa

Rubim Messeder dos Santos, & da Silva César, 2017). The Amazon’s role in the international scenario is a contradictory way. The world’s largest standing rainforest is

also a global provider of mineral and food commodities. As access to electricity is vital to accelerate industrialization and increases local energy demand in a region with abundant hydraulic resources. Pará accounts for a considerable share of Brazil’s new economic development and participation in the global commodity export boom since the

1980s. Increasing energy demand to supply industrial production, it also accounts for a large portion of the generated energy. Soy, cattle, minerals, and ethanol are among the most important economic activities in the state (Backhouse, 2013; Gibbs et al., 2015;

Southworth, Munroe, & Nagendra, 2004; SUDAM, 2016).

Pará has a strategic geographical location. In addition, to available land, fertile soil and abundant natural resources, ocean access offers transportation flow and connection to other markets. The internal waterway connects the Amazon river and other tributaries. Those features make Pará a natural destination for products leaving the Amazon for the international market. Brazil feeds China with mineral and natural commodities and shows no signs of abandoning its extraction-based economy.

In this context, energy becomes even more important. Brazil relies on hydropower as its main energy source for electricity production (65%). Hydropower as a system has also become increasingly important as a driver of changes, including land use and cover; and urbanization of the surrounding areas. Brazil plans to increase the energy grid with 46% more transmission lines to add new substations, and to build six more large-scale and 17 small dams in the Amazon (Brazil, 2018).

Large dams and hydroelectric plants cause large-scale socioeconomic and environmental impacts. The energy system in Pará has relied on the Tucuruí dam since

1984 and represents much of the energy produced in the Amazon. In 2005, Pará

produced over 2.3 million metric tons of oil equivalent, while the remaining Amazon states produced only approximately 1.1 tons (Brasil, 2008b). In 2016, another large dam, Belo Monte, started to operate and more are planned or inventoried.

Tucuruí was the only large dam in the state for over 3 decades. Its operation’s start in 1984 and provides a good baseline for the complex outcomes in hydropower infrastructure. Over the years, dam’s transmission lines and substations have been added to make the system more robust the system. In parallel, Pará’s spatial configuration was also reshaped, in part on the account of impacts from Tucuruí and the energy grid system that evolves from it.

Tucuruí Hydropower Plant: Some Historical Background

The first large-capacity dam in the Amazon, Tucuruí hydropower plant is currently the fourth largest dam in the world. It is located on the Tocantins-Araguaia, the largest 100% Brazilian Amazon river basin. The Tocantins river flows through four states in the BLA into the Atlantic Ocean. A great portion of the energy produced in

Tucuruí travels over 300 kilometers to reach its destination in the aluminum industrial hub of Barcarena.

The Tucuruí hydroelectric power plant was proposed in 1974 to supply energy to

Japanese and Brazilian joint ventures in Albrás and Alunorte (Pinto, 2012; Tavares,

2001; WCD, 2000). The Japanese government backed out after increased construction costs. Construction costs were then covered by the Brazilian government to produce low-priced energy. This subsidized the Japanese-Brazilian company Albrás to produce and export aluminum to Japan (Pinto, 2012). Tucuruí began operating in 1984, and aluminum production of Albrás/Alunorte began in 1985 (Moura & Maia, 2012).

The main purpose of Tucuruí was to supply energy to the mineral industrial complex of Barcarena, which attracted several other mineral companies in the next decades. The energy met the needs of recent industry in Pará, Maranhão, and later the newly formed state of Tocantins. In 1986, 60% of the energy generated in Tucuruí had large-scale industrial consumers, reaching almost 70% in 1992. By 1999, more than

50% of its production was for industrial use (Comissao Mundial de Barragens 2000). A smaller share served the state capital and other residential and municipal uses. In Pará, hydropower energy supply went from 4% in 1980 to 92% in 1985. After Tucuruí begun operating, it increased and to 96% in 2005 (Dias, 2014). Tucuruí and its system are thus the main elements of the state’s energy provision.

A Broader Horizon for a Dam's Impacts: Connection to Urbanization

Past studies of Amazonian urbanization at the basin or state scale concentrated on aggregate urban outcomes or individual urban units. Many relate urbanization to multiple drivers and account for the impact of occupation policies and large infrastructure projects such as roads, mining ventures, and dams (Browder, 2002;

Browder & Godfrey, 1997; Eloy & Brondizio, 2015b; Godfrey & Browder, 1996; Guedes et al., 2009; Thypin-Bermeo & Godfrey, 2012). However, in most cases, urbanization and the hydropower infrastructure systems are related spatially, limiting the impact to the surrounding areas of the reservoir of the major hydropower plant.

The connection between the Tucuruí dam and its direct impacts on urbanization have been widely explored by considering its surrounding areas, especially in the municipality of Tucuruí and its neighbor Breu Branco (Miranda Rocha, 2011; Tavares,

2001; WCD, 2000; Browder and Godfrey, 1997). Lago de Tucuruí subnational regional division accounts for the seven municipalities near the dam’s reservoir. The population

in Lago de Tucuruí before construction of the hydro plant was about 17,000. In 1970, it was 56% rural. The population in 2010 was about 360,000: 68% urban. From 1970 to

2010, the population of Lago de Tucuruí increased by 1,955%, higher than the 879% increase in Tucuruí. The urban population of Tucuruí reached 95% in 2010. Four of the six municipalities forming the area were created in the 1990s, a reflex of space reconfiguration, probably stimulated by the population increase (Miranda Rocha, 2011).

Public infrastructure projects affected industrialization and urbanization outside the areas themselves. The framework for addressing urbanization as one outcome of the hydropower system in Pará is drawn from two interconnected theories. The first is

Central Place Theory, developed by Christaller and published in 1933 (Christaller,

1966), adapted by von Thünen shortly after, and reframed by Lösch and Alonso. This theory explains the weight of centrality on different land uses driven by economic connections and the dominance of land rent (Alonso, 1977; Robert Sinclair & Sinclair,

1967; Sathler et al., 2010). The second is Krugman’s (1996) theory of cumulative causation and industrialization. Its main presumptions rely on the fact that urban areas possess synergies, resulting primarily from trade relations that stimulate economic activity. With increasing trade, and commercial activities propelled by the increase in energy supply, urban centers expand their capacity to develop commercial exchange and a higher degree of specialization. A given city supplies to others what they need and do not produce, making it rational for cities to trade. Therefore, cumulative causation happens within each city, and each city grows quickly than with interurban trade (Fujita, Krugman, & Venables, 1999; Krugman, 1990, 1996). I apply these two theories to explain increased urbanization in response to the hydropower system.

Central place theory helps explain population boosts near municipal headquarters as the system of municipalities reacts to input from the hydropower system.

The hydropower system’s influence on urbanization extend beyond physical connections and is not limited by proximity to power plant or reservoir. This work takes into account urbanization in Pará receives different stimuli. It also accounts the hydropower system will probably continue to expand and its impacts to unfold. Thus, is seeks to answer the question how has hydropower impacted urbanization across all municipalities in a single state.

Methods

Data Analysis

This study uses an empirical analysis of spatial, temporal, and population data for municipalities in the state of Pará, Brazil. Spatial data corresponds to 143 municipalities in Pará and the hydropower infrastructure network. Temporal data consist of population data for 1980, 1991, 2000 and 2010, and starting operation year of the transmission lines. Because municipalities did not all form at the same time, I generated two major datasets to run distinct temporal models. One included all municipalities with entries for all years (83 units). The other included municipalities with data from 1991 onward (143 units).

To better conceptualize impacts of the hydropower grid on urbanization in Pará, I divided the data analysis into two major parts. The first part was GIS mapping by municipal foundation date and the infrastructure of the hydropower grid, with transmission lines, substation and hydro plant. The second part was a temporal statistical analysis, with a longitudinal model assessing increased urban population percent as a function the presence or absence of hydropower (StataCorp, 2017).

For the statistical analysis, I ran two models for each of the time-series sets: the first group formed by years 1980, 1991, 2000 and 2010, and the second for 1991, 2000,

2010. One model used the variable “hydro” from Moretto’s (2016) dataset, which I called conventional hydro. This is a categorical variable, representing municipalities that were impacted by dams and municipality considered not impacted. A binary code establishes values are one for affected and 0 for not affected. The second model assumed a revised classification of the “hydro” variable, called proposed hydro. The proposed hydro was based on visual identification of municipalities physically linked to any elements of the hydroelectric grid, by overlaying shapefiles of the municipalities downloaded from IBGE ( 2019b) with shapefiles of the hydropower system download from Aneel (2016). A time matrix was created for the categorical variable based on the year of operation of transmission lines. Shapefiles corresponded to layers for hydroelectric dams, transmission lines, and substations. Both models use the same total and urban population data from the years of 1980, 1991, 2000 and 2010, and the operation dates of the transmission lines.

Data Set

Outcome variable – urban population percentage

Urban population as a percentage of total population was used as the outcome for all analysis. The decision to use the percentage of urban population takes into account a worldwide tendency toward urbanization, not necessarily proportional increase in total population. Also, in absolute terms, the percentage of urban population allows for an urbanization assessment under a standardized scale. A 10,000-person increase would affect a 20,000-person town differently than it would affect a 200,000- person town. I also considered the average urbanization percentage in the Amazon

(over 70%) show an increase tendency despite differences in rate across the municipalities and state.

Proposed versus conventional “hydro” variable

The project Performances of Brazilian Municipalities Affected by Hydropower plants (Moretto, 2016) differentiates whether a municipality is considered affected by a dam according criteria proposed by Aneel (ANEEL, 2001). This classification guides financial compensation to the municipalities’ areas flooded by the reservoir. This dataset provides what I call as conventional classification of hydropower impact, or

“conventional hydro” (Figure 3-2). There were 39 municipalities affected by hydropower according to the conventional classification.

For the proposed “hydro” I extend the classification as impacted municipalities with physical connection to all elements of the hydropower system. To investigate broader impacts of dams on urbanization, I proposed different criteria to include physical connections with dams, transmission lines and substations in Pará. I called this new dataset “proposed hydro”.

Examples of proposed new classification for municipalities as affected by dams are the municipalities of Belém (the capital) and Barcarena, the destination of most of energy produced by the Tucuruí Dam and consumed by the aluminum activities.

Moretto's compilation (2016) classify both municipalities as not impacted by dams because they are located 300 kilometers away from the Tucuruí reservoir. Proposed hydro sees these municipalities as physically connected to the hydropower system through transmission lines and substations and directly impacted by it. They are direct recipients of energy inputs. There were 82 municipalities affected by hydropower according to the proposed classification.

Categorical variable across time

The time-related categorical variable was created to indicate when the hydropower lines started to operate (ANEEL, 2016). Hydropower lines begun operating in 1990 (28), 2003 (1) and 2004 (4). There were seven municipalities considered affected by conventional classification that were considered not affected in the proposed classification with no entry for the time variable.

Assuming that municipalities were affected by the hydropower system in different periods across the time studied, I created a vector of dummy variable hydro, with a zero value if the municipality was considered not affected by a dam, and 1 if considered affected. Next, I added to the data set the year the transmission lines began operating.

In the Tucuruí Dam, new transmission lines were added to the system after 1984. To map the variability of hydropower, I assumed that if the year observed is less than the year of the starting operation for the transmission lines, then “hydro” is equal to 0. For example, the transmission line crossing Marabá begun operating in 1990. Therefore, this unit has 0 for “hydro” 1980; and 1 for “hydro” 1991, 2000 and 2010.

Models

Models capable of analyzing panel (longitudinal) data were utilized, as the goal was to investigate the hydropower system’s impact on population across multiple periods. As a random effect model, it assumes the variation across entities is random and uncorrelated with the predictor or independent variables included in the model

(Bates, 2010; StataCorp, 2017). Variables used as independent correspond to the percentage of urban population in 1980, 1991, 2000, and 2010. Dependent variables are the dummy variables representing the presence of hydropower across the same

years (Table 3-1). Panel data considers the dimensions of space and time and is written as Yit =βXit +α+uit +εit (StataCorp, 2017). The final best models outcome from testing different combinations of the interaction terms and accounts for urban population percentage as a function of regressed hydro, time, weight matrix of hydro and time, and error terms.

푦푛푡 = 휆푊푦푛푡 + 푋푛푡훽 + 푐푛 + 푢푛푡 (3-1) 푢푛푡 = 휌푀푢푛푡 + 푣푛푡 (3-2) where Ynt is a n x 1 vector of observations for the dependent variable for time period t with n number of panels. With periods 1 to 4, according to the models starting from

1980 or 1991, Xnt is a matrix of the time varying regressors (Proposed_Hydront 훽,

Hydront 훽 and timent 훽). Cn is a vector of panel-level effects. The random effect model assumes that Cn is independent and identically distributed (i.i.d.) across panels with

2 mean and variance σ푐. Unt is the spatially lagged error. Vnt is a vector of disturbances and is i.i.d. across panels and time with variance is σ2. W and M are spatial weighting matrices.

Results and Discussion

The first set of analyses correspond to timeframe 1980-2010. The margins coefficients show an overall higher impact for the conventional hydro. For conventional and proposed there is a consistent increase on impact (Table 3-2). For municipalities affected, the difference between conventional and proposed hydro varies from 2-3%.

And the difference between conventional and proposed hydro for municipalities affected varies from 1-3%. Nonetheless, the impact on urban population is higher on municipalities with hydropower elements. The difference points that a conventional classification indicated stronger correlation. For the proposed hydro, some

municipalities were considered affected because the transmission lines run through them. Results indicate this connection might not be sufficient to overcome the proximity to other elements such as substation and proximity to reservoir. Another possible explanation could be the age of municipalities. This model contemplates municipalities that existed in 1980 and continued to exist to the present. Seniority, I this case, made difference suggesting the conventional classification is more suited to asses hydropower impact on urbanization without spatial reconfiguration of additional state subdivision.

As the State of Pará created 60 municipalities after 1980. The second set of models considered 1991-2010 as the timeframe (Table 3-3). In this model, all municipalities in state of Pará are included in the analyses. It also shows that impact on hydropower over urban population increases after time independently to whether a municipality is affected or not. However, contrary to the previous models, it shows significant differences between the proposed and conventional classification results. For municipalities not affected, the conventional hydro is higher, and results increase with about the same difference (17% in 1991, and 16 in 2000 and 2010). For municipalities affected, the proposed hydro has higher coefficients, and the difference from conventional hydro varies from 11-13%. These models indicate a higher impact on municipalities affected with the prosed hydro.

Comparing the two models, the difference between the conventional hydro affected and not affected remains at 4% for 1991-2010 and varies between 5-6% for

1980-2010. The same difference for the proposed hydro models increases to 6-7% for

1980-2010. The highest difference is observed for 1991-2010 (32% in each year).

These results indicate the model 1991-2010 is more representative of the current stage of spatial division and population distribution in the state of Pará. For this model, there is a higher difference in results between conventional and proposed impacts.

to stable 32% for 1991-2010 (Figure 3-3) (Figure 3-4).

This indicates the proposed hydro shows a significant higher impact on municipalities affected by the hydropower system. According to this result, hydropower system will continue to impact significantly more urban population in municipalities with the physical presence of elements of the hydropower.

A higher impact on urban population might be correlated to the attraction factor infrastructure projects might deliver, drawing population to concentrate where there is more investments. This is a trend overserved in the Amazon and other places that is backed up by the availability of resources in urban areas, or lack of investments and opportunities in rural, originating rural exodus.

Largely, the impact on municipalities with the presence of hydropower infrastructure is higher in all years and considering the two time period sets. The difference is diminished or even reversed in the case of absence of hydropower for the conventional and proposed dataset. The results for 2010 on the model from 1991-2010, the most recent profile, points at a greater gap on the impact over urbanization on municipalities that present hydropower.

New Municipalities, Urbanization and Hydropower Grid

Corroborating the main argument of connection between infrastructure and urbanization, spatial data show increased activity along with the substations of the hydropower grid in Pará. Within one single decade, from 1981 to 1991, 42 new municipalities were created. Most were created along transmission lines and adjacent

areas. From 1992 to the present, the state of Pará has added 18 new seats to its territorial divisions. At total of 60 new municipalities were created. Currently, there are

144 municipalities composing Pará. Just one was added since 2010 (Figure 3-5).

When using urban population increase between 1980 and 2010, the map of Pará shows the western part with smaller rates (Figure 3-6). While this map does not provide a clear connection between hydropower and urban population as Figure 3-3, the map points at the higher outliers (reaching 9,000%) on the eastern part.

Concluding Remarks

Urbanization in the Amazon is becoming an important component of the spatial, social and environmental dynamics of the region. While considering multi-drivers, scholars have to identify boomtowns (B. Becker, 2013; Godfrey, 1990; Thypin-Bermeo

& Godfrey, 2012). In Pará, most studies aim at a specific municipality or region (Ana

Cláudia Duarte Cardoso & Neto, 2013; da Trindade Jr, 2005; Miranda Rocha, 2011; V.

M. dos Santos, 2017; Trindade Júnior & Barbosa, 2017). Among these drivers are infrastructure investiments (Spit 1999; Geist and Lambin 2002; WCD 2000; Becker

1989; Barber et al. 2014; Walker et al. 2017; Southworth, Munroe, and Nagendra 2004;

Richards and VanWey 2015; Brondizio and Moran 2012; Browder 2002).

Dams’ impact are difficult to measure, and often undercounted (Almeida Prado et al., 2016; Fearnside, 2005; Laurance, 2018; Little, 2014; Moore et al., 2010). When urbanization is considered an impact it is usually tied to surrounding areas of reservoirs and large-capacity hydro plants (UNPD, 2012).

This study addressed urbanization process in Pará. It also examined broader impacts of the hydropower system on the municipal units. Based on physical connections with the elements of the hydropower grid. Results show the model with the

proposed hydropower variable, extending the concept of impacted municipalities based on physical connections to the elements of the system, rather than proximity to reservoir, suggest a higher difference of impact between municipalities affected and not affected. Municipalities affected by hydropower present a higher impact on urban population.

Overall, this study points at an increase in hydropower impact on municipalities affected or not affected by the hydropower system, suggesting a stronger influence for a higher percentage of urban population considering the redivision of municipalities into new units, as seen in Pará with the 60 new additions since 1980.

The findings complement the need for a broader understanding of urbanization and the impacts of hydropower infrastructure on the spatial reconfiguration of the

Amazon (Costa & Pires, 2015; da Trindade Jr, 2005; Danilo et al., 2018; Fearnside,

2001, 2016; Miranda Rocha, 2011; P. Richards & VanWey, 2015; V. M. dos Santos,

2017).

Urbanization and the spatial reconfiguration of Pará is increasing, and the hydropower system is strengthening., with plans for the addition of new dams, transmission lines and substations. Most notably, this is the first study to investigate the the hydropower infrastructure upon the current urbanization process at a state scale considering all its municipal units across time and space in the Amazon.

Future work should concentrate of refining impacts of the hydropower system on urbanization and account for it as the system continues to grow its tentacles. This refining includes additional data for elements such as substations, energy consumption

and an updated set of spatial data with the inclusion of recently operating transmission lines.

Figure 3-1. Study area: State of Pará

Figure 3-2. Municipalities considered impacted by hydropower: conventional and proposed classification

Table 3-1. Variables used in the models Variables Description Independent Urban population percentage Percentage of urban population in 1980, 1991, 2000, 2010 Dependent Hydro Temporal categorical variable in 1980, 1991, 2000, 2010 – a positive value reflects the year of operation of transmission line Proposed hydro Temporal categorical variable in 1980, 1991, 2000, 2010 – a positive value reflects the year of operation of transmission line after visual reassessment

Table 3-2. Margins for urban population regression for 1980, 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 83 municipalities affected (yes) and not affected (no) by hydropower (confidence interval 95%) Year Conventional Hydro Proposed Hydro no Conventional Hydro Proposed Hydro yes no yes 1980 .2883774 (.23, .35) .2602142 (.51, .47) .3912118 (.26, .52) .35223 (.14, .56) 1991 .3956259 (.33, .46) .3675694 (.16, .58) .4984603 (.37, .63) .4595852 (.25, .67) 2000 .4601181 (.40, .52) .4320681 (.22, .64) .5629525 (.43, .69) .5240839 (.30, .73) 2010 .4857667 (.43, .54) .4577152 (.25, .67) .5886011 (.46, .72) .549731 (.34, .76)

Table 3-3. Urban population regression for 1991, 2000, 2010 for proposed and conventional hydropower impact classification on 143 municipalities affected (yes) and not affected (no) by hydropower (confidence interval 95%) Year Conventional Hydro no Proposed Hydro no Conventional Hydro yes Proposed Hydro yes 1991 .3577728 (.31,.40) .1891624 (.54,.32) .3966256 (.31,.40) .5107299 (.39,.62) 2000 .4560885 (.40,.50) .2912086 (.16,.42) .4949413 (.42,.56) .6127761 (.49,.73) 2010 .4972598 (.45,.54) .3317114 (.20,.46) .5361125 (.46,.60) .6532788 (.53,.77)

32 32 32

Percentage 4 4 4

1991 2000 2010 Year

Proposed hydro Conventional hydro

Figure 3-3. Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1991-2010

7 7 6 6 6 6

5 5 Percentage

1980 1991 2000 2010 Year

Proposed hydro Conventional hydro

Figure 3-4. Difference between impact on urban population percentage in municipalities affected and not affected by hydropower with proposed and conventional set of categorical variables for model 1980-2010

Figure 3-5. Urbanization change in Pará and hydropower infrastructure 1980-2010

Figure 3-6. Foundation timeline of municipalities in Pará and hydropower system. Source: IBGE 1980, 1991, 2010

Table 3-4. Spatial model of urban population 2010 as a function of hydropower, quadrants, urban population in 2000 and total population in 2000 Variables Coef. Std. Err. z P>|z| [95% Conf Interval] Urban Population 2010 Hydro 0 Base Hydro 1 . 0848297 .0362134 2.35 0.019* .0139528 .1559066 Year 1991 Base 2010 .1659696 .054682 3.04 0.002* -.0587948 .2731444 Urbpop_2000_th .0042423 .0008001 5.30 0.000* .0026741 .0058104

Urban Population 2010 W Hydro 0 Base Hydro 1 .4080889 .3281122 1.24 0.214 -.2349992 1.051177 Year 1991 Base 2000 .0797963 .0777658 1.03 0.305 -.072622 .2322145 2010 .0042971 .0816567 0.05 0.958 -.1557471 .1643413 Urbpop_2000_th .0042423 .0008001 5.30 0.000* .0026741 .0058104

Indirect Hydro 1 .0005233 .0024966 0.21 0.834 -.0043699 .0054165 Quadrant Southeast .0009241 .0043774 0.21 0.833 -.0076555 .0095037 Southwest .000256 .0013437 0.19 0.833 -.0023776 .0028895 Northwest -.0002156 .0009085 -0.24 0.812 -.0019962 .001565 Totpop_2000_th -.000034 .0001599 -0.21 0.832 -.0003474 .0002794 Urbpop_2000_th .0000415 .0001954 0.21 0.832 -.0003414 .0004244 Total Hydro 1 .0482521 .0157963 3.05 0.002* .0172919 .0792122 Quadrant Southeast .0945151 .0178287 5.30 0.000* .0595715 .1294587 Southwest .0260094 .0242013 1.07 0.283 -.0214243 .073443 Northwest -.0246607 .0306825 -0.80 0.422 -.0847974 .0354759 Total Population 2000th -.0035085 .0007862 -4.46 0.000* -.0050494 -.0019675 Urban Population 2000th .0042838 .0008052 5.32 0.000* .0027055 .005862 Notes. * p <0.05.

CHAPTER 4 URBAN EXPANSION IN A RESOURCE FRONTIER: A MUNICIPAL LEVEL APPROACH

Significant changes in the Brazilian Amazon started less than a century ago. The rainforest went from an inaccessible and unproductive mass of forest to a massive political, economic, and physical investment to “occupy”1 and develop the area. The development model based on natural resource exploitation has been producing unforeseen outcomes at considerable environmental expenses. Expansion of urban areas, increased urbanization, loss of forest areas, and increased agricultural activities.

The expansion of urban areas and increase in urban population throughout the Amazon is reshaping boundaries, triggering constant land cover and land use transformation. It shows no signs of retreating (Ana Cláudia Duarte Cardoso et al., 2013; Gomes &

Vergolino, 1997; Mitschein et al., 1989; Sathler et al., 2010; Trindade, 2013). As a worldwide process, (Lefebvre, Bononno, & Smith, 2003; UNDESA, 2018; Seto &

Shepherd, 2009), the ubiquitous characteristics of urbanization need not erase the heterogeneity and singularity of local processes if viewed carefully; however, much research is needed on urbanization of the Amazon2.

Much of the spatial reconfiguration research has been devoted to deforestation as the main representation of physical changes (Davidson et al., 2012; Lovejoy &

Nobre, 2018; Soares-Filho et al., 2006). In most cases, urbanization was treated as a social phenomenon, rather than a physical one (Guedes et al., 2009; Monte-Mór, 2015;

1 The term occupy herein used does not reflect my personal view given that the Brazilian Rainforest occupation by traditional population dates to thousands of years (Denevan, 1992; London & Kelly, 2007). Rather, I reproduce the term extensively used by the Brazilian Government in public policies for the Amazon mainly during the last half of the past century.

2 The term Amazon herein refers to the Brazilian Amazon

Perz, 1999; Thypin-Bermeo & Godfrey, 2012; Trindade, 2013). Nonetheless there seems to be a consensus that both physical and social processes are a result of the unique interaction between the forest and its context (Barbieri, Monte-Mór, &

Bilsborrow, 2009; Bertha Becker, 2013; Monte-Mór, 2015; P. Richards & VanWey,

2015).

The spatial manifestation of urbanization in the Amazon is a reflection and a driver of changes in the use of space. Despite the importance of forest-preservation and environmental issues. Given the intensive process of urban areas expansion reshaping the landcover and land uses in the Amazon, the spatial expansion of urbanization should be accounted as an important element of the new forest reality.

This research investigates the expansion of urbanization and the subsequent spatial reconfiguration of the changes in land use and land cover in Barcarena, motivated by answering the broad question of how urbanization takes place in a forest frontier, in the form of urban land use expansion and land cover transition. The guiding premise relies on the fact that, due to the singular combination of elements and geography, nor the increase in population nor the expansion of urban areas in

Barcarena are sufficiently fit to a traditional path of urbanization.

This research developed land cover maps to identify landscape changes through a period of 33 years, from 1984 to 2017. This spatiotemporal analysis focused on changes in land cover and use within the categories of forest, urban, bare soil, and agriculture to traces the extent of the transitions between these covers. Further, this research focused on the physical reconfiguration of space during urbanization to address urban morphology and verify whether it follows a pre-established pattern

common to other places. The land cover maps also identify whether the Amazon urban sprawl follows generic landscape patterns observed elsewhere.

Study Area

The municipality of Barcarena, in the state of Pará, is the site of notable economic forces and intensive urbanization (Figure 4-1). The municipal geopolitical boundaries sum 1,310,588 km2. Barcarena near one of the two largest metropolitan regions in the Amazon, the Belém Metropolitan Region (BMR). There is an on-going debate to formally incorporate Barcarena into BMR (Lima, Cardoso, & Holanda, 2005;

Tourinho, Pinheiro, & Bello, 2018). Linked to the energy grid from the Tucuruí Dam, it houses one of the world's biggest aluminum industrial plants. Located near the most significant metropolitan areas in eastern Amazon, the municipality has seen its population increase by 500% since 19803. Landscape has been reconfigured as a result of population influx. At the same time, Barcarena further catapults the urbanization begun in the Amazon in past decades as it contains many of the drivers impacting spatial and socioeconomic changes. Industrialization, exportation node, infrastructure, transportation, and hydropower grid are the primary development forces converging in

Barcarena (Estatística Municipal de Barcarena, 2014; Monteiro & Monteiro, 2007;

Moura & Maia, 2012). Location also places Barcarena in global trade. The Port of Vila do Conde is an import node in commodities export and global trading. Barcarena is a city of the forest connected to the world, with important intra-regional and global performances.

3 Include population estimates for 2017 (Tourinho et al., 2018).

As a municipal unit, Barcarena is a heterogeneous layer. Its large area includes undisturbed natural areas and allows a variety of social uses of space. It also evidences an example for blurred frontier between the urban and the rural landform and use in the

Amazon. The diverse use of space in Barcarena includes major industrial zones, solid residues deposit, processing facilities, a Company Town settlement, a large-scale port, local small-scales, and the central area that concentrates the municipal headquarters.

Barcarena is 300 km from the Tucuruí dam, and is spread across five different municipalities. The municipality is not located in the surrounding areas of Tucuruí’s reservoir to be considered directly impacted (Aneel, 2001; Moretto, 2016). However, this does not prevent establishing indirect hydropower impacts on urbanization. I propose to rethink and expand the impacts of dams beyond where the energy is produced or processed to include locations where it is also being received. Thus, establishing a connection between the hydropower system and indirect urbanization impacts.

Theoretical Ground for Urbanization, Urban Morphology, and Urban Land Change

Global urbanization has reached 54% in 2014. World dominant economies show an even higher urban population: 73% in Europe, 81% in the US, 85% in Brazil, and more than 70% in the Brazilian Amazon. Following the urbanization trend, global rural population is expected to decrease from 3.4 billion to 3.2 billion people between 2014 and 2050 (United Nations, 2014).

Urbanization is a process of population migration from rural lands dominated by agricultural activities to areas where economic activities are dominated by manufacturing and service (Montgomery, Stren, Cohen, & Reed, 2013; United Nations,

2014). Urbanization, therefore, is more than spatial. It involves aspatial aspects as social processes manifesting in various forms and scales.

In most cases, urbanization manifests in spatial forms. The city or urban space becomes central, as processes unfold, and is the unit-base for different scales of measurement. Metropolitan area is a synonym for city or urban area. Intrametropolitan scale addresses individual cities, while intermetropolitan scale can include a network of cities. This research consideres the growth of urban space as equivalent to the morphology of urbanization, occurring at an intrametropolitan scale. A variety of theories are used to classify urban morphology, perhaps because of diverse outcomes related to physical and social characteristics space. Using a broad scale of forms, morphology can usually take five shapes: sprawl, infilling, extension, linear development, and large-scale projects. Urban sprawl is scattered suburbanization. Infilling is the creation of new developments in urban areas with the past use deactivated (railroad yard, waterfront, industrial brownfield). Urban expansion through large-scale projects occurs when new zones are created to provide for airports, ports, and industrial sites. Extension is the standard form of urban expansion, wherein land adjacent to urban use becomes urban.

Linear development is an extension driven by transportation infrastructure and shaped by its corridors (Camagni, Gibelli, & Rigamonti, 2002; Japiassú & Lins, 2014).4

4 Three of the five urban expansion forms are usually used interchangeably. Linear development, urban extension, and urban sprawl can be treated as synonyms. Some analysts refer to sprawl as the generalized form of an urban extension. However, sprawl, especially in the English academic literature has captured the headlines for being a phenomenon typical of the North American urbanization pattern, most often linked to a negative connotation that connects vigorous urbanization unrelated to population pressures to unnecessary use of land. Urban sprawl usually produces less densely populated areas and is linked to the excessive spatial growth of cities (Brueckner, 2000; Brueckner & Fansler, 1983; Ciscel, 2001). Given this negative connotation and avoiding discussing the economic merits of this urban expansion format, urban sprawl constitutes the classic model of the United States suburban-based development. It can also be seen elsewhere, although not as a dominant phenomenon. The sprawl phenomenon in Europe, although restricted by land limitation among others, has been addressed as a concern of scholars and governments alike (European Environment Agency, 2006; Rics et al., 2007; United Nations, 2014). In Brazil, on the other hand, not only the classic concept of sprawl is not easily multiplied as a form of urban expansion, but also other distinguishable formats are identified such as “unoccupied land installment” and differentiation between intra and extra-urban occupation and municipal boundaries (Japiassú & Lins, 2014).

Classic theories of urban expansion assume that urban areas develop as a succession to agricultural use of land (Godfrey, 1990; Godfrey & Browder, 1996; Muth,

1961; Simmons et al., 2002; Watkins & Perry, 1977). The classic theory of von Thünen implies an evolution in the pattern of agriculture to a form that maximizes location rent

(Thünen & Hall, 1966). This foundational concept was later adapted to urban areas by

Alonso (1977) and by Walker (2001) to explain urban sprawl. Turner's (Turner, 1920;

Turner & Bogue, 2010) classical yet recently revisited model of frontier expansion was used to explain the process in the Midwestern United States, where urbanization did involve succession to agricultural land use across a broad region. This model has also been applied around the globe, including other parts of Brazil (L. A. Brown et al., 1994;

Bylund, 1960; Hudson, 2018; James, 2018). Location theory helped to ground spatial analysis and geographical elements in economics (Fujita, 1996; Fujita et al., 1999;

Walker & Richards, 2013). To explain urban expansion and the role of agriculture in this transition requires understanding the connections among urban and agricultural land, population, and activities.

Few studies have addressed links between urbanization and land cover change.

Urbanization in a resource frontier was analyzed aspatially. Studies claimed that

Amazônia’s cities and towns grow without the prior agricultural stage commonly found in urban land use models (Becker 1990; Godfrey 1990; Godfrey and Browder 1996;

Guedes, Costa, and Brondízio 2009). Becker said urbanization in the Amazon is not a result of agriculture: instead, the "frontier is born already urbanized." Thus, cities emerge directly as cities and not successors of agricultural lands (1990, p.44). Although this concept accounts for the urbanization process, it fails to consider spatial expansion.

Thus, urbanization as a spatial process in the Amazon lacks empirical analysis. If urbanization is a distinct spatial process, failing to consider its morphology leaves a gap in understanding of physical components of the Amazon.

Mapping urbanization in the Amazon region requires thinking outside box.

Rainforest cities are still poorly defined. The Amazon is a trendy research topic, but few studies focus on the increasingly populated urban zones (Browder, 2002; Guedes et al.,

2009; Perz, 1999; Simmons et al., 2002). And fewer relate increased urbanization in

Barcarena as an outcome of investments and infrastructure elsewhere.

Pioneer urbanization studies of the Amazon include the book Rainforest Cities

(Browder & Godfrey, 1997). Its main theory assumes principles are independent and not originating from a single master principle. Nine independent principles compose the conceptual framework of Rainforest Cities. As seen through the principles of urbanization, Barcarena (1) is a heterogeneous space; (2) has a configuration of settlement systems that is irregular and polymorphous, disarticulated from any single master principle of spatial organization; (3) its urban growth is fundamentally disarticulated from agricultural development; (4) its dynamics of rapid urbanization are disarticulated from the process of regional industrialization; (5) its urbanization is variously linked to economic forces operating at the global level, but is not subordinated to a world economic system; (6) is a technological crossroads linking specific activities to global circuits of information and exchange; (7) is largely a geopolitical creation but remains politically disarticulated within the centralist state; (8) its established dichotomous categories of rural and urban become problematic because of complex and regionally heterogeneous local migration patterns; and (9) environmental change,

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including tropical deforestation processes, are increasingly mediated by regional, urban- based interests. Even considering Barcarena’s urbanization as an outlier among

Amazon municipalities, the municipality fits into the main profile described by Browder and Godfrey (1997).

Historical and Population Context

Barcarena’s fate was shaped by the discovery of large bauxite deposits in

Oriximiná, 600 km away. Bauxite is a mineral compound with high aluminum concentration. Refining bauxite into aluminum is highly water and energy dependent.

Mining extraction dictated the vertex of development policies from the 1970s (Serra &

Fernández, 2004; WCD 2000). Since then, mining is responsible for socio, political, economic and spatial changes in the state of Pará.

In 1973, the first petroleum crisis increased the price of energy worldwide and jeopardized the technological and aluminum-dependent Japanese industrial production.

The aluminum production chain is one of the highest energy-consuming industries.

Along with this international crisis, discovery of sizeable bauxite reserves in the northeastern Amazon led to the opening of an industrial plant to process bauxite into alumina in Barcarena. Up to this point, Barcarena had no prospects of becoming an industrial hub (Pinto 2012; 2000).

The choice for Barcarena relies on geographical advantages. Distance and access to the bauxite mines, and waterway convergence of Amazon rivers and the

Atlantic Ocean allowed for a large-capacity port. In addition, the site has abundant access to water for industrial use and it is near an urban center. Finally, Barcarena offered available and unoccupied land (Nahum, 2008; Pinto, 2012; M. G. da C. Tavares,

2011; WCD, 2000).

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Between 1980 and 2010, Brazil’s population grew 57%, and Amazon’s population grew 134%, above the national average (Figure 4-2). Pará’s total population increased 116%, representative of the regional pattern. Barcarena’s population grew by

399%. A suddenly industrialized economy with a significant population increase makes

Barcarena an interesting case of land use changes.

Comparing population increase in different geographic-temporal scales shows more intense urbanization in Barcarena (Figure 4-3). Amazon’s population growth is higher than Brazil’s, indicating interregional migration. In 1980, Barcarena’s small town population was below national and regional levels, and mostly rural. In 1991, population increased by 129%, despite Brazil facing economic recession and decreased growth rates (IBGE, 1970, 2012). Currently, there are five large-scale mining plants in the municipality.

Urban Versus Rural Population: A Dichotomy

Boundaries between urban and rural areas in the Amazon are blurred. The dichotomy passes through a more in-depth discussion of the meaning of rural and urban, according to local perspectives (Eloy & Brondizio, 2015a; Guedes et al., 2009;

Rego, 2015; Schmink & Wood, 1992). Barcarena’s population defies urbanization trends as rural population continues to increase.

Given Amazon’s urbanization rates, it would be expected that Barcarena’s urban population surpassed rural as industrial activity is a stimuli for urban migration. Higher increase in urban populations is observed even in municipalities with no industrial activities. Partly, this can be explained because the outdated sectorial classification by the Brazilian Institute of Geography and Statistics (IBGE). In Barcarena, 90 municipal sectors are considered rural and 34 sectors are urban. The IBGE sectorial classification

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has remained static for the last three census periods (1991 to 2010) (Tourinho et al.,

2018). Some sectors are considered rural nonetheless the current spatial configuration points at urban land use and cover. Carmo (2015) points to the company town Vila dos

Cabanos as an example of population accounted as rural when, in fact, it is an urban environment.

Despite industrialization, Barcarena labor demand does not absorb the population influx. Intramigration leads people to occupy areas classified as rural, causing urbanization to spread. This sector of the population is caught up between the dichotomy of rural and urban lifestyle, nonetheless, driving urbanization to more remote areas.

Although this study considers the links between the spatial configuration of urban as the main focus, it also acknowledges that spatial reconfiguration of urban areas is, in most cases, directly linked to the increase in urban population. Such dichotomy in

Barcarena’s population calls for a reassessment of urban and rural boundaries.

Spatial Reconfiguration, a Broad Approach for Connections: 30 or 300

Mega infrastructure ventures commonly encompass transportation and energy generation. Such ventures also function as the primary sponsor for economic development (Little, 2014). This is true for the large-scale dam of Tucuruí, whose energy production made viable the development of an industrial hub in Barcarena.

Impacts of the dam extend beyond industrialization support and have been widely discussed (Campos, 2011; de Faria, Jaramillo, Sawakuchi, Richey, & Barros, 2015;

Latrubesse et al., 2017; Moore et al., 2010; Soito & Freitas, 2011; Tundisi et al., 2014).

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Nonetheless, many of the impacts are unaccounted for (Laurance, 2019).

Undercounted socioecological impacts, including on indigenous and traditional population livelihood has not been restricted to the Amazon (Athayde, 2014; Fearnside,

2016). The lack of social and environmental data (Hyde et al., 2018) implicates impact monitoring. In addition, a large number of dams started operating before collecting baseline data (Castello et al., 2013). Frameworks to assess the dam’s impact therefore require a multidisciplinary approach given the multiple layers of interactions, from biophysical to socioeconomic and geopolitical (P. H. Brown, Tullos, Tilt, Magee, & Wolf,

2009).

There is challenge in classifying and defining extents of direct and indirect impacts of dams (Little, 2014). Dams’ impacts are counted based on direct physical connection to the reservoir, downstream and upstream, failing to include interactions of spatial and socio-economic networks outside the direct connection. Few studies link

Barcarena to possible impacts of the Tucuruí Dam (Fearnside, 2016; D. R. De Lima &

Mota, 2009; Pinto, 2012). State research agency Fapespa included Barcarena among the municipalities related (not impacted by) to the Tucuruí Lake Region to proposed integrated policies (Costa & Pires, 2015). Nonetheless many studies acknowledge

Tucuruí Dam’s energy supply is vital to Barcarena’s industrial activities (Moura & Maia,

2012; Nahum, 2006; M. G. da C. Tavares, 2001; Trindade Júnior & Barbosa, 2017). In most cases, Barcarena’s social and environmental impacts are restricted to triggers of industrial activity within the municipal boundaries (Monteiro, 2006; Monteiro & Monteiro,

2007); rarely are they related within the outreach of direct or indirect impacts by

Tucuruí.

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Rethinking the horizon of the dam’s impact seems even more relevant when considering that hydroelectricity accounts for 77% of the country’s energy matrix, and industrial processes consume nearly half of that energy. National energy and development policies continue to rely on building more dams in the Amazon (Almeida

Prado et al., 2016; Brazil, 2018; IIRSA, 2011; Timothy J. Killeen, 2007).

By reinforcing the already mentioned connection between Barcarena and the

Tucuruí dam, I propose to expand the notion of space and its configuration, adding dimensions beyond topographic, and contesting the notions of far and near. Instead, the focus on socio-spatial interactions and consider the relational space as an element that connects Euclidian-distant elements (Harvey, 1994; Warf, 2010). Tucuruí Dam serves as the agent of the sending system, and Barcarena as an agent of the receiving system, with flows of energy, investments, and infrastructure that culminates in urbanization increase— in a certain way, expanding the framework of teleconnections (Munroe,

McSweeney, Olson, & Mansfield, 2014; Karen C Seto et al., 2012).

The 30 kilometers between Barcarena and Belém, and the 300 kilometers between Barcarena and Tucuruí dissolves in the face of other factors. Energy, rather than mineral resources or labor, is the main component of the industrial process

(Fearnside, 2016).

Urban Land Expansion

According to location and urban theories (Alonso, 1977; Jacobs, 2016), urban expansion usually happens through encroachment of agricultural land. Classic location theory considers land rent as a function of distance from the city center, and accounts for agricultural use preceding urban land expansion. Urban land expansion causes agricultural land to expand further from the city. Therefore, urban encroaches onto

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agriculture that encroaches on the forest. It can be inferred that there is no direct transition from urban onto the forest.

Data and Methods

This study used remote sensing and GIS to generate Land Cover classification and a Land Cover Change (LULC) to provide land cover information (Chen, Powers, de

Carvalho, & Mora, 2015; De Carvalho & Szlafsztein, 2018; Herold, Roberts, Gardner, &

Dennison, 2004; Jensen, 2015; Rindfuss, Walsh, Turner, Fox, & Mishra, 2004; Karen C.

Seto & Christensen, 2013). A supervised classification was performed to produce the spatial-temporal land cover change. Imagery was selected for the years 1984, 1999 and

2017, a 15 and 18 year-interval. In total, there were 33 years of land cover assessment.

These dates cover the start of the dam operations, covers a mid-period after a steep population increase, and extends to recent years.

Data

This study used cloud-free satellite imagery from the Landsat Project for 1984 and 1999 (USGS, 2019) and Sentinel-2 for 2017 (ESA, 2019) to generate a thematic cover map of the area. A search for cloud-free images was the reason for using two different satellite sources. For 1984 and 1999 it was used 2 images from Landsat 5, corresponding to Path 223 and 224 and Row 61 of the Landsat images with 30-meter resolution, mosaicked to match study area boundaries, obtained from the Landsat open access global land cover services (www.glovis.usgs.gov). For Sentinel-2, image correspond to tile 22M. Sentinel images was resampled to match Landsat’s 30-meter resolution, obtained from the Satellite Sentinel Project from http://www.satsentinel.org.

Before image preprocessing, the images were subset to match the study area shapefile extracted from the set of municipal boundaries of the state of Pará (IBGE,

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2019). Images from Google Earth (Google Earth, n.d.) for 1984, 1999, and 2017 were used for each land cover type for image validation.

Land Cover Classification

The aim was to examine the expansion of urbanization, assuming urbanization is the expansion of urban areas. Five main land covers were detected in Barcarena: urban, bare soil, agriculture, forest, and water (Table 4-1). Images from Google Earth

(Google Earth, n.d.) were used to better assess the land cover classes and to produce training sample data as part of the supervised classification (Pabi, 2007; Suribabu,

Bhaskar, & Neelakantan, 2012; Wondrade, Dick, & Tveite, 2014).

Between 30 and 60 training samples (TS), corresponding to points and polygons, were collected for each thematic class and for each year of images, created in Google

Earth images for 1984, 1999 and 2017 (Jensen, 2015). The TS is data collection process also considered visual interpretation of the data based on image comparison and dates and previous knowledge of the area. Analysis of TS’s allowed the identification of statistical similarities between the spectral-signatures, contributing to more accurate thresholds of local land cover classification.

Supervised classification with maximum likelihood algorithm was applied to each image (Dean & Smith, 2003; Lu, Mausel, Brondízio, & Moran, 2004; Yuan, Sawaya,

Loeffelholz, & Bauer, 2005). In addition to the raw image bands, additional layers were also created. A Soil-Adjusted Vegetation Index (Huete, 1988), Tasseled Cap transformation (Crist & Cicone, 1984; Healey, Cohen, Zhiqiang, & Krankina, 2005) and a Principal Component Analysis (PCA) (Deng, Wang, Deng, & Qi, 2008; Zabalza et al.,

2014) were all created.

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Supervised classification based on the maximum likelihood algorithm is appropriate given the purpose of this study to survey urban sprawl. The focus was on spatial extent of built-up areas.

Maximum likelihood classification has been widely used, as it reduces the data required (Hassan, 2017; Jensen, 2015; Yuan et al., 2005). Nonetheless, it delivers great potential to extract information from the urban scenes. Erdas Engine was used to apply the maximum likelihood algorithm and thus created the five thematic classes.

Results and Discussion

Barcarena Land Cover Classes

Forest

The largest-area land cover class was forest. Forest cover represents vegetation in any stage, other than agricultural use. The areas have low seasonality and low variation in canopy density. Barcarena has forest cover intertwined with other uses across its entire area. A dense forest area can be near urban areas and industrial sites; or intertwined with agricultural land. Some stages of agricultural areas may be confused with forest. Forest cover was visually easily distinguished from agricultural use based on texture and intensity of green on Google Earth imagery for 2017, with clear resolution.

Natural forest has irregular shapes, and agricultural activities have more defined boundaries. To distinguish agriculture areas with vegetation and forest for lower resolution images in 1984 and 1999, forest cover was divided into subcategories of dense and sparse forest. Next, they were stacked into one single category.

Agriculture

Herein, agricultural land use and cover encompasses agriculture, and pastureland. Land use for crops at all stages, pastures and recently deforested areas

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are classified under agriculture to provide training and test samples. An added challenge consisted of distinguishing the class of bare soil from agriculture as it can be a step for the cultivation process, or to pasture use. In Barcarena, there were many patches with some vegetation surrounded by forest, usually in isolated areas. Such areas usually account for deforestation. Deforestation may happen to extract logs and wood. In some cases, areas go through natural or managed secondary regrowth and reforestation or migrate to agricultural and pastureland. Using this guideline to provide training and test samples, agricultural areas are considered as distinguishable from forest at all other stages, independently from the preceding or proceeding stages.

Bare Soil

As an industrial site, Barcarena presents some open areas for industrial residues. These areas varying land cover depending on the stage, from open plain soil to chemical deposit. Mapping bare soil becomes relevant as outdoor industrial deposits expand. Although representing a small percentage of Barcarena’s land cover, its presence indicates changes in the land use and cover dynamics that include expansion of industrial activities and urban land use. To map these sites expansion in previous years, the training points for bare soil classification were expanded to match the different cover stages, meaning it would eventually pick up soil as in unpaved roads.

Bare soil also occurs as a stage for agricultural use and urban land expansion. Bare soil training and testing samples represent open, non-vegetated areas and also encompass industrial deposits. Over the years, the industrial deposit areas have been extended as the aluminum chain activity also increased. Bare soil in industrial sites usually present high concentration of bauxite or kaolin clay, presenting distinct spectral signatures that

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were combined into a single class that includes other types of open soil that are neither agriculture nor urban.

Urban

Mapping urban land expansion is complicated in Barcarena because other land uses, including agricultural activities, are often intertwined with urban areas. The class that stands for urban cover encompasses built up areas in general. Conversely, the profile of different spectral signatures and urban materials adds an extra challenge in isolating those areas. More specifically, the built structures comprise mostly of different roofing materials, some vegetation and/or gardens, unpaved roads (which provide soil- like signatures), and paved roads, thus providing mixed spectral signatures when considering an overall urban area. The residuals and dirt particles from soil, industrial residues sites and unpaved roads accumulate on top of the roofs, made usually of either ceramic or metal, causing additional mixture of signatures, mixing pixels that might correspond to bare soils instead of urban or buildup areas.

Water

Water covers a significant area in Barcarena. Its boundaries include a large portion of river in the northern part. The area also presents some rivers and creeks running inside the territory. Water is not included in trajectory maps, analysis, nor considered as part of the total land cover assessment, as it presents little variability.

Accuracy Assessment

Since image heterogeneity presents a major challenge in land cover classification, accuracy assessment was carried out in combination with manual interpretation of Landsat images, topographic maps, google earth high resolution images, and random stratified sampling. For each image between 150–180 points were

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created using the equalized stratified random function in ArcMap Pro (Hassan &

Southworth, 2017; Petit et al., 2001). The number and spatial distribution of verification points were varied due to dominance of certain land cover classes. The testing points were cross-checked and validated manually through overlays with the true color original

Landsat and Google Earth imagery corresponding to the year and approximately the month of collection.

The confusion matrices generated the overall accuracy, user’s accuracy, producer’s accuracy, and the non-parametric Kappa coefficient (Table 4-2). The overall classification accuracy was 83–96%, with kappa values ranging between 83% and 96%.

The overall accuracies for 2017 presented the highest value, indicating higher quality in imagery. The Kappa statistics for 2017 was also the higher, 95%. The lowest overall and kappa accuracies were measured for 1999. Water presented the highest user and producer accuracy in all three years. User’s accuracies for urban were over 95%, and producer’s accuracy varied from 76 to 92%. Agriculture presented a reduction in producer’s accuracy for 1999. Bare soil presented the lowest user accuracies in 1984 and 1999. Bare soil cover can often be confusing and perhaps the most challenging task in image classification, particularly in Barcarena, given the similar spectral reflectance as stages of agricultural land use, unpaved roads and open areas within the urban environment. Liquid content from the industrial residue deposits may also be mistaken as water pixels. Forest presented consistent levels of user accuracy, and producer’s accuracy ranged from 85 to 96%.

Land Cover Change and Morphology

Barcarena is a dynamic area, with changes in land use and land cover throughout the area. Urban land use is mostly in the northern portion of the territory,

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around its headquarters and industrial plants. Paved and unpaved roads link the other sparse urban cores. Another area to the west has a higher concentration of urban land use. Forest is the main coverage throughout the territory. Land-cover maps (Figure 4-4) show agricultural use as sparse. In 1984, without considering water bodies as a land cover, Barcarena was 87% forest cover (690 km2), 2% urban land cover (15 km2), 8% agricultural use (63 km2), and 4% bare soil (28 km2). In 1999, all coverages change slightly. Forest cover decreases slightly to 85%, while urban slightly increases to above

2%. Bare soil increases to 5.5% (38 km2), and agricultural use increases to over 9% (65 km2). In 2017, more significant changes are observed in all, but agriculture. Forest cover decreases to 78%, urban cover expands to 6%, and bare soil to 7%. Agricultural use, instead, presents a small decrease to just below 9%.

From 1984 to 2017, there was a loss of 75 km2 of forest, and an increase in 32 km2 in urban area, 30 km%, representing 75 km2 of loss of green coverage. Urban land use expands to almost 9%, 32 km2 of added area. Similarly, bare soil expands 30 km2, while agriculture, even though presenting an overall increase from 63 to 70 km2, presents the smallest increase in percent of total area (7%). There was a slight variation in area across the years, most probably because of non-significant errors and resolution differences.

Urban land expansion in Barcarena is polymorphous. Extension is observed in the areas surrounding the municipal headquarters and company towns. New urban areas are adjacent to the occupation in 1984. Nonetheless, in 1999 and 2017, the focus of urban sprawl is in the central and southern part of Barcarena. The spatial pattern also indicates these urban areas showing some linear development along roads.

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Concluding Remarks

Population increase has restructured Barcarena in the last four decades. The dynamics, outcomes and drivers of urbanization and demographic concentration ought to be included in the priorities of topics that help understand the Amazon’s complex reality.

The temporal frame provided a reliable baseline to understand some of the impacts of an increasing hydropower system. Barcarena serves as an example of the diversity of the Amazon’s physical and social changes. Both population and land cover showed significant changes from the 1980s to the current time.

The main proposition was that urban land conversion into forest and agricultural area would surpass agricultural activities due to the accentuated urbanization process.

Land cover trajectory maps showed that the classic theory of urban land expansion

(Alonso, 1977; Thünen & Hall, 1966) still applies to the urbanization process in

Barcarena. In fact, there has been an expansion of agricultural land cover. Yet, when using the temporal changes, the prediction pattern show that urban land area might surpass agriculture (Figure 4-5).

Linking the hydropower system with spatial and populational changes in

Barcarena seems a logic connection due to the destination of energy, used in the industrial processes that takes place in the municipality.

This study has demonstrated that development processes happening in

Barcarena are complex, and spatial changes occurring as a consequence of the urbanization process and the overall population increase are still not easily assessed, even considering the five main land cover classes.

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Aiming at Amazon’s environmental preservation, it appears prudent to recognize the vital role of the local population and the urbanization tendency Barcarena presents.

The impacts of hydropower systems can unfold in cascade effect onto urbanization processes in places at distant over space but, nonetheless connected by the conduction and destination of energy. These regions should be considered as part of the impacts new dams may have on distant places. Mostly, connection such as energy destination should be taken into account when assessing hydropower impact on urbanization within and outside Amazon’s boundaries.

This study thus strengths the connections and drivers of urbanization beyond what is most often taken into account. As the Amazon acquires an urban profile, investigating the impacts and triggers of these processes contributes to a better assessment of local reality and spatial reconfiguration. Although Barcarena is an outlier, combining major industrial activities and transportation nodes, urbanization shows no sign of retreating. Barcarena shows how those processes are reshaping this region.

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Figure 4-1. Study area: Barcarena and State of Pará and the Brazilian Amazon

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129%

62% 60% 58% 52% 48% 38% 28% 26% 21% 23% 19%22%

15%12% 12% Population Increase Percentage Brazil Amazon Pará Barcarena

1980 1991 2000 2010

Figure 4-2. Total population increase percentage by decade in Brazil, Amazon, Pará and Barcarena (Source: IBGE, 1980, 1991, 2010)

140000

120000

100000

80000

60000 Population 40000

20000

0 1980 1991 2000 2010 2018 Years

Rural Urban Total

Figure 4-3. Barcarena urban, rural and total population 1980-2018 (Source: IBGE 1980, 1991, 2010)

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Table 4-1. Land cover classification Land Cover Description

Urban Built up, residential and commercial, paved and unpaved roads, most metal

and ceramic roof

Agriculture Agriculture and activities in all stages and animal husbandry pastureland; main

crops are banana, cacao, coconut, orange, lemon, papaya, and black pepper

Forest Forest cover in stands for any other vegetation other than agricultural

Bare Soil Soil without coverage, industrial soil and residues deposits of bauxite ore and

kaolin clay

Water Water bodies

Figure 4-4. Barcarena land cover change from 1984 to 2017 per class

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Table 4-2. Accuracy assessment for 1984, 1999, and 2017 with users’ accuracy (UA), producers’ accuracy (PA), overall accuracy (OA) and Kappa accuracy (KAPPA) 1984 1999 2017 UA/PA UA/PA UA/PA Urban 96/79 95/76 95/92 Agriculture 96/88 95/83 92/96 Bare Soil 46/100 40/89 98/98 Water 100/89 100/77 98/98 Forest 96/85 85/100 96/96 OA 87% 83% 96% KAPPA 83% 79% 95%

2 Percentage Percentage of TotalArea

0 1984 1999 2017

Urban Agriculture

Figure 4-5. Prediction of urban and agriculture land cover change

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CHAPTER 5 CONCLUSION

Urban expansion in Brazil is an intensive process based on rural out-migration.

However, empirical accounts of urban expansion in the Amazon appear to challenge existing labels for various reasons. As a relatively recent phenomenon, urbanization in the Amazônia faces the unique context of a forest frontier. The geographical, environmental, historical, and social characteristics that compose this frontier are still responding to unprecedented spatial changes.

Urbanization in the Brazilian Amazon has intensified in recent decades, with urban population rates surpassing 80% and over 400 new municipalities created in the past forty years. The Amazon has presented intense spatial reconfiguration the in the past decades that per passes but is not limited to the loss of forest cover. Under the reginal dynamic, there was a significant population increase and a shift in the geopolitics of the region, with actions that stated in the second half of the last century, led by governmental approach to reordination on how the space and resources were to be used and explored. Urbanization in the Amazon became a dominant process to reshape spatial configurations.

In this work, Chapter 2 shows that hydropower and urbanization rates present clear connections. At a regional scale, stronger impacts on urbanization are felt in the southeastern Amazon, where there is the higher number of hydropower system elements, including the oldest large-scale dam in operation, and its transmission lines and substations. This results from this chapter will allow for future research to concentrate on adding other variables to the statistical analysis, as well as search for a model that better explains the relationship between variables at a unit of analysis level.

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In Chapter 3 approached a state scale, and also presents an overall result of a greater impact on the percentage of urban population in municipalities presenting some form of physical infrastructure of the hydropower network is greater than on those without. The longer a municipality is directly connected to a hydropower system structure, the higher is the effect on urbanization. Furthermore, this work also shows a connection between the creation of new municipalities and the hydropower infrastructure network. The results from this Chapter will allow for the next steps of research considering: a thoroughly revision of the proposed and conventional categorical variable, with updated spatial layers incorporated; and the inclusion of a buffer zone to establish distance to roads.

Chapter 4 presented spatial changes and urbanization expansion at a municipal scale not directly considered affected by dams. This research has shown significant losses of forest and changes in land use and land cover, pointing at expansion of urban areas and gradual reduction of agricultural land. Land conversion from forest into urban and industrial use are significantly higher than agricultural conversion. As the municipality's population growth, chances are there will be the environmental disturbance of natural ecosystems at least along the areas the population is spread.

This Chapter will allow for the next steps of research to be concentrated on building a trajectory model to predict land cover changes; and include additional data with imagery for five year interval.

Overall, this study has evidenced a heterogeneity within the urbanization process in different scales in the Brazilian Amazon, in what is here been called Amazons within the Amazon. Above all, has also demonstrated the importance of urban expansion and

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urbanization increasing rates for the spatial reconfiguration of the rainforest. While attempting to isolate the hydropower system as single driver of the urbanization process, it is acknowledged that such a complex outcome deviates for a complex and diverse set of other elements’ interactions.

Facing the energy demand and the Amazon hydroelectrical potential, investments in amplifying the hydropower system are most likely continue to increase.

In most cases, as a direct and indirect source of environmental disturbance and loss of natural coverage, while encouraging an unplanned and massive urbanization.

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BIOGRAPHICAL SKETCH

Roberta Mendonça De Carvalho was proudly born and raised in the largest city in the Brazilian Amazon - Belém, Pará. She received her PhD from the University of

Florida on urbanization in the Amazon. Her current research interests embrace urbanization as a worldwide process and how it unfolds at the local scale. Her interdisciplinary background gathers social, physical and environmental geography applied to urban studies in a multiscalar approach. She uses Remote Sensing to filter urban spatial reconfiguration as well as the loss of natural areas within city boundaries.

Roberta has also worked in private, government and non-governmental sectors in positions that advocate for local matters related to the environment and development.

She holds a BA in Business Administration and an MA in Natural Resources

Management and Local Development in the Amazon from the Federal University of

Pará. She is a faculty in the Urban Studies Program at the University of Pittsburgh.

Roberta is also a member of the Amazon Dams Network, has won the Ruth McQuown

Scholarship Award 2019 and is a former Water Institute Graduate Fellow Cohort from

2015.

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