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Understanding slums: analysis of the metabolic pattern of the Vidigal favela in Rio de Janeiro, Brazil

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Abstract

This paper illustrates an innovative approach to characterize the metabolic pattern of informal urban settlements or slums with the aim to better understand the factors that affect the material standard of living of slum residents, the dynamics of slum development and the interaction of the slum with its wider socioeconomic context. The proposed system of accounting, multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM), integrates socioeconomic and spatial data and studies energy and monetary flows in relation to the pattern of human activities and land uses. The theoretical basis of the approach is illustrated with data from Vidigal favela in Rio de Janeiro, Brazil. In particular, we show how to construct taxonomies of accounting categories to characterize: (1) the set of activities carried out by the slum dwellers, to which to link assessments of flow rates per hour; (2) the set of land uses or spatial elements making up the slum, to which to link assessments of flow densities per hectare. The analysis of the interaction of Vidigal with its wider socioeconomic context focuses on monetary flows and transport (job commuting).

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Source: IPP (2014)

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Source: Own development

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Source: Own development

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Notes

  1. Although similar in essence, this definition differs slightly from the one in use by the United Nations (2003): Slums are sites with one or more of the following characteristics: lack of basic services–sanitation, safe water, waste collection, water and roads; unhealthy living conditions and hazardous locations (usually related to the lack of services); poor structural quality of housing; overcrowding; insecure residential status–lack of formal documents of tenure.

  2. Slums located in high-income areas have escaped the “clean up” of the 1960s and early 1970s. At the time, the government attempted to remove much of the slums in these areas. The Vidigal was firstly occupied in the 1940s. In the 1970s, there was an attempted eviction of the slum, when some residents were moved to a housing complex in the western zone of the city. However, dwellers led a movement to remain and for the improvement of local infrastructure, thus preventing the complete removal (Oliveira et al. 2012) and benefiting from being near to the city center, which is still the area that concentrates most jobs.

  3. http://portalgeo.rio.rj.gov.br/sabren/index.html.

  4. Assuming that all commercial facilities are on the ground.

  5. Although obtaining favelas data is usually a difficult issue due to its informal nature, there are relatively good data source for Brazil. At 2010, the national census made a specific research for this kind of habitation. Furthermore, much of the Brazilian favelas are already considered as official region and are included in the municipal planning.

  6. We chose to use the Indice Nacional de Preçosao Consumidor (INPC) price index, for this is an index focused on low-income urban households (households with monthly revenues from one to 5 minimum wages). Source: IBGE (2015).

  7. A fact that primes a chain reaction where more cars are purchased, thus further worsening the city traffic.

  8. Besides the increased travel time, it also results in waste of energy and local pollution. Since at least the travel time and urban pollution externalities are not considered in price, those negative aspects are not perceived by economy. Also, while the loss of time and energy are spent waste inherent in who moves, local pollution is an external cost to the whole society, traveler or not (Pearce and Turner 1989; PERMAN et al. 2003).

Abbreviations

BDRgr :

Total built ground area occupied by buildings for the household sector

EMR:

Exosomatic metabolic rate

EMRHH :

Energetic metabolic rate, household sectors

EMRPW :

Energetic metabolic rate, paid work sector

EMRSA :

Energetic metabolic rate, societal average

ET:

Energy throughput

ETHH :

Exosomatic energy consumption for the HH sector

ETPW :

Exosomatic energy consumption for the PW sector

FAR:

Floor–area ratio

H:

Hours

HA:

Human activity

HAHC+LE :

Time allocated to household chores, leisure and education

HAPO :

“Physiological overhead,” that is the time dedicated to sleeping, eating and personal care

HAPW :

Time allocated to paid work in economic activities

HC + LE:

Household chores, leisure and education

IBGE:

Brazilian Institute of Geography and Statistics

LPG:

Liquefied petroleum gas (LPG)

LU:

Land use

Mh:

106 h

MuSIASEM:

Multi-scale integrated analysis of societal and ecosystem metabolism

NBA:

Total unbuilt area

NBApav :

Paved unbuilt area

Pc:

Per capita

PO:

Physiological overhead

PW:

Paid work sector

TBDgr :

Total built ground area occupied by buildings

TBDin :

Building indoors useful for hosting human activities

TET:

Total exosomatic throughput (TET = ETPW + ETHH)

THA:

Total human activity (in h)

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Acknowledgments

This research was supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Union Framework Programme, under project NETEP-European Brazilian Network on Energy Planning (PIRSES-GA-2013-612263). The authors would also like to express their gratitude to the Coordination for the Improvement of Higher Education Personnel (CAPES—Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for the essential support given for this work to be carried out. The authors would like to thank Eduardo Heck de Sá (United Nations Human Settlements Programme, UN-Habitat) for providing valuable data sets, Victoria Neves Santos for her input in an earlier version of this paper and Sandra G.F. Bukkens for editing the paper. We are grateful to the reviewers for their useful comments and suggestions.

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Miranda, R.F.C., Grottera, C. & Giampietro, M. Understanding slums: analysis of the metabolic pattern of the Vidigal favela in Rio de Janeiro, Brazil. Environ Dev Sustain 18, 1297–1322 (2016). https://doi.org/10.1007/s10668-016-9810-y

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