Joao Meirelles

Global sustainability relies on our capacity of understanding and guiding urban systems and their metabolism adequately. It has been proposed that bigger and denser cities are more resource-efficient than smaller ones because they tend to demand less infrastructure, consume less fuel for transportation and less energy for cooling/heating in per capita terms. This hypothesis is also called Brand’s Law. However, as cities get bigger, denser and more resource-efficient, they also get richer, and richer inhabitants consume more, potentially increasing resource demand and associated environmental impacts. In this paper, we propose a method based on scaling theory to assess Brand’s Law taking into account greenhouse gas (GHG) emissions from both direct (energy and fuels locally consumed) and indirect (embedded in goods and services) sources, measured as carbon footprint (CF). We aim at understanding whether Brand’s Law can be confirmed once we adopt a consumption-based approach to urban emissions. By analyzing the balance between direct and indirect emissions in a theoretical urban system, we develop a scaling theory relating carbon footprint and city size. Facing the lack of empirical data on consumption-based emissions for cities, we developed a model to derive emission estimations using well-established urban metrics (city size, density, infrastructure, wealth). Our results show that, once consumption-based CF is considered, Brand’s Law falls apart, as bigger cities have greater purchase power, leading to greater consumption of goods and higher associated GHG. Findings also suggest that a shift in consumption patterns is of utmost importance, given that, according to the model, each new monetary unit added to the gross domestic product (GDP) or to other income variables results in a more than proportional increase in GHG emissions. This work contributes to a broader assessment of the causes of emissions and the paradigm shift regarding the assumption of efficiency in the relationship of city size and emissions, adding consumption behavior as a critical variable, beyond Brand’s Law.

Nos últimos anos, a nova Ciência das Cidades se estabeleceu como uma abordagem quantitativa fértil para o entendimento dos fenômenos urbanos. Um de seus pilares é a proposição de que os sistemas urbanos apresentam comportamentos universais de escala de variáveis socioeconômicas, de infraestrutura e de serviços básicos individuais. Este artigo discute até onde essa proposição é realmente universal, testando-a frente a uma ampla variedade de métricas urbanas de um país em desenvolvimento. Apresentamos uma exploração dos expoentes de escala(mento) de mais de 60 variáveis do sistema urbano brasileiro. A estimação dos expoentes é um desafio técnico, dado que a definição de "município" no Brasil segue critérios políticos e não considera as características da paisagem, a densidade e os serviços domiciliares básicos. Considerando que os municípios brasileiros podem não ser iguais ao que se entende por assentamento urbanizado, selecionamos os mais assemelhados a "cidades" através de um procedimento sistemático de corte da densidade e estimamos os expoentes desse subconjunto. Para validar nossos achados, comparamos nossos resultados para as mesmas variáveis com outros estudos baseados em métodos alternativos. Os resultados mostram que as variáveis socioeconômicas analisadas seguem uma relação de escala superlinear com a população das cidades, enquanto a maioria das variáveis de infraestrutura e de serviços básicos domiciliares seguem os esperados regimes sublinear e linear, respectivamente. No entanto, algumas delas fogem dos regimes esperados, botando em dúvida a hipótese universal do escalamento urbano / leis de escala. Nossa conclusão é de que esses desvios são produto de decisões e políticas impostas "de cima para baixo". Mesmo que nossa análise abranja um período de apenas 10 anos e, portanto, não seja suficiente para permitir conclusões definitivas, há indícios de que os expoentes de escala dessas variáveis estão evoluindo e que os desvios podem ser apenas comportamentos restritos no tempo que não impedem os sistemas urbanos de atingir o regime esperado em algum momento futuro.

Does the scaling relationship between population sizes of cities with urban metrics like economic output and infrastructure (transversal scaling) mirror the evolution of individual cities in time (longitudinal scaling)? The answer to this question has important policy implications, but the lack of suitable data has so far hindered rigorous empirical tests. In this paper, we advance the debate by looking at the evolution of two urban variables, GDP and water network length, for over 5500 cities in Brazil. We find that longitudinal scaling exponents are city-specific. However, they are distributed around an average value that approaches the transversal scaling exponent provided that the data is decomposed to eliminate external factors, and only for cities with a sufficiently high growth rate. We also introduce a mathematical framework that connects the microscopic level to global behaviour, finding good agreement between theoretical predictions and empirical evidence in all analyzed cases. Our results add complexity to the idea that the longitudinal dynamics is a micro-scaling version of the transversal dynamics of the entire urban system. The longitudinal analysis can reveal differences in scaling behavior related to population size and nature of urban variables. Our approach also makes room for the role of external factors such as public policies and development, and opens up new possibilities in the research of the effects of scaling and contextual factors.

Global sustainability relies on our capacity of understanding and guiding urban systems, and their metabolism, in an adequate way. It has been proposed that bigger and denser cities are more resource-efficient than smaller ones because they tend to demand less infrastructure, consume less fuel for transportation and less energy for cooling or heating in per capita terms. This hypothesis is also called Brand's Law. But as cities get bigger, denser and more resource-efficient they also get richer. And richer inhabitants buy more, increasing its resource demand and associated environmental impacts. To fully understand the nexus between population size or density and the environmental impacts generated by a city, one needs to take into account both direct and indirect impacts. Facing the lack of empirical evidence on consumption-based emissions for cities, in this paper we propose a mean-field model to derive emissions estimations out of well-established urban metrics (city size, density, infrastructure, wealth). We aim at understanding if Brand's law holds true after adopting a consumption-based approach to urban emissions. The proposed model shows that when considering consumption-based emissions, in most cases Brand's law falls apart - bigger cities have greater purchase power, resulting in greater consumption of goods and higher associated GHG emissions. The model also shows that decoupling between population and emissions is possible and dependent on the decoupling level between income and impacts. In order to achieve it, a shift in consumption patterns of most cities is of utmost importance, so that each new monetary unit added to the GDP, or any other income variable for that effect does not result in a proportional increase in GHG emissions.

Greenhouse gas (GHG) emission inventories form the basis of evidence-based climate change planning across the local, regional, national, and international levels. In this letter, we present a consumption-based GHG accounting approach for estimating the carbon footprint (CF) comprising direct and indirect emissions of households in Switzerland for 2008, 2011, and 2014 and examine the impact of urbanity and socioeconomic variables on these estimates. The CF model used herein couples regionalized household budget surveys (HBS) with environmentally-extended input-output analysis (EEIOA). We provide greater insight into the obscure process of combining bottom-up consumption data (i.e., HBS) and top-down input-output tables (IOT) in a CF model. The findings show that urban households tend to have lower direct emissions than rural households whereas indirect emissions are higher. Therefore, the nature of both direct and indirect emissions should be considered when evaluating the role of urbanization, as each has a different focus. Overall, our results indicate that income is the most important driver of household total cf Some local features specific to Switzerland have also been found to be important in shaping the relationship between the household CF and its drivers. We argue that household composition should be the focal point for future study of CF mitigation in Switzerland, and that policies should prioritise measures that target consumer behaviour and lifestyles, rather than solely focus on improving physical infrastructure and adopting new technologies.

It is clear by now that climate change mitigation relies on our capacity to guide urban systems towards a low-carbon phase and that the urban transportation sector plays a major role in this transition. It is estimated that around 30% of total CO2 emissions worldwide come from the urban transportation sector. Regardless of its importance, detailed estimations of transport-related emissions in cities are still rare to find, hindering our capacity to understand and reduce them. This work aims to develop a replicable and fast method for GHG estimation from GPS (Global Positioning System) data and to introduce a simple sinuosity-based algorithm for such. We applied the method for 1 year of GPS data in the city of Rio de Janeiro. Our results were compared to top-down estimations from fuel consumption and proved to be valid after a simple data filling process. Our GPS-based approach allowed for much finer spatial and temporal descriptions of emissions and we further showed possible policy insights that can be extracted from the estimated emissions based on the proposed method.

Hosting more than half of the world population, cities are currently responsible for two thirds of the global energy use and three quarters of the global CO2 emissions related to energy use. As humanity becomes more urbanized, urban systems are becoming a major nexus of global sustainability. Various studies have tried to pinpoint urban energy use drivers in order to find actionable levers to mitigate consumption and its associated environmental effects. Some of the approaches, mainly coming from complexity science and industrial ecology disciplines, use city-scale data to find power-laws relating to different types of energy use metrics with urban features at a city-scale. By doing so, cities’ internal complexity and heterogeneity are not explicitly addressed. Moreover, to our knowledge, no studies have yet explicitly addressed the potential scale dependency of such drivers. Drivers might not be transferable to other scales and yield undesired effects. In the present study, power-law relations are examined for 10 cities worldwide at city scale and infra-city scale, and the results are compared across scales. Relations are made across three urban features for three energy use intensity metrics. The results show that energy use drivers are in fact scale-dependent and are city-dependent for intra-urban territories.

From physics to the social sciences, information is now seen as a fundamental component of reality. However, a form of information seems still underestimated, perhaps precisely because it is so pervasive that we take it for granted: the information encoded in the very environment we live in. We still do not fully understand how information takes the form of cities, and how our minds deal with it in order to learn about the world, make daily decisions, and take part in the complex system of interactions we create as we live together. This paper addresses three related problems that need to be solved if we are to understand the role of environmental information: (1) the physical problem: how can we preserve information in the built environment? (2) The semantic problem: how do we make environmental information meaningful? and (3) the pragmatic problem: how do we use environmental information in our daily lives? Attempting to devise a solution to these problems, we introduce a three-layered model of information in cities, namely environmental information in physical space, environmental information in semantic space, and the information enacted by interacting agents. We propose forms of estimating entropy in these different layers, and apply these measures to emblematic urban cases and simulated scenarios. Our results suggest that ordered spatial structures and diverse land use patterns encode information, and that aspects of physical and semantic information affect coordination in interaction systems.

From physics to the social sciences, information is now seen as a fundamental component of reality. However, a form of information seems still underestimated, perhaps precisely because it is so pervasive that we take it for granted: the information encoded in the very environment we live in. We still do not fully understand how information takes the form of cities, and how our minds deal with it in order to learn about the world, make daily decisions, and take part in the complex system of interactions we create as we live together. This paper addresses three related problems that need to be solved if we are to understand the role of environmental information: (1) the physical problem: how can we preserve information in the built environment? (2) The semantic problem: how do we make environmental information meaningful? And (3) the pragmatic problem: how do we enact environmental information in our daily lives? Attempting to devise a solution to these problems, we introduce a three-layered model of information in cities, namely environmental information in physical space, environmental information in semantic space, and the information enacted by interacting agents. We propose forms of calculating entropy in these different layers, and apply these measures to archetypal urban cases and simulated scenarios. Our results suggest that ordered spatial structures and diverse land use patterns encode information, and that aspects of physical and semantic information affect coordination in interaction systems.

During the last years, the new science of cities has been established as a fertile quantitative approach to systematically understand the urban phenomena. One of its main pillars is the proposition that urban systems display universal scaling behavior regarding socioeconomic, infrastructural and individual basic services variables. This paper discusses the extension of the universality proposition by testing it against a broad range of urban metrics in a developing country urban system. We present an exploration of the scaling exponents for over 60 variables for the Brazilian urban system. Estimating those exponents is challenging from the technical point of view because the Brazilian municipalities’ definition follows local political criteria and does not regard characteristics of the landscape, density, and basic utilities. As Brazilian municipalities can deviate significantly from urban settlements, urban-like municipalities were selected based on a systematic density cut-off procedure and the scaling exponents were estimated for this new subset of municipalities. To validate our findings we compared the results for overlaying variables with other studies based on alternative methods. It was found that the analyzed socioeconomic variables follow a superlinear scaling relationship with the population size, and most of the infrastructure and individual basic services variables follow expected sublinear and linear scaling, respectively. However, some infrastructural and individual basic services variables deviated from their expected regimes, challenging the universality hypothesis of urban scaling. We propose that these deviations are a product of top-down decisions/policies. Our analysis spreads over a time-range of 10 years, what is not enough to draw conclusive observations, nevertheless we found hints that the scaling exponent of these variables are evolving towards the expected scaling regime, indicating that the deviations might be temporally constrained and that the urban systems might eventually reach the expected scaling regime.