Assessing cities growth-degrowth pulsing by emergy and fractals: A methodological proposal
Introduction
There is an increasing global interest in understanding and assessing cities shape and limits to growth, and their effects on environmental sustainability and urban sprawl (Sutton et al., 2007; Zhang & Li, 2018). According to the United Nations Environment Program (UN, 2019a), cities generate over 80% of the GDP of many countries in Asia and the Pacific and are engines of economic growth that have lifted millions out of poverty. Cities provide high-quality outcomes in terms of economic development, knowledge transfer, innovation, and social interaction. Cities provide access to recreational areas and public services (e.g. water and sanitation, health care, electricity, and emergency services), and usually feature the central administrative, financial, legislative, and judiciary offices. On the other hand, although occupying 2% of the Earth's surface, cities consume 60–80% of the global energy (Sodiq et al., 2019). In particular, cities demand a high density of energy and goods from their surrounding neighborhood (Odum, 1996; Odum & Odum, 2006; Giannetti et al., 2020), and also import from other regions and countries, which results in the emergence of spatial networks of hierarchical energy transformations (Braham et al., 2019).
In general, in a given region, smaller centers of built-up areas support larger cities with materials and energy, and larger cities act as hubs for manufacturing and distributing goods. This hierarchy results in a convergence of energy, from primary energy transformations to concentration of energy in final goods and services (Giannetti et al., 2020; Odum, 2007). This energy-based network is visualized through night-time lights, with cities as bright nodes connected through major roads (Odum, 2007). Due to the high energy demand and environmental impacts of cities (Fistola, 2011; Jacobi, 2013; Sevegnani et al., 2018), night-time lights observations are being used to understand how urban aggregates growth, are organized, distributed and connected (Ghosh et al., 2009, Ghosh et al., 2010, Ghosh et al., 2013; Henderson et al., 2012; Mellander et al., 2015; Hu & Yao, 2019; Coscieme et al., 2014a, Coscieme et al., 2014b; Sutton et al., 2007), which are important aspects for informing public policies (Evans, 2019).
Since 2007, more than half of the world's population has been living in urban areas, with a 70% increase expected by 2050 (UN, 2019a). The 11th goal of the United Nation's 2030 Agenda for Sustainable Development (SDG, 2019) emphasizes the need for sustainable cities and communities by making cities and human settlements inclusive, safe, resilient and sustainable. To make a city more sustainable, the UN (2019a) highlights the importance of investments in renewable energy, efficiency in water and electricity use, planning for more green areas, and fast, reliable and affordable public transportation and waste and recycling systems (see also Newman (2020) for a case on climate change). Although recognizing the importance of investing in social, economic, environmental and urban governance in cities (Evans, 2019; UN, 2019b), a focus on how cities contribute to approach (and exceed) the Earth's biophysical limits, in particular regarding energy on a larger scale, should be considered in supporting public policies (Agostinho et al., 2019; Sodiq et al., 2019; Wackernagel et al., 2017).
The concentration of economic enterprises and people in cities ultimately depends on the availability of “cheap” fossil fuels, a condition that will much likely not be maintained in the future, with fossil fuels becoming less available and more expensive (Mohr et al., 2015; Ward et al., 2012). According to Odum and Odum's (2006) pulsing-paradigm cycle, the decreasing resource base of the world's fossil fuel economy will force society into a different stage where pursuing economic growth will have to reconcile with the general systems principles of energy, matter and information (see also Bardi (2015) and Brown and Ulgiati (2011)).
Considering that large urban aggregates growth resembles the development of self-organized structures (Schweitzer & Steinbrink, 1998), the complexity of cities can be studied by considering, among other characteristics, their fractal1 structure (or scaling exponent), as a means to understand how cities are spatially organized. This is supported by Chen (2014), and other studies on the fractal dimension of cities (e.g. Batty & Longley, 1994; Benguigui et al., 2000; Frankhauser, 1998; Marques, 2005; Xu & Min, 2013). Fractals can be investigated through different approaches, mainly involving measurements of area, population, and GDP over time. However, none of these approaches considers the biophysical limits of a city, as a consumer system of highly concentrated energy. This hampers the ability of fractals to be used in studies involving future scenarios of city development.
In order to investigate cities' limits to growth beyond physical spatial restrictions from fractals, the use of emergy accounting (with an ‘m’; Odum, 1996) which considers renewable (R) and non-renewable (N) natural resources and resources provided by the economy (F) is proposed. Emergy accounting is a tool based on the thermodynamics and systems theory which provides a number of sustainability indicators (Giannetti et al., 2010; Brown & Ulgiati, 2011). In particular, emergy is able to take into account the sustainability of the entire set of energy flows in urban metabolism (Agostinho et al., 2018), including those flows without a market value, by summing up the equivalent solar energy needed to produce each different form of energy through a series of environmental and anthropic processes. According to Odum (1996), the empower density (i.e. emergy per time per unit surface) can be used to spatially represent the energy transformation hierarchy, indicating how emergy is distributed in a territory (e.g. Braham et al., 2019). Chen and Zhou (2008) indicate that energy and energy transformations determine the spatial order and structure of cities self-organized networks, however, the use of energy rather than emergy would provide incomplete insights on the full range of resources demanded by urban metabolism, as energy does not consider the whole life cycle of energy and material forms. All these characteristics make emergy values a potential reference for investigating how city's growth affects the geography of resources in a territory, allowing to investigate the fractal structure of urban growth from a sustainability perspective.
However, time-series emergy values of cities are scarcely available, calling for the use of proxy measures and other methodological alternatives. In this vein, Coscieme et al. (2014a) used night-time lights satellite observations to estimate and visualize emergy values of urban areas with intensive energy use and rural/wilderness areas. In this “thermodynamic geography”, emergy is used to characterize a territory “as a continuum of physical and morphological elements, infrastructures and urban settlements, rather than a combination of separated systems” (Pulselli, 2010). Following this approach, Neri et al. (2018) used night-time lights images to estimate the non-renewable component of empower density for a list of 57 countries from 1995 to 2012. Some authors (e.g. Agostinho et al., 2010; Lee & Brown, 2019; Mellino et al., 2014) have proposed and used methods to calculate the renewable component of empower density using georeferenced data of spatial distribution of solar radiation, geological heat flow, wind-kinetic energy and precipitation. Combined, these methods can be used to estimate empower density of cities in time series, including the non-renewable and renewable components.
Considering the current restrictions for cities' growth due to reduced availability of biophysical resources, understanding cities development patterns under the pulsing-curve becomes of paramount importance to propose more precise public policies. This city metabolism can be assessed under the emergy perspective, however, large databases containing emergy flows for urban systems are rarely found in literature, mainly on small scales. That said, this paper aims to propose an approach that considers the use of night-time satellite images to estimate non-renewable empower density (NRED) and the fractal dimension of cities. These estimates are used to investigate how the physical dimension of urban growth relates with urban metabolism in terms of emergy. A case study for nine cities of the State of São Paulo, Brazil, selected through a cluster analysis, is presented in order to assess dynamics of urban expansion and metabolism in cities with different characteristics.
Section snippets
The pulsing-paradigm curve
Odum's pulsing-paradigm for general systems self-organization (Odum, 1996) involves stages of slow production, growth and succession followed by a pulse in consumption, a descent and a recession (Fig. 1). “Pulsing” refers to the slow building up, or stocking, of products converging into centers of production, followed by a dispersal of products towards multiple centers of fast consumption and a sharp descent in the overall amount of products. Four main stages of the pulsing cycle can be
Temporal analysis of NRED and fractal dimension of cities
Fig. 5 presents NRED from 1992 to 2012, estimated every two-four years for the nine cities considered in this study. Under a general view, some non-expected concave or convex trends for NRED can be observed, most prominently for Assis and Jaboticabal in 1998, and for Presidente Bernardes in 2008. Since all NRED values were estimated through SOL, at first, significant changes would not occur for short time periods between two-four consecutive years, since urban expansion usually occurs in larger
Conclusions
In this study, a novel methodological approach for estimating the fractal dimension of cities over time from night-time lights observations was developed and applied. The method is effective and relies on a smaller amount of data, and consequently faster processing time, as compared to other methods based on land-cover images. Night-time satellite DMSP-OLS and, more recently, VIIRS images are available free-of-charge, and do not require an image classification process. These data can thus be
CRediT authorship contribution statement
Feni Agostinho: Conceptualization, Methodology, Writing (original draft preparation, reviewing and editing). Márcio Costa: Software, Data curation. Luca Coscieme: Writing (reviewing and editing). Cecília M.V.B. Almeida: Writing (review). Biagio F. Giannetti: Conceptualization.
Declaration of competing interest
None.
Acknowledgements
The authors are grateful for the financial support received from Vice-Reitoria de Pós-Graduação da Universidade Paulista (UNIP). MC is grateful to the scholarship provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES - Finance Code 001). FA is grateful to the financial support provided by CNPq Brazil (452378/2019-2; 302592/2019-9). LC is funded by an IRC/Marie Skłodowska-Curie CAROLINE Postdoctoral Fellowship (IRC-CLNE/2017/567). The work of José Hugo de
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