Russo, Laura (2017) Cities and energy consumption: how to reduce CO2 emissions and address climate change. [Tesi di dottorato]

[img]
Preview
Text
russo_laura_29.pdf

Download (32MB) | Preview
[error in script] [error in script]
Item Type: Tesi di dottorato
Lingua: English
Title: Cities and energy consumption: how to reduce CO2 emissions and address climate change
Creators:
CreatorsEmail
Russo, Lauralaurarusso88@hotmail.it
Date: 7 April 2017
Number of Pages: 109
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Civile, Edile e Ambientale
Dottorato: Ingegneria dei sistemi civili
Ciclo di dottorato: 29
Coordinatore del Corso di dottorato:
nomeemail
Papola, Andreapapola@unina.it
Tutor:
nomeemail
Gargiulo, CarmelaUNSPECIFIED
Date: 7 April 2017
Number of Pages: 109
Uncontrolled Keywords: cities; energy consumption; CO2 emissions; regression analysis; cluster analysis
Settori scientifico-disciplinari del MIUR: Area 08 - Ingegneria civile e Architettura > ICAR/20 - Tecnica e pianificazione urbanistica
Date Deposited: 04 May 2017 15:07
Last Modified: 08 Mar 2018 14:30
URI: http://www.fedoa.unina.it/id/eprint/11555
DOI: 10.6093/UNINA/FEDOA/11555

Abstract

According to IEA (2016), urban areas consume about two-thirds of primary energy demand and produce over 70 per cent of global carbon dioxide emissions (CO2). Consequently “cities are the heart of the decarbonisation effort” (IEA, 2016) and can be the solution to climate change. In order to support local policy makers’ decisions and foster the transition towards a low-carbon future, a growing body of international researchers has been studying the complex and multidimensional relationship between cities and energy consumption. Urban planning policies, indeed, can effectively improve energy saving in cities and reduce urban emissions only if the interactions between urban factors and energy use are investigated and are found to be significant. However, despite the great interest of the literature for this topic, a consistent number of interactions between urban features and energy use at urban scale still lacks consensus. Therefore, this research aimed to investigate the relationship between cities and energy consumption to identify the urban factors that significantly affect a city’s energy and carbon footprint, thus supporting policy-makers in the definition of effective strategies and policies that can be implemented at an urban scale to reduce energy consumption and resulting CO2 emissions. By using a holistic approach rather than a sectoral one, this work considered together a comprehensive set of urban variables – grouped into four categories according to the general system theory (i.e. physical, functional, geographical, and socio-economic variables) – and energy variables. The selection of variables was based on a critical review of the recent interdisciplinary scientific literature on the relationship between cities and energy consumption. The review allowed the definition of a theoretical framework that presented the main urban factors influencing the energy and carbon footprint of a city according to the scientific community and described the key relationships between these features and energy consumption. Furthermore, the theoretical framework also illustrated those relationships amongst the different urban features, which may significantly affect energy consumption but are often ignored by the scientific literature. Based on this framework, a set of eighteen urban variables and five energy variables was selected and included in the model, which was developed and calculated for a sample of seventy-three Italian capital cities, uniformly distributed across the country. After an intensive data collection procedure, the dataset was explored and analyzed using different statistical methods each of which provided useful insights into the complex relationship between cities and their carbon footprint. In particular, an exploratory data analysis (EDA) was performed in order to identify potential outliers and evaluate the distribution of data, thus gaining a better knowledge of the research dataset and sample. After EDA, the data were analyzed using a correlation analysis, which provided two main results: (1) it enabled the measurement of the association between the eighteen urban variables in order to identify redundant information and, most importantly, significant interconnections amongst these factors; (2) it showed the significance of the linear relationship between each individual urban variable and CO2 emissions by sector. Later, three regression models (OLS) were estimated in order to measure the direct relationships between urban and energy features. In these three models, the dependent variables are three of the five categories of CO2 emissions – residential, transport and total – and the eleven independent variables are housing density, house material, green areas, concentration of manufacturing activities, concentration of commercial activities, concentration of touristic activities, degree-days, topography, income, car ownership and household composition. Finally, a multivariate statistical analysis was performed in order to identify groups of cities with similar urban characteristics and compare their energy behaviors. When considering both direct and indirect effects (i.e. the results of regression and correlation analyses respectively), all the four groups of urban variables affect total CO2 emissions per capita. More specifically, three physical variables (i.e. housing density, house material and green areas), one functional variable (i.e. the concentration of commercial activities), and two geographical variables (i.e. degree-days and topography) have a direct effect on CO2 emissions: lower density of dwelling units and lower air temperatures, as well as valley topography and higher concentrations of masonry buildings, green areas and commercial activities increase CO2 emissions per capita. On the other hand, three socio economic variables (i.e. income, education and ethnicity) and one functional variable (i.e. land-use mix) indirectly affect CO2 emissions through the mediators of other urban features. In particular, a higher level of education and a higher share of foreign residents are both associated with higher income that, in turn, is associated with a higher land-use mix that corresponds to higher housing density, which reduces CO2 emissions per capita. herefore, two main policy implications are drawn from the results of the correlation and regression analysis; one at the building scale and one at the urban scale. (a) At the building scale, interventions should focus on buildings materials, especially for reducing the energy use of masonry buildings. (2) At the urban scale, planning strategies should encourage compact developments in order to reduce energy consumption and total CO2 emissions. Moreover, besides the lower energy footprint of compact cities, in Italy, higher densities of housing units correspond to higher densities of jobs, which in turn are characterized by higher incomes, and therefore strategies for promoting urban compactness can also have positive economic effects. Furthermore, the results of the cluster analysis corroborate the findings of the correlation and regression analysis and provide additional insights about the sample of Italian cities considered in this research. The cluster analysis, indeed, shows that Italian colder-valley-inland-wealthier cities – such as Torino, Bolzano, Padova, Mantova, etc. – produce higher level of both residential and total CO2 emissions, and are mainly located in the northern part of the country. On the contrary, Italian cities by the sea, with warmer climate and densely urbanized – such as Genova, Salerno, Bari, etc. – emit less CO2 per capita, thus being more energy efficient than the others. The results of this research substantiate the complexity and multidimensionality of the relationship between cities and energy consumption and the strategic role of both building and urban interventions for energy saving (Zanon & Verones, 2013). Furthermore, these results, which only partially support previous findings, suggest that important trade-offs exist between the different urban characteristics and cities’ energy consumption and CO2 emissions (Doherty et al., 2009; Lee & Lee, 2014; Papa et al., 2016). Measuring all of the trade-offs is a very challenging task, and this research proposed a first step in this direction.

Actions (login required)

View Item View Item