Urban Morphology & Energy Demand: A Review

Samir Shaikh | CUSE | 134426001

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Urban morphology and energy demand: A review Samir Shaikh

Centre for Urban Science and Engineering (CUSE), IIT-Bombay April 2014 Abstract Urban areas hold a central position in the search for feasible climate change mitigation opportunities as a significant share of all the global greenhouse gas (GHG) emissions is closely related to urban structures. This paper reviews the various methods and approaches to establish and quantify the relationship between urban morphology and energy consumption. The methods mainly use energy modelling, simulation, empirical data, geospatial mapping and 3D visualization techniques, or a combination of two or more of these. The results indicate that there is a direct correlation between urban form and energy consumption. Though there is substantial body of research on the subject, there is no definite and precise methods available to objectively establish the relationship. This is true especially for Indian cities. _______________________________________________________________ Contents

1 Introduction ............................................................................................................. 2 2 Review Methodology ............................................................................................... 2 3 Definitions ................................................................................................................ 3

3.1 Defining an urban energy system .................................................................................. 3 3.2 Defining urban morphology .......................................................................................... 4

4 Approaches & Methods ............................................................................................ 5

4.1 Urban energy system ..................................................................................................... 5 4.2 Urban morphology ......................................................................................................... 8

5 Summary of findings .............................................................................................. 10 6 Challenges & Opportunities .................................................................................. 11

6.1 Complexity of variables ............................................................................................... 12 6.2 Data availability and uncertainty ............................................................................... 12 6.3 Applicability of models ................................................................................................ 12 6.4 Knowledge gaps ........................................................................................................... 12

7 Conclusion .............................................................................................................. 13 8 Acknowledgements ................................................................................................ 14 9 References .............................................................................................................. 14


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1 Introduction Buildings are key to a sustainable future because their design, construction, operation, and the activities in buildings are significant contributors to energy-related sustainability challenges reducing energy demand in buildings can play one of the most important roles in solving these challenges. The buildings sector and peoples activities in buildings are responsible for approximately 31% of global final energy demand, approximately one-third of energy-related CO 2 emissions, approximately two-thirds of halocarbon, and approximately 2533% of black carbon emissions [1]. "The balance of evidence, from changes in global mean surface air temperature and from changes in geographical, seasonal and vertical patterns of atmospheric temperature, suggests a discernible human influence on global climate" [1]. Over the past two decades buildings (its construction and operations) has been recognized as one of the main consumers of fossil fuels. Buildings are responsible for approximately 70% of sulphur oxides and 50% of the CO2 emissions released into the atmosphere. The building sector consumes 40% of the worlds energy, 16% of the worlds fresh water, and 25% of the timber taken from forests. [2]. Increase in urbanization around the world has multiplied the urban population exponentially, thus making them highly complex systems. Though cities cover only 2% of the land surface, its citizens consume 75% of the total resources of the world. This has resulted in the ecological footprint of the world being much larger than the actual physical areas they occupy [3]. Urban form has profound effects on the energy consumption of a city. With the growth in urbanization, especially the exponentially fast face of urbanization in the developing world, energy in the context of cities will be critical. As our cities grow the demand for energy will also rise. Growing attention on making our buildings more efficient from an energy point of view. Cities and urban areas are complex socio-economic-ecological systems. Major factors influencing levels of energy consumption in buildings include climate, urban context (or morphology), building design, systems efficiency and occupant behaviour. [4] This paper has six sections. Section 2 defines and discusses some of the terms used in this paper. Section 3 discusses the various methods used to quantify and analyse urban morphology and energy systems. Section 4 discusses the relationship between urban morphology and energy consumption. Section 5 discusses some of the challenges and opportunities in methodologies and approaches. Section 6 presents the paper's conclusions. 2 Review Methodology A review of scientific papers was carried out using the Web of Science online (www.webofknowledge.com) and Science Direct portals. For this review, published papers as well as conference papers were selected. All the papers were in English only. The key words used for the searches were: urban morphology, energy consumption, energy modelling, urban form, urban texture and low carbon cities. A preliminary reading of the abstracts were carried out, based on which the papers were short-listed for further reading. The citations were downloaded using Endnote software. The various papers were manually categorised by the following topics: 1. Energy consumption patterns


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2. Energy modelling 3. Energy management 4. Urban form modelling 5. Urban scale While manually categorizing it was noted that some of the papers did not have a direct relevance to the specific topic. These papers were stored in a separate folder for later review. Finally, 20 research literature were short-listed for detailed reading and review. The literature included published scientific papers, conference papers, as well as a reports. 3 Definitions 3.1 Defining an urban energy system Before we begin the review it is important to understand the concept of "urban energy systems model". Urban environments receive both natural and anthropogenically derived energy inputs. Solar radiation is the primary source of natural energy input in urban areas. This energy is stored in natural as well as man-made features which act as reservoirs. Anthropogenic energy is generated outside the city and transported through infrastructure for consumption in the city [5] Energy consumed in urban environments can be grouped into three categories [6]: Embodied energy is the energy consumed in the manufacture, distribution and deployment of materials for construction and manufacture of buildings and infrastructure. Operational energy is the energy consumed in the running and operation of various equipments, machinery and appliances. Transport energy is the energy consumed during travel and commutes via public transportation as well as private vehicles. Building energy performance is currently understood as dependent upon: (see Fig. 1.) [7] 1. urban geometry 2. building design 3. systems efficiency 4. occupant behaviour

Fig. 1. Variables affecting energy consumption [7] From the above, it can be determined that urban context has an implication on the energy performance of buildings by a factor of 2. This is generally neglected in energy analysis of individual buildings [7].


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3.2 Defining urban morphology Urban morphological analysis can be understood in the following three principles: [8] 1. Urban form is defined by three fundamental physical elements: i) buildings and open spaces ii) plot size, and iii) streets 2. Urban form can also be defined at different scales: i) building and lot level ii) street and block level iii) city and the region level 3. Urban form has to be understood historically also. Cities and its form undergoes transformation over time. This reveals the fourth dimension of time, which helps to reveal the historical layers of cities. Mehaffy (2013) defines urban morphology is a system of various formal elements interacting in complex ways. The various elements that define urban morphology are: Population Density Building Density Building heights Ratio of Built/ Open Space Mix of Uses Walk-ability Bikability Availability of Public Transport Urban Network (street grid)

Density The concept of density in urban environments is plagued by a plethora of definitions that vary depending on purpose. Confusion arises partly because density can refer to either dwellings or people per unit area, and most calculations of people per unit area are based on an assumed average number of people per dwelling. Urban density has been defined in various ways. But there has never been an integrated and comprehensive definition that covers all the aspects and variables. It is quite evident that density has both physical as well as social forms. Therefore it is difficult to completely separate population density from building density [9]. Although the terms urban morphology and urban form are sometimes used interchangeably, they are generally discussed and treated at different spatial scales. Urban morphology, sometimes referred to as the urban fabric or urban texture, deals primarily with the particular shape and dimensions of the built environment and with the aggregations and configurations of


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building types. At this fine scale the configurations of cities directly affect both exterior and interior environments and have a direct bearing on embodied and operational energy use [5]. Use of landscape metrics for urban analysis is used, combining the analysis with remote sensing data (see Fig. 2.) [6]. The development and advancement of remote sensing and GIS has significantly helped in the representation, analysis and quantification of urban forms [6].

Fig. 2. Use of remote sensing and landscape metrics for urban analysis [6]

4 Approaches & Methods The reviewed papers consist of research at macro as well as micro levels. Following is a review of the various methods and techniques that have been used in calculating and modelling (1) energy system and (2) urban morphology. 4.1 Urban energy system Surface-to-volume ratio: Surface-to-volume ratio is an important determinant and variable of urban form. Surface-to-volume ratio (S/V) determines the heat gain/ loss through a building. Lower the S/V lesser is


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the heat gain/ loss. In hot dry climates lower S/V helps in reducing the heat gain, whereas in cold and dry climates it helps in reducing heat loss. In warm and humid climates S/V is not as critical a factor because the primary concern is to increase ventilation [7]. The following factors influence heat transfer through the building: Temperature of ground surface Wind speed and direction Solar radiation incident on the building In order to minimize the heat gain/ loss through a building, the shape of the building should be as compact as possible; a cube being the ideal form. But achieving the optimum built form while also giving due consideration to the factors above, is much more complex. Basam Behsh found that the S/V ratio may not be useful in analysing the thermal behaviour of buildings with complex forms. For complex shapes of buildings that do not fit into the S/V parameters, advanced computer simulations have to be applied to compare different options.

Fig. 3. Passive Zone [7] Passive zone concept: S/V, though an important parameter of urban morphology, does not give a true representation of the total energy consumption in urban areas. Passive zone method is considered a better method, compared to S/V. Passive zone is the area in a building where passive, low-energy techniques can be applied that help in reducing the total energy consumption of the building. The ratio of passive zones to non-passive zones provides an estimate of the potential for implementation of passive energy techniques [7]. Another aspect to consider is that minimizing the S/V results in the reduction of wall surface through which natural light can be admitted into the building. With natural light dependence on artificial lighting can be reduced, (though the optimum window-wall ratio has to be achieved). Therefore, striking the correct balance between S/V and the window-wall ratio is critical for the overall energy performance of the building. There emerge two contradictory parameters of urban morphology that affect the energy consumption of buildings; (1) minimizing the building envelope to reduce heat gain/ loss, and (2) maximizing the building envelope to increase daylight. The decision on the relative weightage to be given to each of the parameters will


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mainly be dependent upon the local climatic conditions, and therefore differ from one city to the other [7]. LT Method (Lighting and thermal method) is a form-based method created by Baker and Steemers (2000) to estimate a buildings lighting, heating and cooling energy demand. The LT Method is a design tool that uses a set of graphs for its analysis. The LT Curves give the unit energy consumption (per square meter) of all four orientations of the facade (North, South, East and West). The graph provides separate data for cooling, heating and lighting, along with a curve that gives a combined consumption data as well. Data, in the form of graphs, is provided for two climatic zones. Bahu et al. (2013) describe an agent-based modelling approach to model urban energy demand [10]. An Agent-based model (ABM) is a type of computational model that simulates the actions and interactions of agents (individual entities or groups) to assess its effects and implications on other agents and the system as a whole. ABM is a dynamic modelling system, where the values of variables can be changed to simulate different scenarios. Combining ABM and GIS (geographical information systems) will result in a model that is dynamic as well spatially represented [10]. Heinonen & Junnila (2014) used the Life-cycle assessment (LCA), utilising streamlined input- output (IO) method. This method is capable of capturing the emissions of production and supply chains globally. The selected methods were assessed as the most suitable due to the complex nature of the research object, the carbon emissions of private consumption . Bose and Anandalingam (1996) employed the Goal Programming model (GP) to identify the multiple objectives of energy management in an urban area. "The basic structure in the GP model is a Reference Energy System (RES) that maps the optimum flow of energy from supply side to demand nodes." [12]. The goals used were (i) Energy demand, (ii) Energy budget, (iii) Emissions, (iv) Vehicle utilization capacity, (v) Power supply capacity. Wong, et al. (2011) used the STEVE tool and TAS software to assess the ambient air temperature around buildings and its energy consumption as a function of given different urban morphology variables (see Fig. 4.) [13].


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Fig. 4. Use of STEVE tool to assess ambient air temperature around buildings [13] 4.2 Urban morphology Traditional houses and streets in many parts of the world used to be laid out so as to provide a significant amount of self-shading. The spacing of buildings close enough to provide significant self-shading will diminish wind strength near the ground, reducing the potential for ventilation, although daytime ventilation is not always useful. Close spacing also reduces solar access in winter, but such access will not be needed in those hot-summer regions with mild winters. Traditional streets are about five degrees cooler than contemporary streets, whether oriented north-south or east-west. This is due both to greater shading of traditional streets, which reduces direct solar irradiance, and the smaller sky viewing angles, which reduces diffuse solar irradiance [2]. In hot-dry desert climates of India, the urban-scape is defined by narrow roads banked by tall and compact houses with thick walls and small openings all of these strategies help keep heat out of buildings. Building shape (length, width, and height), form and orientation are architectural decisions that have significant impacts on heating and cooling loads, as well as on day-lighting and opportunities for passive ventilation, passive solar heating and cooling, and for active solar


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energy systems. For instance, in temperate climates, the optimal orientation for rectangular buildings is the long axis running east-west, as this simultaneously maximizes passive solar heating in the cold season and minimizes solar heating in the warm season. Traditional houses in warm climates in India were mostly designed around courtyards and front courts (Aangan) that served as congregation spaces for families and for sleeping during night time, as these are naturally cool outdoor spaces. Developing countries, such as India, have a rich legacy of architecture that uses indigenous techniques to ensure thermal comfort. There have been attempts to classify and categorize the various urban forms throughout the world, from the urban sprawl of the US to the "compact cities" of Europe. Comparison of the spatial metrics was made between the various cities. Cluster analysis to classify cities in groups in terms of their spatial metrics [14]. Since there is no universal and consistent way to represent the urban area among various countries using the different maps typically used to represent urban areas, like topography maps, administrative maps, tourist maps, etc., remote sensing images using satellite imagery is used for comparison between different cities [14]. Urban morphology types: There have been some attempts at modelling the urban form, for example [15] used CitySim by modelling a city block of Paris. The various variables of height, footprint, glazing ratios, facade orientations were considered in the model. Thus it was possible to define a city block using a set of variables. [16] developed building archetypes for the city of Milan, and categorised them into 4 types; (1) Semi-detached building, (2) Line block building, (3) Tower block building and (4) Central patio building. This is possible in an urban region where the building types do not show great variation. DEM is an effective tool to derive the various parameters of urban morphology in a graphical format quickly (See Fig. 5.). Some of these parameters are then transferred to a simulation tool like LT model, to simulate and quantify energy consumption. LT (Lighting and Thermal) models are ideal for modelling energy consumption at the urban scale. Though LT models capture the energy flow of buildings with enough accuracy without requiring full dynamic simulation, it requires numerous inputs like U-values, reflectance values of interior as well as exterior surfaces, luminance data, heating efficiency and set point, etc. [7]

Fig. 5. Digital Elevation Model (DEM), London, Toulouse and Berlin [7]


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5 Summary of findings The papers reviewed deal with energy and the city in the following categories: 1. Technology design 2. Building design 3. Urban design 4. System design 5. Policy assessment 6. Transportation and land-use Of the above, 1. Technology design, 2. Building design, and 3. Urban design are directly related to urban morphology and 4., 5., and 6. have indirect implications to the study. 1. There is a very clear correlation between urban density and CO2 emissions. Lower the density higher is the per capita emission of CO2 (see Fig. 6.). Based on the available research there is good reason to believe that higher densities can indeed lead to lower carbon emissions [17].

Fig. 6. Average urban densities in large cities and average CO2 emissions per capita [17] 2. The combination of 3D geo-data and energy models is mutually benefitial. Using 2D models in combination with 3D techniques allows for not only dynamic modelling but also visualization of the results [10] 3. There is increasing body of research that identifies urban form as an important parameter to manage urban energy systems. But though research points to the evidence


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that urban form optimization can lower energy consumption, the fabric (2-dimensional) of the city is prioritized over the form (3-dimensional) of the city [18]. 4. Research on the relationship between urban form and energy consumption found that [6]: 1. Urban size is directly proportional to energy consumption 2. Fragmented urban land use patterns lead to higher energy consumption 3. Large high density urban areas showed lower consumption of energy 5. Three main elements influence the urban temperature at the local scale; (1) buildings, (2) greenery, and (3) pavement. Other related parameters, such as "green plot ratio" (GnPR), sky view factor (SVF), surrounding building density, wall surface area, albedo can also be evaluated for a better understanding of the impact of change in urban morphology on energy consumption [13]. 6. The relationship between density and energy use is not straightforward. Changes to urban form and density are part of wider changes to the demographic structures of cities, including decreasing household sizes, increasing numbers of households, and increasing income levels. The changing nature of households can have a significant impact on energy consumption in cities [5]. 7. Commercial office blocks consume more energy per square metre than both residential dwellings and industrial land uses; the largest land use by type in urban areas is, however, residential [4]. 8. The total energy demand may either be impacted positively or negatively by densification of cities [3]. When densities are increased, there is an increase in the overall cost of living, in turn affecting wages, rents and prices of goods. This results in relocation of offices and households to remote areas in search of lower rents and prices. This reshapes the urban system in such a way that it increases pollution due to increased commutes. Therefore, though density and compactness is desirable from an environment point-of-view, it may not be desirable if the general equilibrium effects are considered [19]. 6 Challenges & Opportunities 1. Studies often focus on specific aspects of energy use, and do not consider the full set of combined processes. Though system design models look at the combined processes of energy systems, they often exclude transportation energy [20]. 2. Studies often looked at either only supply side or demand side of the energy system. Very few models considered both supply and demand [20]. 3. Most of the papers examined specific cities, or geographic regions. This implies an appreciation of local context [20].


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6.1 Complexity of variables Urban areas are complex socio-economic structures. The models representing these natural systems tend to be complex in nature too. Different models have different capabilities, and processing times, depending on the method, scope and computing power. Over-simplifying or over-complicating a model may not deliver the desired output and accuracy [20]. Assumptions within a model also play an important part. Less complexity, generally means more assumptions. The methodology will be rendered invalid if the core assumptions are wrong [20]. The audience for whom the model is intended is also usually ignored. For example, a model intended for a government authority will be quite different from the model intended for a technical research. The variables, assumptions and methodology will vary [20] 6.2 Data availability and uncertainty Data is critical for any form of modelling. Lack of good quality information impacts the quality of analysis and evaluation, in turn affecting the end results. There are two major issues: data availability and data uncertainty [20]. Obtaining precise data of energy consumption is very difficult. In general, the energy data is collected in mainly three ways: (1) From previous studies. (2) From surveys, and (3) Statistical data. A combination of the above three methods can be useful in areas where the data is not precise, inaccurate or not easily available [6]. Another challenge is defining the boundaries of an urban energy system. This has been a challenge to model the energy system with all the variables, due to uncertainty of data sets. 6.3 Applicability of models Climate change and urbanization have a strong link, and many local authorities have started to realize the importance of climate change policies to address urban issues. It is essential that more and more local bodies and authorities understand the relationship between urbanization and climate change and implement policies that reduce greenhouse gas (GHG) emissions. There is a strong and urgent need for measurable and quantifiable evidence to evaluate the implemented policies and its effectiveness. Currently there are no clear mechanisms that the government can implement to measure the success or failure of their policies at the local or regional levels [21]. 6.4 Knowledge gaps At the building scale, a number of studies on heat-energy demand have called into question the generally accepted view that more compact building types, such as apartment blocks, outperform small-scale, individual building units most commonly represented by detached, single-family housing [19]. However, these relationships can be better understood when the more complex trade-offs between the solar heat gains and the surface heat losses of different urban forms are considered. Indeed, this is now well understood at the level of the individual building. The tools have recently become available to move this research up to the scale of the urban block and neighbourhood.


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City planners and policy makers do not differentiate the urban fabric by geometric values, but rather by neighbourhood qualities and building types. There is still lacking a large body of literature on the general categorisation of urban morphology types [5]. Because the relationships between morphology, form, density and energy are complex, there is no one-size-fits-all solution for optimising embodied, operational and transport energy within the built environment. Comparisons between countries or even within a country are problematic. A comparison of morphology, density and energy use between cities with similar climates is possible, for example, because climate is the key factor influencing operational energy demand in dwellings. With the exception of isolated studies looking at the thermal performance of blocks and archetypal building forms, there has apparently not been a study of the thermal performance of general urban morphology types. There is still a lack of a larger body of literature on the general categorisation of urban morphology types in modern cities, and thereby, a knowledge gap as to the effect of urban morphology type on building heat energy demand [8]. Much of the literature focuses on mitigation as a way of addressing the issue of urban energy demand. Pre-emptive planning, that looks at an integrated approach to the urban form-energy continuum, is still large unaddressed. 7 Conclusion Over the past few decades there have been various attempts at modelling urban energy systems and characterizing urban form. These models addressed urban form and energy systems at various scales and used a variety of methods. There have been numerous attempts to understand the relationship between urban morphology and energy demand also. However, there does not exist a comprehensive literature on this topic that casts a wider net and covers all the aspects of the subject. The existing literature looks at individual variables and its impacts, and not the urban system as an entity in itself, with its inherent complexity and unpredictability. This paper is a first step towards filling in this knowledge gap, to attempts to give an overall view of the full scope of the activities in this area. The paper started with defining the key terms, "Energy system" and "Urban morphology". Energy, being obtained from natural as well as anthropogenic sources can be classified in various ways. Specifically, embodied energy, operational energy and transport energy were looked at. Urban morphology is also a complex system with a number of variables. The paper tried to identify the various components of urban morphology. The various methodologies and approaches to modelling energy demand and urban form were discussed. Based on the literature reviewed two methods were looked at in detail; the passive zone concept and the LT method. For modelling urban form, mainly geospatial mapping and remote-sensing data was used to model urban landscape, whereas DEM and raster based software were used to model individual buildings. The relationship between urban form and energy were carried out using CitySim and Energy plus. Later, the various opportunities and challenges in the various approaches and methods were discussed. One thing that clearly came out of the review was that there is no "one-size-fits-all" solution. Each urban area has a unique climate, urban morphology, and energy system.


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Therefore, every city will have a unique set of variables, which will determine a unique methodology and model. There is a strong and direct relationship between economic activity which requires energy consumption, which further leads to CO2 emissions that are major cause for global climate change. So, though economic activity and energy consumption is a localised phenomenon, it has global environmental implications [17]. Indian cities are growing at an exponential rate, and India is one of the fastest urbanizing countries in the world. [1] In spite of this there seems to be a dearth of research and data for India related to energy consumption and its relationship to urban form. India has a unique climate and a unique urban morphology. Research has shown that each climatic region should have its own unique solutions. There are three types of intervention that could play equally important roles in reducing heat-energy demand: behavioural adjustments, technological advancement and design considerations [22]. This research will provide an impetus to push for effective urban development policies that will have a positive impact on the energy consumption in Indian cities. There is tremendous scope to develop an integrated model of urban form and energy using digital techniques for the city of Mumbai. This methodology could provide the much-needed impetus to push for sustainable policies at the government level. This paper is a starting point for a thorough and detailed study for a similar study for Indian metropolises like Mumbai. 8 Acknowledgements The findings and methodologies used in this paper are part of a broader research towards establishing a more definitive relationship between urban morphology and energy consumption. The author is thankful to the faculty at the Centre for Urban Science and Engineering (C-USE) at IIT Bombay; Prof. Krithi Ramamritham, Prof. Monika Jain, Prof. Arnab Jana, Prof. Ronita Bardhan, Prof. Arnab Chakraborthy, and to Prof. Rangan Banerjee (Head-C-TARA) for their extremely stimulating critiques and inputs. 9 References 1. Habitat, U., Sustainable Urbanization in Asia: ASourcebook for Local Governments, F.C.

Bernhard Barth, Editor 2012, United Nations Human Settlements Programme: UNON, Nairobi.

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5. Doherty, M., et al., Relationships between form, morphology, density and energy in urban environments. Sustainable Ecosystems, 2012.


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IntroductionReview MethodologyDefinitionsDefining an urban energy systemDefining urban morphology

Approaches & MethodsUrban energy systemUrban morphology

Summary of findingsChallenges & OpportunitiesComplexity of variablesData availability and uncertaintyApplicability of modelsKnowledge gaps

ConclusionAcknowledgementsReferences

Urban Morphology & Energy Demand: A Review

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