Over 50 of the worlds population currently lives in urban areas, a figure expected to rise to 70 by 2050 UN, 2008. This trend can be explained in large part by economic and social forces, as cities offer their citizens new opportunities for business, education, security, and community (Kotkin, 2005; WB, 2008). However supporting these activities requires significant resource flows. A recent research suggests that two-thirds of global primary energy consumption can be attributed to urban area, which in turn leads to 71% of global direct energy-related greenhouse gas emissions (IEA, 2008). Thus, for urban planners or urban policy makers, a major challenge at present is the development of practical tools to evaluate and plan urban energy use. However, it appears that there is little method developed for planning efficient urban energy use. It is therefore the aim of this study to provide an experimental simulation platform to explore the complex and interdependent relationships between the various factors, and investigate the possible outcomes of different scenarios under alternative planning or government policies on urban energy use and other market-driven factors beyond government control. The platform is fully implemented in a GIS. The findings are expected to assist in the process of government's policy decision making concerning the use of urban energy.
2. Literature Review
The existing research about urban energy planning mainly focused on the following several aspects:
2.1 Urban sustainability assessment
Urban sustainability assessment is increasingly a part of urban planning. Although the assessment approach and techniques do not directly deal with the efficiency of urban energy use, it indirectly reflects the potential application of GIS or spatial analysis technique in assessing urban energy use, which is sub-sector of urban planning. Xu and Coors (2012) argued GIS are commonly used to represent the current status and plan the future of the urban environment. Technically GIS are well suited to address spatial data and visualization issues associated with multiscale geographical data. An important feature of a GIS is the ability to generate new information by integrating the existing diverse datasets sharing a compatible spatial referencing system. Further, Xu and Coors (2012) proposed that residential development, from the small to the large, all make a long-term impact on our communities and the surrounding environment. With the majority of new residential buildings and houses to build in urban areas, it is extremely important to achieve residential development in a sustainable way. They introduced an integrated analysis system mainly consisting of geographical information system analysis, system dynamics model and 3D visualization. The system covers sustainability assessment of urban residential development and spatial distribution analysis of residential buildings based on sustainability indicators categorized in four groups, namely, housing, society, economics and environment. The integrated analysis system is characterized by three main advantages: a) its capacity to explore the housing equilibrium utilizing sustainability indicators. b) its economic-social-environmental features especially on residential buildings. c) the visualization of the simulation data in GIS technology. Yigitcanlar and Dur (2010) developed "The Sustainable Infrastructure, Land-use, Environment and Transport Model (SILENT). The SILENT model is an advanced geographic information system and indicator-based comparative urban sustainability indexing model. The GIS-based model produces a grid cell system for sustainability analysis. It also provides a tabular report for exact unit values of the comparative sustainability level of each grid cell. These grid cell values via the aggregation method in the GIS environment are converted into other geographical scales of analysis, such as street, neighborhood etc. Theodoridou et al (2012) argued that designing measures for the reduction of energy consumption in urban area is a complex venture. In terms of urban sustainability, such measures affect energy efficiency as well as environmental, economic and social aspects. Furthermore, they suggested that energy performance depends on building density, occupancy and consumer profile, climatic conditions, not least construction quality, factors linked, directly or not, to socio-economic aspects. The proposed methodology approach regarding retrofitting in the existing residential building stock is based on a thorough research concerning all parameters involved with this task. The use of GIS analysis technique can extrapolate the result of each building into a larger scale , namely a building block, a city. The extrapolation can be even more precise in case of available GIS data. In addition, the results can be automatically graphically depicted on maps. Similar work was introduced by Brownsword et al (2005) based on a botton-up methodology the urban energy demand of the city of Leicester is be being determined for various occupancy profiles and consumer behaviors.
2.2 Urban form
Issues related to the spatial distribution of homes and workplaces and the travel patterns that they generate have raised interest and debate about the desirability of integrating land use policies to promote higher urban densities and transport policies aimed at reducing energy consumption. Understanding the connection between land use and transport can be promoted by analyzing the urban spatial structure. Spatial structure can be defined as a combination of land use formation, its densities and the spatial design of infrastructure such as transportation and communication (Carty and Ahern, 2010). It can alternatively be characterized by three elements: urban form, the human interaction in the city and the organizing principles that define the relationship between the two (Bourne, 1982).
Chen et al. (2011) estimated the relationship between urban forms and energy consumption by adopting the panel data analysis. The urban land use patterns were quantified using a set of landscape metrics, which serve as explanatory variables in the estimation. Zhou et al (2013) argued that it is important to understand the settle morphology and its transition process in the rapid urbanization cities of developing countries. It is equally important to learn about the relationships between transport energy consumption and the transition of settlement morphology and its underlying process. An integrated land use and transportations modeling system, TRANUS was used to demonstrate this transition will bring great changes to the urban spatial distribution of population, jobs and land use, and to residents' travel patterns. The TRANUS uses the relationships of dynamic equilibrium between urban transportation and land use to simulate the evolutionary process of cities. Other examples include an empirical analysis of the influence of urban form on household travel and energy consumption (Liu and Shen, 2011), Geospatial technologies to improve urban energy efficiency (Hay et al, 2011), the relationship between the characteristics of transportation energy consumption and urban form (Shim et al, 2006), a global comparative analysis of urban form by applying spatial metrics and remote sensing (Huang et al, 2007), the spatial influence research of the energy efficiency in China by using the spatial panel data model (Wei et al, 2012), Mapping the relationship between transport energy consumption and urban form characteristics for the Greater Dublin Area (Carty and Ahern, 2010).
2. 3 Buildings energy
An understanding of the energy performance in buildings in an entire municipality or an entire district is important for sustainable energy planning strategies that accelerate the energy renovation process in existing buildings that are not energy efficient. Dall'O' et al (2012) described a methodology which is largely based on information that is already available on building stock (i. e., cartographic documentation, thematic maps, geometric data and others). Data regarding the energy performance of buildings were collected using energy audits on sample buildings, which were selected using a statistical approach. Using the tools in a GIS platform, the integration of two data sources allows for a low cost, comprehensive framework of the energy performance of buildings.
Howard et al (2012) argued that the current energy distribution infrastructure in many urban areas either cannot support anticipated future energy use or would require significant rehabilitation even if current use were maintained. Understanding the dynamics of local energy use is an important precondition of understanding how to remedy this situation. A model was built to estimate the building sector energy end-use intensity for space heating, domestic hot water, electricity for space cooling and electricity for non-space cooling applications in New York City. Applying the energy intensities to all of the building area in New York City produced the spatial distribution of building energy consumption in New York City. The resulting spatially explicit energy consumption can be a valuable tool for determining cost-effectiveness and policies for implementing energy efficiency and renewable energy programs. Similar study includes urbanization and its impact on building energy consumption and efficiency in China (Li and Yao, 2009).
While some researchers considered the energy performance of new and existing buildings as their research focus, Fabbri et al (2012) emphasized the heritage buildings. They used Ferrara, an old own, as an example to analyze the distribution of environmental energy performance indexes and the related cartographic representation. GIS technique was used to report energy class in order to suggest a zone energy indicator for measuring the city and the old town. Other similar study includes a GIS analysis of the impact of modern practices and policies on the urban heritage of Irbid, Jordan (Al-Kheder, et al, 2009).
2.4 Transportation energy
The current trends of urban dynamics in the Third World are alarming with regard to climate change, because they are giving an increasingly important role to cars - to the detriment of public and non-motorized transportation. The energy consumption is expected to grow the fastest in business-as-usual scenarios (Lefevre, 2009). Lefebvre (2009) adopted an integrated "transport-land uses" model TRANUS to demonstrate that transit technologies affordable to an emerging city like Bangalore can significantly curb the trajectories of energy consumption and the ensuing carbon dioxide emissions. Further, Lefevre analyzed the evolution of the spatial distribution of homes and jobs, as well as the growth in land consumption using GIS maps. These maps allow us to understand how the policies tested impact the location decision. Arampatzis et al (2004) argued that computer-based tools that perform travel demand analyses are extremely useful for transportation planning and policy development in a study area. Apart from predicting reasonable estimates for the extent of pollutant emissions and energy consumption caused by road traffic, efficient methodologies can be extracted for promoting and quantifying the penetration of public transport within a unified traffic management framework. They suggested transport modeling, given a transport network and a set of data representing the spatial distribution of urban activities and their intensities, four basic components of the travel pattern in a study area, i.e. trip generation, trip distribution, modal share and traffic assignment should be considered. In addition, they suggested the implementation of a traffic management strategy would involve a set of mathematical models to perform transport network analysis and estimate the corresponding impact on environmental and energy indicators. A GIS-based decision support system (DDS) for planning urban transportation policies were established working on three level: to perform the transport network analysis, to assess the energy consumption and pollutant emissions and to evaluate the several policies selected. Models were integrated in a GIS environment, which serves as the repository of the data as well s the user interface of the tool. Examples also include a simulation platform for computing energy consumption and emissions in transportation networks (Corte et al, 2008).
2.5 Integrated energy model
Ramachandra (2009) argued that the energy planning endeavors for a particular region involves the finding of a set of sources and conversion devices so as to meet the energy requirement/demand of all the tasks in an optimal manner. This optimality depends on the objective to minimize the total annual cost of energy and the dependence on non-local resources or maximize the overall system efficiency. Factors such as availability of resources in the region and task energy requirements impose constrains on the regional energy planning exercise. Thus, regional energy planning turns out to be a constrained optimization problem. Furthermore, he argued that the central theme of the energy planning at decentralised level would be to prepare regional energy plans to meet energy needs and development of alternate energy sources at least- cost to the economy and environment. Regional integrated energy planning (RIEP) mechanism takes into account various available resources and demands in a region. This implies that the assessment of the demand supply and its intervention in the energy system, which may appear desirable due to such exercises, must be at a similar geographic scale. Keirstead and Shah (2011) developed an optimization-based sketch modeling framework to design minimum energy layouts. The research was motivated by an interest in the potential limits to low energy urban design. Conceptually, this question is very similar to the issue of excess commuting found in the land use and transportation modeling literature. The model extends by jointly optimizing building and activity location, as well as transportation flows. A key benefit of the model's formulation is its relative speed, which facilitate Monte Carlo-based uncertainty analysis. This technique was applied to examine the consequences of tow planning decisions on the minimum energy consumption benchmark. Ramachandra (2007) presented a decision support systems (DSS) approach for Uttara Kanada district energy planning focusing on renewable resources that could be harnessed for energy, land use database, and optimal allocation of energy resources for various tasks, and explored the energy use consequences of an alternative scenarios, such as, base case scenarios, high-energy intensity and improved end use efficiency options. Linear programming formulation for optimum allocation was used. A GIS database with data on wind, topography, urban area and special activities have been developed and used for the evaluation of theoretical potential through spatially continuous mapping of regional energy resources.
3. Research Question Statement
Despite the diversity of practice highlighted by the review, some common limitations and issues can be seen. From the above review, it appears that there is little method developed for planning efficient urban energy use by using GIS and spatial analysis technique. In addition, it also appears that current urban energy planning practices do not apply the systems thinking approach to plan urban energy use. For example, a tool that is used to design a solar hot water heating system, may find it easier to acquire sufficient data for the generation of useful results than a more complex system design model. From a policy perspective, it fails to account for the conflicting effects of different polices. For example, congestion charging intended to reduce traffic congestion and emissions in the city centre could lead to land use changes with businesses relocating outside the charging zone, which in turn leads to an increase in travel distances and therefore more energy consumptions. As urban systems face close integration through development, it becomes increasingly important to account for the combined effects of policies (Keirstead et al, 2012).
Thirdly, one major weakness of these studies is that they do not consider the impact of spatial dynamic interactions between various factors affecting urban energy use. Factors in a system, especially an urban system, are usually not fixed and not independent of each other, but rather are influenced by each other (Fang et al, 2005). The exploration of interaction of dynamic factors offers a powerful frame to analyze why a particular level of energy use is observed, and the ways in which changes in the performance of the system occur. Furthermore, a dynamic mechanism investigation focuses on the causal structure of problems and enables urban planner and decision-making to take into account explicit multiple and conflicting criteria in the decision-making process. It can assist decision-makers or urban planner not only to understand better why the performance is as it is, but also to develop and eventually select better-informed and justifiable improvement solutions.
It is therefore the aim of this study to provide an experimental simulation platform to explore the complex and interdependent relationships between the various factors, and investigate the possible outcomes of different scenarios under alternative planning or government policies on urban energy use and other market-driven factors beyond government control. The platform is fully implemented in a GIS. The findings are expected to assist in the process of government's policy decision making concerning the use of urban energy. This research will focus on the planning for urban transportation energy use.
4. Methodology
1) To identify key factors that affects the urban transportation energy use
Identification of key factors that affects the urban transportation energy use will be achieved mainly through examining the existing studies on urban transportation energy use, literature reviews, official guidelines/policy papers. Interview discussions with industry professionals are conducted to ensure the comprehensiveness and suitability of the factors.
2) To analyze the relationship between the characteristics of transportation energy consumption and various factors affecting the use of transportation energy.
The main themes in this part are to investigate how the key factors, such as city size, density, transport network, employment, car ownership etc. affect the transportation energy consumption, and how the relationships between various key factors interact.
Spatial statistical analysis will be conducted to examine these relationships.
3) To develop a GIS-based dynamic model to plan efficient urban transportation energy use
System dynamics is a methodology and computer simulation modeling technique for framing, understanding and discussing complex issues and problems. And it is widely used to gain understanding of a system with complex, dynamic and nonlinearly interacting variables. The urban energy planning can be viewed as a complex system because of the complex interactions between various dynamic factors affecting energy use. Apart from the application of GIS and spatial analysis in planning urban energy use, system science, and dynamic approach should be seen as one of the core underpinnings of successful planning for urban energy use.
Dynamic approach is descriptive models based on a logical representation of a system, and they are aimed at reproducing a simplified operation of the system. It is referred to as dynamic if the output of the current period is affected by evolution or expansion compared with previous periods. This model allows exploring the effects of different hypotheses via scenarios. The impacts of different assumptions and policies can be evaluated by creating different scenarios.
In this part of the research work, the system dynamics approach will be used to develop a dynamic model for planning efficient urban transportation energy use. The model which includes various dynamic factors affecting urban transportation energy consumption will be implemented in a GIS environment.
4) To demonstrate the application of the dynamic model by using a practical case
A city or a district in Greater Toronto Area will be selected to illustrate the application of the dynamic model considering the fastest growing. In the application process, the adequacy of the dynamic model will be analyzed, and the applicability of the model in planning the efficient urban energy consumption will be also demonstrated by different policy scenarios.
(About the methodology, I will give more detailed explanations for each section later on.)
5. Resources
6. Timetable
7. Domains of Background Reading
