The results are quite remarkable: of the 576 micropolitan statistical areas covered by the database, in-fill or redevelopment has accounted for all of the new development in 472 of them (82%). In total, 4950 million square feet are expected to be built between 2010 and 2030 of which 3740 million square feet will be on in-fill sites according to the book. The histogram below shows the distribution of results.

Estimated amount of in-fill development in US micropolitan statistical areas, 2010-2030. Source: Reshaping Metropolitan America database.

For more information on the Reshaping Metropolitan America book, please see the publisher’s website.

A model, applicable to a range of innovation diffusion applications with a strong peer-to-peer component, is developed and studied, along with methods for its investigation and analysis. A particular application is to individual households deciding whether to install an energy efficiency measure in their home. The model represents these individuals as nodes on a network, each with a variable representing their current state of adoption of the innovation. The motivation to adopt is composed of three terms, representing personal preference, an average of each individual’s network neighbors’ states, and a system average, which is a measure of the current social trend.

As the paper notes, many energy innovations are invisible to outside observers. If my neighbour installs loft insulation, I won’t know this even if I go into his or her house since I try not to make a habit of rooting around in my friends’ lofts. The proposed model therefore assumes that the benefits of this technology are only communicated by conversations between peers, which probabilistically might touch upon the installed innovation. From there, it’s basically a formalized model of Rogers’ Diffusion of Innovation theory.

Nick points out in the press release that one of the challenges of validating this kind of model is getting appropriate data. One approach would be to use telecommunications data, as in this paper. But this kind of data is so ‘big’ that one might potentially lose out on important local features and so I wonder if there isn’t scope for a more detailed study of a single neighbourhood.

My doctoral research focused on domestic photovoltaics. While the diffusion of this innovation wasn’t the main area of interest, clearly photovoltaics are a highly visible energy innovation, as might be an obviously electric vehicle parked out front, in contrast to a more hidden technology like insulation.

If this was parked in front of your neighbour’s house, would you know it was an electric car? Source: Darrenm540 at Wikimedia Commons.

This suggests that McCullen et al’s model could be modified to include a separate term for visible innovations and by using a fine-grained data set, one could test the relative strengths of the invisible social signal and the visible proximity signal. In fact there are two possible variants of this approach. In one, the physical observation of an energy innovation would increase the likelihood of adoption independently of the social network mechanism. But in a subtle variant, the visible observation mechanism might play an important role in prompting a conversation on the social network. For example, ‘I saw an loft insulation truck earlier today. Which reminds me, didn’t you guys get insulation installed last year?’ This latter mechanism is sort of covered already by the ‘system average’ term in Nick’s model, but it would be interesting to make this mechanism more explicit even if the γ term of the model has relatively little impact on adoption (see below).

Figure 4(a) from McCullen et al (2013). Note that uptake varies primarily as a function of β, the weight on social network messages (versus personal beliefs or general social norms)

Urban metabolism is an important technique for understanding the relationship between cities and the wider environment. Such analyses are typically performed at the scale of the whole city using annual average data, a feature that is driven largely by restrictions in data availability. However, in order to assess the resource implications of policy interventions and to design and operate efficient urban infrastructures such as energy systems, greater spatial and temporal resolutions are required in the underlying resource demand data. As this information is rarely available, we propose that these demand profiles might be simulated using activity-based modeling. This is a microsimulation approach that calculates the activity schedules of individuals within the city and then converts this information into resource demands. The method is demonstrated by simulating electricity and natural gas demands in London and by examining how these nontransport energy demands might change in response to a shift in commuting patterns, for example, in response to a congestion charge or similar policy. The article concludes by discussing the strengths and weaknesses of the approach, as well as highlighting future research directions. Key challenges include the simulation of in-home activities, assessing the transferability of the complex data sets and models supporting such analyses, and determining which aspects of urban metabolism would benefit most from this technique.

Keirstead, J., & Sivakumar, A. (2012). Using Activity-Based Modeling to Simulate Urban Resource Demands at High Spatial and Temporal Resolutions Journal of Industrial Ecology, 16 (6), 889-900 DOI: 10.1111/j.1530-9290.2012.00486.x

Part IV: Implementing Solutions

In the first chapters, we stressed that improving the performance of urban energy systems is not just a question of technology. A new CHP engine or smart ICT system may look promising but unless it is affordable and people know how to use it correctly, the full benefits of that technology might be lost. The final part of the book is therefore about implementing solutions and how changes in urban energy systems might be achieved in practice.

Managing UES transitions

Cities are constantly changing and one of the key questions is how can these transition processes be managed and steered towards more sustainable energy solutions. I introduce a couple of theoretical ideas about transitions in urban energy systems, including the so-called ‘energy ladder’ model of energy use in developing countries and the technological transitions model for large socio-technical systems. These provide a starting point for the discussion but the main focus of the chapter are two case studies.

The first asks how did Copenhagen come to be one of the most energy efficient cities in the world, with about 95% of the population getting their heat from highly efficient district heat networks and CHP systems. While there is no one answer, the case study shows that a consistent policy environment over decades and a willingness to directly steer the energy market towards district energy systems played a vital role. In contrast, London has a fragmented local governance structure and a liberalized energy market that makes it difficult to coordinate the investments required for a successful district energy system.

Map of district heating network in Copenhagen

The second case looks at Nakuru, Kenya. Nakuru is Kenya’s fourth largest city and most of its energy is supplied not by modern energy sources like electricity or natural gas, but by locally sourced biomass and charcoal. This creates severe deforestation which has gotten worse over the past 30 years as the city’s population has grown, despite improvements in the efficiency of cooking stoves. The challenge here is to create a business offering that will provide households with a more sustainable energy source (perhaps based on biogas from anaerobic digestion) at a cost that low-income households can afford.

Cities of the future

In the next chapter, David Fisk asks how visions of the future of our cities influence the planning of large infrastructure projects and energy systems in particular. Drawing on representations of urban futures in films and literature, the chapter shows how such visions provide a safe space in which to discuss present concerns under the masquerade of a hypothetical future.

The energy centre from Fritz Lang’s Metropolis. Image courtesy of Eureka Entertainment Ltd.

Traditional land-use master plans are somewhat similar, in that they provide the overarching view of a city, say 30 years in the future, without specifying the full details of the energy system. But one can query these plans and ask what the requisite energy systems might look like, or alternatively whether present ambitions for low carbon futures, for example, are consistent with these plans. The development of alternative narrative scenarios is a practical tool for stimulating these sorts of discussions.

Conclusion

The last chapter of the book tries to provide an overall summary. When I was writing this chapter, the key thing that struck me was how the field of urban energy systems had matured since we first started working in the area around 2006. It’s not that urban energy systems hadn’t been studied before but the past five to ten years have seen an expansion of interest in the topic, encompassing both technical innovations and a growing appreciation of the associated economic and social challenges and opportunities.

Our conclusion then is that an integrated approach is needed if urban energy systems are to make a significant contribution to energy and climate policy goals. This means combining a qualitative appreciation of the unique circumstances and multiple stakeholders in each city, with state-of-the-art computational models for the analysis of alternative system configurations. The chapter includes a convenient flow-chart outlining how these principles might be applied in a practical design setting, but I don’t want to give the ending away — you’ll have to get the book to see it.

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Photo by Mark Bellingham. Used under CC BY-NC-SA 2.0 license.

I think the answer lies in five factors, all of which I’ll be trying to take on board in my own work.

1. The paper has a sexy topic and a clear title. Bullet trains! China! “Megacities”! One glance and you know exactly what the paper’s about and why you should read it. Compare that with the dredded “compound” title, where a colon is used to divide the paper’s title into general/specific clauses. (Interestingly, this paper suggested that compound titles tend to get vetoed in multi-author papers. Wisdom of the crowd perhaps?)

2. The authors present a unique data set. The paper performs regression analyses on a set of 262 Chinese cities, which covers local real estate prices, income, healthcare, education, transport infrastructure and other variables. Given the paper’s focus on the change in house prices and infrastructure, and China’s unique contemporary status as a rapidly urbanizing and developing country, there are few places in the world that offer this sort of analytical opportunity. The only other option for researchers might be extensive historical data collection, which could be extremely time-consuming.

3. The analysis is rigorous. While I like to think that no academic wants to publish sloppy analysis, sometimes the peer-review process doesn’t catch little methodological issues that could distort the results. In this paper, the authors use instrumental variable regression, alongside a standard OLS formulation, to clearly identify the role of bullet-trains in affecting house prices separate from other possible drivers. This sort of analysis isn’t particularly difficult but it shows that the authors (or reviewers) have considered the potential problems of a naive regression model and have taken steps to address them.

4. The conclusions are restrained. I had a lecturer during my MSc course who told us the story of an over-zealous researcher, who estimated the economic cost of soil erosion in Europe by extrapolating from experimental work conducted on 8 small plots in a Belgian field. In contrast, Zheng and Kahn acknowledge the limits of their analysis and China’s unique circumstances before making modest claims for the relevance of their findings to other settings, like Europe and California.

5. And finally, perhaps the most important reason why this paper got published in PNAS: it was submitted to PNAS. If you don’t submit to the top journals, how are you ever going to get published there? This is something I struggle with, as it’s very easy to say “These findings will definitely appeal to disciplinary journal X. I’ll just submit there again.” Sure, submitting to a bigger journal might lead to more rejections but hopefully you will learn from the process and eventually crack it.

¹ PNAS Plus is an electronic-only offshoot of the main journal but it has the same review process

Part III: Techniques

Part III is titled Analysing Urban Energy Systems. The goal here is to introduce specific modelling and analytical techniques for urban energy systems, with an emphasis on optimization models which are helpful for planning new system designs.

Modelling urban energy systems

Nilay Shah opens the section with a brief chapter on the variety of modelling approaches that might be used to analyse urban energy use. One could focus on energy supply or demand, the environmental impacts, or narrow cost objectives. However a recent review shows that there is a growing interest in integrated modelling approaches that combine the strengths of different techniques in order to give a broader view of urban energy systems at all stages of development. To this end, we developed the SynCity hierarchical modelling framework which is illustrated below. It incorporates four models that respectively cover the design of energy efficient land-use plans, the simulation of citizen activities within an existing city, the design of efficient integrated energy systems to meet carbon reduction goals, and the operation of energy systems to maximize the benefits for system operators or households. The remaining chapters then explore some of these models in detail.

Overview of SynCity modelling platform

Optimization and systems integration

Nouri Samsatli and Mark Jennings discuss the role of optimization models in planning urban energy systems. Such models are usually described as being ‘normative’, i.e. they show the world the way it should be and not necessarily the way it is. However when designing a new urban energy system, this is usually a good thing as you want to design a system, for example, that meets a given level of performance but for a minimum cost.

The chapter gives the detailed formulation of a mixed integer linear programming model called TURN (Technologies and Urban Resource Networks). The model allows multiple energy system technologies and fuels to be evaluated simultaneously, choosing the number of technologies, their locations, and operating rates, and other parameters so that a given pattern of energy service demands are met.

An example TURN for an urban energy system using biomass. Boxes represent conversion processes, circles resources.

Two example applications of the modelling framework are shown. The first investigates the optimal use of bioenergy resources within a proposed eco-town development, selecting a wood-chip based district heating solution with storage that illustrates the benefits of an integrated assessment framework. The second case study considers the optimal timing of retrofit investment decisions over a ten-year period and takes into account the different preferences of owner-occupiers versus private or public landlords.

Ecologically-inspired UES models

The objective of an optimization model like the TURN model is typically to minimize the total system cost subject to some constraints. However an alternative perspective would be to minimize environmental impact. In her chapter, Nicole Papaioannou develops such a model using a range of environmental impact measures. The case study she presents looks at the energy requirements of a typical urban area and investigates how the optimal energy system configuration will vary depending on whether one’s priority is to minimize global warming potential, local air pollution, or resource consumption. A second analysis uses scenarios instead of an optimization model to describe the energy system options and then applies three different environmental impact assessment methods: life cycle analysis, material flow analysis, and ecological footprinting.

Activity-based modelling

Energy demands are described by economists as a ‘derived demand’; in other words, people don’t consume energy for its own sake but only do so in order to achieve other goals, like travelling from A to B or keeping one’s house warm. [link][Aruna Sivakumar] therefore presents activity-based modelling as a way of explicitly describing what people are doing within the city and using this information to then infer energy demands. It is a very powerful technique that allows complex, and more realistic, urban behaviour to be captured. The figure below, for example, shows how a change in work patterns might create an opportunity for a longer shopping period, leading to a ‘cold start’ of the vehicle’s engine and worse environmental performance.

If you got off work early, you might spend a longer time shopping which could have an impact on vehicle emissions.

We have been developing our own activity-based modelling platform, known as AMMUA (Agent-based Microsimulation Model of Urban Activities), but as the chapter describes, there are many difficulties in gathering the required data, understanding the behaviour of such complex model systems, and evaluating their performance as a tool for policy analysis. Nevertheless, activity-based modelling is one of the most powerful and unique tools for urban energy systems analysis.

Uncertainty and sensitivity analysis

The final chapter in this section discusses uncertainty and sensitivity analysis. These techniques are not modelling techniques /per se/, but rather general methods that could be applied to any modelling framework in order to assess the impact of uncertainty on the model results. Having formal methods for evaluating uncertainty is important for urban energy systems because of the complexity of such systems: even if we manage to make a nice neat quantitative model of the system, there will still be questions about the choice of values for specific model parameters.

Penetration of domestic energy efficiency measures as part of Newcastle’s overall energy strategy

The chapter illustrates how uncertainty analysis — the description of model output variability given uncertain inputs — can be performed using the R package and Monte Carlo simulation. I also discuss the use of sensitivity analysis — the attribution of model output variability to specific uncertain inputs — as a way of improving the quality of model results and promoting more robust policy conclusions. The methods are applied to a case study from Newcastle-upon-Tyne where we worked with the local council to develop an energy systems strategy to 2050 that clearly illustrated the benefits of prioritising energy efficiency interventions.

These chapters show that no one modelling technique can answer all questions about urban energy systems. Optimization models work well for technical design problems and long-term planning, but simulation models based on citizen activities are valuable for capturing the complexity of urban life and its impact on energy consumption. But even more to the point, computational models are only part of an analyst’s toolkit. Part IV therefore looks at some qualitative lessons that be drawn from experience in urban energy systems around the world.

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Part II: Technologies

The second part of the book turns from theoretical considerations about how to define an urban energy system to the actual nuts and bolts of four key technological systems: buildings, distributed multi-generation, urban renewable energy, and transportation systems.

Buildings

First, Mark Jennings provides an overview of one of the main sectors of urban energy demand, the built environment. Buildings account for approximately 60% of global primary energy demand and they are often iconic elements of any given city. After all, what would New York be without its skyscrapers or Paris without its Haussmannian terraces? Regardless of where they are located, energy use within buildings can be divided into four main categories — cooking, lights and appliances, space conditioning (heating and cooling), and water heating. However the choice of technologies and fuels used to satisfy these service requirements will depend on a city’s local energy resources, climate, and other factors.

Source: ~guidoanselmi at Deviant Art.

These issues are illustrated by focusing on the question of how to retrofit existing buildings for improved energy efficiency. In the UK, approximately 1–1.5% of buildings are retrofitted in any given year which presents a significant challenge if the country’s approximately 27 million dwellings are to be improved sufficiently to satisfy future carbon targets. A number of technologies, from supply-side innovations like heat pumps and biomass boilers to demand-side measures like improved insulation and smart controls, offer significant potential. However perhaps the key lesson from the chapter is that successful retrofits require not just appropriate technologies and favourable economics, but also the coordination of multiple actors including landlords, tenants, technicians, and more.

Distributed multi-generation and district energy systems

Distributed multi-generation refers to technologies which can produce multiple energy services from a fuel (e.g. heating, cooling, and electricity from gas). Located directly within the urban fabric, they are often connected to district energy systems that distribute these products to homes, offices, and other centres of demand. Pierluigi Mancarella, from the University of Manchester, provides an excellent overview of this subject which is one of the most promising technologies for improving urban energy efficiency.

The Avedøre CHP power station in Copenhagen

Multi-generation and district energy systems leverage the density of urban energy demands to create energy systems of almost unrivaled variety. Systems can be designed to provide heating, cooling, power, or combinations of all three. Key technologies include internal combustion engines, micro-turbines, gas turbines, Stirling engines, fuel cells, heat pumps and more, and they can be powered using fossil fuels, bioenergy, solar thermal, and other sources. This variety means that it is important to have an easy way of assessing alternative system configurations and the chapter presents a simple method for this purpose based on the idea of a reference technology.

Urban bioenergy and other renewables

While distributed multi-generation systems benefit from the spatial concentration of energy demands in a city, this creates problems for renewable energy sources. Rough calculations show that, for London, the power density of demand is approximately 11 W/m² whereas wind energy only offers 2 W/m². Other renewable energy technologies like solar can provide more power, but often at the wrong time relative to demand.

Urban bioenergy is therefore one of the more interesting urban renewables as it can make use of wastes produced within the city and can used for dispatchable heat and power provision. Marco Pantaleo explores this unique category of urban renewable resources, considering the size of the potential resource and the relative merits of the multiple conversion pathways and technologies that can be used. Ultimately the choice of which biomass feedstock, intermediate biofuel, and final bioenergy technology to use will depend on the specific city in question, as planning constraints, local resource availability, and supporting infrastructures for transport and storage are all important design considerations.

Urban transport technologies

In the last chapter of this section, Aruna Sivakumar, Salvador Acha, and I examine the transportation sector. While transportation energy consumption in cities is significantly affected by longer term decisions relating to land use and activity location, this chapter focuses primarily on technological options such as fuels, vehicle power trains, and the potential impact of electrifying transportation. A brief case study is presented to illustrate how these different changes might affect transportation energy consumption and greenhouse gas emissions in London to 2050.

Source: Qurren via Wikimedia Commons

However another way of creating a more efficient urban transport system is through the use of ‘smart’ transportation technologies. By using information and communication technologies, it is possible to use existing infrastructures more effectively (e.g. through congestion pricing and real-time notifications of traffic conditions) and develop innovative business models for vehicle sharing, integrated logistics, and other mobility services. The difficulty here is that many of these innovations are just beginning to be deployed and it remains to be seen just how much of an impact they might have on urban energy systems.

This part of the book therefore provides a necessarily brief survey of the many technologies that can be used to improve the efficiency of urban energy systems. While it’s lovely to have that variety, it does create difficulties when deciding which specific system configuration to invest in, and so the next post will focus on techniques for urban energy systems analysis.

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The book itself is divided into four main parts and over the next few posts, I’ll give you a bit of a sneak peak, looking at the key findings in each section.

Part 1: Introduction

The introductory section has three aims: to explain why a person should care about urban energy systems, to provide a theoretical foundation for analyzing such systems, and to intrigue the reader with a practical case study.

Motivation

As shown in the figure below, when we first started working on urban energy systems in 2005, the phrase “urban energy systems” wasn’t very common in the academic literature. But that has certainly changed over the past five years, owing to four factors:

• Over half of the world’s population now lives in cities with substantial growth in the cities of the developing world expected in the coming decades. These urban residents will need energy systems if they are to achieve their social and economic goals, and such systems can have significant environmental impacts.

• Cities offer spatial concentrations of people and activities that create unique opportunities. This has been widely recognized in the economic geography literature, but debates about ‘smart growth’ and urban sustainability also emphasise the potential energy efficiency benefits of urban living.

• In the absence of substantial national and international progress on climate change, many cities are beginning to take action directly as demonstrated by emerging city networks like the C40.

• Similarly there is a growing literature on urban governance processes and the ways in which local authorities can encourage innovation through partnerships with residents, other levels of government, and the private sector.

Plot showing trends in urban energy system publications (Data from Web of Science)

These factors motivated our original interest in the subject and led us to specific hypotheses about the potential for more sustainable urban energy systems.

Conceptualizing UES

Before we can begin any meaningful analysis, it is helpful to have a theoretical framework. The very word ‘theory’ brings back bad memories from grad school, but theory does not have to mean impenetrable jargon. It is simply a way of structuring one’s thoughts so that you can analyze and interpret the world.

For urban energy systems, there are two theoretical perspectives of interest. The first is the view of cities as physical systems which can be interpreted with the tools of thermodynamics, industrial ecology, or complex systems analysis. These are helpful for calculating key metrics like the efficiency of a city, but it doesn’t say much about why any given city uses energy in the way it does. For that, a second set of socio-technical theories are needed. These deal with the ways in which technologies and people co-evolve when establishing both basic energy consumption habits and the structure of large infrastructure systems.

The chapter concludes with a definition of urban energy systems that captures most of these elements: “the combined processes of acquiring and using energy to satisfy the energy service demands of a given urban area”.

The history of London’s urban energy system

The section concludes with a brief history of London’s urban energy system. The idea was to reveal how the current configuration of an urban energy system, and the future options available to it, are shaped by past choices and place-specific factors.

Over the centuries, London’s energy system has evolved from the collection and burning of locally available biomass, to the use of sea-imported coal, to early experiments with electricity and gas networks, through to today’s national grids and integrated transport systems. Each transition was marked by pressures that encouraged a shift away from the old technology (be they economic, environmental or social) and an opportunity created by new innovations. In a classic example, London’s early electricity system was highly fragmented with 70 authorities, 50 different types of system, 10 different frequencies and 24 different supply voltages. But national-level reforms introduced standardization and rationalizing and paved the way to today’s seamless electricity grid.

Part I therefore sets the stage for the rest of the book, which will cover urban energy technologies, analytical techniques, and policy guidance. In the next post, I’ll turn to Part II: Urban Energy Use and Technologies.

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Are businesses sleep-walking into a resource crunch? Good luck answering that with this figure.

While the overall research on whether businesses are prepared for future resource scarcity is interesting, this figure falls down in all sorts of ways.

• First we’ve got an hourglass metaphor wrecked by the fact that “now” (i.e. the pinch point in the glass) is actually 3–5 years in the future and the past sand includes “up to three years” in the future. If you’re going to use this kind of visual metaphor, at least give it some respect.
• Next there are the percentages which are appear to represent a vertical distance, not volume of sand or width of the hourglass. Maybe that’s a pedantic point, but the amount of ink on the yellow 15% is not even close to the amount on the green 19%. Tufte may be an obsessive, but he’s got a point when he talks about the lie factor of a plot.
• And finally, there’s a strange color scheme in which green goes from dark to light to dark again. So is dark green a good thing or not?

The look is just the default Twenty Twelve wordpress theme, a painless way to replicate those snazzy Octopress sites you see around (e.g. Kieran Healy‘s site). I did look into switching but it looks pretty time-consuming when converting a large existing site.