Author Archives: James Keirstead

The end of urban sprawl in America?

Via the Atlantic Cities, a fascinating post about possible future patterns of US urban development between 2010 and 2030. While the post focuses mainly on those places where urban sprawl is likely to persist as the dominant development pattern, I wanted to play with the database and have a look for myself.

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.

Diffusion of energy innovations in a social network

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Nick McCullen and colleagues have a new paper out in SIAM Journal on Applied Dynamical Systems entitled ‘Multiparameter Models of Innovation Diffusion on Complex Networks‘. There’s a nice press release summarizing the paper on the SIAM website, and the first few lines of the abstract get right to the point:

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.

Can you guess that this is an electric car? Source: Darrenm540 at Wikimedia Commons.

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)

New paper: Activity-based modelling of urban energy demands

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I’m not sure how this slipped between the cracks, but we had a new paper published recently in the Journal of Industrial Ecology. The main idea is that you can use detailed patterns of individual activities (e.g. working, shopping, resting at home) as produced by transport models to estimate the stationary energy demands for heat and power at high spatial and temporal resolutions. The paper presents a basic proof of concept but there is lots of room for improvement here.

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

Urban Energy Systems: Conclusion

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This post is part of a four-part series giving an overview of our recently published book Urban Energy Systems: An Integrated Approach. Parts I, II, and III can be read here.

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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How to write a PNAS paper

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Siqi Zheng and Matthew Kahn have just published a paper in PNAS Plus entitled ‘China’s bullet trains facilitate market integration and mitigate the cost of megacity growth‘. After reading the abstract on Kahn’s blog (via the soon-to-be-missed Google Reader), I have to confess that my first thought was: What’s the big deal? The main finding of the paper, that faster transportation options enable people to escape from expensive polluted cities while maintaining access to economic opportunities, is hardly new. A similar effect was seen in the UK in the 1970s with the introduction of the Intercity 125 trains and the result is also consist with the foundational Alonso-Mills-Muth model of urban economics from the 1960s. Why would a major journal like PNAS ¹ publish this?

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.

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?)
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.
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.
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.
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

Urban Energy Systems: Techniques

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This post is part of a four-part series giving an overview of our recently published book Urban Energy Systems: An Integrated Approach. Parts I and II can be read here.

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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Available now from Amazon and other bookshops.