The results allow us to offer robust policy advice, such as installing loft insulation and cavity wall insulation over the next ten years, because it is cost- and carbon-effective in almost all future scenarios. Here’s the key figure, with the maximum rates of penetration indicated by the dashed line and the grey areas showing the interquartile range:

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

The abstract:

Local authorities often rely upon urban energy and carbon modelling tools to develop mitigation policies and strategies that will deliver reductions in greenhouse gas emissions. In this paper the UK example of Newcastle-upon-Tyne is used to critique current practice, noting that important features of urban energy systems are often omitted by bottom-up tools including interactions between technologies, spatial disaggregation of demand, and the ability to pursue over-arching policy goals like cost minimization. An alternative optimization-based approach is then described and applied to the Newcastle case, at the scale of both the whole city and the South Heaton district, and using Monte Carlo techniques to address policy uncertainty. The results show that this new method can help policy makers draw more robust policy conclusions, sensitive to spatial variations in energy demand and capturing the interactions between developments in the national energy system and local policy options. Further work should focus on improving our understanding of local building stocks and energy demands so as to better assess the potential of new technologies and policies.

Keirstead, J., & Calderon, C. (2012). Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study Energy Policy DOI: 10.1016/j.enpol.2012.03.058

If you’ve ever read the work of Vaclav Smil, Roger Fouquet and Peter Pearson or others, you’ll know that this is a huge field and it really deserves a book length treatment. But, for the moment, we’ve written a paper that tries to emphasize the main themes and sets the stage for further work.

The abstract:

Modern cities depend on energy systems to deliver a range of services such as heating, cooling, lighting, mobility, communications, and so on. This article examines how these urban energy systems came to be, tracing the major transitions from the earliest settlements through to today’s fossil-fuelled cities. The underlying theme is “increasing efficiency under constraints” with each transition marked by increasing energy efficiency in service provision, increasing per capita energy use, increasing complexity in the energy system’s structure, with innovations driven by a strategic view of the overall system, and accompanied by wider changes in technology and society. In developed countries, the future of urban energy systems is likely to continue many of these trends, with increased efficiency being driven by the constraints of climate change and rising fuel prices. Both supply and demand side technologies are discussed as potential solutions to these issues, with different impacts on the urban environment and its citizens. However in developing countries, rising urban populations and access to basic energy services will drive the next transition.

Rutter, P., & Keirstead, J. (2012). A brief history and the possible future of urban energy systems Energy Policy DOI: 10.1016/j.enpol.2012.03.072

The abstract:

Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in a number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified – technology design, building design, urban climate, systems design, and policy assessment – each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date. Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

Keirstead, J., Jennings, M., & Sivakumar, A. (2012). A review of urban energy system models: Approaches, challenges and opportunities Renewable and Sustainable Energy Reviews, 16 (6), 3847-3866 DOI: 10.1016/j.rser.2012.02.047

In 2011 the average London household spent £1140 on their combined electricity and gas bills when paying by direct debit, up 8.6% on the year before. Since high energy bills are a sure-fire way to get bad headlines, it’s no wonder that politicians are looking for ways to bring down costs. But given the structure of the UK’s energy markets and urban governance, there’s usually very little that local authorities can do besides encouraging their residents to shop around.

This is why Ken’s big idea, if it works, would be a significant change in the way UK cities approach their energy systems. By using a Transport for London energy supply contract to buy in cheaper energy from the wholesale market, the Livingstone campaign claims that London households could switch to the new London Energy Co-operative (LEC) and save around £130 per year. Per unit gas and electricity costs for the median industrial consumer are about half of what domestic customers pay so this certainly sounds feasible.

We can double-check the maths with the help of this factsheet (pdf) from the energy regulator Ofgem, which gives a breakdown of domestic gas and electricity bills. For both fuel types, about 35% of the price is fixed and covers metering provision, transmission and distribution charges, taxes and the cost of energy saving and climate change policies; the LEC would still have to pass on these charges to consumers.

The remaining 65% is made up of the actual fuel cost, the cost of running the business, and the profit margin. Recent Ofgem analysis indicates that operating costs account for about 11% of the final bill, and average net profit margins are around 4.5%. Assuming similar operating costs for the LEC, having access to cheaper fuels and operating as a non-profit would enable a potential annual savings of around £220 per household.¹ Naturally there will be set-up costs and the operations may not be as efficient as existing suppliers, but expected savings of £130 are not unreasonable.

There are many obstacles before Ken Livingstone makes this experiment a reality, not least of which is getting elected. Furthermore a number of firms have expressed their reluctance to offer wholesale contracts to these types of bulk purchase schemes. The matter may eventually need to be settled in court.

But if it were to happen, this model could provide a significant disruption to the UK’s energy supply market. Not all UK cities currently have access to wholesale supply contracts via an entity like TfL (London’s largest single electricity consumer), but with the template in place, it could encourage them to think about how they might promote more affordable urban energy provision. And once started, these urban energy co-ops could expand to offer a range of improvements in energy efficiency, renewable energy supply, and customer experience. Elected mayors, if granted appropriate powers in this area, could be at the forefront of a renaissance of urban energy innovation.

Over a hundred years ago, modern energy services like gas and electricity began to emerge in cities worldwide, led by local firms and local governments. As the networks grew, the pendulum swung towards the centralized national grids we have today. Perhaps now’s the time for the pendulum to swing back the other way.

¹ There are a lot of assumptions in this calculation. I’m only guessing at TfL’s costs for gas and electricity based on DECC’s commercial price survey and it’s hard to work out the exact cost of fuel within these prices, as Ofgem doesn’t provide a breakdown for commercial bills in the same way that it does for the domestic sector.

But when I saw the Guardian’s headlines this week about increasing commercial hesitation and plans for a sneaky subsidy, I thought back to our final point on the “limitations of facilitative action”. It’s a bit long, but I’ll post it in full below since I think our analysis has stood up pretty well. By threatening to pull out, nuclear companies have the government over a barrel and will use this position to squeeze for as many financial concessions as they can reasonably get.

Q16: The public interest and new nuclear power stations: the limitations of facilitative action

The consultation document makes it clear that the government’s role in this matter is only to decide whether or not the private sector should be “allowed” to construct nuclear power stations and if necessary, to initiate “facilitative action”. As this position is broadly consistent with the belief that competitive markets can best fulfill the UK’s energy needs, it begs the question: what happens if the private sector decides not to invest?

Our concern is as follows. The Energy White Paper stresses the importance of new nuclear power investment, warning that without it “we would be in danger of not meeting our policy goals” on energy security and reducing carbon emissions. This dependence, combined with nuclear power’s long-lead times, creates a risk that should private sector investment in nuclear power not be immediately forthcoming, the government might feel compelled to offer further incentives beyond the proposed “facilitative action”. There are two possible drivers for such a scenario. First, investors may be reluctant to commit significant resources to nuclear power without a guarantee on the price of carbon; E.ON, for example, recently stated that this would be a prerequisite for new nuclear development. However the consultation document’s assumption of a future carbon price of €36/tonne CO2 cannot be guaranteed under the quantity-based cap-and-trade EU ETS, as shown by the work of Profs Michael Grubb and David Newbery. The second concern can be found in Shimon Awerbuch’s work on the impact of nuclear power on financial risk in the electricity sector. Of course if further measures were needed to encourage nuclear power investment, a public consultation would be required to justify additional market intervention and this would create further delays in meeting our energy policy goals.

We recognize that these concerns represent a somewhat speculative, but not unimaginable, ‘what if’ scenario; nonetheless it does highlight two important issues which should be considered before proceeding on nuclear power. First, it demonstrates the tight timescales implied by our energy policy goals, particularly in the area of reducing carbon emissions. The government must be clear about the critical paths necessary to meet these targets and act quickly if progress is not being made. Secondly, it serves as a reminder that the UK’s energy policy challenges can be met in many different ways and that all alternatives should be fully considered before committing to a particular course of action. Box 10.1 of the white paper is an excellent demonstration of this, showing that there are at least 16 other policy measures that are likely to be more cost-effective, with shorter lead-times, and greater carbon mitigation potentials that nuclear power.

Our conclusion therefore is that it is right for the private sector to have the option of investing in new nuclear power stations, provided that they bear the full costs. However any further market intervention by the government would require a full and transparent comparison with other policy options.

Low EU ETS price? Check. New consultation for improved support? Check.

Somehow this video seems appropriate:

While the book is sensibly laid-out and pretty comprehensive in its choice of topics, it is also a very hard read. My linear algebra may be rusty but I’ve heard some mathematicians describe the conventions used in the book as “an affront to notation”. So just be aware that if you try to work through the book, you will need to be patient. It’s not a cookbook that clearly spells out how to do everything step-by-step.

That said, I have now worked through the basics of Gaussian process regression as described in Chapter 2 and I want to share my code with you here. As always, I’m doing this in R and if you search CRAN, you will find a specific package for Gaussian process regression: gptk. The implementation shown below is much slower than the gptk functions, but by doing things manually I hope you will find it easier to understand what’s actually going on. The full code is given below and is available Github. (PS anyone know how to embed only a few lines from a gist?)

Step 1: Generating functions

With a standard univariate statistical distribution, we draw single values. By contrast, a Gaussian process can be thought of as a distribution of functions. So the first thing we need to do is set up some code that enables us to generate these functions. The code at the bottom shows how to do this and hopefully it is pretty self-explanatory. The result is basically the same as Figure 2.2(a) in Rasmussen and Williams, although with a different random seed and plotting settings.

Example of functions from a Gaussian process

Step 2: Fitting the process to noise-free data

Now let’s assume that we have a number of fixed data points. In other words, our Gaussian process is again generating lots of different functions but we know that each draw must pass through some given points. For now, we will assume that these points are perfectly known. In the code, I’ve tried to use variable names that match the notation in the book. In the resulting plot, which corresponds to Figure 2.2(b) in Rasmussen and Williams, we can see the explicit samples from the process, along with the mean function in red, and the constraining data points.

Example of Gaussian process trained on noise-free data

Step 3: Fitting the process to noisy data

The next extension is to assume that the constraining data points are not perfectly known. Instead we assume that they have some amount of normally-distributed noise associated with them. This case is discussed on page 16 of the book, although an explicit plot isn’t shown. The code and resulting plot is shown below; again, we include the individual sampled functions, the mean function, and the data points (this time with error bars to signify 95% confidence intervals).

Example of Gaussian process trained on noisy data

Next steps

Hopefully that will give you a starting point for implementating Gaussian process regression in R. There are several further steps that could be taken now including:

• Changing the squared exponential covariance function to include the signal and noise variance parameters, in addition to the length scale shown here.
• Speed up the code by using the Cholesky decomposition, as described in Algorithm 2.1 on page 19.
• Try to implement the same regression using the gptk package. Sadly the documentation is also quite sparse here, but if you look in the source files at the various demo* files, you should be able to figure out what’s going on.

The code

This isn’t a problem that’s unique to Britain. In the United States for example, municipalities are struggling to maintain aging water infrastructures where, in some places, the pipes are over a hundred years old and made out of wood. And like in the US, the need for major infrastructure investments raises substantial questions about the role of such facilities and who should pay for it.

In this post, I want to sketch out some thoughts on infrastructure and look at three related questions:

• Is infrastructure a private or public good?
• How should it be financed?
• How do infrastructure investments relate to the wider economy and our prospects for growth?

Is infrastructure a private or public good?

Infrastructure is traditionally viewed as a public good, meaning that people can’t be excluded from its use and one person’s use doesn’t affect its availability to others. This is a somewhat stylised view – think of congestion on a roadway – but the important point is that the alternative view, infrastructure as a private good, would lead to a shortfall of provision. Left to its own devices, the private sector would only build infrastructure that provided a sufficient return on its capital, at commercial terms, and so roads would only be built on routes where there was sufficient toll revenue to justify their construction and similarly energy and communication networks would be built only in areas of high demand.

Governments have historically taken the view that society benefits from providing (near) universal access to these facilities at low or no cost, and hence they have been willing to pay for infrastructure projects with public money. This doesn’t necessarily mean that such investments are uneconomic, because governments are typically able to borrow at lower costs than the private sector and are willing to wait decades for such assets to pay off. And in the meantime, households and businesses benefit from the public provision of these goods. Unfortunately, these borrowings show up on public balance sheets which, in times of economic turmoil, may not be what governments want.

Cameron’s speech recognized the public good nature of infrastructure several times, remarking that these systems are part of a nation’s assets which “sustain future prosperity” and that exclusive private sector provision risks “leaving some people behind”.

How should infrastructure be financed?

One can therefore conclude that infrastructure in the UK is still seen primarily as a public good, but one for which the public purse is increasing reluctant to pay. So what alternatives are available?

Over the past two decades, one of the most popular options in Britain has been public-private partnerships and private finance initiatives in particular. The precise detail of these arrangements can vary significantly from project to project, but the general idea is that government contracts with a private sector firm for the provision of a public good or service. So the M6 toll route for example was built with private funds, in return for the ability to collect tolls over a 53 year period.

Another busy day on the M6. Source: Wikipedia

These arrangements have been widely critized, with a recent Treasury Select Committee describing PFI as an “extremely inefficient” method of financing public sector projects. The simple financial reason for this inefficiency is that “[t]he average cost of capital for a low risk PFI project is over 8%, double that of government gilts”. In other words, the government can borrow money much more cheaply than the private sector. The only problem is that this borrowing ends up on the national balance sheet and chancellors are loath to do this. This sort of short-term thinking is also common in United States, where long-term infrastructure leases are provided to private firms “in exchange for one-off lump sum payments of a few billion bucks at best, usually just to help patch a hole or two in a single budget year.”

Having been criticised by the Treasury Select Committee and by the current chancellor, the political question is how can a PPP deal be structured that doesn’t look like PFI but still delivers new and improved infrastructure without a significant impact on government borrowing figures. The chancellor’s current strategy is to encourage investment from pension funds and sovereign wealth funds, but as the Guardian’s excellent fact check article discusses, the new proposals aren’t substantially different from previous PFI arrangements. And what’s more, new projects won’t magically appear as the UK still faces long-term challenges with the cost of its infrastructure projects (60% higher than in Germany). The proposed planning reforms are intended to help reduce these costs by speeding up the process and removing uncertainty for investors, but whether these changes will be effective is another issue.

Funding infrastructure in tough economic times is clearly a challenge. Public-private partnerships could be a good alternative but the UK experience suggests that these arrangements are very expensive and only have the benefit of deferring large line items to future budgets. However that may be enough for the current government to proceed with PFI Mark 2.

What do infrastructure investments mean for the wider economy?

One of the reasons why infrastructure is in the news at the moment is that constrained capacity in transportation, house building, and communications networks is seen as a barrier to economic growth. Build more capacity and the good times will roll, or so the theory goes.

There are two issues here. The first is the stimulus value of immediate infrastructure investment. If the private or public sector spends heaps of money on new roads, houses, and so on, then this boosts employment, demand for construction materials, and generally helps the recovery. I won’t get into the detail but plenty has been written about the merit of such projects, but with somewhat conflicting messages. On the one hand, when private sector demand is down, Keynesian economists would argue that it makes sense to use the public purse to boost aggregate demand. In the case of Japan’s lost decade, for example, Paul Krugman argues that the only problem with their infrastructure investment programme was that it wasn’t bigger, but at the very least, their spending on roads and rails kept things from getting substantially worse. On the other side, the Austrian school thinks that stimulus spending simply delays the inevitable realignment of the economy by propping up losing industries. (Reading between the lines, Michael White implies there might be some sort of benefit in selling our expertise in writing the PFI contracts.)

While I’m not a card-carrying economist, on the question of infrastructure as stimulus, my intuition would be similar to Dieter Helm’s argument: if the economy is depressed and savings can’t achieve good returns anywhere else, then you might as well invest them in long-term productive assets like infrastructure.

To my mind though, the more interesting question is whether or not these investments are likely to deliver long-term growth. Chris Kennedy’s recent book on the wealth of cities makes the point that infrastructures create patterns of consumption that last decades. Motorway networks for example have established an entire economy around vehicle manufacturing and repair, and support a suburban lifestyle with larger houses that need to built and filled with more stuff.

These sorts of effects can be seen in the following plot. It shows the UK’s GDP since 1500 and I’ve fitted three regression lines: one to the pre-Industrial Revolution period, another to the period after the UK railway boom of the 1830s and 40s, and a third to the post-war period or modern industrial age. The results are clear: each stage has a distinctly different slope, meaning that average rates of growth are in different in each period (0.07% prior to the industrial revolution, 1.1% after the railway boom, and 2.1% in the modern era). I would suggest these changes are due to the infrastructure investments made at the start of the period: railway investments at the start of the 19th century boom, and modern manufacturing methods and the use of oil in the 20th century.

Historic UK GDP and major infrastructure periods. Data from Angus Maddison's historic GDP statistics. Click for bigger.

This analysis isn’t entirely novel and fits in with the notion of Kondratiev waves. But it does suggest that only some of the proposed infrastructure improvements will lead to long-term economic benefits. New investments in the roads? That doesn’t change the structure of the economy and lead to new patterns of consumption; it only ensures that the current system keeps working. Investments in 4G mobile and high-speed broadband? These are more promising and could enable a range of new products and services.

Conclusion

In concluding his speech, the Prime Minister asked “what is it that people want for the future? They want reasonable things; a decent home, a clean environment, jobs for their children, the ability to get around without hassle, huge costs or endless jams.” These are modest aims and, despite his subsequent call to “be bold”, the discussion presented here suggests that the proposed infrastructure reforms will indeed have only minor consequences. We have seen that infrastructure will continue to be viewed as a public good in the UK, even if the private sector has a significant role as a co-provider of these services. The financing of infrastructure projects will continue to driven by a desire to keep borrowing costs off the public balance sheet, even if current low bond rates suggest and the poor value of past PFI contracts suggests that this may not be the best strategy. And finally, while there may be some short-term boosts in economic activity, the long-term prospects are mixed. Improvements in communications infrastructure should help support long-term growth but investments in roads and housing are largely maintenance, part of the ongoing costs of supporting our current way of life.

Further reading

• Dieter Helm comment piece on the relationship between infrastructure, GDP and national prosperity

• Chapters 18 and 19 of E.F. Schumacher’s Small is Beautiful contain a useful discussion on public versus private goods, in particular noting the benefits that private firms receive from the public provision of these services.

The authors of this paper however (economists themselves) are careful to point out that the function of economic models is slightly different. The confusion arises when we think of economic models as “rule-based knowledge”, that is representative pieces of knowledge that summarize the results of multiple individual cases. For example, if we watched the arc of thousands of balls fly through the air at different velocities and angles, we could state a general “rule” of Newton mechanics to explain this path. Economic models, on the other hand, are examples of “case-based knowledge” and should be treated like a piece of experimental data, a paper from the literature, an anecdote, or any other piece of information that helps the analyst think about a particular problem. In other words, theoretical models allow economists to play around with situations which may be intentionally unrealistic, but can nevertheless provide valuable insight to a real problem.

This seems like such a fundamental bit of epistemology that I find it hard to believe it’s not more widely known, among engineers, the general public, but also economists themselves. Indeed the wealth of quotes about economic models needing “beauty before truth” suggests that perhaps the economics profession itself has forgotten the difference between case-based and rule-based knowledge.

In any event, it’s an excellent paper with a nice summary of the standard critiques of economic modelling, the difference between rule and case-based knowledge and, ironically, an economic model explaining the argument. The key conclusion is that, whatever form of knowledge one uses, one should be clear about the limits of relevance and validity inherent in any given tool. The abstract:

People often wonder why economists analyze models whose assumptions are known to be false, while economists feel that they learn a great deal from such exercises. We suggest that part of the knowledge generated by academic economists is case-based rather than rule-based. That is, instead of offering general rules or theories that should be contrasted with data, economists often analyze models that are “theoretical cases”, which help understand economic problems by drawing analogies between the model and the problem. According to this view, economic models, empirical data, experimental results and other sources of knowledge are all on equal footing, that is, they all provide cases to which a given problem can be compared. We offer some complexity arguments that explain why case-based reasoning may sometimes be the method of choice; why economists prefer simple examples; and why a paradigm may be useful even if it does not produce theories.

Recent years have shown a marked interest in the construction of eco-towns, showcase developments intended to demonstrate the best in ecologically-sensitive and energy-efficient construction. This paper examines one such development in the UK and considers the role of biomass energy systems. We present an integrated resource modelling framework that identifies an optimized low-cost energy supply system including the choice of conversion technologies, fuel sources, and distribution networks. Our analysis shows that strategies based on imported wood chips, rather than locally converted forestry residues, burned in a mix of ICE and ORC combined heat and power facilities offer the most promise. While there are uncertainties surrounding the precise environmental impacts of these solutions, it is clear that such biomass systems can help eco-towns to meet their target of an 80% reduction in greenhouse gas emissions.

It is primarily an application paper, but it does demonstrate the value of incorporating spatial and temporal dynamics within urban energy systems. Biomass feedstocks for example can be stored between seasons, depending on availability and price, and the resulting district heat will have losses as its transported around the city. There’s plenty of scope for expanding this type of model too, in particular to look at local air pollution impacts rather than just greenhouse gas emissions.

Keirstead, J., Samsatli, N., Pantaleo, A., & Shah, N. (2012). Evaluating biomass energy strategies for a UK eco-town with an MILP optimization model Biomass and Bioenergy DOI: 10.1016/j.biombioe.2012.01.022

These are some of the questions that Chris Kennedy’s new book, The Evolution of Great World Cities, seeks to answer. Kennedy is a professor of civil engineering at the University of Toronto with a soft spot for cities and economics. Launching the book in London last week, he noted that he originally wanted to investigate the wealth of cities in much the same way that Adam Smith had done for the wealth of nations. But along the way, the book changed into something subtly different and the result is a fascinating mix of macroeconomics, infrastructure engineering, history, and ecology.

Philadelphia's City Hall

The second theme is the connection between economic growth and the physical structure of cities. Take the automobile as an example. Its introduction in the early twentieth century led to a new mode of urban development, suburban sprawl, which although it has many disadvantages certainly leads to increased consumption. Cars need to manufactured, sold, and serviced; larger suburban homes need to be constructed with more materials and filled with consumer goods. The key issue here is not the infrastructure itself, but the modes of consumption that it necessitates. For example, one might expect that the 1990s IT revolution would have led to a demand side crisis. Just like Smith’s pin-makers, those displaced by the productivity gains of IT – bank tellers, backroom operations, and so on – would be unemployed and unable to consume. However IT also created new opportunities in PC manufacturing, software design, and innovative business models like EBay and Amazon. This new online way of life drove a new cycle of demand, rejuvenating the economy.

To me, this is the book’s most important contribution. This idea of the autonomous consumption of infrastructure, that is the societally mandatory level of minimum consumption created by these systems, is hugely important for understanding not just the economic growth that Kennedy is concerned about, but more broadly the sustainability of cities. North American urban sprawl is a perfect example. While it engenders high levels of consumption, maintaining that lifestyle depends on a throughput of resources, most importantly abundant and affordable transport fuel. Should these fuels become significantly more expensive, these cities would grind to a halt. The same level of infrastructure-mandated autonomous consumption would still be there, but it would now consume a much larger portion of a household’s income, leading to reduced savings, reduced investment in new opportunities, and eventual stagnation. In the language of sustainable development, the autonomous consumption of infrastructure represents a liability that must be serviced on an on-going basis.

The third theme is an analysis of urban economic processes as ecological systems. The relevant chapter offers several noteworthy ideas but it felt incomplete compared to the rest of the tightly-argued book. Nevertheless, an excellent description of Detroit’s decline is provided and the statistics cannot fail to astound: a population decline of 50% between 1930 and today, over 60 square miles of vacant abandoned land (44% of the city’s area), local tree species poking through factory floors that once produced millions of automobiles. Kennedy argues that, in much the same way that an ecosystem needs diversity to survive a changing environment, Detroit was too focused on cars in order to survive, both in terms of the limited diversity of its economy and the inflexibility of its infrastructure. One of the great what-ifs posed by the book is what Detroit might look like now had a 1923 proposal for a subway and integrated transit system been implemented.

Illustrated with case studies on cities as diverse as Toronto and Montreal, Philadelphia and New York, Seville, Paris, Dubai, and London, The Evolution of Great World Cities is a unique work of economic geography. Engineers often complain that economic models are too abstract to offer a meaningful understanding of the real world. Kennedy has therefore done both professions a great service by presenting a strong argument that it is the links between our built environment and economies that matter most.

Chris Kennedy has also written a blog post about the book over at the World Bank’s Sustainable Cities site.