A Model to Include Risks of Peak Oil in Current Urban Planning

High gasoline<!–[if supportFields]> XE “gasoline”<![endif]–><!–[if supportFields]><![endif]–> prices in 2005 made it clear that spikes in fuel costs will cause people to modify their behaviors. These changes ranged from the increase in consumption of more efficient vehicles, changes in driving behavior such as driving at lower speeds and carpooling, to the reduction of optional trips that an individual may have wanted to take but could not afford. Given these effects, it would make sense for risk models in urban<!–[if supportFields]> XE “urban”<![endif]–><!–[if supportFields]><![endif]–> planning to include risks to certain development forms caused by reduced supply of fossil fuels, but thus far no one has attempted to model these effects. Krumdieck et al. (2010) attempt to rectify this by aggregating various predictions on peaks in conventional oil, they created probabilistic models showing the probability of a peak at a given time and the amount of oil available in the future. They also create impact models to show how these changes will affect behavior and the ultimate value of certain urban developments. Their model showed that the larger the urban sprawl, the greater the calculated risk factor. They conclude that it is essential that transportation risks created by an oil peak must be considered when planning urban areas and that these risks would be best mitigated by concentrating population centers and creating incentives to ride public transportation or use active modes of transportation like cycling or walking. —Steven Erickson
Krumdieck, S., Page, S., Dantas, A., 2010. Urban form and long-term fuel supply decline: A method to investigate the peak oil risks to essential activities. Transportation Research Part A 44, 306–322.

          Krumdieck et al. must first calculate the probable supply of oil in order to know how large of an impact an oil production peak will have on civic life. They create this distribution by fitting a curve to peak projections by various experts rather than trying to pick a single expert to base their claims on. These predictions tend to cluster around 2010, and all of them fall before 2030.  The resulting distribution is bell-shaped and results in a probability function that projects the probability of a peak in a given year. They then take the cumulative probability of this function to calculate the probability that a peak will have occurred by a certain year.
          Then, in order to calculate future oil supply for a given year, similar probabilistic functions need to be estimated in order to find future growth rates and rates of decline. Before peak, growth rates tend to center around 1.6% and estimates of post-peak declines center around 3% per year. A Gaussian probability function was then created for each of these rates.
          Finally, to create the ultimate probabilistic estimation of oil supply, a Monte Carlo simulation was used to integrate the three probabilistic models, generating a set of possible oil supplies spread across a probability function. Among the figures generated by this production, it is predicted that there is only a 5% chance of having more than 28 billion barrels/year in 2035, which is about the yearly production of conventional oil in 2005. The authors suggest that it is likely that by 2035 we will have about 60% of the oil supply as existed in 2005. As urban<!–[if supportFields]>XE “urban”<![endif]–><!–[if supportFields]><![endif]–> planning projects typically look forward as much as 40 years it is clear that an oil peak needs to be considered when looking at potential risks to viability.
          After creating a function for oil supply, an impact function was also necessary to illustrate the effects of an oil peak on individual behavior and aggregate this to find the ultimate effect on various modes of urban<!–[if supportFields]> XE “urban”<![endif]–><!–[if supportFields]><![endif]–> planning. The authors theorize that the primary consequence of rising fuel prices will be a change in travel demand. This will manifest itself by a change in the quantity and nature of trips. They create a metric to measure the essentialiality of trips. Trips are divided into three groups: optional, necessary, and essential. Optional trips are trips that can be cut without an overall loss of utility, necessary trips are trips which an individual would not cut if they could avoid it, and result in a loss of either social or economic wellbeing. Finally, essential trips cannot be cut without significant loss to personal health and overall quality of life. The authors define all trips as 20% optional, 30% necessary, and 50% essential.
          Given the projections on fuel supply produced by the model, it is likely that there will be a disparity between unconstrained—or business as usual—demand and energy supply. Impact is then characterized by the types of strategies available to deal with this disparity, which would depend on organization of the city and transportation methods available. Low impact strategies are defined as those in which energy consumption is lowered but all economic and social participation is maintained. This would be realized by an increase in the use of fuel efficient vehicles, public transportation and active modes of transportation such as walking or cycling as well as through decreased trip distances and other behavioral changes. Medium impact results imply a cut in optional trips. High impact strategies are those in which necessary trips are cut, and very high impact strategies result in a loss of essential trips. The larger the supply and demand disparity, the more difficult it will be to avoid suffering medium and high impact effects.
          These models of fuel supply and supply demand disparity impact are then used to calculate the risk factor of a peak in conventional oil. This model, which incorporates unconstrained energy demand, energy supply, and the various ways in which people can change their behavior to deal with the disparity between the two, comes up with several conclusions. The first is that behavioral changes generate the least impact. These are facilitated by a dense city requiring little driving with good public transportation. The other conclusion is that a reduction to trips significantly increases the risk factor, a possibility faced by more spread out cities with poor public transportation. Although these conclusions seem trivial, the authors point out that this is the first time they have been modeled in a mathematical fashion that can be included in risk analysis models for the planning of future projects.
          In order to test this model, the authors applied their formula to the city of Christchurch, New Zealand<!–[if supportFields]>XE “New Zealand”<![endif]–><!–[if supportFields]><![endif]–>. Christchurch was predicted to nearly double in population between 2005 and 2041, and the city has four different plans for expansion: an unplanned, business as usual approach, Plan A, in which the city borders expand little and the focus is on population density, Plan B, which would allow growth to push out along developed areas, and Plan C, which would primarily involve growth in the suburbs of Christchurch. The development model used had oil supply about 20% over what the model showed was likely for 2041, and risk was calculated with this assumption. Unsurprisingly, although the risk factor was significant for all plans, Plan A had the lowest risk.

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