Customers & Industries: Cambridge University

Cambridge University's Judge Business School uses @RISK from Palisade to Advise on Climate Change

  • Industry: Academic
  • Product(s): @RISK
  • Application: Climate Change

Summary

The Stern Review on the Economics of Climate Change, a project of Cambridge University's Judge Institute of Management, developed an analysis model using @RISK to investigate climate change across the world.

We needed the flexibility offered by @RISK in order to build a range of models to handle the uncertainty that goes hand-in-hand with climate change and its effects. The fact that the software works in Excel also makes it easy to use and ensures that the results generated are transparent. This ensures that the outputs are understood by everyone.
Dr. Chris Hope, Judge Business School, University of Cambridge

The Judge Institute of Management was established in 1990 to provide management teaching and research for the University of Cambridge. As well as reflecting the world-renowned status of the university, its work needs to be relevant to both its academic audience and people outside the organisation. The latter group includes government bodies such as the Organisation for Economic Co-operation and Development (OECD) and the Department for Energy and Climate Change (DECC), for whom it undertakes research. Renamed the Judge Business School in 2005, its management focus also fosters technical and scientific analysis.

@RISK Used for Stern Review Analysis Model

One such topic covered under this remit is that of climate change, and the Management Science Research Group provided key input to the Stern Review on the Economics of Climate Change. Released in October 2006, this report undertaken for the British government by Lord Stern discusses the effect of climate change and global warming on the world economy. It is the largest and most widely referenced report of its kind.

The research group developed an analysis model, PAGE2002 (for Policy Analysis of the Greenhouse Effect) using @RISK from Palisade. @RISK is an Excel add-in using Monte Carlo simulation to show all potential scenarios, as well as the likelihood that each will occur, thereby providing the decision-maker with the most complete picture possible.

PAGE 2002 was used by the staff at Stern to investigate climate change across the world. They researched issues such as the impacts of the sea level rising and increases in temperature making land infertile or unfarmable, and balanced these against the costs of various options available to tackle global warming. At one end of the scale, doing nothing costs nothing, but the environmental consequences will be high. However, activity that reduces the severity of the impacts may itself be very expensive. The aim of the model is to enable people to make informed decisions on the optimum way to deal with climate change (ie how much to cut back on damaging activity and what methods to use).

The PAGE 2002 @RISK model is an integrated assessment one, in that it aggregates information from various other sources to use as inputs. For example it uses scientific studies and knowledge for details on the climate's sensitivity on an increase in CO2 emissions. It also combines this with economic expertise to look at the effect of an increase in temperature on gross domestic product (GDP).

@RISK Quantifies Uncertainty of Climate Change Variables

Dr Chris Hope, reader in policy modelling at Judge Business School, explains: “A key problem with investigating climate change is that the different effects of the various factors which influence it are themselves, undetermined. For example, the historical evidence does not pin down exactly how much global temperatures will increase if CO2 emissions double. @RISK enables researchers to quantify this uncertainty in order that they have a measurement of the accuracy of their findings.”

In particular, the Stern Review looked at the social cost of carbon (SCC), measured in terms of the economic impact of the extra damage done by each additional tonne of CO2 in the atmosphere. From there it could determine that if, for example, one extra tonne was going to cause $100 worth of damage in the long term, then any activity costing less than $100 which resulted in at least one less tonne of emissions was therefore both viable and desirable.

A key input to the @RISK-powered model is climate sensitivity which, using scientific evidence, is usually taken to be an increase in climate temperature of anywhere between 1.5 and five degrees Celsius per extra tonne of CO2 released into the atmosphere. Measurements like this feed into the economic impact of increased CO2 emissions, and therefore help determine the desired cutbacks on the production of greenhouse gases.

Another uncertainty the model takes into account is the length of time it could take the earth to respond to increases in greenhouse gases – for example, will this be 20 years or 50 years? This is also relevant when looking at corrective activity because it will not have an immediate effect. In addition, PAGE2002 aims to model the point at which 'climate catastrophes', such as the West Antarctic ice sheet melting or the Gulf Stream switching off, become possible. Referred to as the 'Tolerable Before Discontinuity’ parameter, this is set at an increase in global temperature of between two and four degrees Celsius. (To put this in context, the present temperature rise since pre-industrial times is about 0.75 degrees).

RISKOptimizer Enables Recommendations on Activity

Judge Business School then uses the RISKOptimizer element of @RISK to calculate more specific details on how much it would be best to cut back on CO2 emissions. For example, should they be reduced by sixty or eighty percent by 2050?

The next step is to work out how much it will cost to do this, in different regions of the world. Options include using alternative energy sources, such as wind turbines and nuclear power stations, reducing oil-fuelled traffic and transport and halting the destruction of rainforests. As with the impacts, the exact costs of any of these activities are still not certain. However, RISKOptimizer quantifies uncertainty and, by drawing together the impact, cost implication and potential for success of each option, it enables recommendations on which cut backs will be most effective.

RISKOptimizer can also take into account that increasingly accurate information, such as a more precise figure for climate sensitivity, will become available as research progresses. It can indicate the value of this better information in terms of its potential to influence the overall recommendations.

The Judge Business School selected @RISK as a result of Dr Hope's knowledge of PRISM, Palisade's original desktop risk analysis software that was developed into @RISK. He confirms: “We needed the flexibility offered by @RISK in order to build a range of models to handle the uncertainty that goes hand-in-hand with climate change and its effects. The fact that the software works in Excel also makes it easy to use and ensures that the results generated are transparent. This ensures that the outputs are understood by everyone.”

Conclusions

The Stern report proposed that one percent of global GDP should be invested per year in order to avoid the worst effects of climate change. Failure to do so has the potential to reduce global GDP by 20 percent.

As climate change continues to be a global issue, the Judge Business School continues to use its PAGE2002 @RISK modelling tool to advise on ways in which to tackle it.

More detail

The Judge Business School uses the Industrial version of @RISK.

Key @RISK software features used on this project by the Judge Business School

Latin Hypercube sampling is used for uncertain sampling Graphic capability (specifically to show outputs) Tornado charts used to show which inputs are having the biggest effect on which outputs

Distributions used on this project by the Judge Business School

Triangular distributions are used most often because they are the most simple distributions that are not symmetrical. Non-symmetrical distributions are needed when the sign of an effect is known, but its magnitude is very uncertain. Log logistical distributions are used for inputs that have long tails: the potential to differ greatly from their most likely value, but with only a small chance.

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