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Lead Papers
Niyi Oyewole niyioyewole@yahoo.com David S. Evans, C. Coulthard, I. Henderson, P. Jones made a presentation to describe how to measure social, environmental and political elements of sustainable development using risk analysis. They show the impact of these sustainable development elements on the construction, operation and closure of a large construction project. They have selected a large mining project to measure the impact and value of “social, environmental and political elements” on the total life cycle (30 years) of the project. They have tested the impact of the elements in three political jurisdictions. For risk analysis, we use a conventional Monte Carlo simulation technique, influence diagram modeling, XL data input, random number generator and commercially available graphical software for output results. They use modern group facilitation techniques in a “workshop” environment to frame the problem decision, to identify the risk issues, to assess the ranges and probabilities, to “reality check” the outputs and to provide input for sensitivity analysis and “sustainable development” optimization. They have “grounded” the mining project by fixing the technical parameters, commodities, durations, operating conditions, closure and marketing decisions and constraints. CSC Project Management Services is a Calgary-based partnership specializing in risk analysis, risk management and strategic risk management services to the natural resource sector for 20 years. Risk analysis is a rigorous and comprehensive process that, when properly done, is accountable, defensible and transparent. Risk analysis is more than “range estimating” as it incorporates “real life” situations, and, models “real life” over the life cycle of a project….Risk analysis readily incorporates social, environmental and political conditioning into an “influence diagram” (risk model) to gain understanding of of sustainable development impacts on project economics. CSC uses a five step process to conduct risk analysis. For risk analysis calculation, we use a conventional Monte Carlo simulation technique, influence diagram modeling, XL data input, random number generator and commercially available software for output results. We us modern group facilitation techniques in a "workshop" environment to frame the problem decision, to identify the risk issues, to assess the ranges and probabilities, to "really check" the outputs and to provide input for sensitivity analysis and "sustainable development" optimization. A “Strategy & Decisions Table” is used to show the grounded mining project in Canada, Russia and Indonesia. The “Influence Diagram” or “Risk Model” is the heart of the risk simulation exercise. The rectangular boxes are “results” in terms of time and value (money). The open ovals are the input impact variables which represent a range of estimates or uncertainties in terms of time and value (money). The shaded ovals are the input conditioning variables, or indirect variables, which define non-financial performance (sustainable development) indicators and values. The polygons are output values (money). We have “conditioned” NPV (Net Present Value) with a Discount Rate which is normally applied to most economic output to satisfy financial security uncertainty. The Environmental Performance Conditioning Variable is used to calibrate and measure “environmental uncertainty”. In a workshop setting, we ask “expert participants” to calibrate the conditioning variable by asking questions about “poor”, “expected” and “high” environmental performance for a host country. The average nominal values (i.e.. 2.5, 5.5 and 7.5 on a scale of 0 to 10) indicate that this group believe that the host country’s environmental performance could range from 2.5 to 7.5 for the 30 year life cycle of the mining project. We then ask the same group “what is the probability that the host country will have environmental performances of 2.5, 5.5 and 7.5 over the life cycle of the major construction project in a host country”. The answers must add to 100%. These probabilities are then used in the risk simulation model to “condition” the impact variables for the host countries. The Socio-Cultural Conditioning Variable is used to calibrate and measure “social and cultural uncertainty”. In a workshop setting, we ask “expert participants” to calibrate the conditioning variable by asking questions about “poor”, “expected” and “high” socio-cultural environment for a host country. The average nominal values (i.e.. 4.5, 6.0 and 7.5 on a scale of 0 to 10) indicate that this group believe that the host country’s socio-cultural environment could range from 4.5 to 7.5 for the 30 year life cycle of the mining project. They then ask the same group “what is the probability that the host country will have a socio-cultural environment of 4.5, 6.0 and 7.5 over the life cycle of the major construction project in a host country”. The answers must add to 100%. These probabilities are then used in the risk simulation model to “condition” the impact variables for socio-cultural environment of the host countries. The Political Climate Conditioning Variable is used to calibrate and measure “political climate uncertainty”. In a workshop setting, we ask “expert participants” to calibrate the conditioning variable by asking questions about “poor”, “expected” and “high” political climate for a host country. The average nominal values (i.e.. 3.0, 5.5 and 7.5 on a scale of 0 to 10) indicate that this group believe that the host country’s political climate could range from 3.0 to 7.5 for the 30 year life cycle. They then ask the same group “what is the probability that the host country will have a political climate of 3.0, 5.5 and 7.5 over the life cycle of the major construction project in a host country”. The answers must add to 100% These probabilities are then used in the risk simulation model to “condition” the impact variables for political climate of the host countries. They then ask technical specialists and other expert participants to assess or provide opinions all the “duration” range impact variables and “cost” range impact variables (on the influence diagram) for the pre-construction, construction, operating and closure components of the large mining project. Where impact variables are influenced by “conditioning variables” we ask these same experts to assess or provide opinion on duration and cost matrices around the “superior”, “expected” and “poor” political, social and environmental probabilities previously estimated. The assessment data is tabulated into an XL spread sheet, loaded into the risk model and the simulation (Monte Carlo or Latin Hypercube) probability curves are produced. Interpretation features of the curves are shown above. The “flatter” the curve, the more uncertain the “rolled-up” sustainability of the project is likely to be; a “steeper” curve indicates a project with higher, more superior sustainability... Here is the probabilistic comparison of the “rolled up” 30 year life cycle mining project in Russia, Canada and Indonesia. The Russian project is clearly uneconomic (i.e unsustainable), the Indonesian project is “at best” a break-even project, while the Canadian project appears to have good sustainable (economic, social & environmental) prospects. There is a slight probability (less than 10%) that the Indonesian project could exceed the value of the Canadian project (where the two curves intersect @ 90%!). The tornado diagram shows the “value” of sustainable development elements to the large mining project in Canada. Given an expected value of $147MM for the Canadian Project, good management of socio-cultural environment could ADD (positive side) up to an additional $150MM to this project…..poor management of socio-cultural environment may SUBTRACT (negative side) up to $160MM…
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