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Risk Analysis to Measure Sustainable Development Elements for Large Construction Projects

This presentation describes how to measure social, environmental and political elements of sustainable development using risk analysis. We show the impact of these sustainable development elements on the construction, operation and closure of a large construction project.

We 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. We 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. We 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. We 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.

Definitions and Assumptions for the “grounded” Project. Note the last item in the right hand column. Conditioning Variables (also known as indirect or intangible variables) are used to “condition” the risk model with social, environmental and political input elements.

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.
We 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.
We 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.

We 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…
Similarly, for environmental performance, good management is valued at about an additional $80MM during the full life cycle and, poor management may cost about $90MM…
The over-riding variable is “political climate”….a poor political climate could make this project “valueless”or, could add almost $200MM in value over the life cycle of the project...
Similar outputs can be produced for Russia and Indonesia.

The step diagram shows the risk-based “departures” from the Canadian case for impact and conditioning variables to the equivalent Indonesian and Russian sustainable development elements and impact variables…
There is little ‘value difference’ between the three countries in terms of the Political Climate, but there is need to influence Socio-Cultural Environment and Environmental Performance in Indonesia and Russia to optimize sustainability.…close and proper mitigation attention to these elements and the adoption of better management practices will improve the feasibility and sustainability metrics of the mining project in these jurisdictions….

This diagram shows the “economic payouts” for the large mining project in Canada, Indonesia and Russia…again, the Russian example will not likely produce a cashflow to payback the capital cost without a commitment to better manage political, social and environment host country performance; and, to change these risks into opportunities....
Although the Canadian project “pays out” earlier than the Indonesian project, there is opportunity in Indonesia to better manage the socio-cultural environment and environmental performance to meet or beat the Canadian case. A “high performing mine owner organization” could affect management changes and mitigation with the Indonesian case that will not result in a financially attractive, but economically, socially and environmentally beneficial project to shareholders and stakeholders alike...

Risk analysis is increasingly used to ensure success in large construction projects. When risk models are appropriately conditioned with sustainable development elements, the impact on future shared benefits can be demonstrated with a high degree of transparency, accountability and defensibility.
To date, ‘Sustainable Development Performance’ has largely been a qualitative assessment or measurement. Quantifying risk-based political, social and environmental values and impacts using risk analysis can demonstrate “future economic added value” for mitigation and management opportunities.
Failure to incorporate sustainable development into financial and economic analysis may allow “poor projects” to proceed with questionable outcomes. Using the structure and rigor of well-framed and designed risk analysis will identify future mitigation needs; and, previously unidentified opportunities for the ultimate benefit of shareholders and stakeholders alike.