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Green Decision Analysis and the Long Tailpipe

Sean Salleh 03 Jul 2013 Energy and environment

Going green is held to be ecologically sound and financially advantageous; and in many cases, it is. In many cases however, it’s also a decision involving a number of different criteria. Building more environmentally friendly buildings, using greener modes of transport and adopting more ecological industrial technologies and processes can be complex decisions, with surprises along the way for the unwary. Think electric vehicles are the answer to the pollution produced by the combustion engine? Maybe – but only if the electricity generating supply is clean in itself.

Coal generated electricity means a 'long tailpipe' Image source: commons.wikimedia.org



The Long Tailpipe Problem

When gas or diesel fuel is burned in engines, the exhaust gases are evacuated via the tailpipe. Images of car tailpipes puffing clouds into the air are standard symbols of hydrocarbon pollution and non-sustainable energy consumption. A purely electric car burns no fossil fuels, has no exhaust gases to get rid of, and therefore is a cleaner and greener solution, right? Critics however assert that if electric cars are using electricity generated by coal-burning power stations, then all that has happened is that the problem has been pushed back up the chain. They call this the long tailpipe problem.

Green decision analysis process Image source: nasa.gov

Green Decision Process and MCDA

A green decision analysis needs a process that starts by understanding what kind of decision is to be made, and that identifies all the relevant factors (including side-effects such as the long tailpipe). Where several desirable characteristics exist for a solution and are in potential conflict with one another (for instance, lowest cost versus greenest technology), different multiple criteria decision analysis (MCDA) processes exist to help reach a conclusion. In a deterministic sense, examples include the Pugh Method, Kepner-Tregoe and the Analytic Hierarchy Process (AHP).

AHP – Analytic Hierarchy Process

AHP can be applied to a wide variety of decision problems; it is also a popular technique for green decision analysis. The basis of the Analytic Hierarchy Process is to decompose a problem into a hierarchy of criteria and alternatives. Outcomes include ranking of different options from most to least desirable. While both qualitative and quantitative aspects can be taken into account, AHP depends on fixed scores and criteria weightings being applied to the different criteria being considered. Sensitivity can be evaluated to some degree, but the propagation of uncertainty is not part of the basic AHP process.

Example of Analytic Hierarchy Process Image source: commons.wikimedia.org

Will Stochastic Methods Save the Planet?

While deterministic methods may be sufficient in decision processes to choose a new car or a fleet of new vehicles, stochastic methods can improve green decision analysis when variables like electricity generating policies, market prices and changing locations of green resources are to be taken into account. Green analytic simulations using Monte Carlo sampling (or Latin Hypercube for faster, reasonably reliable conclusions) can cover electrical energy, air quality, climate assessment, and the future of the automobile.

Meanwhile, green decision analysis continues across the globe. From commercial enterprises and government agencies to public figures going green, decisions are being taken every day. Clear, reliable and relevant processes have an important part to play. As Joe Borden, president of the Anahola Farmers and Ranchers Association in Hawaii said about a renewable energy project that requires thousands of acres of land to be cleared, ‘We’ve been burned too much by (just) going on someone’s word’.

If you’d like to know how Analytica, the modeling software from Lumina, can help you build and analyze better environmental models, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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