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Basic Premise
Having chosen to visit this site, you are probably interested in minimizing your environmental impacts. Below, I take this as a given, but if that is not so, or if you are mildly receptive yet not convinced, please read this.

Toyota Prius For most people who are committed to mitigating their environmental impact (footprint), this commitment is merely a starting point; it offers no guidance about direction, strategy, priorities.

Some are comfortable choosing among a few often promoted one-size-fits-all strategies—get a Prius, buy local, install photovoltaic panels—and moving on. This approach is not unreasonable; in most cases, each of the endoresed actions is indeed better than none.

Yet the impacts of these promoted generic actions vary widely. For example, for a heavy commuter from warm, sunny San Diego, getting a Prius may well save the most greenhouse gas emissions. For a T-taking Boston family living in a large old house, on the other hand, the money they could have spent on a Prius may well save much more emissions if spent instead on retrofitting their aging home to conform to modern energy efficiency standards.

And is emission reduction always the most logical goal?! It need not be. If you live in Tuscon, like your showers long and your laundry triple washed, water considerations may well trump greenhouse gas emissions as your key environmental impact. And if you live in Montana and like your beef local and grass fed, displacing endangered species may well be your most significant footprint, so that efforts to minimize either greenhouse gas emissions or water use could have achieved more—by some measures—if spent on biodiversity preservation.

How to best allocate your limited resources, or what environmental investment yields the most impact minimization bang for the buck, are thus hard questions, whose answers vary widely from setting to setting, household to household, business to business. Inescapably, optimzing your impact minimization efforts requires calculations. You can't simply Google for optimal choices, nor are generic recommendations (even those issued by well meaning, impartial not-for-profit organizations) likely to provide authoritative, pertinent guidance.

A first, and crucial, step is devising a metric, your choice of the way you measure impact, the prime consideration that determines your choices among competing viable alternatives. Do you wish to minimize greenhouse gas emissions per invested Dollar?! To minimize greenhouse gas emissions, period?! To minimize water consumption under the constraint of zero patronage of sweatshop labor-based products?! A cogently defined metric goes a long way toward optimal choices.

Important though it is, the choice of a metric still leaves an inevitable subjective element to every environmental decision, as the scope of possible metrics is literally infinite, and no metric is universally better than another. Even in combining multiple objectives into a unified metric, the weights given to participating objectives in the combined total are not unique. For example, in the labor-minded water example above, is avoiding labor force exploitation twice, or half, as important to you as minimizing water use? equally? Subjectivity is inescapable, and optimality of environmental mitigation strategies is thus entirely specific to the setting, the preferences and priorities of the person or organization, the circumstances.

If any decision is only as good as the metric guiding it, and if no metric is objectively superior to others, is there even such a thing as an optimal choice?! And if there is, how does one identify it?! The above brief discussion makes optimality appear prohibitively elusive, perhaps even impossible.

The answers to these two questions are related. Yes, optimality exists, and it can be found, provided the unique combination of objectives and constraints of a particular, highly specific user—you—are clearly, quantitatively articulated. Once objectives and constraints are verbally articulated and suitably quantified, identifying optimal choices for the unique set of objectives and constraints at hand is possible.

The tools necessary to identify optimality vary from situation to situation. In almost all cases, however, they involve advanced mathematics: optimization, linear and nonlinear programming, numerical modelling, data collection and data analysis.

In some cases, the data the above tools process, the information stream they use as input, exist (e.g., solar radiation or wind data when PV panels or a wind turbine are among the considered options). In such cases, the challenge is to identify, locate, download, read, and process the relevant data, while likely eliminating vast volumes of unwanted data inevitably present in most data set. In more challenging cases, off-the-shelf data would not suffice, and goal-specific, original data must be collected before the analysis can proceed. For example, a business may be able to allocate for solar panels only land covered by a mix of deciduous and evergreen trees. In that case, the expected actual solar energy yield will be markedly lower than the area representative solar radiation values, because of partial shading. But how much lower will be hard to estimate without measuring actual available radiation in the proposed site where and when it matters, in the proposed site, between November and February. In such cases, the challenge extend to also include setting up and recording the appropriate measurements.

double click to check out my new data analysis book at the Princeton Univ Press website In many cases, even the best data sets will not fully resolve all unknowns. In such cases, identifying optimal options must also rely on environmental modelling, using the relevant physics and the governing equations to predict possible outcomes under a wide range of conditions possible in the considered site. For example, if considering better insulating a structure (a house, say), the proposed insulation's so-called R factor—a measure of the rate of heat loss from the house to the environment through the walls—is often used. But the manufacturer issued R value addresses mean conditions; if the house in question is on top of a commanding—and windy—Catskill summit, the nominal R factor has to be suitably modified by modelling turbulent heat loss unique to the house in question.

If you, or somebody in your organization, has the ability to carry out all the above tasks, you are well on your way to identifying your optimal options yourself. In most cases, however, the combination of all those disparate skills is not available. This combination of skills is precisely the defining characteristic of environmentalCalculations.com, and the reason environmentalCalculations.com is a unique one-stop-shop for carrying out the necessary analyses from start (preliminary data gathering) to finish (carrying out the necessary data analysis and modelling as needed, identifying the scope of optimal choices for the unique combination of circumstances you present.

Last modified on GMT by Gidon Eshel