Assessing Green Technology: Towards More Insightful Analysis

Posted Jul 6th, 2020 in Design, Current Issues

Assessing Green Technology:  Towards More Insightful Analysis

Ian Ellingham, MBA, PhD, FRAIC and William Fawcett MA, PhD, RIBA            Cambridge Architectural Research Limited 

The research initiatives upon which this article is based were funded under the Seventh Framework for Research programme of the European Commission and the Partners in Innovation programme of the U.K. Department of Trade and Industry.  The final report of the European Cileccta project can be found at:

Mathematical methods of assessing project alternatives are well known and widely used.  While the research projects forming the background to this chapter included extensive mathematical analysis, the process of developing and exploring case studies for evaluation yielded insights about many uncertainties that often need to be considered in an analysis.  Often the identification of them can be more important in arriving at appropriate decisions than the mathematical analysis itself.  The astute analysis of alternatives is an essential aspect of moving towards a state of sustainability, yet, frequently, uncertainty is not fully integrated into the decision process.  This is not surprising, given the difficulty of dealing with the uncertainties that will affect the long lives of buildings, but ignoring uncertainties undermines the conclusions from analysis.  

In one sense, the objectives of an analysis for sustainability are reasonably clear:  one is seeking balance - neither overinvesting nor underinvesting.  It is very easy to overinvest in the pursuit of environmental concerns, paying too much now to achieve future benefits that may not materialise, or may ultimately be of little value to future generations.  The general objective is simple, but the issues of time and uncertainty make analyses complex.  

The pursuit of sustainability implies making decisions about intertemporal transfers - relating and balancing flows of resources that occur in different times.  We do something in the present or in the near future, in anticipation of someone receiving some benefit in the more distant future.  For buildings, in contrast with most human-produced goods, can last centuries.   It becomes necessary to compare near-term flows of resources (money, CO2, energy, construction activities…), which are almost certain in nature, with highly uncertain long-term flows.

Unfortunately, for evaluating sustainability matters there is no single, simple measure.  If one considers resource flows on an abstract basis, the conceptual and analytical models found in finance, in particular discounting, are a way of evaluating uncertain resource flows.  One can work by replacing money as the unit of measurement with energy, CO2 emissions, natural resources or anything else:  although once one starts counting in multiple terms; it is desirable to try to relate them to each other.

Relating Alternatives

In an energy or sustainability analysis, alternatives are being considered – often a base product or system is being compared with a more expensive alternative, one that is expected to return some future benefits.  That is the starting point, but the concept of opportunity costs needs to be considered.  Opportunity costs are benefits that are foregone when choosing one alternative over another.  For example, if investment in an energy-efficient boiler means that super insulation cannot be afforded then the energy-saving of the super insulation is an opportunity cost.  Decision-makers can use the concept when they have multiple alternatives before them - and that is effectively all the time.  Most budgets tend to be limited, it is not just choosing one window type over another, but whether it is appropriate to spend more on windows or on the mechanical system.  Another problem is that not all alternatives are immediately visible, and some thought may be required to identify them.  This means that good alternatives can be missed.

Attempts should be made to balance the three aspects of sustainability, environmental, economic, and social, although this adds another layer of complexity, but if a project is not economically and socially viable it is at risk of being demolished prematurely - which can happen – and will fail to deliver the expected environmental benefits.  

Identifying Uncertainties

Buildings, because of their long lives, exist in a context of rampant uncertainties.  Key ones need to be recognised by decision-makers.  Life expectancies are important, and building types and components have different lives.  Restaurants tend to be very short-lived, so investing in 'green' elements may be difficult, because it is likely that the use will change or the restaurant will be reformatted in a few years, or less.    

Change can affect the best-laid plans.  One advantage of housing over building types such as offices or retail, is that the use tends to be reasonably stable over time, although, interiors including kitchens and bathrooms, are subject to the vagaries of changing taste.  However, there are other risks that can affect housing:  for example, a community largely reliant on high-tech industries is likely to experience substantial change (expansion, change of use, business failure) than those with a more diversified economy.  

Different building elements have different life patterns, all of which are uncertain.  Unfortunately, data is difficult to find:  manufacturers tend to give unsupported single-point estimates of component life.  In part this is because they may not understand the nature of uncertainty, but also because of the long lives of most building elements; by the time data is available, the identical product is likely no longer in production.  In our discussions with building managers it became apparent that one reason for the end of the lives of much mechanical equipment was that replacement parts were no longer available.  One manager who dealt with numerous buildings and mechanical systems carefully scavenged parts from discarded equipment for use in other systems in the property portfolio.  This suggests that service life uncertainty can be reduced by selecting equipment that is either based on generic parts, or has a very large installed base, so parts are likely available long into the future.

Real estate decisions involve looking into the future so there are numbers of factors requiring some consideration.  One is the possibility that our society is not around to collect the future benefits, or that the benefits are of no value.  A thermo-nuclear war or a deadly epidemic would thin out the human population and destroy society, but less dramatic systemic disruptions, such as rising oceans, might mean that expected benefits do not materialise.  

Even without catastrophic scenarios, expected future benefits might simply be of less value to future generations, rather as coal is much less important to us than to the Victorians.  Technology is likely to improve or change, so being an early-adopter may not be the best strategy - something better may be just around the corner.  If people in the future are wealthier than they are now - continuing a trend that in developed economies goes back to the industrial revolutions – investing now for long-term future benefits may be questionable.  Most people would regard it as inappropriate for a poorer society to transfer resources to a more affluent one.  

It is very tempting, to look at trends and project them into the future; however, the force of mean reversion is very strong.  The concept of mean reversion is rooted in financial theory, and proposes that continuous processes, including rents or energy prices, will eventually return back to a long-run mean or basic trend.  It is easy to fall prey to popular hysteria, and believe that something will continue to rise for ever, but forces tend to limit this.  Obviously, in the face of rising energy prices, it is likely that more capacity appears, other energy sources are exploited and energy-saving efforts are made, all working to push energy prices down; rather as increasing rents leads to more construction, hence more supply and a downwards pressure on rents.  

Real Options

The concepts and techniques of real options analysis were developed in the management disciplines through the 1980s.  It is a significant tool in understanding future events, and developing good strategies to deal with them.  It has clear application in making decisions relating to buildings.  

A fundamental mathematical breakthrough with real options was made by Myron Scholes, Fischer Black, and Robert Merton, which lead to the 1997 Nobel Prize in Economics.  Unfortunately, the typical lack of quality data associated with real estate decisions tends to make their specific technique less workable than other approaches.

When we design resilience and flexibility into buildings we are creating real options - making provision for things that might, or might not, happen in the future.   As well as deliberately created real options, others occur naturally – unfortunately, they may be difficult to identify, and we can never be sure that some future decision-maker will know they exist.

Merely understanding the concept of real options can be valuable, even if the decision-maker never attempts a mathematical evaluation.  The possessor of a real option has the right, but not the obligation to undertake some action in the future, perhaps expanding, abandoning, or changing the use of a building.  Real options may run with a project, but may be used when deemed appropriate, by some future decision-maker.  This is an important point - we do not have to make all the decisions now, but can leave options in the hands of future decision-makers.  The conditions that trigger the use of an option may or may not occur in the future – many options are never exercised.  Future decision-makers will be in a better position to make good decisions; after all, they will know what has happened between now and then, may have new technologies, and a different understanding of social values and priorities - things we can only guess at in the present.  

Typical options in real estate projects include:   - Options to build or expand - Options to change use - Options to contract or demolish - Options to upgrade

Options are a mechanism for managing uncertainty, and the more uncertainty that exists, the more value they have.  This means that in more uncertain situations: (i) it is worth paying more to get them, and (ii) it is worth holding onto them rather than exercising before reaching their full value.

We can measure uncertainty of continuous processes by calculating the volatility - a measure of how much something, such as rents, capital values or energy prices, moves over time.  A decade ago, many were forecasting $US 200 barrel oil, as the prevailing trends were clearly upwards.  A different point of view was that energy prices are historically very volatile, and that options to manage price uncertainty are of very high value.  If, ten years ago, a decision had been made to invest in energy-saving strategies based on the expectation of a continuing rise in energy costs, it would have been overinvestment.  Given the high volatility of oil prices, it would have been better to have provided some real options, such as the ability to upgrade windows or the mechanical system.  If prices soared, the options could be exercised, if they fell back the options could be left unexercised.  This underlines the centrality of uncertainty, no-one could have predicted, with certainty, the course of future oil prices, so an optimum decision has to recognise volatility.

Housing, like all aspects of society, will face change and challenges over the coming decades.  The rise of home-working and internet businesses, means that space for possible office use is valuable.  In the area one of the authors lives, homes built in the 1920s had spaces off the main entry where people could be received, without having to encounter the rest of the family - or the servants.  They are being rediscovered after decades as family rooms or bedrooms and converted back to business uses.  In another area, houses were routinely switched from one to two family dwellings and back again, as families went through generational cycles, when there were children, more space was required.  This was, of course, strictly forbidden under the planning regulations, which are usually suspicious of such activities.

An option-holder can wait to see if conditions come to justify action..  Some actions are irreversible, but others can be revisited.  For example, insulation under concrete slabs is very difficult and expensive to install later.  If a minimal amount is installed now, there is little that can be done later.  In contrast, windows can be replaced or upgraded, indeed secondary glazing can often be installed at very reasonable cost later - the decision not to triple-glaze now is not final, and has the option to upgrade later, which can be passed on to future decision-makers.   Analysis approaches

Financial options make use of complex mathematical formulae, but they are rarely useful for the analysis of real options for construction.  Because of multiple uncertainties and poor or non-existent data the researchers at Cambridge Architectural Research have found treated sustainability decisions in real estate as a flow, best represented through Monte Carlo simulation.  This technique yields, not just one scenario, but, typically thousands, all of which are possibilities, only one of which will occur, but which one cannot be predicted

A significant advantage of simulation is that it maps out, usually year-by-year, the stream of resources associated with the project alternatives previously identified, taking account of continuous or discontinuous uncertainties.  Management decisions in response to the ongoing processes can be included in the simulation.  The process of framing the decision, together with variables and future decisions and possibilities, forces the analyst, who hopefully is the decision-maker or in very close communications with him/her, to discuss what variables and uncertainties are important.  

Exploring the ‘What ifs’

It is very tempting to act as if we can predict the future or that it will resemble the present, but that is seldom the case.  Unpredicted changes occur; in the 1980s the massive transformations caused by the internet would not have been predicted.

While unforeseen events can sideswipe even the best researched and calculated decision-making, it is unwise to ignore the uncertainties we can be aware of - in part because it will make the decision-maker aware that the future is always uncertain.  

Some useful techniques exist, but they depend on doing an analysis, such as Monte Carlo simulation, that generates ranges of possible outcomes.

Uncertainties can be explored through sensitivity tests, which should be a part of every sustainability analysis.  This process is a systematic exploration of how uncertainty in the output of a mathematical model relates to the various elements of uncertainty in the inputs.  The benefits from such an exploration, include identifying the aspects of uncertainty which affect the decision most significantly and which justify more analysis - or simply seeing where it is worth collecting more data.

Another way of understanding uncertainties is through confidence limits.  Confidence limits, in statistical terminology, are a pair of numbers between which one can calculate the probability that the true value of the process will lie.  In the building world it is of most interest to avoid the outcome falling in an undesirable range; for example, what is the probability that a candidate green investment might turn out to be unacceptable?  The unacceptability level may indicate some other alternative would be preferable..  

The exploration of alternatives through sensitivity testing or confidence limits helps the decision-maker deal with another important characteristic - robustness.  Some alternatives will be more able to deliver benefits in the face of uncertainty, and this is a key element of making good decisions.  'Optimality' will often be an unachievable, elusive and illusory objective.  

Of course major surprise events do occur, and can affect the project under consideration.  It is exceedingly difficult to model these, for the obvious reason that they are, by their nature, unexpected.  They have very low probabilities, but major impacts, and likely may not be able to be identified in advance.  As they are unknowable, robustness in the design of an alternative is a good defence, and one way of generating robustness is through real options that can give future decision-makers freedom to manoeuvre.  

Making the best decision How do you know if you made the right decision?  The problem is that you never will know, because decisions have to be made using only the information available at the time.  An unfortunate outcome does not necessarily mean the wrong decision was made – the world might have just unfolded in an unlikely way.  This process can be represented with binomial trees, which model continuous uncertainties and allow real options to be valued.  Binomial trees, are generated effectively by flipping coins creating a diverging set of possibilities (the tree) embedding the volatility of the process being assessed.  Each path in a tree represents a specific sequence of coin flips.  There are many more possible paths to reach the middle areas of the tree, some paths lead to the edges of the tree.  You know the exact sequence that will be followed; you can only make the 'right' decision within the context of knowable information.  


The result of poor data and rampant uncertainty means that there is rarely a single numerical answer for a real estate analysis.  Analysis should therefore be regarded as a process to yield information to assist the decision-maker, who will ultimately be relying, at least in part, on informed judgement.

When designing and managing buildings myriads of decisions have to be made, and in most cases a complete analysis is not warranted, but having insights into how quantitative methods function will help expand the knowledge base that underlies informed intuition, in particular assisting people in being able to frame decisions, and understand the implications of the key uncertainties that may determine outcomes.  

Further Readings 

Amram, Martha and Kulatilaka (1999) Real Options, Boston, Harvard Business School Press. 

Assaf, Sadi et al (2002) Assessment of the Problems of Application of Life Cycle Costing in Construction Projects, Cost Engineering, 44:2.

Atkinson, Giles and Mourato, Susana (2008) Environmental Cost-Benefit Analysis, Annual Review of Environmental Resources, 33, pp.317-44.

Ayres, Robert U.; van den Bergh, C.J.M.; and Gowdy, John M. (2001) Strong versus week sustainability: Economics, Natural Sciences, and Consillience, Environmental Ethics, 23(2) pp.155-168.

Baron, Jonathan (2008) Thinking and Deciding (4th Edition), Cambridge: Cambridge University Press.

Bordass, Bill (2000) Cost and Value: Fact and Fiction. Building Research and Information, 28(5/6), pp.338-352. Boer, F. Peter (2002) The Real Options Solution: Finding Total Value in a High-Risk World, New York: John Wiley & Sons, Inc.

Clift, Mike and Bourke, Kathryn (1999) Study on Whole Life Costing, Report Number CR366/98, Watford, British Research Establishment Limited.  

Davis Langdon Management Consulting, (2007) Life Cycle Costing (LCC) as a contribution to sustainable construction: a common methodology.

Edwards, Brian (2005) Rough Guide to Sustainability, 2nd Edition, London: RIBA Enterprises.

Ellingham, Ian and Fawcett, William (2013) Whole-Life Sustainability, London: RIBA Publishing.

Ellingham, Ian and Fawcett, William (2006) New Generation Whole-Life Costing, London: Taylor & Francis.

Fisk, D.J. and Kerherve, J. (2006) Complexity as a cause of unsustainability, Ecological Complexity 3, pp.336-343.  

Frowen, Stephen F. (1990) Unknowledge and Choice in Economics, Proceedings of a conference in honour of G.L.S. Shackle, Basingstoke: Macmillian.  

Gardner, Dan (2008) Risk, Toronto: McClelland & Stewart.  

Hauff, Volker (2007) Brundtland Report: A 20 Years Update, Keynote speech, European Sustainability, Berlin: EXB07.

Hayes, John R. (1981) The Complete Problem Solver, Philadelphia: The Franklin Institute.

Hepburn, Cameron (2007) Valuing the far-off future: discounting and its alternatives. in: Atkinson, Giles; Dietz, Simon; and Neumayer, Eric (eds) Handbook of Sustainable Development, Cheltenham: Edward Elgar.

Kahneman, Daniel; Slovic, Paul; and Tversky, Amos (eds), (1985) Judgement under Uncertainty: Heuristics and biases, Cambridge: Cambridge University Press.

Knight, Frank H. (1921) Risk, Uncertainty and Profit, Boston: Houghton Mifflin (reprinted 1971) Chicago: University of Chicago Press.

Liddell, Howard (2008) Eco-minimialism: The antidote to eco-bling, RIBA Publishing, London.

Lutzkendorf, Thomas; and Lorenz, David (2005) Sustainable property investment:  Valuing sustainable buildings through property performance assessment. Building Research & Information, 33(3) 212-234.

Milne, Glen (2011) Foresight: Understanding the future security environment. Vanguard, accessed 15 April, 2011, Mithraratne, Nalanie; Vale, Brenda; and Vale, Robert (2007) Sustainable Living: the Role of Whole Life Costs and Values, Oxford: Elsevier.

Moor, Steven A. (ed) (2010) Pragmatic Sustainability: Theoretical and Practical Tools. London: Routledge.

Obasli, Aylin (2008) Architectural Conservation, Oxford: Glackwell Science.

Pearce, D.W., and Nash, C.A. (1981: 1991 reprint) A Social Appraisal of Projects: A Text in Cost-Benefit Analysis, Basingstoke and London: Macmillan.

Pollard, Douglas (2009) Changing into Tails, OAA Perspectives. Spring 2009, pp 11-12. Pope, Stephen (2006) Green green everywhere, but not a moment to think or Not there yet, OAAPerspectives, Fall, 24(3).

Price, Richard; Thornton, Simeon, and Nelson, Stephen (2007) The Social Cost of Carbon And The Shadow Price of Carbon: What They Are, And How To Use Them In Economic Appraisal in the UK, Department for Environment, Food and Rural Affairs.

Quinn, Robert E. (1988) Beyond Rational Management. Oxford: Jossey-Bass

Rosenhead, Jonathan; Elton, Martin; and Gupta, Shiv (1972) Robustness and Optimality as Criteria for Strategic Decisions (1972) Operations Research Quarterly, 23(4),  pp.413-431.

Ross, Barbara (2009) Teaching Architecture Students to Think Critically about Sustainable Design, Unpublished Paper: Centre for Teaching Excellence, University of Waterloo.  

Ross, Barbara (2009) Design with Energy in Mind. Unpublished Masters dissertation, University of Waterloo.

Russell, Clifford S. (2001) Applying Economics to the Environment, Oxford: Oxford University Press.

Shackle, G.L.S. (1961) Decision, Order and Time in Human Affairs, Cambridge: Cambridge University Press.  

Smith, Laurence C. (2010) The Word in 2050: Four forces shaping civilization’s northern future, Dutton, New York.

Sørnes K. and Thunshelle K. (eds) (2015) Sustainability within the Construction Sector. CILECCTA – Life Cycle Costing and Assessment. Oslo: SINTEF Academic Press.

Standardized Method of Life Cycle Costing for Construction Procurement: A supplement to BS ISO 15686-5 Buildings & constructed assets - Service life planning - Part 5: Life cycle costing, (2008) BSI Technical Subcommittee B/500/3, Durability, London:  BSI.

Steele, James (1997) Sustainable Architecture. New York: McGraw-Hill.

The Green Book: Appraisal and Evaluation in Central Government, (2018) HM Treasury.

Thornton, Mark (2007) Richard Cantillon and the Discovery of Opportunity Cost, History of Political Economy, 39(1), pp.97-119.  

Torbert, W.R. (1987) Managing the Corporate Dream: Restructuring for long-term success, Homewood, Ill: Dow-Jones Irwin.

Veefkind, Menno (1998) Life Cycle Costing for Green Product Development, Technical University of Delft paper.

Weybrecht, Giselle (2010) The Sustainable MBA: The Manager’s Guide to Green Business. Chichester: Wiley.

Woolley, Simon (2009) Sources of Value: A practical guide to the art and science of valuation, Cambridge: Cambridge University Press.


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