Course literature

Day 1

1.1 Davoudi, S. Dilley, L and Crawford, J. 2014, "Energy consumption behavior: rational or

habitual? " DiSP, forthcoming.

1.2 Alberti, M. 1999, "Modeling the urban ecosystem: a conceptual framework", Environment

and Planning B, vol. 26, pp. 605-630.

1.3 Kennedy, C., Cuddihy, J. & EngelYan, J. 2007, "The changing metabolism of

cities", Journal of Industrial Ecology, vol. 11, no. 2, pp. 43-59.

1.4 Kennedy, C., Pincetl, S. & Bunje, P. 2011, "The study of urban metabolism and its

applications to urban planning and design", Environmental pollution, vol. 159, no. 8, pp.

1965-1973.

1.5 Ozaki, R. & Shaw, I. 2014, "Entangled Practices: Governance, Sustainable Technologies,

and Energy Consumption", Sociology, vol. 48, no. 3, pp. 590-605.

1.6 Wachsmuth, D. 2012, "Three Ecologies: Urban Metabolism and the SocietyNature

Opposition", The Sociological Quarterly, vol. 53, no. 4, pp. 506-523.

1.7 (downloadable) FP7 Project: Sustainable Urban Metabolism in Europe (SUME)

http://www.sume.at/project_downloads [Working paper 1.1.] Downloadable online (after

brief registration)

Day 2

2.1 De Boer, J., Zuidema, C. 2013, "Towards an integrated energy landscape", AESOP-ACSP

joint congress, Dublin.

2.2 Loorbach, D. 2010, "Transition management for sustainable development: a prescriptive,

complexitybased governance framework", Governance, vol. 23, no. 1, pp. 161-183.

2.3 Newman, P.W. 1999, "Sustainability and cities: extending the metabolism

model", Landscape and Urban Planning, vol. 44, no. 4, pp. 219-226.

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2.4 Pincetl, S., Bunje, P. & Holmes, T. 2012, "An expanded urban metabolism method:

Toward a systems approach for assessing urban energy processes and causes", Landscape

and Urban Planning, vol. 107, no. 3, pp. 193-202.

2.5 Barles, S. 2010, "Society, energy and materials: the contribution of urban metabolism

studies to sustainable urban development issues", Journal of Environmental Planning and

Management, vol. 53, no. 4, pp. 439-455.

Day 3

3.1 Beck, M.B. & Cummings, R.G. 1996, "Wastewater infrastructure: challenges for the

sustainable city in the new millennium", Habitat International, vol. 20, no. 3, pp. 405-

420.

3.2 Gandy, M. 2004, "Rethinking urban metabolism: water, space and the modern

city", City, vol. 8, no. 3, pp. 363-379.

3.3 Marks, J.S. & Zadoroznyj, M. 2005, "Managing sustainable urban water reuse: structural

context and cultures of trust", Society and Natural Resources, vol. 18, no. 6, pp. 557-572.

3.4 Zaman, A.U. & Lehmann, S. 2013, "The zero waste index: a performance measurement

tool for waste management systems in a zero waste city", Journal of Cleaner

Production, vol. 50, pp. 123-132.

3.5 Barles, S. 2009, "Urban metabolism of Paris and its region", Journal of Industrial

Ecology, vol. 13, no. 6, pp. 898-913.

Day 4

4.1 Bettencourt, L.M., Lobo, J., Helbing, D., Kuhnert, C. & West, G.B. 2007, "Growth,

innovation, scaling, and the pace of life in cities", Proceedings of the National Academy of

Sciences of the United States of America, vol. 104, no. 17, pp. 7301-7306.

4.2 Grubler, Arnulf, Xuemai Bai, Thomas Buettner, Shobhakar Dhakal, David J. Fisk,

Thoshiaki Ichinose, James Keirstead, Gerd Sammer, David Satterthwaite, Niels B. Schulz,

Nilay Shah, Julia Steinberger and Helga Weisz: Chapter 18: Urban Energy Systems. In

Global Energy Assessment: Toward a Sustainable Future. L. Gomez-Echeverri, T.B.

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Johansson, N. Nakicenovic, A. Patwardhan, (eds.), IIASA, Laxenburg, Austria and

Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA (2012):

[selected parts].

4.3 Weisz, H. & Steinberger, J.K. 2010, "Reducing energy and material flows in

cities", Current Opinion in Environmental Sustainability, vol. 2, no. 3, pp. 185-192.

4.4 Timmeren, A. "Environmental Technology & Design; the Concept of the Urban

Metabolism" TU Delft ABE/U/ETD (unpublished)

4.5 Holden, E. & Norland, I.T. 2005, "Three challenges for the compact city as a sustainable

urban form: household consumption of energy and transport in eight residential areas in the

greater Oslo region", Urban Studies, vol. 42, no. 12, pp. 2145-2166.

4.6 Lenzen, M. & Peters, G.M. 2010, "How city dwellers affect their resource

hinterland", Journal of Industrial Ecology, vol. 14, no. 1, pp. 73-90.

Day 5

5.1 Bulkeley, H., Castn Broto, V., Hodson, M. & Marvin, S. 2011, "Cities and the low

carbon transition", Europ.Finan.Rev, , pp. 24-27.

5.2 Watson, V. 2009, "The planned city sweeps the poor away: Urban planning and 21st

century urbanisation", Progress in Planning, vol. 72, no. 3, pp. 151-193.

5.3 Swilling, M. 2010, "Sustainability, poverty and municipal services: the case of Cape

Town, South Africa", Sustainable Development, vol. 18, no. 4, pp. 194-201.

5.4 van Bueren, E. & ten Heuvelhof, E. 2005, "Improving governance arrangements in

support of sustainable cities", Environment and planning B: Planning and Design, vol.

32, no. 1, pp. 47-66.

5.5 Bulkeley, H., Watson, M. & Hudson, R. 2007, "Modes of governing municipal

waste", Environment and Planning A, vol. 39, no. 11, pp. 2733.

5.6 (suggested reading) selected chapters from book Slimme Steden by Maarten Hajer and

Ton Dassen.

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1

Energy Consumption Behaviour: Rational or Habitual?

Simin Davoudi, Luke Dilly, Jenny Crawford

Abstract: Reducing energy demand is not simply about developing energy efficiency measures and technologies, but also changing behaviour and everyday practices. Although the over-emphasis on individual behaviour as the main driver of transition to low-carbon societies may be contested on the grounds that it distracts attention from the wider structural, economic and political factors, it is widely acknowledged that pro-environmental behaviours play an important part in such a transition. This paper aims to address these questions by drawing on three dominant perspectives on environmental behaviour and its drivers: the rational economic, the psychological and the sociological perspectives. The aim is to provide a conceptual understanding of behaviour, illustrated with example from energy consumption.

Keywords: Urban Energy Consumption; Consumption Behaviour; Rational Economic Perspective; Psychological Perspective; Sociological Perspective

1 Introduction

In the United Kingdom (UK) households are responsible for around half of the national carbon emissions through energy consumption in the home and personal transport (DECC, 2013). While residential energy consumption has been falling per household this is more than offset by growing population and household formation (Committee on Climate Change, 2013). It is argued that reductions in household energy use could be much greater if improved domestic technologies and products were to be more rapidly adopted and used more effectively. Individual energy behaviour is perceived as a significant barrier to achieving a major step change in energy efficiency. This barrier exists in spite of growing environmental awareness and the financial and environmental benefits of energy efficiency measures (Christie, et al., 2011; Crosbie & Baker, 2010; Gram-Hanssen et. al., 2007). In addition, when such measures are adopted their benefits may be negated by poor use (Gill, et. al., 2010) or changes in other household characteristics such as increase in the number of appliances in the home (Vale & Vale, 2010), preferred temperature (Lomas, 2010) or the floor area of the house (Summerfield, et. al., 2010). This offsetting of increased efficiency by increased consumption is known as the rebound, or take back effect. The terms suggest that household energy efficiency measures can encourage more profligate use of energy because energy users feel they do not have to be as miserly with energy usage (Jenkins, 2010; Greening et al., 2000). For example, it has been shown that instalment of efficient washing machines correlates with an increase in the amount of washing done (Sorrel et al., 2009). This has led to a growing argument that reducing energy demand is not simply about developing energy efficiency measures and technologies, but also changing behaviour and everyday practices. Indeed, there is a commonly held assumption that changes in individual behaviour can achieve a step change in global energy use, as indicated in the following statement from the Stern Review:

In the case of climate change, individual preferences play a particularly important role. Dangerous climate change cannot be avoided through high level international agreements; it will take behavioural change by individuals and communities, particularly in relation to their housing, transport and food consumption decisions (Stern, 2007:395)

Similar assumptions are made by the UK government (DECC / Defra, 2009) which consider behavioural change to be central in pulling society towards the development of alternatives to carbon intensive forms of living (Parag & Darby, 2009: 3985).

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Although the over-emphasis on individual behaviour as the main driver of transition to low-carbon societies may be contested on the grounds that it distracts attention from the wider structural, economic and political factors, it is widely acknowledged that pro-environmental behaviours play an important part in such a transition (Defra, 2008). The question, however, remains: what constitutes such behaviour? Why do people behave in the way they do? What motivates them to change their behaviour? What are the key factors in behaviour formation and change?

One response to these questions has been to bundle everything in what may be called Attitudes-Behaviours-Context (ABC) models (Stern 2010) in which a multitude of factors are considered as contextual factors including:

interpersonal influences (); community expectations; advertising; government regulations; other legal and institutional factors (); monetary incentives and costs; the physical difficulty of specific actions; capabilities and constraints provided by technology and the built environment (); the availability of public policies to support behaviour (); and various features of the broad social, economic and political context () (Stern 2000: 417)

However, as Shove (2010: 1275) argues, the more factors are added to ABC models the more muddled the picture becomes. At the same time, the more complex the models become the less their empirical applicability (Jackson, 2005).

This paper aims to shed some light on this complex picture by presenting a clearer grouping of the factors that drive behaviour. We draw on the broader literature on decision making which cuts across several disciplines to frame specific discussion about environmental behaviour with a focus on energy consumption. A large part of the decision making literature is normative and prescribes how decisions ought to be made. The focus of this paper, however, is on how decisions are actually made by individuals. It aims to provide a conceptual understanding of behaviour. We believe that such an insight is crucial for policies aimed at encouraging pro-environmental behaviour. The following sections focus on three broad perspectives on behaviour and review the discussions on values and norms which play a critical role in the environmental behaviour literature. The concluding section highlights a major shift in understanding energy consumption behaviour in terms of the interplay of individual and social drivers.

2 Three perspectives on environmental behaviour

There are three dominant perspectives for understanding environmental behaviour and its drivers. We call them the rational economic, the psychological and the sociological perspectives (Tetlock, 1991). Below, we elaborate on these in turn.

2.1 The rational economic perspective

The rational economic perspective suggests that people are utility maximisers and their decisions are based on rationally ordered preferences, which in turn are based on the level of utility attached to, and probability of securing, each choice. In doing so, they follow a number of logical steps: define the problem, identify the decision criteria, weight each criterion, assess risk, generate options, rate options on each criterion, compute the optimum option, and monitor and evaluate (Bazerman, 2001: 3-4). This model suggests that peoples choices are based on rationally calculating the costs and benefits of a particular course of action and taking the one which maximises their net benefit. Access to information is crucial for making optimal decisions with highest benefit and lowest cost. This implies that people will reduce their energy use, invest in energy efficient measures, or retrofit their houses, if they possess the requisite information and if their self-interested benefits outweigh costs (Wilson & Dowlatabadi, 2007; Jackson, 2005). According to the model, a key role of intervention is therefore to provide information. This has led to a myriad of policy initiatives

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based on giving feedback to households on their use of energy and providing them with new, actionable information on consumption that could be clearly understood Darby (2008: 450). The idea is that having the information about energy use of different appliances and different patterns of use, people will be motivated to reduce their consumption (Hargreaves et al., 2010; Thaler & Sunstein, 2009; Gronhoj & Thogerson, 2011; Gyberg & Palm 2009).

Another role of policy intervention, according to this model, is to ensure that the market allows people to make optimal choices by correcting price signals through internalisation of social and environmental externalities. This is the basis of a growing number of environmental taxes and levies (such as carbon tax) that are aimed at incorporating environmental costs into economic cost-benefit calculations.

Critics point to key complicating factors such as: the influence of variable future discount rates and the non-linear way in which the value of costs and benefits changes over time; the significance of framing and how preference is depended on a reference point (Lindenberg & Steg, 2007), and the importance of various forms of heuristic, habit and emotion (Wilson & Dowlatabadi, 2007; Jackson, 2005). These latter will be discussed in more details below. Empirical studies have also demonstrated that people do not always behave as utility maximisers. For example, Christie, et al. (2011) highlight that adoption of energy-efficiency technologies are assessed by potential users not only in terms of utility maximisation but also, and more significantly, in terms of risks to, among other things, perceptions of social belonging and other aspects of personal identity and safety.

At the same time, the rational model suggests that besides cost-benefit calculation, the probability of achieving the preferred outcome also plays a part in decision-making. Perceived behavioural control (PBC), as advocated by Ajzen (1991), describes the individuals perception of the ease or difficulty with which they can adopt behaviour (Turaga et al., 2010: 216). Self-efficacy is defined as the perception of how well one can execute a course of action required to deal with prospective situations (Jackson, 2005: 49). The implicit assumption within notions of PBC and self-efficacy is that if a behaviour is perceived as being impossible within a particular context it will not be adopted despite the motivation being present (Darnton, 2008: 19). It is, however, suggested that encouragement and emotional arousal can increase feelings of self-efficacy (Darnton, 2008: 20). Again, information plays a key part because it is argued that feelings of self-efficacy can be strengthened through positive feedback (Grohoj & Thogersen, 2011) on, for example, the level of reduction in energy use. However, if the feedback is negative (no reduction), it may act as a deterrent for those with low perceptions of self-efficacy. Wilson & Dowlatabadi (2007) argue that it is crucial for interventions to enhance individuals perceptions of self-efficacy through feedback mechanisms as well as education and training.

The rational economic model was dominant in the spatial planning field in the 1960s and 1970s in Europe and America. Since then, it has been subject to criticism by planning theorists who argue that it fails to match the seemingly disjointed and incremental processes of decision making by individuals and institutions (including planning systems) alike. However, despite a great deal of research indicating the limitations of the rational model, its assumptions have crept into the debate about attitude and its assumed determining role in environmental behaviour. Peoples behaviour is understood to be preceded by their attitude towards that behaviour. This attitude is in turn informed by a rational evaluation of the characteristics of that behaviour (Jackson, 2005). For example, the attitude towards purchasing and installing a low energy light bulb might be based upon an evaluation of its environmental impact, money saving potential, its aesthetic qualities, the quality of the light and so on (Crosbie & Baker, 2010). Such assumptions imply that if we modify attitudes, we can modify behaviour and this can be done primarily through education, information provision and awareness raising (Stern, 2000; Hargreaves, 2008).

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2.2 The psychological perspective

The psychological perspective does not consider people as irrational, but it argues that their rationality is bounded by certain limiting cognitive characteristics and patterns. It draws on an evolutionary perspective, in which the human species has developed to respond to complex, changing environments by developing mental shortcuts or heuristics (Gigerenzer et al., 1999; Calne, 1999).These rules of thumb are simplifying mechanisms that allow us to make quick decisions whenever full analysis is either not possible or not wise due to the urgency of action (such as escaping from imminent danger) (Nicholson, 2000). While these mechanisms have proved useful and practical, they lead to a number of biases which run counter to some of the fundamental assumptions of the rational model. Some key biases are outlined below, following Kahneman and Tversky (1979).

Firstly, we tend to treat choices differently depending on the manner in which they are described or framed, not what they actually are. If they are framed in terms of losses, we attach more risk to them than if they are framed in terms of gains. This cognitive illusion means that people are more risk averse in relation to potential losses than for potential gains; they are indeed loss averse. This has important implications for environmental policy in terms of, for example, choosing between policies that are based on peoples willingness to pay (buying price) and those focusing on willingness to accept (selling price). The latter is shown by Kahneman and Tversky (1979) to be up to 20 times the former. Layard (2005) provides an intriguing example, suggesting that most people would expect to be paid much more to mow their neighbours lawn than they would be prepared to pay to have their own lawn mowed by their neighbours. This implies that we tend to pay only a little to have something, and demand a lot to give it up (Dawnay and Shah, 2005: 17). Framing, therefore, is significant in economic cost-benefit analyses. More importantly, such analyses are not sufficient in assessing the potential for a given policy being accepted and taken up by people. For example, Christie et al. (2011) found that householders who were resistant to the installation of solar panels remained so even when they had to make no initial expense and were assured that their subsequent payments would not exceed the financial savings that the equipment generated. Clearly, factors other than financial concerns have influenced their decisions, such as the trust in the reliability of panels or the level of disruptions involved.

Secondly, in assessing information we pay more attention to information that is easily available and to memories that are easily retrievable because they have personal relevance or are emotionally vivid. For example, we may put more weight on our own experience of a malfunctioning energy efficient device than on the published statistics about the probabilities of such defaults. We also tend to cherry pick evidence to support our chosen options (a self-serving bias) or the decisions that have already been made (a confirmation bias) (De Bondt, 1998).

Thirdly, in making judgements about which options to choose we use our intuition to filter the huge amount of information received, so that we can make decisions in the face of uncertainties and ambiguities. While this helps with the problem of so called analysis paralysis, it can also lead to over-confident estimates or unwillingness to acknowledge new information. In situations of repeated decision making (such as picking the right temperature for washing laundry) we tend to identify emotionally and cognitively with familiar options that have been tried and tested rather than rationally weigh alternative options. That may explain why a great majority of households wash at 40 degrees Centigrade despite the availability of several other temperature options and improved washing detergents that wash equally well at 30C.

Finally, in evaluating the decisions that have been made, two further biases may occur. The first one is a tendency to attribute any good outcomes to our own actions, and any bad outcomes to factors outside our control, often in the attempt to maintain self-esteem. The second bias relates to the illusion that we have control over the risks of our actions. This then leads us to discount information that suggests otherwise (Fenton-OCreevy et al., 2003).

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In summary, the psychological perspective shows how peoples rationality is bounded by their cognitive characteristics. However, while for some this perspective implies that peoples judgments are always coloured by their biases and destined to systematic mismatch (Nisbett and Ross, 1980), for others, they are signs of strength indicating that people can use their tacit knowledge to arrive at timely decisions. In practice, people move between the two extremes, from simple heuristics to complex cognitive strategies, depending on the significance of the decision that they have to make (Fiske and Taylor, 1991). The psychological perspective stresses the habitual, ritual and conventional bases of human behaviour. It suggests that people are not always calculating rational beings; that, they may not know their costs and benefits; and that they may not act in their own self-interest.

Habit plays a vital role in peoples lives (Darnton et al, 2011). Contrary to the rational choice models, peoples behaviour is often habitual based on short cuts and routines rather than rational deliberation. Only when these routines are disrupted, do conscious deliberations come to play a part. It is in this context that feedback mechanisms, mentioned above, may work. By re-materialising energy which is abstract and by making what is hidden in peoples mundane routines visible (Burgess & Nye, 2008; Thaler & Sunstein, 2009:82), entrenched habit can be disrupted and a space opened which may allow for new habit formation. In other words, feedback may bring energy use back into peoples economic and environmental consciousness.

A distinction, however, can be made between indirect and direct feedback. Indirect feedback occurs sometime after consumption has taken place (such as on households energy bill), while direct feedback happens immediately at the time of consumption (such as energy monitors or smart meters). Direct feedback has been shown to be more effective at saving energy than indirect feedback. It has led to improved energy literacy and interest in purchasing energy efficient appliances or renewable energy technologies (Gronhoj & Thogerson, 2011; Hargreaves, Nye & Burgess, 2010). This underpins the UK governments plan for every household to have a smart meter and energy monitor by 2020 in order to electronically display instant and detailed information about energy use. Research has shown that such devices can produce savings of around 5-15% (Gronhoj & Thogerson, 2011) by motivating a range of actions such as: turning off appliances, using energy more thoughtfully, replacing inefficient appliances, and so on (Darby, 2010). However, research has also shown that the positive effects of energy monitors often decrease overtime (Hargreaves et al. 2010; van Dam et al., 2010). Furthermore, rather than enhancing peoples sense of self-efficacy, their use may lead to a sense of disempowerment as energy monitors can, on occasion, make the challenge of energy saving seem larger and even more insurmountable (Hargreaves et al., 2010: 6119). This has led to calls for more careful examination of their positive and potentially counter-productive effects (Pierce et. al. 2010).

2.3 The sociological perspective

What is common between the rational and the psychological perspectives is that both portray people as information-processors albeit often with highly biased (and limited) processing capacity and bounded rationality (Simon, 1957). Both focus on individual behaviour rather than social and cultural processes that play crucial roles in habit formation, in providing categories within which we think, and in framing what is legitimate or normal.

In line with the psychological perspective outlined above, the sociological perspective also considers peoples rationality as bounded, not just by their cognitive capacity to process information, but also by the social context in which they operate. From this perspective, people are seen as being driven to control not just their environment (as is the case in psychological approaches), but also to respond to social pressures. Three types of social pressures are particularly influential in decision-making. The first is coercive and involves social sanctions if people do not act in socially legitimate ways. Legislation, regulations and rules are among this type of pressure. Non-conformity leads to punishments. A large part of pro-environmental behaviour emanates from the enforceable rules and regulations.

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The second type of social pressure is mimetic and involves imitating what others do (Routledge, 1993). In order to reduce complexity and save time, we may either choose or be compelled to copy others without necessarily considering the potential contextual differences. We tend to do what our neighbours do especially if we trust their judgment. Research has shown that households are motivated to take energy-saving action only after others have been seen to do so (GfK NOP Social Research 2012).

The third type of social pressure is normative, based on the values we hold and the acceptability of behaviours. It involves what we think we should do to not only avoid social censure but also maximise social reward. A great deal of the literature on environmental behaviour considers values and norms as central to the understanding of behaviour and the design of effective policies and programmes aimed at behavioural change (see for example: Stern, 2000; Barr, 2003; Gilg et al. , 2005; Turaga et al., 2010). It is, therefore, justified to dedicate a section to these and elaborate them further.

3 Values and norms

Values are considered to be higher level social constructs than attitudes or beliefs (Jackson, 2005). Some commentators have suggested that individuals hold general values that can be placed on continua ranging from egoistic to altruistic, from conservative to open to change, and from bio-centric (nature has intrinsic value) to anthropocentric (nature has instrumental value) (Barr, 2003: 229). In relation to environmental behaviour, Stern (2000) proposes a value-belief-norm model in which the above values are linked to beliefs about human relationship to nature (also see Davoudi, 2012). It is argued that, altruistic and bio-centric value orientations are positively correlated to an ecological worldview which considers nature as being in a delicate balance that can be offset by unchecked human actions and growth. This ecological worldview, in turn, leads to a sense of moral obligation to engage in pro-environmental behaviour and to perform such behaviour. In contrast, egoistic values correlate negatively to the activation of a sense of responsibility towards the environment (Stern, 2000).

As Hargreaves (2008) argues, Sterns model implies that values are socially, rather than individually, constructed. Despite this, attempts to change values continue to rely on information provision and moral suasion/education aimed at individual consumption (Wilson & Dowlatabadi, 2007: 185; see also Stern, 2000:419) rather than steering the normative basis of society towards more altruistic and reflexive environmentalism (Jackson 2009). It is also important to note that while other studies support the link between altruistic and bio-centric values and environmental behaviours, they nevertheless emphasise that values are not easily manipulated (Gilg et al., 2005: 499) and that, there are other factors that determine pro-environmental behaviour.

In Ajzens (1991) Theory of Planned Behaviour a subjective norm is the perception of what (important) others think about a particular behaviour (Jackson, 2005: 46-47). If we perceive that others would see our behaviour in a positive light, we are more likely to perform that behaviour (Harland et al., 1999). Subjective norms are therefore social norms and as such they refer to what is perceived to be normal or legitimate in a given social context. Social norms can be powerful drivers for pro-environmental behaviours (Evans, 2007). This means that people are likely to engage in energy reduction behaviours if they are a member of a group in which such behaviour is normal (Dono et al., 2010; McKenzie-Mohr, 2000). If switching off lights is normal in our workplace, we are more likely to do so. An individuals ability to observe social norms is important to how they are perceived and accepted by their peer group especially in relation to what is interpreted as socially (un)acceptable (smoking is a clear example).

It is in this context that normative feedback (i.e. comparing one households energy use with that of other households) as opposed to informative feedback (i.e. providing households with information about their own energy use) is suggested to be more effective because it can

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activate a social norm and hence a change of behaviour (Fischer, 2008; De Young, 2000). However, empirical findings on this claim are mixed: some argue that normative feedback stimulates energy saving (Darby, 2010), others suggest that the effect is often under-detected (Nolan, et. al., 2008) and a third group find that none of the studies utilising normative feedback could demonstrate an effect on consumption (Fischer, 2008: 99). While, more research is needed in the exact effects of normative feedback, it is widely acknowledged that social norms refer to what is conceived of as appropriate forms of behaviour in a given circumstance or a given social group (Jackson, 2005:60). Adjusting ones behaviour to the norm can therefore have a positive or negative impact on their energy consumption. So, as Fischer (2008) suggests, low energy households could actually increase their energy consumption if comparative feedback suggests that their consumption is below the norm. So, social norms can act both ways depending on the nature of the norm (pro, anti or neutral towards the environment) and the extent to which it is embedded in the social consciousness. Lorenzoni et al. (2007) argue that a significant barrier to adopting pro-environmental behaviour in the UK is the perception that low-consumption green living is both abnormal and undesirable.

Overall, it is important to note that a focus on values and norms in policy-making recognises that energy behaviour is an inherently political as well as a technical issue and requires the development of energy-sensitive politics as well as policy integration.

4 Conclusion

Reducing households energy consumption is a significant part of strategies for transition to low carbon societies. Such reduction can take place through technological advances such as energy efficient building materials and appliances and physical interventions such as retrofitting of the built environment. However, problems of rebound effect, low levels of take up and acceptability have directed attention to behavioural issues. Changing behaviour has increasingly become the buzzword of public policy. However, as mentioned in the introduction to this paper, progression towards more sustainable forms of energy demand and supply requires more than a shift in the attitudes and intentions of individuals (Walker & Cass, 2007: 467). Attempts to steer society towards sustainable energy systems should go beyond a focus on influencing individual behaviour. It requires a radical re-working and re-alignment of technologies, routines, forms of knowhow, markets and expectations (Shove, 2012:1278) as well as institutional practices and systems of provision.

Peoples consumption of energy is based on a set of social practices which are influenced by both their lifestyle choices and by the institutions and structures of society, including those which determine the dynamics of energy systems. For policy to be effective, it needs to be developed with a sound understanding of the complexity of these relationships.

The need for systemic change does not mean an abandonment of attempts to promote pro-environmental behaviour. What we have demonstrated in this paper is the existence of at least three different conceptualisations of behaviour with each being rooted in different disciplinary traditions and presenting different views of individuals and the drivers of behavioural change. In practice, what constitute our behaviour is far from the neat dividing lines presented above. As Jackson (2005) puts it, peoples behaviour is a function of their attitude and intentions, their habitual responses and the situational constraints and conditions under which they operate. Their intentions are then influenced by social, normative, and affective factors as well as rational deliberations.

Effective policies have to take into account the importance of the social context of behaviour, while also renegotiating habits and encouraging new habit formation. An important element of changing habit is to unlock existing behaviour or, in other words, raise the behaviour from the level of practical (everyday routine) to discursive (intentional, goal-oriented) consciousness (Jackson, 2005). This can be done more effectively with a focus on communities rather than individuals (Brulle, 2010; Bunt and Harries, 2010, Heiskanen et al

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2010). Through both place-based and group-based communities, reducing energy consumption could become the new social norm, shaping both individual and systemic behaviour. Many of the current pro-environmental behaviour change approaches do recognize the importance of information, norms and attitudes and take a collective approach at the level of community. And yet, there appears to be a lack of stress on the facilitative structural conditions and institutional practices within which these community initiatives are situated. The evidence in this paper suggests that a shift in energy behaviours requires a multi-level and cross-sectoral approach which addresses material, institutional, social and subjective determinants of behaviour simultaneously.

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Environment and Planning II Planning and Design IW), volume 26, pages 603 630

Modeling the urban ecosystem: a conceptual framework

M Aibcrtt Department of Urban Design and Planning, University of Washington, Box 355740, Seattle, WA 98195, USA; e-mail: maibcrttou.wasliington.edu Received 14 October 1998; in revised form 22 March 1999

Abstract. In this paper I build on current research in urban and ecological simulation modeling to develop n conceptual framework for modeling the urban ecosystem. Although important progress has been made in various areas of urban modeling, operational urban models are still primitive in terms of their ability to represent ecological processes. On the other hand, environmental models designed to assess the ecological impact of an urban region are limited in their ability to represent human systems, I present here a strategy to integrate these two lines of research into an urban ecological model (UEM). This model addresses the human dimension of the Pugct Sound regional integrated simulation model (PRISM)a multidisciplinary initiative at the University of Washington aimed at developing a dynamic and integrated understanding of the environmental and human systems in the Pugct Sound. UEM simulates the environmental pressures associated with human activities under alternative demographic, economic, policy, and environmental scenarios. The specific objectives of UEM are to: quantify the major sources of human-induced environmental stresses (such as land-cover changes and nutrient discharges); determine the spatial and temporal variability of human stressors in relation to changes in the biophysical structure; relate the biophysical impacts of these stressors to the variability and spatial heterogeneity in land uses, human activities, and management practices; and predict the changes in stressors in relation to changes in human factors.

1 Introduction Planning agencies worldwide are increasingly challenged by the need to assess the environmental implications of alternative urban growth patternsand policies to control themin a comprehensive manner. Urban growth leads to rapid conversion of land and puts increasing pressure on local and global ecosystems. It causes changes in water and energy fluxes. Natural habitats are reduced and fragmented, exotic organisms arc introduced, and nutrient cycles are severely modified. Although impacts of urban development often seem local, they cause environmental changes at larger scales. Assessments of urban growth that are timely and accurate, and developed in a transparent manner, are crucial to achieve sound decisions. However, operational urban models designed to analyze or predict the development of urban areas are still primitive in their ability to represent ecological processes and urban ecosystem dynamics. Though important progress has been made in various areas of urban modeling (Wegener, 1994; 1995), only a few scholars have attempted to integrate the environmental dimension. The majority of these models are designed to answer a set of fundamental but limited planning questions relevant to housing (Anas, 1995; Anas and Arnott, 1991; Kain and Apgar, 1985), land use (Landis, 1992; 1995; Prastacos, 1986; Waddell, 1998), transportation (Boyce, 1986; Kim, 1989) and in some cases the inter-actions among them (de la Barra, 1989; Echenique et al, 1990; Mackett, 1990; Putman, 1983; 1991; Wegener, 1983).

On the other hand, the environmental models designed to assess the ecological impact of an urban region are limited in their ability to represent human systems. These models represent people as static scenarios of land uses and economic activities and predict human-induced disturbances from aggregated measures of economic develop-ment and urban growth. Only with the increasing attention paid to the role of human

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activities in global environmental change has the need emerged to represent more explicitly human systems in environmental models. Whereas integrated assessment modeling can be traced back to the late 1960s (Forrester, 1969; Meadows et al, 1972), the first generation of operational integrated models has emerged only in the mid-1980s. During the last decade, integrated assessment modeling has been proposed as a new approach to link biophysical and socioeconomic systems in assessing climate change (Dowlatabadi, 1995). At present more than thirty integrated assessment models (IAMs) have been developed (Alcamo, 1994; Dowlatabadi, 1995; Rotmans et al, 1995). The focus of current IAMs is global; however, a new generation of spatially explicit regional integrated models is now emerging (Maxwell and Costanza, 1995). These models have started to treat human decisions explicitly but are still too limited in the repre-sentation of human behavior and the heterogeneity of urban land uses (Alberti, 1998).

Recent progress in the study of complex systems (Schneider and Kay, 1994) and the evolution of computer modeling capabilities (Brail, 1990) have made possible a more explicit treatment of the link between human and ecological systems. The development of GIS has provided the capability to integrate spatial processes. However, the greatest challenge for integrating urban and environmental modeling will be in interfacing the various disciplines involved. Urban subsystems have been studied for several decades but progress in urban-ecological modeling has been limited because of the difficulty in integrating the natural and social sciences. A recent National Science Foundation workshop on urban processes pointed out that ecologists, social scientists, and urban planners will need to work together to make their data, models, and findings compatible with one another and to identify systematically where fruitful clusters of multidisci-plinary research problems can be developed (Brown, 1997). Such an approach can offer a new perspective on modeling urban systems.

In this paper I build on research in urban and ecological simulation modeling to develop an integrated urban-ecological modeling framework. This framework is part of a current effort to develop an urban-ecological model (UEM) at the University of Washington as part of the Puget Sound regional integrated simulation model (PRISM). UEM simulates the environmental impacts associated with human activities under alter-native demographic, economic, policy, and environmental scenarios. Its objectives are to: (1) Quantify the major sources of human-induced environmental stresses (such as land-cover changes and nutrient discharges); (2) Determine the spatial and temporal variability of human stressors in relation to changes in the biophysical structure; (3) Relate the biophysical impacts of these stressors to the variability and spatial hetero-geneity in land uses, human activities, and management practices; and (4) Predict the changes in stressors in relation to changes in human factors.

The development of an integrated urban-ecological framework has both scientific and policy relevance. It provides a basis for developing integrated knowledge of the processes and mechanisms that govern urban ecosystem dynamics. It also creates the basis for modeling urban systems and provides planners with a powerful tool to simulate the ecological impacts of urban development patterns.

2 The urban ecosystem Early efforts to understand the interactions between urban development and environ-mental change led to the conceptual model of cities as urban ecosystems (Boyden et al, 1981; Douglas, 1983; Duvigneaud, 1974; Odum, 1963; 1997; Stearns and Montag, 1974). Ecologists have described the city as a heterotrophic ecosystem highly dependent on large inputs of energy and materials and a vast capacity to absorb emissions and waste (Boyden et al, 1981; Duvigneaud, 1974; Odum, 1963). Wolman (1965) applied an 'urban

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metabolism1 approach to quantify the Hows of energy and materials into and out of a hypothetical American city. Systems ccologists provided formal equations to describe the energy balance and the cycling of materials (Douglas, 1983; Oclum, 1983). Although these efforts have never been translated into operational simulation models, they have laid out the basis for urban-ecological research. Urban scholars were rightly skeptical about the attempts to integrate biological and socioeconomic concepts into system dynamics models. None of these models represented explicitly the processes by which humans affect or are affected by the urban environment. At best, human behavior was reduced to a few differential equations. These models simplified the interactions of natural and social systems so much that they could provide little useful insight for planners and decisionmakers. Since then, however, urban and ecological research has made important progress with respect to understanding how urban ecosystems operate and how they differ from natural ecosystems.

Urban-ecological interactions are complex. Urban ecosystems consist of several interlinked subsystems -social, economic, institutional, and environmental each representing a complex system of its own and affecting all the others at various structural and functional levels. Urban development is a major determinant of eco-system structure and influences significantly the functioning of natural ecosystems through (a) the conversion of land and transformation of the landscape; (b) the use of natural resources; and (c) the release of emissions and waste. The earth's ecosystems also provide (d) important services to the human population in urban areas. Thus (e) environmental changes occurring at the local, regional, and global scale such as the contamination of watersheds, loss of biodiversity, and changes in climate -affect human health and well-being. Humans respond to environmental change through (f) management strategies (figure I).

Global ecosystem

Figure 1. Human - natural systems interactions.

2.1 Human systems Human drivers are dominant in urban ecosystem dynamics. Major human driving forces are demographics, socioeconomic organization, political structure, and technol-ogy. Human behaviorsthe underlying rationales for the actions that give rise to these forcesdirectly influence the use of land and the demand for and supply of resources

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(Turner, 1989). In urban areas these forces combine to affect the spatial distribution of activities and ultimately the spatial heterogeneity of natural processes and distur-bances. It is increasingly clear to both social (Openshaw, 1995) and natural (Pickett et al, 1994) scientists that it is absurd to model the urban ecosystem without explicitly representing humans in them. Would ecologists exclude other species from models of natural ecosystems? However, as Pickett et al (1997) point out, simply adding humans to ecosystems without representing the way they function is not an adequate alter-native. Today, social and natural scientists have the tools to explore the richness of interactions between the social and ecological functions of the human species.

Representing human actors and their institutions in models of urban ecosystems will be an important step towards representing more realistically the human dimension of environmental change. Many of the human impacts on the physical environment are mediated through social, economic, and political institutions that control and order human activities (Kates et al, 1990). Also, humans consciously act to mitigate these impacts and build the institutional settings to promote such actions. They adapt by learning both individually and collectively. How can these dimensions be represented? Lynch (1981, page 115) suggested that "a learning ecology might be more appropriate for human settlement since some of its actors, at least, are conscious, and capable of modifying themselves and thus changing the rules of the game", for example by restructuring materials and switching the path of energy flows. Humans, like other species, respond to environmental change but in a more complex way.

2.2 Natural systems Environmental forcessuch as climate, topography, hydrology, land cover, and human-induced changes in environmental qualityare important drivers of urban systems. Moreover, natural hazardssuch as hurricanes, floods, and landslidescan cause significant perturbations in social systems. Most models of human systems, however, simply ignore these forces. In urban models, biophysical processes are at best included as exogenous variables and treated as constants. This is a severe limitation because human decisions are directly related to environmental conditions and changes. Surprisingly, urban modelers cannot remove the behavior of the job market or degradation of housing stocks from their models but can represent the dynamics of urban systems without considering the degradation of the environment and depletion of natural resources.

As we cannot simply add humans to ecological models, representing biophysical processes in urban models will require going beyond simply adding environmental variables to existing urban models. A number of models currently extend their modules to include changes in environmental variables such as air quality and noise (Wegener, 1995). However, these models may misrepresent complex ecological responses. Before we can model these responses, we need to recognize explicitly the properties of ecosystem organization and behavior that govern them. According to Holling (1978, pages 25-26) four properties of ecological systems determine how they respond to change. First, parts in ecological systems are connected to each other in a selective way that has implications for what should be measured. Second, events are not uniform over space, which has implications for how intense impacts will be and where they will occur. Third, sharp shifts in behavior are natural for many ecosystems. Fourth, variability, not constancy, is a feature of ecological systems that contributes to their self-correcting capacity.

2.3 Integrated modeling In modeling the interactions between human and natural systems, we need to consider that many factors work simultaneously at various levels. Simply linking these models in an 'additive' fashion may not adequately represent system behavior because interactions occur at levels that are not represented (Pickett et al, 1994). On the basis of hierarchy

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theory* Pickett et nl (1994) argue that the consideration of interactions only at the upper level may provide statistical relationship but cannot help explain or predict important feedback for future conditions. This is particularly true in urban ecosystems because urban development controls the ecosystem structure in complex ways. Land-use decisions affect species composition directly through the introduction of species and indirectly through the modification of natural disturbance agents. Production and consumption choices determine the level of resource extraction and generation of emissions and waste. Decisions about investing in infrastructures or adopting control policies may mitigate or exacerbate these effects. Because ecological productivity controls the regional economy, interactions between local decisions and ecological processes at the local scale can result in large-scale environmental change.

We also need to challenge the implicit assumption of most models that decisions arc made by one single decisionmaker at one point in time. Urban development is the outcome of dynamic interactions among the choices of many actors, including house-holds, businesses, developers, and governments (WaddcII, 1998). These actors make decisions that determine and alter the patterns of human activities and ultimately affect environmental change. Their decisions arc interdependent; for example, housing location is affected by employment activity and affects retail activity and infrastructure, which in turn affect housing development.

Human and natural systems, including their equilibrium conditions, change over time. One major problem in describing their relationships is that they operate at very different temporal and spatial scales. The lag times between human decisions and their environmental effects further complicate any attempt to understand these interactions. Moreover, the environmental effects of human actions may also be distanced over space (Moiling, 1986). Simulating the behavior of urban-ecological systems requires not only an explicit consideration of the temporal and spatial dynamics of these systems, but also achieving consistency across the different temporal and spatial scales at which various processes operate.

Another source of difficulty in spelling out these interactions is their cumulative and synergistic impacts. In general, environmental impacts become important when their sources are grouped closely enough in space or time to exceed the ability of the natural system to remove or dissipate the disturbances (Clark, 1986). Human stresses in cities may cross thresholds beyond which they may irrevocably damage important ecological functions. In most ecological systems, processes operate in a stepwise rather than a smoothly progressive fashion over time (Holling, 1986). Sharp shifts in behavior are natural This property of ecosystems requires the consideration of resilience: the amount of disturbance a system can absorb without changing its structure or behavior.

In modeling urban-ecological systems we also need to consider feedback mecha-nisms between the natural and human systems. These are control elements that can amplify or regulate a given output. At the global level, an example of negative feedback in the biosphere described by ecologists is the homeostatic integration of biotic and physical processes that keeps the amount of CO2 in the air relatively constant. Feed-back loopsboth positive and negativebetween human and environmental systems are not completely understood. We know that human decisions leading to the burning of fossil fuels and land-use change affect the carbon cycle, and that in turn the associated climate changes will affect human choices, but the nature of these inter-actions remains controversial. In particular, the feedback of environmental change on human decisions is difficult to represent because environmental change affects all people independently of who has caused the environmental impact in the first place, whereas the impact of each individual decisionmaker on the environment depends on the choices of others (Ostrom, 1991).

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Modeling urban-ecological systems will require special attention to uncertainty. Uncertainty can arise from limited understanding of a given phenomenon, systematic and random error, and subjective judgment. Change in natural systems can occur in abrupt and discontinuous ways, and responses can be characterized by thresholds and multiple domains of stability. The knowledge of the environmental systems is always incomplete and surprise is inevitable (Holling, 1995). The explicit characterization and analysis of uncertainty should be a central focus of modeling integration.

3 The environmental dimension in urban models Although extensive urban research has focused on the dynamics of urban systems, it has been described only partially through numerical models. Most operational urban models focus on a few subsystems such as housing, employment, land use, and trans-portation, with a limited set of elements influencing their dynamics. These models predict the spatial distribution of activities based on simple spatial interaction mech-anisms and economic axioms. No operational urban models have attempted to describe the interactions between urban and environmental processes in a systematic way. Recently a few modelers have started to address a number of direct impacts of human activities, such as air pollution and noise, on the environment. However, as the idealized urban model proposed by Wegener (1994) depicts quite well, only unidirec-tional links between urban systems and the environment have been conceived in urban modeling. Today a vast literature synthesizes the theoretical and methodological foun-dation of urban simulation models (Batty and Hutchinson, 1983; Harris, 1996; Mackett, 1985; 1990; Putman, 1983; 1995; Wegener, 1994; 1995; Wilson et al, 1977; 1981). In this section I draw on this literature to explore how environmental variables are considered and how environmental processes are represented.

Operational urban models can be classified according to the approach they use to predict the generation and spatial allocation of activities or according to the solution proposed to a variety of model design questions (see table 1, over). It is difficult to classify the vast literature on urban modeling because of the great variability in empha-sis that authors place on theory, techniques, and applications. Moreover, the various approaches are interrelated in complex ways. Six major classes of operational models discussed in the literature are relevant here: those relying on gravity, the economic market, optimization, input - output, microsimulation, and cellular automata.

3.1 Gravity, maximum entropy, and discrete-choice models The dominant approach in urban modeling can be traced to Lowry's (1964) model, a simple iterative procedure in which nine equations are used to simulate the spatial distribution of population, employment, service, and land use. The model is based on the simple hypothesis that residences gravitate toward employment locations. Two schools of research have provided a statistical basis for the gravity model, guided by Wilson (1967, the entropy-maximizing principle) and McFadden (1973, utility maximization). The results obtained by the two methods were later shown to be equivalent (Anas, 1983). The models most often used by planning agencies in the USAthe disaggregated residential allocation model (DRAM) and the employment allocation model (EMPAL)are derivatives of Lowry's model using maximum entropy formulation. Developed by Putman (1979), and incrementally improved since the early 1970s, DRAM and EMPAL are currently in use in fourteen US metropolitan areas (Putman, 1996). The integrated transportation land-use package (ITLUP), also devel-oped by Putman (1983), provides a feedback mechanism to integrate DRAM, EMPAL, and various components of the urban transportation planning system (UTPS) models implemented in most metropolitan areas. Although these models substantially improve

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upon the initial Lovvry model, they are based on the same simple assumption. No environmental variables are used in determining the spatial distribution of residence. The allocation of residential and employment activities must of course meet physical constraints and planning restrictions within the available zones. However, other than these constraints no other environmental considerations are included in the equation.

3.2 Economic market-based models A second urban modeling approach is based on the work of Wingo (1961) and Alonso (1964), who introduce the notion of land-rent and land-market clearing. Wingo was the first to describe the urban spatial structure in the framework of equilibrium theory. Given the location of employment centers, a particular transportation technology, and a set of households, his model determines the spatial distribution, value, and extent of residential land requirements under the assumption that landowners and households both maximize their return. Wingo uses demand, whereas Alonso uses bid-rent func-tions to distribute the land to its users. The aim of both models is to describe the effects of the residential land market on location. Under this approach, households are assumed to maximize their utility and select an optimum residential location by trading off housing prices and transportation costs. The trade-offs are represented in a demand or bid-rent functional form which describes how much each household is willing to pay to live at each location. Anas (1983) introduced discrete-choice behavior into models with economically specified behavior and market clearing. Two models that use this approach arc UrbanSim developed by Waddcll (1998), and CUF2 devel-oped by Landis and Zhang (1998a; 1998b). Both models arc based on random utility theory and make use of logit models to implement key components. However, they differ in a substantial way. UrbanSim models the key decisionmakershouseholds, businesses, and developersand simulates their choices that impact urban develop-ment. It also simulates the land market as the interaction of demand and supply with prices of land and buildings adjusting to clear the market. UrbanSim simulates urban development as a dynamic process as opposed to a cross-sectional or equilibrium approach. CUF2 models land-use transition probabilities based on a set of site and community characteristics such as population and employment growth, accessibility, and original use in the site and surrounding sites.

As indicated in table 1, most current operational models are based on an economic market-based approach and rely on random utility or discrete-choice theory. In these models, environmental variables are not part of the equation, except for environmental constraints. The value of the ecological servicessuch as clean air, clean water, and flood controlthat ecosystems provide to households are not reflected in market prices. This is a severe limitation, because changes in environmental quality and other ecolog-ical services provided by ecosystems will affect the market behavior of the households (Maler et al, 1994). 3.3 Mathematical programming-based models A third approach to describing urban activity allocation is based on optimization theory. By using mathematical programming, these models design spatial interaction problems in order to optimize an objective function that includes transportation and activity establishment costs. Herbert and Stevens (1960) used linear programming to simulate the market mechanisms that affect location. Wheaton (1974) developed an optimization model by using nonlinear programming. More recently Boyce et al (1993) and Boyce and Southworth (1979) have explored the options for integrating spatial interactions of residential, employment, and travel choices within a single optimized modeling framework. The projective optimization land-use system (POLIS) developed by Prastacos (1986) is one of the few optimization land-use models used in

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Table 1. Urban models.

Model

Clarke

CUF2

IRPUD

ITLUP

Kim

MASTER

MEPLAN

POLIS

TRANUS

UrbanSim

Subsystems

Land use and/or cover

Population Employment Housing Land use Population Employment Housing Land use

Population Employment Land use Travel Population Employment Transport Travel Population Employment Housing Land use Travel Population Employment Housing Land use Transport Travel Population Employment Housing Land use Travel Population Employment Housing Land use Transport Travel Population Employment Housing Land use

Theory or approach

Complex systems Cellular automata Monte Carlo simulation Random utility Multinomial logit

Random utility Network equilibrium Land-use equilibrium Monte Carlo micro-simulation Random utility Maximization Network equilibrium

Random utility General equilibrium Input-output

Random utility Maximization Monte Carlo micro-simulation

Random utility Maximization Market clearing Input-output

Random utility Optimization

Random utility Network equilibrium Land-use equilibrium Input-output

Random utility Partial equilibrium Multinomial logit

Population or sectors

Aggregated

Aggregated

Partially disaggregated

Partially disaggregated

Aggregated

Disaggregated

Aggregated

Aggregated

Aggregated

Partially disaggregated

Modeling the urban ecosystem 613

Model

Clarke

CUF2.

IRPUD

ITLUP

Kim

Time

Dynamic

Static

Quasidynamic

Static

Static

Space

Dynamic Cirid cell

Static I00x 100 m grid cell

Static Zone

Static Zone

Static Zone

Environmental (actors

Land cover Topography Hydrography Percent slope

Zone space constraints CO2 emissions by transport

Zone space constraints Mobile source emissions

Zone space constraints

Source

Clurkc etui, 1997

Landis and Zhang, 1998a; 1998b

Wegener, 1995

Putman, 1983; 199!

Kim, 1989

MASTER Quasidynamic Static Zone

Zone space constraints Mackctt, 1990

MEPLAN Static Static Zone

Zone space constraints Echcnique et al, 1990

POLIS Static Static Zone

Zone space constraints Prastacos, 1986

TRANUS Static Static Zone

Zone space constraints de la Barra, 1989

UrbanSim Quasidynamic Static Parcels

Topography Stream buffers Wetlands 100 years floodplain area

Waddell, 1998

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planning practice. This model, which has been implemented in the San Francisco Bay area, seeks to maximize both the location surplus and the spatial agglomeration benefits of basic employment sectors. As in previous models, only land availability is included as a determinant of employment allocation to zones.

3.4 Input-output models Another important contribution from economic theory to urban modeling is the spatially disaggregated intersectoral input-output ( I -O) approach, developed initially by Leontief (1967). The approach provides a framework for disaggregating economic activities by sector and integrating them into urban spatial interaction models. This transforms the basic structure of an I - O table, allowing the modeler to estimate the direct and indirect impacts of exogenous change in the economy on a spatially dis-aggregated scale. Operational urban models that use such an approach include MEPLAN, TRANUS, and the models developed by Kim (1989). MEPLAN includes three modules: LUS, the land-use model; FRED, which converts production and consumption into flows of goods and services; and TAS, a transportation model which allocates the transport of goods and passengers to travel modes and routes. The land-use component of MEPLAN is based on a spatial disaggregation of production and consumption factors that include goods, services, and labor. Total consumption is estimated by using a modified I - O framework subsequently converted into trips. MEPLAN, TRANUS, and Kim's models use I - O tables to generate interregional flows of goods. MEPLAN uses the results of the I - O framework to evaluate environmental impacts. I - O models have been extended to include environmental variables and incor-porate pollution multipliers, but no urban model has attempted to implement this approach for describing economic-ecological interactions. The regional applications of such an approach have encountered various difficulties related to the specification of the ecological interprocess matrix and the assumption of fixed coefficients. A major limita-tion is that inputs and outputs are measured in values as opposed to physical flows.

3.5 Micro simulation One major limitation in the way most urban models represent the behavior of house-holds and businesses stems from the fact that they are aggregated and static. Individuals behave in ways that are influenced by their characteristics and the opportunities from which they choose. Without the explicit representation of these individuals it is impos-sible to predict the trade-offs they make between jobs, residential locations, or travel modes. A distinct approach to model the behavior of individuals is microanalytic simulation that explicitly represents individuals and their progress through a series of processes (Mackett, 1990). Microsimulation is a modeling technique that is particularly suitable for systems where decisions are made at the individual unit level and where the interactions within the system are complex. In such systems, the outcomes produced by altering the system can vary widely for different groups and are often difficult to predict. In microsimulation the relationships between the various outcomes of decision processes and the characteristics of the decisionmaker can be defined by a set of rules or by a Monte Carlo process. Furthermore, the actions of a population can be simu-lated through time and incorporate the dynamics of demographic change. An example is the microanalytical simulation of transport, employment, and residence (MASTER) model developed by Mackett (1990). The model simulates the choices of a given population through a set of processes. The outcome of each process is a function of the characteristics of the household or business, the set of available choices, and a set of constraints. This approach is applied less extensively in Wegener's (1982) Dortmund model. Although these models do not explicitly use microsimulation for modeling environmental impacts, it is clear that the greater disaggregation of the

Modeling the urban ecosystem cm

actors and behaviors has enormous advantages for modeling consumer behavior and environmental impacts,

3.6 Cellular automata The use of cellular automata (CA) has been proposed to model spatially explicit dynamic processes not currently represented in urban models (Hatty and Xie, 1994; Couclclis, 1985; White et al, 1997; Wu, 1998). Existing operational models arc spatially aggregated and, even when they use or produce spatially disaggregated data, they rely on simple spatial geometric processing. A number of modelers have stressed the need to represent more realistically the spatial behavior of urban actors (White and Engelen, 1997). CA consist of cells arranged in a regular grid that change state according to specific transition rules. These rules define the new state of the ceils as a function of their original state and local neighborhood, Clarke et al (1997) have developed a CA urban growth model as part of the Human-Induced Land Transformations Project initiated by the US Geological Survey. The model aims to examine the urban transition in the San Francisco Bay area from a historical perspective and to predict regional patterns of urbanization in the next 100 years (Clarke ct al, 1997). These predictions are then used as a basis to assess the ecological and climatic impacts of urban change. There are four types of growth: spontaneous, diffusive, organic, and road-influenced. Five factors regulate the rate and nature of growth: a diffusion factor which determines the dispcrsiveness; a breed coefficient which specifics the likelihood of a settlement to begin its growth cycle; a spread coefficient which regulates growth of existing settle-ments; a slope resistance factor which influences the likelihood of growth on steeper slopes; and a road gravity factor which attracts new settlements close to roads.

4 The human dimension in environmental models Environmental models have been developed for several decades to simulate atmos-pheric, land, and ecosystem dynamic processes, and to help assess the effects of various natural and human-induced disturbances. However, the use of these models in environ-mental management has become widespread only in the last three decades (J0rgensen et al, 1995). Since the early 1970s major environmental problems such as eutrophication and the fate of toxic substances have attracted the attention of environmental modelers, and very complex models were developed. More recently, the prospect of major changes in the global environment has presented the scientific community with the challenge of modeling the interactions between human and ecological systems in an integrated way. Over these decades a rich literature on'environmental models has developed, but this is well outside the scope of this paper. In this review I focus on the treatment of the human dimension in these models (table 2, over). 4.1 Climate and atmospheric models Atmospheric models can be classified according to the scale of the atmospheric pro-cesses they represent. At the global scale, sophisticated coupled atmospheric-ocean general circulation models (AOGCM) predict climate conditions by considering simul-taneously the atmosphere and the ocean (Washington and Meehl, 1996). Using a set of climate parameters (that is, solar constant) and boundary conditions (that is, land cover, topography, and atmospheric composition), these models determine the rate of change in climatic variables such as the temperature, precipitation, surface pressure, and soil moisture associated with alternative scenarios of CO2 concentrations. These models are currently being used by the Intergovernmental Panel on Climate Change (IPPC) to assess the impact of alternative greenhouse-gases emission scenarios up to the year 2100.

Regional models have been developed primarily to tackle the issue of acid rain. Aggregated emissions of sulfur and nitrogen compounds, estimated on the basis of

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Table 2. Environmental models.

Model Class Media or subsystems Scale

NCAR

CMAQ

UAM

Ocean-climate general circulation model

Atmospheric model

Atmospheric model

Climate - ocean

Meteorological emission, Chemistry transport

Photochemical processes

Global

Local or regional

Local or regional

OBM Biogeochemical model Terrestrial biosphere Global

HRBM Biogeochemical model Terrestrial biosphere Regional

DHSVM Distributed hydrology Hydrology soil vegetation model

JABOWA/ Population-community Trees FORET dynamic model

CENTURY Biogeochemical model Nutrient cycles

Regional

Local

Local

GEM

PLM

IMAGE2

ICAM-2

RAINS

Process-oriented ecological model Process-oriented landscape model

Process-oriented integrated simulation model

Optimization - simulation model

Optimization - simulation model

Ecosystems

Terrestrial landscape

Energy - industry Terrestrial Environment Atmosphere - ocean Climate Economy Policy Emissions Atmospheric transport Soil acidification

Local

Regional

Global, 13 regions

Global, 7 regions

Continental, Europe

TARGETS Integrated simulation model

Population or health Energy or economics Biophysics, land, soils, or water

Global, 6 regions

Modeling the urban ecosystem 617

Model

NCAR

CMAQ

UAM

OBM

HRBM

DHSVM

JABOWA/ FORET

CENTURY

GEM

PLM

IMAGE2

ICAM-2

RAINS

TARGETS

Time

Dynamic Minutes 100 years

Dynamic 8-hour to 72-hour period Dynamic 8-hour to 72-hour period Dynamic 1 year

Dynamic 6 days

Dynamic Hours Dynamic Up to 500 years 1 year Dynamic 1 month Thousands of years Dynamic 12 hours Dynamic 1 week

Dynamic 1 day to 5 years

Dynamic 5 years

Dynamic 1 year

Dynamic 1 year

Space

Dynamic 4.5" x 7.5 (latitude x longitude 9 layers Dynamic Variable 3-D grid

Dynamic Variable 3-D grid

Dynamic 2.5 x 2.5

Dynamic 0.5 x 0.5

Dynamic 30- 100 m Dynamic 10 x 10 m grid

Dynamic 1 x 1 m grid cell

Dynamic 1 km cell Dynamic 200 m grid 1 km grid Dynamic Variable from 0.5 x 0.5 grid to region Static Latitude bands

Static 150 km x 150 km in deposition submodel and 0.5 x 1