**12. Appendix B**

What is cognitive mapping methodology? Example cognitive map and fuzzy cognitive map from the public health study is depicted. Explanation on main factors and how these factors

Which factors, which things, come into your mind spontaneously if I mention to you the Mediterranean (Black) Sea as a system where humans, marine animals and plants are all

How strongly a factor A influences another factor B? A scale having 12 grades capable to

C2 **Tourism** Tourism C15 **IA** Industrial

C5 **BD** Biodiversity C18 **MSW** Municipal

 **Concepts Abbreviation Description** 

C14 **Sphi** Sulphide

C16 **S** Urban

C17 **ISP** Invasive

C19 **MR** MR= Marine

C21 **SLR** Sea level rise

C20 **RP** Riverine

**of concepts** 

Increase

Sewage

species

solid waste pollution to the sea

Research

Pollutants

Activities or Industrial Pollution

Is there any positive or negative relationship between these factors?

Strong words/phrases they used, general comments of the interview.

**of concepts** 

oil spill pollution

Sea Trade

wastes

State & Institutions

Fish Stocks

C9 **RAW** Radio-active C22 **BW** Ballast

Development

describe any kind of relationship between two things is given.

Which is the Aim of the study?

are interrelated is presented.

living together?

Stage (3): Conclusion

**12. Appendix B** 

Stage (2) Creation of individual FCM

**Concepts Abbreviation Description** 

C1 **AOSP** Accidental

C3 **PST** Pollution of

C4 **CD** Coastal

C6 **ChemW** Chemical

C7 **D** Distrust to

C8 **DFS** Depletion of


Table A. 26 clustered concepts describing stakeholders' perceptions.

Fig. B1. A cognitive map defined by an individual/stakeholder from Ukraine.

Using Fuzzy Cognitive Mapping in Environmental

*Systems*, vol. 3, pp. 1873-1877.

*Management Studies* vol. 29, pp. 309–324.

*Agricultural Systems*, vol. 55, No. 2, pp. 173-193.

261–265.

USA.

1, pp. 135–151.

pp. 1321–1329.

Management 38, 304-315

vol. 41, pp.197-210.

University, Maastricht.

*Society,* vol.13, no. 1, pp. 21.

Decision Making and Management: A Methodological Primer and an Application 447

Craiger, J. P. & Coovert, M. D. (1994). Modeling dynamic social and psychological processes

Desvousges W. H. and V. K. Smith (1988). Focus Groups and Risk Communication: The "Science" of Listening to Data. *Risk Analysis* vol. 8, no. 4, pp.479-484 Eden C. (1992). On the nature of cognitive maps. *Journal of Management Studies* vol. 29, pp.

Eden C; Ackermann F, & Cropper S (1992). The analysis of cause maps. *Journal of* 

Eisenack K.; M.K.B. Lüdeke; G. Petschel-Held, J. Scheffran & Kropp, J.P. (2006).

Grimble R. & Wellard, K. (1997). Stakeholder Methodologies in Natural Resource

Grosskurth J. & Rotmans, J. (2005). The scene model: getting a grip on sustainable

Grosskurth J. (2008). *Regional Sustainability: tools for integrative governance*, Maastricht

Harary, F.; Norman R. Z. & D. Cartwright, (1965). Structural Models: An Introduction to the

Hermans, L. M. (2008). Exploring the promise of actor analysis for environmental policy

[online URL: http://www. ecologyandsociety.org/vol13/iss1/art21/] Hjortsø CN; Christensen SM; & Tarp P (2005). Rapid stakeholder and conflict assessment for

Enterprise, Vietnam. *Agriculture and Human Values* vol. 22, pp. 149–167. Hobbs, B. F., S. A. Ludsin, R. L. Knight, P. A. Ryan, J. Biberhofer & Ciborowski, J. J. H.

Isaac M. E., Dawoe, E. & Sieciechowicz, K. (2009). Assessing Local Knowledge Use in

Karageorgis A., Kapsimalis V. Kontogianni A., Skourtos M. Turner R.K, Salomons W (2006)

Kardaras, D., & Karakostas, B. (1999). The use of fuzzy cognitive maps to simulate the

complex ecosystems. *Ecological Applications* vol. 12, pp.1548–1565.

Theory of Directed Graphs. John Wiley & Sons, New York.

Qualitative modeling techniques to assess patterns of global change. In: J.P. Kropp and J. Scheffran, Editors, *Advanced Methods for Decision Making and Risk Management in Sustainability Science*, Nova Science Publishers, Hauppage, NY,

Management: a Review of Principles, Contexts, Experiences and Opportunities.

development in policy making, *Environment development and sustainability* vol. 7, no.

analysis: lessons from four cases in water resources management. *Ecology and* 

natural resource management using cognitive mapping: the case of Damdoi Forest

(2002). Fuzzy cognitive mapping as a tool to define management objectives for

Agroforestry Management with Cognitive Maps. *Environmental Management* vol. 43,

Impact of 100-year human interventions on the Deltaik coastal zone of the inner Thermaikos Gulf (Greece): A DPSIR framework analysis. Journal of Environmental

information systems strategic planning process. *Information and Software Technology*,

with fuzzy cognitive maps. *Proceedings of the Third IEEE World Conference on Fuzzy* 

Fig. B2. The collective FCM with the eighteen most mentioned concepts and their related interconnections.

#### **13. References**


HAB

Tourism

0.525

MSW

0.525

.66

RP

**Bd**

**D-Distrust**

Fig. B2. The collective FCM with the eighteen most mentioned concepts and their related

Biloslavo, R.; & Dolinšek, S. (2010). Scenario planning for climate strategies development by

Brouwer, R.; Powe, N.; Turner, R.K.; Bateman, I.J. & Langford, I.H. (1999). Public Attitudes

Bougon, M., Weick, K., and Binkhorst, D. (1977). Cognition in organizations: An analysis of the Utrecht jazz orchestra. A*dministrative Science Quarterly*, vol. 22, pp.606-639. Bueno S., & J.L. Salmeron (2008). Fuzzy modeling Enterprise Resource Planning tool

Carley, K. & M. Palmquist, 1992. Extracting, representing, and analyzing mental models.

Contreras J., Juan P. Paz, David Amaya & Pineda, A. (2007). Realistic Ecosystem Modelling

with Fuzzy Cognitive Maps. *International Journal of Computational Intelligence* 

selection. *Computer Standards & Interfaces*, vol. 30 137–147.

*Research*. ISSN 0973-1873 Vol.3, No.2, pp. 139-144

Buzan T. (1993). *The Mind Map Book*. London: BBC Books

*Social Forces* vol. 70, pp.601–636.

integrating group Delphi, AHP and dynamic fuzzy cognitive maps. *Foresight,* vol.

to Contingent Valuation and Public Consultation. *Environmental Values* vol. 8 no. 3,

.51

MC

DFS

ISP


CD

0.35

S-Urban Sewage

**ECOL**

**red: -** 0.45

1 0.8 0.75 0.9

.6

HA

IA

0.7

AOSP

Sphi

LF

interconnections.

**13. References** 

12, no.2, pp. 38-48.

pp. 325-347

CW

PSA


[online URL: http://www. ecologyandsociety.org/vol13/iss1/art21/]


Using Fuzzy Cognitive Mapping in Environmental

*Computing,* vol. 11, pp. 500–513.

*Austral Ecology* vol. 34, pp. 409–421.

*Economics*, vol. 10(1-2), pp.70-81.

review. Biological Conservation 141: 2417–2431

pp. 905–916.

vol.90, pp.1933–1949

*Applications*, vol. 36, no. 10, pp.12399-12413.

Modeling and Assessment vol. 4, no. 4, pp. 295–314.

Putnam, R. A. (1985). Creating facts and values. *Philosophy* vol. 60, pp. 187-204

Decision Making and Management: A Methodological Primer and an Application 449

Papageorgiou E.I.; Markinos, Ath. & Gemtos, Th. (2009). Application of fuzzy cognitive

Papageorgiou E.Ι.; Markinos Ath. & Th. Gemtos, (2010). Soft Computing Technique of Fuzzy

Papageorgiou E.I. (2011). A new methodology for Decisions in Medical Informatics using

Petschel-Held G.; Block, A.; Cassel-Gintz, M.; Kropp, J.P.; Lüdeke, M.K.B.; Moldenhauer O.;

Rajaram T., & A. Das, (2009). Modeling of interactions among sustainability components of

Ramsey, D. & Norbury, G. L. (2009). Predicting the unexpected: using a qualitative model of

Reed M. S. (2008). Stakeholder participation for environmental management: A literature

Reed M. S.; Graves, A. Dandy, N. Posthumus, H. Hubacek, K. Morris, J. Prell, C. Quinn, C.

Robson, M. & Kant, S., (2007). Structure of causation and its influence on cooperation: A

Rodriguez-Repiso, L., Setchi, R., and Salmeron, J.L. (2007). Modelling IT projects success with Fuzzy Cognitive Maps. *Expert Systems with Applications*, vol. 32, pp. 543-559. Sharif, A.M. and Irani, Z. (2006). Exploring Fuzzy Cognitive Mapping for IS Evaluation.

Skov, F. & J. -C. Svenning, (2003). Predicting plant species richness in a managed forest.

W. Stach, L. A. Kurgan, and W. Pedrycz, Expert-based and Computational Methods for

Developing Fuzzy Cognitive Maps, In: Glykas, M., *Fuzzy Cognitive Maps: Advances in Theory, Methodologies and Applications*, Springer (ISBN-10: 36-42032-19-2), 2010. Stoney, C., & Winstanley, D., 2001. Stakeholding: confusion or utopia? Mapping the conceptual terrain. *Journal of Management Studies* vol.38, pp.603–626.

*European Journal of Operational Research*, vol. 173, pp.1175-1187.

*Forest Ecology and Management* vol. 6200, pp. 1–11.

maps for cotton yield management in precision farming, *Expert Systems with* 

Cognitive Maps to connect yield defining parameters with yield in Cotton Crop Production in Central Greece as a basis for a decision support system for precision agriculture application, in book: *Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools, Applications*, edited by M. Glykas, Springer Verlag, pp. 325-362.

Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction techniques, *Applied Soft* 

Reusswig F. & H.J. Schellnhuber, (1999). Syndromes of global change: a qualitative modeling approach to support environmental management, Environmental

an agro-ecosystem using local knowledge through cognitive mapping and fuzzy inference system, *Expert Systems with Applications,* vol. 37, no. 2, pp. 1734-1744. Ramsey D. & Veltman, C. (2005). Predicting the effects of perturbations on ecological

communities: what can qualitative models offer?*Journal of Animal Ecology* vol. 74,

a New Zealand dryland ecosystem to anticipate pest management outcomes.

H. & Stringer L. C. (2009). Who's in and why? A typology of stakeholder analysis methods for natural resource management. *Journal of Environmental Management*

comparative study of forest management in Ontario, Canada. *Forest Policy and* 


Khan, M.S., Quaddus, M.A., & Intrapairot, A. (2001). Application of a Fuzzy Cognitive Map

Kontogianni A., Tziritis I., Skourtos M. (2005), Bottom-up environmental decision making

Kontogianni A., M. Skourtos (2008), Social Perception of Risk informing Integrated Coastal

Kok K., (2009). The potential of Fuzzy Cognitive Maps for semi-quantitative scenario

Kosko, B. (1986). Fuzzy cognitive maps. *International Journal on Man-Machine Studies*, vol. 24,

Kosko, B. (1992). *Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to* 

Kouwen F. V.; P.P. Schot & M.J. Wassen, (2008). A framework for linking advanced

Montazemi, A.R.& Conrath D.W. (1986) The use of cognitive mapping for information

National Research Council (NRC). (1996). Understanding Risk: Informing Decisions in a

Novak J. D. (1993). How do we learn our lesson? Taking students through the process. The

Ozesmi, U. & S. L. Ozesmi, (2003). A participatory approach to ecosystem conservation:

Ozesmi, U. & S. L. Ozesmi, (2004). Ecological Models based on People's Knowledge: A Multi-Step Fuzzy Cognitive Mapping Approach. *Ecological Modeling* vol. 176,pp. 43–64. Ozesmi, U., (1999). Modeling ecosystems from local perspectives: Fuzzy cognitive maps of

Papageorgiou E.Ι. & Stylios C.D. (2008). Fuzzy Cognitive Maps, in book: *Handbook of* 

Fuzzy cognitive maps and stakeholder analysis in Uluabat Lake, Turkey.

the Kizilirmak Delta Wetlands in Turkey. *Proceedings of 1999 World Conference on* 

*Granular Computing*, editors: Witold Pedrycz, Andrzej Skowron and Vladik

Democratic Society. Washington, DC: National Academy Press.

OECD (2005). Evaluating Public Participation in Policy Making. Paris: OECD.

*Natural Resource Modelling*, Halifax, Nova Scotia, Canada

Kreinovich, Chapter 34, John Wiley & Sons, Ltd, pp. 755-775.

environmental change. Human Ecology Review 12.2, 87-95

Economics 37: 123-138, Elsevier Publishers

*Machine Intelligence*. Prentice-Hall. New York.

requirements analysis *MIS Quarterly*, March, 45-46

*Environmental Management* vol. 31,pp. 518–531.

*Software* vol. 23 no. 9, pp. 1133–1144.

Science Teacher 60: 50-55

(forthcoming)

122–133.

pp.65-75.

for Analysing Data Warehouse Diffusion. Applied Informatics: Artificial Intelligence and Applications; Advances in Computer Applications. *Proceedings of IASTED International Symposia*. 19-22 February, Innsbruck, Austria, pp.32-37. Kontogianni A., Skourtos M., Langford I., Bateman I., Georgiou S. (2001), Integrating

Stakeholder Analysis in non- market valuation of environmental assets, Ecological

taken seriously: Integrating stakeholder perceptions into scenarios of

Zone Management on accidental oil - spill pollution: 'the reason you pollute matters, not numbers', in book: "Integrated Coastal Zone Management – The Global Challenge" (eds) R. Krishnamurthy, B. Glavovic, A. Kannen, D. Green, R. Alagappan, H. Zengcui, S. Tinti, T. Agardy. Research Publishing pp. 207-225. Kontogianni A., Papageorgiou E., Salomatina L., Skourtos M., Zanou B., Assessing

perceptions of marine environmental futures through FCM in Ukrain

development, with an example from Brazil, *Global Environmental Change* vol.19, pp.

simulation models with interactive cognitive maps. *Environmental Modeling &* 


**22** 

Ingrid Muenstermann

*Australia* 

*Charles Sturt University, Wagga Wagga,* 

**Wind Farming and the Not-in-My-Backyard** 

**Syndrome: A Literature Review Regarding** 

Let me begin with a personal note. When several Australian newspapers reported about people objecting to the establishment of wind farms in rural and regional Australia, the Notin-my-Backyard syndrome (NIMBYism) entered my thoughts. Knowing that Australians were emitting more than reasonable amounts1 of greenhouse gasses into the atmosphere, this literature review was started. It is the result of trying to understand the objections to wind farming. I must admit that I like the turbines, their imposing height, the way they enhance the landscape, and their capacity to produce electricity. In 2002, I was standing under a turbine on the Isle of Fanø in Denmark, it was noisy but not overwhelming, I was in awe, admired a manmade product fitting perfectly into the landscape. Reading years later the objections to wind farming in Australia triggered concern and an interest into

According to the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2009, pp. 1-5) climate change is the greatest ecological, economic and social challenge of our time. Globally, CO2 emissions, temperature and sea levels are rising faster than expected and average temperatures are increasing (CSIRO, 2009, p. 3). These trends are recorded on all continents and in the ocean. "Since the Industrial Revolution global CO2 concentration has risen by 37%" which "is mainly due to fossil-fuel use and land-use change" (CSIRO, 2009: p. 5). CO2 is a contributing factor in the enhanced greenhouse effect which is resulting

Approximately 25% of the CO2 emitted in the atmosphere is absorbed by the ocean and another 25% is absorbed by the natural environment on land. In water, CO2 makes the oceans more acidic. Ocean acidification interferes with the formation of shells and corals, and has far reaching implications for the health and productivity of the world's oceans

The CSIRO also finds that "the likelihood of observed warming being due to natural causes

**1. Introduction** 

researching NIMBYism.

in climate change.

(CSIRO, 2009, p. 5).

alone is less than 5%" (p. 5). And they continue:

1 What constitutes a reasonable amount?

**Australia's Challenge in Relation to** 

**Climate Change and CO2 Emissions** 

