**Introduction**

[83] Bandura, A., & Cervone, D. (1983). Self-evaluative and Self-efficacy Mechanisms Governing the Motivational Effects of Goal Systems. *Journal of Personality and Social*

[84] Todd, P., & Benbasat, I. (1999). Evaluating the Impact of DSS, Cognitive Effort, and

[85] Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead times, and Infor‐

[86] Eierman, M., Niederman, F., & Adams, C. (1995). DSS Theory: A Model of Constructs

[87] Guimaraes, T., Igbaria, M., & Lu, M. (1992). The Determinants of DSS Success: An In‐

[88] Grabowski, M., & Sanborn, S. (2001). Evaluation of Embedded Intelligent Real-time

[89] De Lone, W., & Mc Lean, E. (1992). Information Systems Success: The Quest for the

[90] Lawrence, M., & Low, G. (1993). Exploring Individual Satisfaction Within Use-led

[91] Seddon, P. (1997). A Re-specification and Extension of the DeLone and McLean Mod‐

[92] Lawrence, M., Goodwin, P., & Fildes, R. (2002). Influence of User Participation on

[93] Goodwin, P., Fildes, R., Lawrence, M., & Stephens, G. (2011). Restrictiveness and

[94] Jarvenpaa, S. L. (1989). The Effect of Task Demands and Graphic Format on Informa‐

[95] Bannister, F., & Remenyi, D. (2000). Acts of Faith: Instinct, Value and IT Investment

[96] Lin, C., Huang, Y., & Burns, J. (2007). Realising B2B e-commerce Benefits: The Link with IT Maturity, Evaluation Practices, and B2BEC Adoption Readiness. *European*

Incentives on Strategy Selection. *Information Systems Research*, 10, 356-374.

*Psychology*, 41, 586-598.

196 Decision Support Systems

mation. *Management Science*, 46, 436-443.

and Relationships. *Decision Support Systems*, 14, 1-26.

Dependent Variable. *Information Systems Research*, 3, 60-95.

el of IS Success. *Information Systems Research*, 8, 240-253.

DSS Use and Decision Accuracy. *Omega*, 30, 381-392.

Guidance in Support Systems. *Omega*, 39, 242-253.

tion Processing Strategies. *Management Science*, 35, 285-303.

Decisions. *Journal of Information Technology*, 15, 231-241.

*Journal of Information Systems*, 16, 806-819.

tegrated model. *Decision Sciences*, 23, 409-430.

Systems. *Decision Sciences*, 32, 95-124.

Development. *MIS Quarterly*, 17, 195-208.

European legislation calls for a well-planned sustainable development. As such, it has to in‐ clude a social, economic as well as an environmental dimension. According to Agenda 21 (http://www.un.org/esa/dsd/agenda21/), countries should undertake efforts to build up a comprehensive national inventory of their land resources in order to establish land informa‐ tion systems. The overall objective is to provide information for the improvement or the re‐ structuring of land-use decision processes including the consideration of socio-economic and environmental issues.

In the last decades conflicts caused by competing land uses have increased, particularly in urban areas. Consequently, a lot of research has been done aiming to develop methods and tools that assist complex spatial decision problems. The development of Spatial Decision Support Systems (SDSS) has turned out to be very beneficial in assisting to the solution of complex land-use problems [1, 3].

In addition, any planning process must focus on a mix of hard (objective) and soft (subjec‐ tive) information. The former are derived from reported facts, quantitative estimates, and systematic opinion surveys. The soft information denotes the opinions (preferences, priori‐ ties, judgments, etc.) of the interest groups and decision makers. The idea of combining the objective and subjective elements of the planning process in a computer based system lies at the core of the concept of SDSS [1, 3].

SDSS can be defined as an interactive, computer-based system designed to support a user or a group of users in achieving a greater degree of effectiveness in decision making when solving a semi-structured spatial decision problem [3]. SDSS also refers to the combination of GIS and

sophisticated decision support methodologies, e.g. in terms of multicriteria analysis techni‐ ques [3, 6], and are therefore suitable to manage sustainable development of urban areas.

In this chapter, we compare the results obtained by the application of two distinctive landuse suitability analyses to the location of industrial sites, applying two different multicriteria analysis techniques. The multicriteria analysis employed has been performed in a raster en‐ vironment and been used for two objectives. During the site search analysis each pixel was considered a potential location alternative. This analysis used a SAW method which signi‐ fies a weighted summation. It can thus easily be performed in GIS [24, 25, 30, 31]. A site se‐ lection analysis then used the PROMETHEE-2 methodology [32] and a set of predefined alternatives [30, 33]. All of the techniques used in the project were coded and integrated

Comparison of Multicriteria Analysis Techniques for Environmental Decision Making on Industrial Location

http://dx.doi.org/10.5772/51222

199

A problem in the application of multicriteria analysis is the definition of weights for a given set of criteria. A variety of approaches does exist, see for example [26], and the probably best known weight evaluation method is the AHP [35], which we have used in our case as well.

Another problem is the specification of the criteria performance scores which are often sub‐ jective in their determination. Data which have been measured directly will certainly be re‐ garded as more reliable than data which have been estimated, interpolated, taken from a map or simply interpreted. Thus, the method of criteria data collection plays a central role [5]. A stochastic approach which takes account of the uncertainty of input values and which

Zaragoza city and its surroundings are located in the Ebro corridor, a highly dynamic eco‐ nomic area within the Iberian Peninsula. The climate in this area is semi-arid with mean an‐ nual precipitation of about 350 mm and a mean annual temperature of about 15° C.This city is crossed by the cited Ebro river and two of its main tributaries, the Gállego and Huerva rivers (Figure 1). Geologically, Quaternary alluvial terraces of the Ebro river were deposited above Tertiary gypsum formations, forming a covered karst area with intense karstification processes. The Quaternary materials are an important source of sand and gravel which are needed for civil engineering purposes. In addition, it hosts important groundwater reser‐

The availability of these resources has been one of the reasons of the fast development of the city in the last decades. But this fast development has also led to negative interactions with the environment and man-made infrastructure. Intense irrigation triggered land subsidence which in turn caused costly damage and/or destruction of infrastructure such as roads, buildings, gas and water supply networks [36]. Many infrastructures that have been built occupy areas where soils of high fertility had naturally developed, making these areas inac‐ cessible to agriculture. Also, many ecologically important areas have been harmed and an

is presented at a last step in this chapter could be a way out of this dilemma.

voirs, used for domestic, industrial and agricultural purposes.

increased contamination of the aquifer has been observed [37].

within ArcGIS by Marinoni [5, 34].

**1. Background and Methodology**

**1.1. Study area and project background**

Although the development of multicriteria analysis began mainly in the '70s (the first scien‐ tific meeting devoted entirely to decisionmaking was held in 1972 in South Carolina) its ori‐ gins can be dated back to the eighteenth century [4]. Reflections on French policies in the action of judges and their translation into policy (social choice), led people like Condorcet to deepen in decision taken supported in several criteria [4].

In the last two decades of the twentieth century there was an increased trend of integration of Multicriteria Evaluation techniques (MCE) and Geographic Information Systems (GIS), trying to solve some of the analytical shortcomings of GIS "For example see [4, 7, 15]". Wal‐ lenius et al. [16], made a study of the evolution in the use of MCE techniques from 1992 to 2006, showing that the use of multiattribute techniques has increased 4.2 times during this period. In recent years, there has also been a great effort in the integration of MCE and GIS techniques on the Internet "For example see [17, 20]".

Since we consider land-use decision making in general as an intrinsic multicriteria decision problem, in our opinion these are valid methodologies to support the land-use decision process by means of a land-use suitability analysis.

Land-use suitability analysis aims to identify the most appropriate spatial pattern for future land uses according to specified requirements or preferences [3, 21, 22]. GIS-based land-use suitability analyses have been applied in a wide variety of situations, including ecological and geological approaches, suitability for agricultural activities, environmental impact as‐ sessment, site selection for facilities, and regional planning [3, 6, 11, 17, 21,23, 28].

Different attempts to classify Multicriteria Decision Making (MCDM) methods by diverse authors exist in the literature [4, 6, 7, 11, 26, 29]. The majority of them agree that additive decision rules are the best known and most widely used Multiattribute Decision Making (MADM) methods in GIS based decision making. Some of the techniques more commonly described in literature are: Simple Additive Weighting (SAW), Ordered Weighting Averag‐ ing (OWA) technique, the Analytical Hierarchy Process (AHP), ideal point methods (e.g. TOPSIS), concordance methods or outranking techniques (e.g. PROMETHEE, Electre).

Nevertheless, the integration of these techniques continues to pose certain problems or diffi‐ culties at the time of developing specific applications. Among the most notable drawbacks are [4]:


In this chapter, we compare the results obtained by the application of two distinctive landuse suitability analyses to the location of industrial sites, applying two different multicriteria analysis techniques. The multicriteria analysis employed has been performed in a raster en‐ vironment and been used for two objectives. During the site search analysis each pixel was considered a potential location alternative. This analysis used a SAW method which signi‐ fies a weighted summation. It can thus easily be performed in GIS [24, 25, 30, 31]. A site se‐ lection analysis then used the PROMETHEE-2 methodology [32] and a set of predefined alternatives [30, 33]. All of the techniques used in the project were coded and integrated within ArcGIS by Marinoni [5, 34].

A problem in the application of multicriteria analysis is the definition of weights for a given set of criteria. A variety of approaches does exist, see for example [26], and the probably best known weight evaluation method is the AHP [35], which we have used in our case as well.

Another problem is the specification of the criteria performance scores which are often sub‐ jective in their determination. Data which have been measured directly will certainly be re‐ garded as more reliable than data which have been estimated, interpolated, taken from a map or simply interpreted. Thus, the method of criteria data collection plays a central role [5]. A stochastic approach which takes account of the uncertainty of input values and which is presented at a last step in this chapter could be a way out of this dilemma.
