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A Study of finding Sites for Ecological Villages, Using Multi Criteria Evaluation
Md. Tauhid-Ur-Rahman
32, Brinnelvagen, Land and Water Resources Engg.,
Royal Institute of Technology,
KTH, Stockholm, Se-100 44, Sweden
Email : taurah98@kth.se
N. Bernard
32, Brinnelvagen, Land and Water Resources Engg.,
Royal Institute of Technology,
KTH, Stockholm, Se-100 44, Sweden
Abstract
Environmental risks and uncertainties of a high-energy future have become a great concern in the light of the need to develop sustainable community. Thus the search for an alternative to life in the city has led to the development of ecological villages, small societies with 50 to 100 households which are largely self reliant through the creation of integrated and ecological food and energy production systems. This study seeks to use decision support system embedded in Geographical Information Systems to find five sites for ecological villages in Stockholm. In order to ensure that the ecological villages function in a holistic, beneficial mutualism with the surrounding rural and natural environment, Multi Criteria Evaluation (MCE) technique was adopted for analysing the complex trade-offs between choice alternatives with different environmental and social-economic impacts. The findings indicate that most of these suitable sites are located in the agricultural land and pasture with small areas in other parts of the land use.
Introduction
Today, the use of natural resources is very high. Modern city life is highly dependent on energy and matter whose cost is high in terms of transportation, water and wastewater treatment, distribution of food and other commodities, heating and lightning of houses and offices among others. Life in the city thus causes both environmental problems as well as alienation to nature and ecological principles (Lorenz, 1973). The environmental risks and uncertainties of a high energy future are now disturbing. Thus according to the Brundtland Commission (WCED, 1987) and Agenda 21 a new land use planning strategy for sustainable housing and living is needed. And low energy path is thus seen as the best way towards a sustainable future. The search for an alternative to life in the city has led to the development of ecological villages, small societies with 50 to 100 households.
An ecological village is described as a community that is largely self reliant through the creation of integrated and ecological food and energy production systems. Ecological villages are situated near nature, and built up around local supply of energy, water and food (Eronn, 1991; Malbert, 1993; Berg, 1993; Kullinger and Stromberg, 1992; Wiberg, 1998). As a result, the use of natural resources in ecological villages decreases because the people take care of sewage and waste products with a circulation of nutrients and heat their houses with environmentally friendly sources. Thus over time the capacity of local communities to manage their resource base in a sustainable manner will increase. In addition it will provide families with food security, improved health, and increased income and reduce their dependence on outside assistance.
The idea of ecological village has been evolving over the years. For instance in Sweden during the 1970s, ecological villages (referred to as first generation ecological villages) were located on sites with favourable soil and moisture conditions for building and construction purposes as well as with beneficial microclimate. Those villages focused more on internal social and ecological patterns; environmentally accepted materials; low energy demand by sharing common space and structures among others. The second generation of ecological village, now being planned for, aim at finding local supply for primary food, but also for energy and water (Gunther, 1993). They also aim to minimize the need for transportation (cf. Harmaajärvi, 2000). And a pattern of villages comprising a rural center will be able to support social services, job places among others. A third generation of ecological villages will hence aim at functioning in a holistic, beneficial mutualism with the surrounding rural and natural environment. Now the question is how to select sites for ecological villages.
Currently, a variety of analytical techniques have been developed to help decision makers solve location problems with Multi Criteria. GIS software such as IDRISI has incorporated Multi Criteria Evaluation (MCE) and multi objective land evaluation in a decision support module (Eastman et al., 1993; Eastman, 1997). MCE and some others provide tools for analyzing the complex trade-offs between choice alternatives with different environmental and socio-economic impacts. MCE technique with GIS provides the user with means for evaluating various alternatives on the basis of multiple and conflicting criteria and objectives. The criteria can be either constraints or factors. This study therefore seeks to find spots for ecological villages in Stockholm using criteria such as nature reserve, energy, food, water waste and sewage, service and job places radon-gas risk and technical accessibility.
Data and method
The study area is located in southwest of Stockholm and it includes the towns of Sodertalje and Tumba. The data used for this project are summarised in table 1 below
Table 1: A summary of Data used
To find suitable spots for the ecological villages certain factors and constraints were seriously taken into consideration in the evaluation. Based on the evaluation of suggested criteria, four important factors that facilitate ecological villages were identified. These factors include, close proximity to agricultural areas (food), proximity to aquifer (water), proximity to wetland (waste and sewage) and proximity to service centres (jobs and services). Other factors that restrict the usage of an area (called constraints) were also noted. They were technical accessibility (which prevents residential areas to be built on areas regarded as inaccessible), radon-gas risk areas, higher grounds, areas close to beaches and natural reserves.
All that is needed for the constraints is the creation of Boolean image -an image containing only zeros (excluded areas) and ones (permitted areas). These images were created by dividing the maps containing the constraints into two, one for unacceptable area (zeros) and one for the acceptable ones
Since we are interested in finding 5 areas, each being able to house 50 to 100 families, areas required should be about 0.25km2 to 0.5km2 in size. This means that small areas need to be excluded. This was achieved by using appropriate series of methods in Idrisi to get the size of the individual areas in hectares. Areas below a certain threshold were removed to get maps of extensive areas. For instance, thresholds set for factors like agricultural areas; aquifers, wetlands etc. were areas larger than 20, 5, and 5 hectares respectively. Distances on these maps were calculated based on the scores according to proximity to these desired factors (resources). The factor maps were then stretched to get the interval score of 0-255. These stretched maps with low values for close proximity were inverted to obtain high scores for close vicinity.
After checking all the criteria maps, a set of weights were developed for the factors based on relative importance. These weights were real numbers ranging from 1/9 to 9/1 that sum to 1.0. In the matrix below, agric was assigned as the most important, followed by aquifer, job and wetland as the least important. The exercise produced respective eigenvector (values) of weights that show the relative importance of the factors with the most important one having higher score and likewise for the least.
To test the robustness of this decision rule, the weights assigned to the factors were slightly changed (as shown in fig.2 below). This means that importance attached to the factors by way of values were changed. Here aquifer was the highly important one, followed by agriculture, wetland and job in that order.
Matrices for the weights have been summarised below. Figure 1 is the original weights whiles figure 2 represents the slightly altered weights.
Fig 1: matrix for relative importance for pairwise factors
Fig 2: matrix for relative importance for pairwise factors (2nd iteration)
Multi criteria evaluation (mce) was then conducted on the factors and the constraints to get the statistics for the chosen areas. The statistics comprises size (hectares), average, standard deviation, mean, variance etc. Best areas were selected based on their average score, the area (size), mean and variance values. This was done manually by assessing the summary statistics of the different sites. Five areas that are quite extensive in size and have higher average values were selected as the suitable sites.
This methodology has been summarised and may be visually comprehended from the macro modeler below:
Figure 3: Macro modeler
Results
In all, four factors and five constraints were identified. The factors included agriculture, job, wetland and aquifer. Beach, nature reserve, radon-gas risk, technical accessibility and slope were the possible constraints considered for this project.
The factor maps created from the data provided consisted of fragments of plots scattered all over. The maps showing only areas higher than the preset threshold indicated all the areas capable of supporting eco-village. But when the weights were assigned to the factors the areas were limited to those that are more suitable. In the first weights, agriculture is noted to be more important than the other factors and that resulted in five suitable sites. When the robustness of the decision rule was tested by altering the weights, the results showed 4 sites but did not deviate greatly from the original weights results. This can be visually assessed in the images below:
Figure 4: The two outcomes of the MCE and MCE_2(altered)
Final map created from the combination of the suitable sites and the land use map composes of different land use types. Among the suitable sites, there are areas that are more recommended than the others. This is due to their geographical extent and average scores. The suitable sites have therefore, been ranked from best to good. They are indicated on the map by arrows. From Figure 5, the relatively best sites for ecological village are mostly located far away from the highly exploited areas whiles the least good ones are quite close to these areas.
Figure 5: image showing five suitable sites for eco-village
Table 2: Areas and average scores of the chosen sites
The total area seems to be dominated by agricultural lands and pasture with total land area of about 5690 hectares followed by water bodies (4259 ha).
The results obtained indicated that all the chosen areas are surrounded by agricultural lands and forest and also nearer to water bodies.
Discussion
The factors and the constraints are the two criteria used to create this suitability map. The factors seek to enhance the suitability whiles the constraint serves to limit the alternatives. When the factors were introduced to weighting based on their importance, agriculture was noted to be the most important and wetland, the least. This could be attributed to how important food is in the society. Wetland, for instance, for waste and sewage is also important but not as important as food, job and aquifer (water).
In the first weighting, agriculture was regarded as strongly important but not very or extremely important than the others due to its dependence on some of the factors like aquifer (water) and wetland (waste and sewage). The outcome of these weights indicated a suitability map of five sites. Since one of the objectives of this project is to evaluate the robustness of the decision rule, the weights were slightly changed to see whether or not significant changes would occur. In fact, the alteration of the weights caused a slight changes but not a dramatic one. The decision rule is therefore regarded as a robust since only slight changes occurred when the weights were slightly altered. In figure 4 above, it is realised that three out of five sites were at the same spots on both images, indicating that they are more similar than different.
The suitable areas seem to be located in areas dominated by agriculture and pasture on the final image (fig.5). This is because agriculture was the most important factor and also covers large part of the area. Even though the selection of the suitable sites was based on the area and the average scores, the size of the areas was used more than the average scores. This eco-village is being developed to house 50-100 families and therefore requires quite a large area. That is the reason why areas of large sizes but smaller average values were also considered. Apart from using the area coverage in ranking the sites, the results showed that the best sites for eco-village are located far away from the highly exploited areas. This confirms the idea that the ecological villages are situated near nature, local supply of energy, food and water (Eronn, 1991; Malbert, 1993; Berg, 1993; Kullinger and Stromberg, 1992; Wiberg, 1998).
Conclusion
Multi criteria evaluation is a valuable tool to solve problems of this caliber. The finding of this exercise throws much light on differences that exist between different sites in terms susceptibility to a particular problem or suitability for a phenomenon.
The results of this particular exercise indicated that the proposed areas are the best areas that should be designated for eco-village development. They are found in areas closer to most of the above-named factors like agriculture, water, jobs etc. and are capable of hosting 50-100 families. Since all the constraints were identified before citing the appropriate areas, there is no effect from any constraint.
The maps generated from the original weights and altered weights look similar but not the same. The difference between them measures the impact of the alteration. In this project, the weight changes caused reduction in the number of proposed sites as well as the size of some sites( as shown in Fig 4). It could therefore, be concluded that the use of multi criteria evaluation is the best tool for such problems and also its decision rule is robust in outlook.
Reference:
A Study of finding Sites for Ecological Villages, Using Multi Criteria Evaluation
Md. Tauhid-Ur-Rahman
32, Brinnelvagen, Land and Water Resources Engg.,
Royal Institute of Technology,
KTH, Stockholm, Se-100 44, Sweden
Email : taurah98@kth.se
N. Bernard
32, Brinnelvagen, Land and Water Resources Engg.,
Royal Institute of Technology,
KTH, Stockholm, Se-100 44, Sweden
Abstract
Environmental risks and uncertainties of a high-energy future have become a great concern in the light of the need to develop sustainable community. Thus the search for an alternative to life in the city has led to the development of ecological villages, small societies with 50 to 100 households which are largely self reliant through the creation of integrated and ecological food and energy production systems. This study seeks to use decision support system embedded in Geographical Information Systems to find five sites for ecological villages in Stockholm. In order to ensure that the ecological villages function in a holistic, beneficial mutualism with the surrounding rural and natural environment, Multi Criteria Evaluation (MCE) technique was adopted for analysing the complex trade-offs between choice alternatives with different environmental and social-economic impacts. The findings indicate that most of these suitable sites are located in the agricultural land and pasture with small areas in other parts of the land use.
Introduction
Today, the use of natural resources is very high. Modern city life is highly dependent on energy and matter whose cost is high in terms of transportation, water and wastewater treatment, distribution of food and other commodities, heating and lightning of houses and offices among others. Life in the city thus causes both environmental problems as well as alienation to nature and ecological principles (Lorenz, 1973). The environmental risks and uncertainties of a high energy future are now disturbing. Thus according to the Brundtland Commission (WCED, 1987) and Agenda 21 a new land use planning strategy for sustainable housing and living is needed. And low energy path is thus seen as the best way towards a sustainable future. The search for an alternative to life in the city has led to the development of ecological villages, small societies with 50 to 100 households.
An ecological village is described as a community that is largely self reliant through the creation of integrated and ecological food and energy production systems. Ecological villages are situated near nature, and built up around local supply of energy, water and food (Eronn, 1991; Malbert, 1993; Berg, 1993; Kullinger and Stromberg, 1992; Wiberg, 1998). As a result, the use of natural resources in ecological villages decreases because the people take care of sewage and waste products with a circulation of nutrients and heat their houses with environmentally friendly sources. Thus over time the capacity of local communities to manage their resource base in a sustainable manner will increase. In addition it will provide families with food security, improved health, and increased income and reduce their dependence on outside assistance.
The idea of ecological village has been evolving over the years. For instance in Sweden during the 1970s, ecological villages (referred to as first generation ecological villages) were located on sites with favourable soil and moisture conditions for building and construction purposes as well as with beneficial microclimate. Those villages focused more on internal social and ecological patterns; environmentally accepted materials; low energy demand by sharing common space and structures among others. The second generation of ecological village, now being planned for, aim at finding local supply for primary food, but also for energy and water (Gunther, 1993). They also aim to minimize the need for transportation (cf. Harmaajärvi, 2000). And a pattern of villages comprising a rural center will be able to support social services, job places among others. A third generation of ecological villages will hence aim at functioning in a holistic, beneficial mutualism with the surrounding rural and natural environment. Now the question is how to select sites for ecological villages.
Currently, a variety of analytical techniques have been developed to help decision makers solve location problems with Multi Criteria. GIS software such as IDRISI has incorporated Multi Criteria Evaluation (MCE) and multi objective land evaluation in a decision support module (Eastman et al., 1993; Eastman, 1997). MCE and some others provide tools for analyzing the complex trade-offs between choice alternatives with different environmental and socio-economic impacts. MCE technique with GIS provides the user with means for evaluating various alternatives on the basis of multiple and conflicting criteria and objectives. The criteria can be either constraints or factors. This study therefore seeks to find spots for ecological villages in Stockholm using criteria such as nature reserve, energy, food, water waste and sewage, service and job places radon-gas risk and technical accessibility.
Data and method
The study area is located in southwest of Stockholm and it includes the towns of Sodertalje and Tumba. The data used for this project are summarised in table 1 below
Table 1: A summary of Data used
| Serial No. | Data type | Data description |
| 1 | Land use | Data of different ways the land has been put to use. It ranges from uncovered to covered areas and low exploited to high exploited areas. |
| 2 | Soils | Information on various categories of soils within the study area. Examples include clay, silt, sand, gravel till etc. |
| 3 | Elevation | Information on the topography of the area. |
| 4 | Bedrock | Data on the types of underlying or parent rock in the area |
To find suitable spots for the ecological villages certain factors and constraints were seriously taken into consideration in the evaluation. Based on the evaluation of suggested criteria, four important factors that facilitate ecological villages were identified. These factors include, close proximity to agricultural areas (food), proximity to aquifer (water), proximity to wetland (waste and sewage) and proximity to service centres (jobs and services). Other factors that restrict the usage of an area (called constraints) were also noted. They were technical accessibility (which prevents residential areas to be built on areas regarded as inaccessible), radon-gas risk areas, higher grounds, areas close to beaches and natural reserves.
All that is needed for the constraints is the creation of Boolean image -an image containing only zeros (excluded areas) and ones (permitted areas). These images were created by dividing the maps containing the constraints into two, one for unacceptable area (zeros) and one for the acceptable ones
Since we are interested in finding 5 areas, each being able to house 50 to 100 families, areas required should be about 0.25km2 to 0.5km2 in size. This means that small areas need to be excluded. This was achieved by using appropriate series of methods in Idrisi to get the size of the individual areas in hectares. Areas below a certain threshold were removed to get maps of extensive areas. For instance, thresholds set for factors like agricultural areas; aquifers, wetlands etc. were areas larger than 20, 5, and 5 hectares respectively. Distances on these maps were calculated based on the scores according to proximity to these desired factors (resources). The factor maps were then stretched to get the interval score of 0-255. These stretched maps with low values for close proximity were inverted to obtain high scores for close vicinity.
After checking all the criteria maps, a set of weights were developed for the factors based on relative importance. These weights were real numbers ranging from 1/9 to 9/1 that sum to 1.0. In the matrix below, agric was assigned as the most important, followed by aquifer, job and wetland as the least important. The exercise produced respective eigenvector (values) of weights that show the relative importance of the factors with the most important one having higher score and likewise for the least.
To test the robustness of this decision rule, the weights assigned to the factors were slightly changed (as shown in fig.2 below). This means that importance attached to the factors by way of values were changed. Here aquifer was the highly important one, followed by agriculture, wetland and job in that order.
Matrices for the weights have been summarised below. Figure 1 is the original weights whiles figure 2 represents the slightly altered weights.
| Job | Agric | Aquifer | Wetland | |
| Job | 1 | |||
| Agric | 5 | 1 | ||
| Aquifer | 3 | 0.33 | 1 | |
| Wetland | 0.33 | 0.142 | 0.20 | 1 |
| Aquifer | Agric | Job | Wetland | |
| Aquifer | 1 | |||
| Agric | 0.2 | 1 | ||
| Job | 0.142 | 0.33 | 1 | |
| Wetland | 0.33 | 3 | 5 | 1 |
Multi criteria evaluation (mce) was then conducted on the factors and the constraints to get the statistics for the chosen areas. The statistics comprises size (hectares), average, standard deviation, mean, variance etc. Best areas were selected based on their average score, the area (size), mean and variance values. This was done manually by assessing the summary statistics of the different sites. Five areas that are quite extensive in size and have higher average values were selected as the suitable sites.
This methodology has been summarised and may be visually comprehended from the macro modeler below:
Figure 3: Macro modeler
Results
In all, four factors and five constraints were identified. The factors included agriculture, job, wetland and aquifer. Beach, nature reserve, radon-gas risk, technical accessibility and slope were the possible constraints considered for this project.
The factor maps created from the data provided consisted of fragments of plots scattered all over. The maps showing only areas higher than the preset threshold indicated all the areas capable of supporting eco-village. But when the weights were assigned to the factors the areas were limited to those that are more suitable. In the first weights, agriculture is noted to be more important than the other factors and that resulted in five suitable sites. When the robustness of the decision rule was tested by altering the weights, the results showed 4 sites but did not deviate greatly from the original weights results. This can be visually assessed in the images below:
Figure 4: The two outcomes of the MCE and MCE_2(altered)
Final map created from the combination of the suitable sites and the land use map composes of different land use types. Among the suitable sites, there are areas that are more recommended than the others. This is due to their geographical extent and average scores. The suitable sites have therefore, been ranked from best to good. They are indicated on the map by arrows. From Figure 5, the relatively best sites for ecological village are mostly located far away from the highly exploited areas whiles the least good ones are quite close to these areas.
Figure 5: image showing five suitable sites for eco-village
| Sites | Area(ha) | Average Scores |
| Best | 144.2 | 236.7 |
| Better | 127 | 240.6 |
| Very good | 105.7 | 229.7 |
| Quite good | 93.7 | 237.6 |
| Good | 84.75 | 236.4 |
The total area seems to be dominated by agricultural lands and pasture with total land area of about 5690 hectares followed by water bodies (4259 ha).
The results obtained indicated that all the chosen areas are surrounded by agricultural lands and forest and also nearer to water bodies.
Discussion
The factors and the constraints are the two criteria used to create this suitability map. The factors seek to enhance the suitability whiles the constraint serves to limit the alternatives. When the factors were introduced to weighting based on their importance, agriculture was noted to be the most important and wetland, the least. This could be attributed to how important food is in the society. Wetland, for instance, for waste and sewage is also important but not as important as food, job and aquifer (water).
In the first weighting, agriculture was regarded as strongly important but not very or extremely important than the others due to its dependence on some of the factors like aquifer (water) and wetland (waste and sewage). The outcome of these weights indicated a suitability map of five sites. Since one of the objectives of this project is to evaluate the robustness of the decision rule, the weights were slightly changed to see whether or not significant changes would occur. In fact, the alteration of the weights caused a slight changes but not a dramatic one. The decision rule is therefore regarded as a robust since only slight changes occurred when the weights were slightly altered. In figure 4 above, it is realised that three out of five sites were at the same spots on both images, indicating that they are more similar than different.
The suitable areas seem to be located in areas dominated by agriculture and pasture on the final image (fig.5). This is because agriculture was the most important factor and also covers large part of the area. Even though the selection of the suitable sites was based on the area and the average scores, the size of the areas was used more than the average scores. This eco-village is being developed to house 50-100 families and therefore requires quite a large area. That is the reason why areas of large sizes but smaller average values were also considered. Apart from using the area coverage in ranking the sites, the results showed that the best sites for eco-village are located far away from the highly exploited areas. This confirms the idea that the ecological villages are situated near nature, local supply of energy, food and water (Eronn, 1991; Malbert, 1993; Berg, 1993; Kullinger and Stromberg, 1992; Wiberg, 1998).
Conclusion
Multi criteria evaluation is a valuable tool to solve problems of this caliber. The finding of this exercise throws much light on differences that exist between different sites in terms susceptibility to a particular problem or suitability for a phenomenon.
The results of this particular exercise indicated that the proposed areas are the best areas that should be designated for eco-village development. They are found in areas closer to most of the above-named factors like agriculture, water, jobs etc. and are capable of hosting 50-100 families. Since all the constraints were identified before citing the appropriate areas, there is no effect from any constraint.
The maps generated from the original weights and altered weights look similar but not the same. The difference between them measures the impact of the alteration. In this project, the weight changes caused reduction in the number of proposed sites as well as the size of some sites( as shown in Fig 4). It could therefore, be concluded that the use of multi criteria evaluation is the best tool for such problems and also its decision rule is robust in outlook.
Reference:
- BERG PG (1993) Biologi och bosättning. Naturanpassning i samhällsbyggandet. Natur och kultur/Institutet för framtidsstudier
- EASTMAN JR (1997) IDRISI for Windows. User's Guide, Version 2.0. Clark University, USA.
- EASTMAN JR, PAK KYEM, J TOLEDANO, W JIN (1993) GIS and decision making. United Nations Institute for Training and Research. Explorations in Geographic Information
- ERONN R (1991) Ecological living in Sweden - Ideas and practical experiences. The Swedish Institute. Current Sweden No 378.
- GUMBRICHT T AND MCCARTHY (2003) Multi Criteria Evaluation of Sites for Ecological Villages. Natural Resource Management, KTH
- GÜNTHER F (1993) Systemekologi och samhällsbyggande. In Berg, P.G., (Ed), 1993. Biologi och bosättning. Naturanpassning i samhällsbyggandet. Natur och kultur/Institutet för framtidsstudier. pp 113-154.
- HARMAAJÄRVI I (2000) EcoBalance model for assessing sustainability in residential areas and relevant case studies in Finland. Environmental impact assessment review, 20: 373-380. KULLINGER B AND UB STRÖMBERG (EDS) (1992) Planera för en bärkraftig utveckling. Byggforskningsrådet, Stockholm.
- LORENZ K (1973) Die acht Todsünden der zivilisierten Menchheit. R. Piper & Co. Verlag, München
- MALBERT B (ED) (1993) Ekologiska utgångspunkter för planering och byggande. Byggforskningsrådet, Stockholm.
- WCED (1987) Our common future.
- WIBERG K (1998) Ecological villages, a European reality. Renewable energy, 15: 101-106.