T HE GOAL AND THE METHOD OF THE STUDY

The primary aim of the research is to help by the accurate and numerical assessing of the possibilities and to mark the important objectives, which could be applied in the future. This is necessary because when the empirical research was performed it was experienced that the Mayors and the notaries consider the tourism as the only survival chance for the settlements. They think it it easier to establish and develop the tourism than an industrial investment and they judge it could be a workplace-generating process as well. This is the reason, why we wanted to collect and demonstrate numerical data, which could be show that how many and what kind of investments are neccessary to perform the plans and whether there is a realistic chance to carry out them at all.

Also it is very important to cooperate with the local decision-makers to implement such useful projects, which could be submitted for the appropriate tender resources. The authors have applied several methods through the investigation, among them the complex application of the emphirical research was found to be the most effective. In consequence, questionnaire surveys and interviews were carried out in Hungary and in Austria as well, and statistical data were processed.

Among the literature relating to the closure of mines we can find the “Closure Risk Classification Model”, which is created by the experts of the University of New South Wales. It is a complex index, which contains several basic data:

CRF = Σ (RE + RSH + RC + RLU + RLF + RT),

where the CRF is the closure risk factor, the RE is the environmental risk, the RSH is the safety and health risk, the RC is the community risk, the RLU is the final land use risk, the RLF is the legal and financial risk, while the RT is the technical risk (Laurence, D. C. 2001). An advanced version of the above mentioned model is a complex indicator, which could be appropriate for modeling the characteristics and the facilities of the ex-mining areas. The data-rows of the index are complex thereby they can help by deciding, which factors should be taken into consideration and take advantage in the aspect whether of workplace-creation, or in the touristic utilization.

The first and second steps in the construction of composite indicators are the following: • Theoretical framework: A theoretical framework should be developed to provide the

basis for the selection and combination of single indicators into a meaningful composite indicator under a fitness-for-purpose principle.

• Data selection: Indicators should be selected on the basis of their analytical soundness, measurability, country coverage, relevance to the phenomenon being measured and relationship to each other. The use of proxy variables should be considered when data are scarce (OECD Handbook, 2008.).

The factor of the economical and social underprivileged areas (ESUA) was created by summarizing different indexes ordered into five main groups (Siskáné Szilasi, B. 2010). These are the followings:

1. Natural factors (NF)

- Agricultural areas (AA) - Forests (F)

- Meadows and other natural habitats (MONH) - Areas interfered with mining (AIM)

- Protected area (PA)

Because of the mining, the change of landuse is remarkable, and unfortunately there are many unsolved problems: one of them is the vegetation-recovering in the cultivation areas and on the surface of the spoils. The index would can be visualized on map aright thereby it is getting to be easily interpreted (Figure 2.).

Fig. 2 The land use of the sample area.

2. Social factor (characteristics) (SF) - Number of population (NP)

- The distribution of the popultion by age and gender (DPAG) - The educational level of the population (EP)

- The economic activity of the population, rate of unemployment (EAP-U) - The employment structure of the population (ESP)

Salgótarján, 04. – 06. October 2012

In the second group we could find such basic indexes, which demonstrate the general characteristics of the population. Due to the closure of the mines, several local workplaces have ceased and the qualified manpower and the youngsters have migrated. When performing a new project, we have to take into account the characteristics of the local population (Figures 3., 4.).

Fig. 3 The population of the settlements (2010) in the sample area

3. Built environment factor (BEF)

- Buildings are appropriate for further utilization, accomodations (BFU-A) - Infrastructural supply (IS)

- Situation of transport (ST) - Commercial supply (CS)

Fig. 4 The net migration of the settlements (2010) in the sample area

The built environment could be differ in the ex-mining areas from the other settlements, since building estates were built for the miners and these have an effect on the present image. The mining activity has facilitated the development of the infrastructure, the transport and the commercial supply of these settlements.

4. Cultural factor (charcteristics) (CF)

- Architectural relics, historical areas (AR-HA) - Monuments (M)

- Museums, exhibitions (M-E)

- The relics of the mining, identity, traditions (RM-I-T)

The cultural characteristics are individual as well, since the miner profession had got typical traditions, wear and lifestyle, it is especially important to preserve that in the aspect of tourism.

5. Economical factor (characteristis) (EF)

Salgótarján, 04. – 06. October 2012

- The economical situation of the population (ESP) - Workplace-creating investments (WCI)

- Enterprises (E)

Of course, one of the most important index-group contains the characteristics of the economical situation. The different size of the settlements has an effect on the data, so it is important to create dimensions and the unified metric.

In the construction of composite indicators the normalisation is very important. Indicators should be normalised to render them comparable. Attention need to be paid to extreme values as they may influence subsequent steps in the process of building a composite indicator. Skewed data should also be identified and accounted for (OECD Handbook, 2008.).

Different normalisation methods will produce different results for the composite indicator (Ranking; Standardisation; Min-Max; Indicators above or below the mean etc.). The authors demonstrate the theoretical background of the “ Indicators above or below the mean” normalisation type.

This transformation considers the indicators which are above and below an arbitrarily defined threshold, p, around the mean:

1 if w>(1+p) Itqc= 0 if (1-p) w (1+p) where w = 0 t c qc t qc x x -1 if w < (1-p)

The threshold p builds a neutral region around the mean, where the transformed indicator is zero. This reduces the sharp discontinuity, from -1 to +1, which exists across the mean value to two minor discontinuities, from -1 to 0 and from 0 to +1, across the thresholds. A larger number of thresholds could be created at different distances from the mean value, which might overlap with the categorical scales. An indicator that moved from significantly below the mean to significantly above the threshold in the consecutive year would have a positive effect on the composite (OECD Handbook, 2008.).

SUMMARY

The composite indicator should ideally measure multidimensional concepts which cannot be captured by a single indicator, e.g. competitiveness, industrialisation, sustainability, single market integration, knowledge-based society, etc (OECD Handbook, 2008.).

The factor of the economical and social underprivileged areas (ESUA) was created by summarizing different indexes ordered into five main groups (Siskáné Szilasi, B. 2010).