Academy of Economic Studies of Bucharest

PUBLIC INVESTMENTS EVALUATION - MULTI-CRITERIA ANALYSIS

introduction

Any capital placement on long or short time, with the purpose of obtaining profit, is an investment. No matter if the funds are placed in fixed assets or floating assets, the investment is an important capital placement, as volume and time, its purpose being a future optimal profitability.
Financially, the investment are the total amount of spending generated when replacing the used fixed assets, building, reconstruction, rehabilitation or improvement of the assets or production capacities. They are similar with the current activity investments and spending, geological prospecting, designing different investment projects, documentation spending, transport and remaking of the transferred fixed assets. Also, are included site management spending and personal training spending.

Project stages:

Option analysis and feasibility - It is known that feasibility doesn't mean only technical aspects, but in many cases, includes marketing, management and implementation aspects.

Financial analysis - Generated cash flow forecast for implementing the project helps us to calculate and analyze different rates demanded by the owner - financial profitability rate (FRR) and Financial net present value. (FNPV).

Economical analysis - Social costs and benefits are main aspects when analyzing a proposed project through European funds. Economical analysis, the instrument that includes all that, is the method which establishes the project contribution at the region or country welfare.

Multi-criteria analysis - Is, maybe, the most important part of the Feasibility study, because it analysis and makes a hierarchy of the project depending on its general and specific objectives.  This can make the difference between an eligible and not eligible project.

Risk and sensitivity analysis - The purpose is to select the critical variables that may determine the deviations from the best estimation. If those variables are determined, it can be determined actions to control those variables. This section is very important for investor, that why is underlined this part, being an important guide for project implementation.

Multi-criteria decision making (MCDM) methods deal with the process of making decisions in the presence of multiple objectives. A decision-maker is required to choose among quantifiable or non-quantifiable and multiple criteria. The objectives are usually conflicting and therefore, the solution is highly dependent on the preferences of the decision-maker and must be a compromise. In most of the cases, different groups of decision-makers are involved in the process. Each group brings along different criteria and points of view, which must be resolved within a framework of understanding and mutual compromise. Applications of MCDM include areas such as integrated manufacturing systems Putrus P. et All, 1990) evaluations of technology investment (Boucher TO et all, 1991), water and agriculture management (Ozelkan EC, 1996), in addition to energy planning (Afgan NH et all, 1998, 2000).

Multi-Criteria Decision Making is a well known branch of decision making. It is a branch of a general class of operations research models which deal with decision problems under the presence of a number of decision criteria. This major class of models is very often called MCDM. This class is further divided into multi-objective decision making (MODM) and multi-attribute decision making (MADM) (Climaco J et. All,1997). There are several methods in each of the above categories. Priority based, outranking, distance based and mixed methods are also applied to various problems. Each method has its own characteristics and the methods can also be classified as deterministic, stochastic and fuzzy methods. There may be combinations of the above methods. Depending upon the number of decision makers, the methods can be classified as single or group decision making methods. Decision making under uncertainty and decision support systems are also prominent decision making techniques (Gal T. et all, 1999).

2. literature review

These methodologies share common characteristics of conflict among criteria, incomparable units, and difficulties in selection of alternatives. In multiple objective decision making, the alternatives are not predetermined but instead a set of objective functions is optimized subject to a set of constraints. The most satisfactory and efficient solution is sought. In this identified efficient solution it is not possible to improve the performance of any objective without degrading the performance of at least one other objective. In multiple attribute decision making, a small number of alternatives are to be evaluated against a set of attributes which are often hard to quantify. The best alternative is usually selected by making comparisons between alternatives with respect to each attribute.

The different methods are described as follows.

Weighted sum method (WSM)

The WSM is the most commonly used approach, especially in single dimensional problems. If there are M alternatives and N criteria then the best alternative is the one that satisfies the following expression:

for i=1,2,3.M (1)

Weighted product method (WPM)

The WPM is very similar to WSM . The main difference is that instead of addition in the model there is multiplication. Each alternative is compared with the others by multiplying a number of ratios, one for each criterion. Each ratio is raised to the power equivalent to the relative weight of the corresponding criterion. In general, in order to compare the alternatives AK and AL the following product is obtained:

(2)

Analytical hierarchy process (AHP)

The essence of the process is decomposition of a complex problem into a hierarchy with goal (objective) at the top of the hierarchy, criterions and sub-criterions at levels and sub-levels of the hierarchy, and decision alternatives at the bottom of the hierarchy. Elements at given hierarchy level are compared in pairs to assess their relative preference with respect to each of the elements at the next higher level.

(3)

After obtaining the weight vector, it is then multiplied with the weight coefficient of the element at a higher level (that was used as criterion for pair wise comparisons). The procedure is repeated upward for each level, until the top of the hierarchy is reached. The overall weight coefficient, with respect to goal for each decision alternative is then obtained. The alternative with the highest weight coefficient value should be taken as the best alternative.

Preference ranking organization method for enrichment evaluation (PROMETHEE)

This method uses the outranking principle to rank the alternatives, combined with the ease of use and decreased complexity. It performs a pair-wise comparison of alternatives in order to rank them with respect to a number of criteria. Brans et al. have offered six generalized criteria functions for reference namely, usual criterion, quasi criterion, criterion with linear preference, level criterion, criterion with linear preference and indifference area, and Gaussian criterion.

(4)

(5)

(6)

(7)

The elimination and choice translating reality (ELECTRE)

This method is capable of handling discrete criteria of both quantitative and qualitative in nature and provides complete ordering of the alternatives. The problem is to be so formulated that it chooses alternatives that are preferred over most of the criteria and that do not cause an unacceptable level of discontent for any of the criteria. It is defined as follows.

(8)

The technique for order preference by similarity to ideal solutions (TOPSIS)

This method is developed by Huang and Yoon as an alternative to ELECTRE. The basic concept of this method is that the selected alternative should have the shortest distance from the negative ideal solution in geometrical sense. The method assumes that each attribute has a monotonically increasing or decreasing utility. This makes it easy to locate the ideal and negative ideal solutions.

Compromise programming (CP)

Compromise Programming defines the best solution as the one in the set of efficient solutions whose point is the least distance from an ideal point Zeleny M. et al, 1982).

(9)

Multi-attribute utility theory (MAUT)

Multi-attribute Utility Theory takes into consideration the decision maker's preferences in the form of the utility function which is defined over a set of attributes. The utility value can be determined by determination of single attribute utility functions followed by verification of preferential and utility independent conditions and derivation of multi-attribute utility functions. The utility functions can be either additively separable or multiplicatively separable with respect to single attribute utility. The multiplicative form of equation for the utility value is defined as:

3. empirical case study

Waste generation is an element of the everyday life of humans. All human activities generate waste, meaning condensed work, energy, and natural resources in the form of end-of-life products. However, the completion of the lifetime of a certain product means that it has lost its value in its present form, but the natural resources, energy and work that were allocated to generate this product are still locked in it.

The so called developed countries soon realized that the production of millions of new products each year as well as the great increase in consumption create two parallel trends. The waste quantities are exponentially increased and also the variety and difference of the various waste streams is exponentially increased.

In Romania municipal waste seem to increase by approximately 1 - 2 % annually. If this is the case then in few years it will be extremely hard and expensive to manage municipal solid waste in a sustainable manner. And this situation is seen in most developing countries.

In general solid waste management needs to be environmentally efficient, economically affordable and socially acceptable. Solid waste management systems need to ensure human health and safety. The environmental impacts of waste management, including energy consumption, depletion of natural resources, pollution of air, land and water and loss of amenity need to be minimized. This is the main reason why the EC policy on waste is turning from waste management to resource management and utilization. The cornerstone of this policy is addressed by the following requirements:

Waste prevention

Increase of waste reuse, recycling and energy utilization

Drastic reduction of the biodegradable waste that is diverted to landfill

The alternative scenarios will refer to three major categories:

Alternatives in terms of technical waste treatment / management

Alternative locations of waste management infrastructure (referring mainly to waste treatment plants, material recovery and recycling facilities, waste transfer stations and landfills)

Alternative solutions in terms of the settlements that will receive common waste management (management zones) as well as the number/capacity of waste management facilities (referring mainly to waste treatment plants, material recovery and recycling facilities, transfer stations and landfills)

The selection of the appropriate location of waste management infrastructure and especially for waste landfills and treatment plants has always been a tricky part in every integrated waste management system. The Not In My back Yard (NIMBY) and even worse the Built Absolutely Nothing Anywhere Near Anyone (BANANA) Syndromes may generate significant problems in finding a suitable location for the development of the waste management infrastructure.

In this framework, it is necessary for the selection of the location of the treatment and disposal facilities to be transparent based on solid technical, environmental and financial criteria. Moreover the development of the infrastructure should be such in order to ensure the absolute protection of the environment and public health.

In this way the selection may be accepted by the public and future delays in the actual development of the disposal and treatment facilities will be avoided.

The methodology for the selection of the locations of the main waste management infrastructure will consist of a set of exclusion and selection criteria.

It is noted that the existing specifications for the waste treatment infrastructure are strict enough to allow their development close to urban areas, cultural sites, environmental protected areas, etc. However, this is usually avoided in order to reduce potential public opposition.

Usually the location of waste treatment and disposal facilities is not:

In areas of archaeological and cultural interest

In traditional areas

In protected natural areas (SPA, NATURA 2000, etc)

Near residential areas

In forests

In areas with specific land uses such as:

o      Urban development

o      Sports and leisure infrastructure development

o      Constantly irrigated areas

o      Vineyards

o      Crop land

o      Industrial zones

Besides these general criteria the exclusion and selection criteria presented in Annex 5.2 are proposed to be used for the selection of appropriate locations for the waste treatment and disposal facilities.

Geological - Hydro geological - Hydrological criteria

o      Criterion EC1 - Minimum distance from river bed or large ghylls: in order to avoid the pollution of surface and groundwater the minimum proposed distances from river and ghyll beds is 1 km. For waste treatment infrastructure this distance limit could be reduced to 0.5 km

o      Criterion EC2 - Minimum distance from water sources: in order to avoid the pollution of surface and groundwater the minimum proposed distances from water sources is 0,5 km

o      Criterion EC3 - Minimum distance from lakes: in order to avoid the pollution of surface and groundwater the minimum proposed distances from lakes is 1 km. For waste treatment infrastructure this distance limit could be reduced to 0.5 km

o      Criterion EC4 - Distance from seismic fault: in ideal conditions, no infrastructure should be developed in seismic areas, due to the fact that severe damages could occur. However since Romania is a seismic area this cannot be the case, however a criterion concerning the minimum distance from earthquake gaps is introduced. The minimum proposed distance is 0.5 km.

Physical planning criteria

o         Criterion EC4 - Minimum distance from residential areas: the minimum proposed distances from residential areas is 1 km. For transfer stations the limit of 0.5 km could be applied

o         Criterion EC5 - Minimum distance from archaeological and cultural monuments: the minimum proposed distances from such areas is 0.5 km. In addition the waste management infrastructure should not be visible by such areas, in order not to deteriorate the cultural heritage of the area

o         Criterion EC6 - Minimum distance from military installations: the minimum proposed distances from military infrastructure is 1 km.

Techno-economic criterion

o         Criterion EC Maximum distance from current road network the solutions, which are located far from the exiting road network, should be avoided. In this respect the proposed maximum distance from the road network is 20 km. For transfer stations the distance from existing road infrastructure should not exceed 2 km. For waste treatment plans the distance from existing road infrastructure should not exceed 10 km

o         Criterion EC9 - Maximum distance from major waste generators in order to reduce the transportation costs the waste management infrastructure should be located close to the main waste generators (in terms of averages). This distance should not exceed km

o         Criterion EC10 Maximum distance from existing public utilities (mainly for waste treatment plants) the existence of public utilities is necessary for the operation of waste management infrastructure and especially for treatment plants. For this purpose the proposed maximum distance from public utilities is 5 Km.

Considering two projects in different areas of the country, for every project has been made a multi-criteria analysis regarding the selection of a given location. This analysis was made to show the model used for a project when is needed to chose a technical option, a location, a investment source or other options included in an investment project.

After the selection of the criterions and the percentage chosen for each criteria, the results of the analysis are the following:

The multi-criteria analysis between the two projects, X and Y, considering the chosen selection criteria: economic, environmental, existing situation, technical, public health, has shown the following results:

4. conclusions and proposal

Multi-criteria analysis helps us to choose between the two investment projects. It seems that the most feasible one is project X with 6.5 points, having a great impact over the environment, inhabitants and social life compared with project Y.

Once again we can see that multi-criteria analysis is the most important part of the Feasibility study, because it analysis and makes a hierarchy of the project depending on its general and specific objectives. This can make the difference between an eligible and not eligible project or between two or many projects that can be financed through European funds, but, considering the limited funding, it has to be made a decision to choose just one project. When the financial or economic analysis can't help us to make a decision, multi-criteria analysis is the best solution.

5. bibliography

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Afgan NH, Gobaisi D, Carvalho MG, Cumo M. Sustainable energy management. Renewable and Sustainable Energy Reviews 1998

Androneceanu Armenia, Managementul proiectelor cu finantare externa, Editura Universitara, Bucuresti, 2004

Boucher TO, McStravic EL. Multi-attribute evaluation within a present value framework and its relation to analytic hierarchy process. Editura: The Engineering Economist, 1991

Climaco J, editor. Multicriteria analysis. Editura: Springer-Verlag, New York, 1997.

Dias L, Climaco J. Exploring the consequences of imprecise information in choice problems using ELECTRE. Editura: Kluwer Academic Publishers, 2002.

Gal T, Hanne T, editors. Multi-criteria decision making: Advances in MCDM models, algorithms, theory, and applications. Editura: Kluwer Academic Publishers, New York, 1999.

Ghid pentru analiza Cost-Beneficii a proiectelor de investitii

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Huang CL, Yoon K. Multi attribute decision making: methods and applications. Editura: Springer -Verlag, New York, 1981

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