Acta Polytechnica CTU Proceedings https://doi.org/10.14311/APP.2024.46.0065 Acta Polytechnica CTU Proceedings 46:65–84, 2024 © 2024 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague MUNICIPAL WASTE MANAGEMENT PERFORMANCE: A FOCUS ON SLOVAKIA AND ITS LAU-1 DISTRICTS Eleonóra Marišová∗, Peter Fandel Slovak University of Agriculture in Nitra, Faculty of European Studies and Regional Development, Institute of Law, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia ∗ corresponding author: eleonora.marisova@uniag.sk Abstract. In this paper, we analyse the changes made to the basic EU directive on waste and assess its impact on the waste legislation of EU members. We then examine the Slovak waste strategies/programs that have implemented the EU directive on waste, namely the Waste Prevention Program, the Waste Management Program, and the Envirostrategy 2030. Based on EU waste legislation, the Environmental Strategy 2030 sets the waste treatment aims for Slovakia until 2030. However, it is questionable whether Slovakia will achieve the set goals. Our research indicates that as of 2021, Slovakia’s rate of waste incineration with energy recovery and landfilling rate of municipal waste are below the EU average, while the recycling rate, both for materials and composting and digestion, is higher. In our quantitative analysis, we examine the progress of waste management performance in Slovakia from 2017 to 2021, focusing on the LAU-1 districts. We estimate composite efficiency indicators using the techniques of Data Envelopment Analysis and Malmquist Indices. In accordance with the hierarchy of waste treatment methods, the applied models consider desirable waste operations variables (recycling and incineration with energy recovery) and undesirable waste operation variables (landfilling). Our results reveal significant variations in efficiency across the LAU-1 districts. The average technical efficiency of the 72 districts has improved from 0.714 in 2017 to 0.852 in 2021, indicating that the performance of districts is generally improving and catching up with the best-performing districts. The total performance, as measured by the Malmquist index, has improved by 45.5 %. Districts with access to waste incineration facilities with energy recovery have exhibited higher efficiency scores, benefitting from this advantage. Keywords: Waste legislation, strategies, municipal waste management, LAU-1 districts, performance, composite indicators, Data Envelopment Analysis, Malmquist Index. 1. Introduction Sustainability issues are closely connected to economic growth of all countries in the world and waste gener- ated by developed and less developed countries. Slo- vakia, similarly, to other countries worldwide commit- ted to the fulfilment of 17 sustainable development goals (SDGs) [1]. The philosophy includes environ- mental, economic, and social pillars of sustainability. We focus mainly on the Goal 12 of SDGs: Ensure sus- tainable consumption and production patterns. This goal should be fulfilled by 2030 as it is formulated: sub- stantially reduce waste generation through prevention, reduction, recycling, and reuse. Our paper examines how Slovak and European waste legislation and waste programs boost munic- ipal waste recycling and advance the circular economy that affect each other. If we talk about municipal waste in connection with circular economy, each Eu- ropean produces about 500 kg of waste per year. Less than half of it is 46 % recycled, 27 % is incinerated and 24 % is landfilled [2]. The paper is aimed at analysis of the fundamental EU legislation and programs relating to minimizing the waste in the EU member states and their impact on waste management performance in Slovakia. First partial objective is to analyse the changes of the basic EU directive on waste – Directive 2008/98/EC of the European Parliament and of the Council of 19 Novem- ber 2008 [3] on waste that was amended by Directive (EU) 2018/851 of the European Parliament and of the Council of 30 May 2018 [3] (hereafter Waste Frame- work Directive). This revised Directive brought many stipulations that had to be implemented into the waste legislation of the EU member states to increase waste incineration with energy recovery, use good techniques for its recycling and minimize landfilling. EU member states are to take among others, measures to support the design, production and use of products that use resources efficiently, are durable, repairable, reusable, and updatable. Except of this, the measures have to set aims how to reduce food waste as a contribution to the United Nations Sustainable Development Goal [4] of reducing global food waste per capita by 50 % by 2030 at retail and consumer level. Member States must by January 1, 2025, establish a sorted collection for textiles and hazardous waste from households and ensure that by December 31, 2023, biological waste is either sorted or recycled at the source (e.g., by composting). Since the Waste Framework Directive establishes the legal basis in the field of waste man- 65 https://doi.org/10.14311/APP.2024.46.0065 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings agement in the European Union, we deal with the Slovak waste legislation and strategies/programs that implemented this EU directive. We analyse the imple- mentation of EU waste legislation into Slovak Waste Prevention Program 2018 [5], Slovak Waste Manage- ment Program, 2021 [6] and Environmental strategy 2030 [7]. The second objective of this paper is to analyse how Slovak legislation and adopted strategies influence the municipal waste management performance. The analysis is conducted at the level of LAU-1 districts. The aim is to assess the trends in waste management performance using waste treatment-specific indicators, as well as composite indicators based on Data Envel- opment Analysis and Malmquist index methodology. This paper aims to bridge a gap in the current liter- ature by providing a comprehensive analysis of munic- ipal waste management performance at subregional level of LAU-1 districts in Slovakia, in relation to the environmental goals of EU and Slovakia, employing both partial waste treatment-specific indicators and composite indicators The findings will be relevant to waste management practitioners, policymakers, and researchers who are interested in enhancing the effi- ciency and sustainability of waste management prac- tices and achieving the environmental objectives set by the EU. The paper is structured as follows: Section 2 pro- vides the theoretical background of the topic, Section 3 describes the data and methodology used, Section 4 presents and discusses the results, and Section 5 con- cludes the paper. 2. Theoretical background The theoretical background is processed mainly through the interpretation of the basic EU legislation on waste – Directive (EU) 2018/851 [3] of the Euro- pean Parliament and of the Council of 30 May 2018 amending Directive 2008/98/EC on waste [3], which has undergone several serious changes. In July 2018, the EU Circular Economy Package was introduced by the EU Commission with particular relevance for man- agement of municipal waste. It is intended to serve the objectives of growth and employment while at the same time ensuring and strengthening environmental protection [8]. Rogge et al. [9] state that focus of EU municipal solid waste policy shifted from basic waste handling standards to promoting, recycling, re-use, and energy recovery. An important guiding principle in the designs of EU waste legislation and policy is the so called “waste management hierarchy”. The Slovak legislation on waste is represented by waste legislation Act No. 79/2015 Coll. as amended [10] and Slovak programs: Waste Preven- tion Programme [5] for years 2019-2025, Environmen- tal strategy 2030 [7] and Waste Management Pro- gramme [6] for years 2021–2025, responding to the EU legislation in the field of waste. We deal in the theory also by amended legislation on waste in the Slovak Republic – Act on Waste No. 79/2015 Coll. [1] in the valid wording and opinions of foreign and domestic authors who make research in the field of waste. Environmental strategy 2030 [7] set the Slovakian aims till 2030, that the municipal waste recycling rate, including the preparation for re-use, will be increased to 60 %, and the land-filling rate will be reduced to less than 25 % by 2035. A green procurement will cover at least 70 % of the total number of all public procurements, and the support for green innovation, science and research will be at a comparable level to the EU average. The energy intensity of the Slovak industry will be closer to the EU average, and by 2020, the sustainability criteria for all renewable energy production sources will be developed and accepted. The production of electricity and heat from coal will be gradually reduced [7]. Cleaning and wasting are quite familiar to us, and once discarded their products have to be dealt with somehow or managed. Yet in many ways research on what becomes of all that we discard has only just begun [11]. In charge of the waste management in Slovakia are both state and self-government. Today, adequate waste services are considered vital to the governance of cities, industries, and refugee camps: a basic hu- man right, an economic opportunity and an ecological imperative [11]. Municipal waste is the total amount of used materials coming from households and smaller local businesses, where the collection is ensured by the local government [12]. Mura [13] states that municipalities and regions are part of the public administration system, which are most closely connected with the everyday life of citizens. The waste management system consists of the whole set of activities related to handling, treating, disposing or recycling the waste materials [14]. We agree with Kahle et al. [15] who states that sustainability is the ability to remain productive in- definitely. But knowledge is still lacking on local waste prevention, especially regarding the methods for monitoring and how local waste management sys- tems can be designed to encourage waste reduction in the households [16]. Waste management is one of the major environ- mental concerns in the world. Human activities and changes in lifestyles and consumption patterns have resulted in an increase in solid waste generation rates [14]. The differences in terms of waste handling among the EU members states are immense. Slovakia belongs to the lowest quartile of EU states in terms of waste volumes disposed of by landfilling [17]. The current EU legislation increases the require- ments on knowledge of food producers, in particular on the packaging of the product, which must fit even more information in a reasonably large font, and on 66 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . the environment, as the amount of waste produced increases with increasing packaging area [18]. We appreciate that beside the EU member states, the Republic of Serbia as part of the negotiations for EU accession, has begun the process of establishing a waste management system and adapting it to the goals and acquis Communautaire. The key document in Serbia that aims for environmental awareness is called the Waste Management Program [6] of the Re- public of Serbia and it harmonizes the waste manage- ment in the candidate state with EU regulations [19]. There has been a significant body of literature ad- dressing municipal waste management performance. Simões and Marques [20] conducted a literature review of 107 studies published from 1965 to 2011, provid- ing a comprehensive overview of the topic. In the past decade, numerous studies have been published, focusing on various aspects of waste management per- formance. The first aspect examined in the literature is the waste management performance of different geopoliti- cal entities, with a particular focus on EU countries or EU NUTS2 regions. Examples of such studies in- clude Chiaotto [21, 22], Khan et al. [23], Rios [24], and Rogge et al. [9, 25], which commonly utilize Eurostat datasets. While these studies offer valuable insights, less frequent are subregional analyses that specifically analyse district or municipal-level data. Some no- table examples of these subregional studies include Rogge-Jaeger [25], Peréz-López [26], and Struk and Boďa [27]. The second aspect explored in the literature per- tains to the different methods employed to estimate performance indicators in waste management. While partial waste treatment-specific indicators are fre- quently used, their ranking ambiguity has led to the adoption of more sophisticated methods that enable the estimation of composite indicators. A mathemati- cal programming approach to model composite indica- tors can be found in Zhou et al. [28]. Other approaches such as multi-criteria decision-making methods, em- ployed in Castillo [29], have found wide applicabil- ity in this field. Another commonly used method is Data Envelopment Analysis (DEA), utilized in studies by Castillo [29, 30], Delgado-Antequera [31], Peréz- López [26], Rios [24], and Struk and Boďa [27]. Other DEA derivatives, such as the Benefit-of-Doubt (BoD) method used in Chiaotto [22] and Rogge [9], Direc- tional Distance Functions applied in Villavicencio [32] and Ye [33], and the Free Disposal Hull (FDH) em- ployed by Rios [24], have also been applied. The third topic thoroughly analysed in literature is the selection and classification of variables in waste management performance models. Key discussions revolve around the hierarchy and weighting of vari- ables [34], as well as the identification of desirable and non-desirable variables and their controllability [31]. By considering the insights provided by these differ- ent strands of literature, a comprehensive understand- ing of municipal waste management performance can be achieved, resulting in the design of a reliable model, what is the aim of this study. 3. Material and methods The theoretical background of the paper is processed mainly through the interpretation of the Waste Frame- work Direction and above-mentioned strategies of the Slovak Republic, the amended legislation on waste in the Slovak Republic – Act on Waste No. 79/2015 Coll. [10] in the valid wording and opinions of foreign and domestic authors who deal with the issue of waste. Method of analysis is used to evaluate the Slovak strategies in accordance with EU legislation. We ex- amine to what extent the goals set by the Slovak government in the above-mentioned strategies have been met in the field of waste in the Slovak Republic. In the quantitative analysis of the municipal waste management performance at the subregional level of LAU-1 districts, we used data of the Statistical Office of the Slovak Republic. We have analysed data of 72 districts of the Slovak republic, using the database coded zp3802rr [35] and named as “Municipal waste and small construction waste from municipalities ac- cording to the waste treatment categories per district (in Tonnes)”, 2021–2017 period. The data were used to calculate both partial waste treatment-specific indicators and composite perfor- mance indicators. Following treatment specific indicators were calcu- lated: • total municipal waste [kg per capita], • recycling [kg per capita], Recycling rate [%], • landfilling [kg per capita], Rate of landfilling [%]. As composite indicators, we used the technical effi- ciency, technical super-efficiency, and Malmquist in- dices to consider simultaneously multiple metrics of the municipal waste management efficiency, employing the Data Envelopment Analysis (DEA) methodology in this study. Technical efficiency (TE) is used to measure per- formance (productivity, efficiency) of the waste man- agement of analysed districts in comparison to the best performing districts. In economic terminology, it is defined as the total factor productivity (TFP ) of an evaluated district expressed relative to the highest TFP in the sample of districts. Formally, technical efficiency can be expressed by the following equations Eq. (1) and Eq. (2): T F P = agregate output of the district under evaluation agregate input of the district under evaluation , (1) TE = TFP of the district under evaluation maximum TFP in the sample of districts . (2) We estimate technical efficiency using the CCR DEA (Charnes, Cooper, Rhodes, [36]). To determine 67 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings the ranking of districts based on their efficiency in waste management, we used an adjusted CCR DEA model for calculating super-efficiency, as proposed by Andersen and Petersen [37]. The formal notation of the DEA models used to estimate the technical efficiency is presented in the Scheme (3): CCR DEA model max φ subject to n∑ j=1 yijλj ≥ φyro, r = 1, 2, . . . , s n∑ j=1 xijλj ≤ xio, i = 1, 2, . . . , m λj ≥ 0, j = 1, 2, . . . , n φ – free (3a) Super-efficiency CCR DEA model max φ subject to n∑ j=1 yijλj ≥ φyro, r = 1, 2, . . . , s n∑ j=1 xijλj ≤ xio, i = 1, 2, . . . , m λj ≥ 0, j = 1, 2, . . . , n; j ̸= o λo = 0 φ – free (3b) where xij ith input of the district j, yrj rth output of the district j, xio ith input of the district under observation o, yro rth output of the district under observation o, λj intensity variable of the jth district, φ technical efficiency (TE) measure. The measure is from the interval [1; ∞). However, for the con- venience, in the following sections, we report and interpret the inverse value 1/φ, which is from the interval [0; 1]. • If TE = 1, then the evaluated district is effi- cient, that is, it achieves the maximum performance (TFP ) in the sample of evaluated districts and serves as a benchmark for other districts in the sample. • If TE < 1, then the evaluated district is inefficient and achieves only (TE∗100) % of the performance of the best districts. The level of the best districts can be achieved either by generating [(1 − TE) ∗ 100] % higher outputs from the inputs used, or by using only (TE ∗ 100) % of the inputs to generate their outputs. • When determining the ranking of districts according to TE, efficient districts with TE = 1 are ranked at the top and districts with the lowest TE value are ranked at the bottom. In a situation where several districts achieve TE = 1, it is not possible to unambiguously determine the ranking of efficient units, and in such cases, super-efficiency measures are calculated for efficient districts. Super-TE mea- sures are from the interval [1; ∞), and the best district is the one with the maximum value of the super-TE. The Malmquist index (MI) proposed by Färe et al. [38] expresses changes in total productivity over time. It is based on estimating Shepard distance func- tions. In our study, we comprehensively evaluate the development of district performance in waste manage- ment over time using the output-oriented Malmquist index and its components, simultaneously considering several performance indicators. The Malmquist index of total productivity can be expressed as follows: MI = (yt+1, xt+1; yt, xt) = dt+1 o (yt+1, xt+1) dt o(yt, xt) [ dt o(yt+1, xt+1) dt+1 o (yt+1, xt+1) × dt o(yt, xt) dt+1 o (yt, xt) ] 1 2 , (4) • if MI > 1, then the TFP of the evaluated district has improved, • if MI = 1, then the TFP of the evaluated district has not changed, • if MI < 1, then the TFP of the evaluated district has worsened. Malmquist index can be decomposed into the index of technical efficiency change (TEC) and the index of technological change (TC): MI = TEC × TC, where TEC = dt+1 o (yt+1, xt+1) dt o(yt, xt) , (5) • if TEC > 1, then the evaluated district improved its TE (catching up with the best districts), • if TEC = 1, then the evaluated district did not change its TE, • if TEC < 1, then the evaluated district worsened its TE (lagging behind the best districts). TC = [ dt o(yt+1, xt+1) dt+1 o (yt+1, xt+1) × dt o(yt, xt) dt+1 o (yt, xt) ] 1 2 (6) • if TC > 1, then we observe progress in technology (innovation in technology) in the evaluated district, • if TC = 1, then there has been no change in tech- nology in the evaluated district, • if TC < 1, then we observe regression in technology in the evaluated district. 68 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . Figure 1. The scheme of the transformation process in waste management system. We estimated the Shepard distance functions using following output-oriented CCR DEA models:[ dt o(yt, xt) ]−1 = max φ,λ φ subject to φyot − Ytλ ≤ 0 Xtλ ≤ xot λ ≥ 0 (7) [ dt+1 o (yt+1, xt+1) ]−1 = max φ,λ φ subject to φyot+1 − Yt+1λ ≤ 0 Xt+1λ ≤ xot+1 λ ≥ 0 (8) [ dt o(yt+1, xt+1) ]−1 = max φ,λ φ subject to φyot+1 − Ytλ ≤ 0 Xtλ ≤ xot+1 λ ≥ 0 (9) [ dt+1 o (yt, xt) ]−1 = max φ,λ φ subject to φyot − Yt+1λ ≤ 0 Xt+1λ ≤ xot λ ≥ 0 (10) In modelling the waste management performance of districts, we assume that the waste management system corresponds to the process of transforming in- puts into outputs. In our analysis, the total quantity of municipal waste generated in districts serves as the input, which enters the waste management system. The quantities of municipal waste processed by the alternative treatment methods are regarded as the out- puts of the system. The scheme of the transformation process is depicted in Figure 1. The inputs and outputs in our analysis are defined in accordance with the above assumption as follows: • Input (input variable) (1.) Total municipal waste generated in a district per capita per year. We work with the only input variable, which we consider to be a short-term uncontrollable variable. • Outputs (output variables) (1.) Recycling – material (R02-R13, except R03) (2.) Recycling – composting and digestion (R03) (3.) Incineration with energy recovery (R01, D10) (4.) Landfilling (D01) The four selected outputs variables represent the prevailing quantities of municipal waste in Slovakia. To account for population density, all outputs, as well as the input, are expressed in per capita values. Con- sistent with the waste management strategies adopted by the EU and Slovakia, the objective is to mini- mize the disposal of municipal waste in landfills and encourage recycling and environmentally friendly in- cineration with energy recovery. For this reason, the selected output variables are hierarchically defined into two groups: (a) Good outputs: These represent desirable treat- ment methods of waste, including material recycling, organic recycling, and incineration with energy re- covery. For these outputs, we adopt the preference of “the more – the better”. (b) Bad outputs: These represent undesirable waste treatment methods, specifically landfilling, with the preference of “the less – the better”. Since outputs are generally modelled in DEA models as maximiza- tion variables, the maximization preference for bad outputs needs to be transformed into minimization. In our study, we adopt the transformation approach proposed by Seiford and Zhu [39]: firstly, each un- desirable (bad) output is multiplied by a coefficient of −1, and then a suitable transformation vector, 69 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings denoted as w, is determined to convert all negative outputs into positive ones: yj b = −yb j + w > 0 yj b = −yb j + max(yb j) + 1 (11) where yb j is the bad output and yj b is the transformed bad output. The CCR DEA model with bad outputs then has the following form: max f = φ output constraints for good outputs n∑ j=1 yg ijλj ≥ φyg ro, r = 1, 2, . . . , s output constraints for bad outputs n∑ j=1 yij bλj ≥ φyro b, r = 1, 2, . . . , s standard input constraints n∑ j=1 xijλj ≤ xio, i = 1, 2, . . . , m λj ≥ 0, j = 1, 2, . . . , m φ – free, yg j are good outputs. (12) We applied the same adjustment in models for calcu- lating the super-efficiency and the Malmquist indices. 4. Results 4.1. Municipal Waste – EU legislation The Waste Framework Directive lays down measures to protect the environment and human health by preventing or reducing the generation of waste, the adverse impacts of the generation and management of waste by reducing overall impacts of resource use and improving the efficiency of such use, which are crucial for the transition to a circular economy and for guar- anteeing the Union’s long-term competitiveness [3]. This directive stresses the transition to a circular economy in all EU member states and changes of the former waste directive 2008/98/EC by new definition the municipal waste [3]: (a) mixed waste and separately collected waste from households, including paper and cardboard, glass, metals, plastics, bio-waste, wood, textiles, pack- aging, waste electrical and electronic equipment, waste batteries and accumulators, and bulky waste, including mattresses and furniture; (b) mixed waste and separately collected waste from other sources, where such waste is similar in nature and composition to waste from households. Municipal waste does not include waste from pro- duction, agriculture, forestry, fishing, septic tanks and sewage network and treatment, including sewage sludge, end-of-life vehicles or construction and demo- lition waste. We can see that the new stipulation of the Waste Framework Directive includes separately collected waste that is more and more important for the EU. As for the municipal waste, it excludes mainly waste from production, agriculture, forestry, fishing. Newly is defined also construction and demolition waste like waste generated by construction and demolition activ- ities, as well as bio-waste that means biodegradable garden and park waste, food and kitchen waste from households, offices, restaurants, wholesale, canteens, caterers and retail premises and comparable waste from food processing plants. The original characteristics of the waste in article 4 Waste Framework Directive [3] was much simpler: any substance or object which the holder discards or intends or is required to discard. As for the waste management, the new directive states that it is not only collection, transport, re- covery and disposal of waste, including the supervi- sion of such operations and the after-care of disposal sites, but includes into management also the waste sorting. Except of waste sorting, the new directive also stipulates a new tool for producers’ responsibility by adding so called extended producer responsibility scheme that means a set of measures taken by mem- ber states to ensure that producers of products bear financial responsibility or financial and organisational responsibility for the management of the waste stage of a product’s life cycle [3]. 4.2. Implementation of EU waste legislation into Slovak one This amendment was transposed into Slovak waste legislation by amending the act on waste No. 79/2015 Coll. Waste Act [10]. As part of the extended respon- sibility, producers of reserved products basically bear all the costs of dealing with the reserved waste stream, from its sorted collection to the recovery and eventual disposal of unrecoverable residues. Exceptions to this rule are cases where the obligation of the producer of a reserved product, which is not registered in the Register of Producers, is transferred from the Waste Act to the distributor of this product. Pursuant to § 81 par. 4 of the Waste Act, the manufacturer is obliged to bear the costs of providing the necessary collection containers (e.g., containers or bags). The transfer of costs for separate collection from cit- izens to producers also creates financial motivation for the improvement of sorting. Although the principles supporting extended producer responsibility create room for an increase in the rate of sorting of munic- ipal waste, extended producer responsibility cannot be considered a direct tool for its increase. It is only a means for shifting the costs of separate collection and recycling from local governments and taxpayers directly to producers, which stimulates waste pre- vention on the part of producers and, on the other hand, creates room for citizens to manage waste more responsibly [40]. 70 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . OECD [41] defines the extended producer responsi- bility as an environmental policy tool that extends the producer’s responsibility for the product to the post- consumer phase of its life cycle. In other words: the manufacturer is responsible for handling the product even after the product has become waste. “Not the consumer, but the producer of the prod- uct is the one who creates the demand for packaging and determines the requirements for its composition. Therefore, if the customer makes the effort and sorts the packaging, the subsequent costs associated with the waste are transferred to the manufacturer. In the extended responsibility of producers, producers are responsible for sorted waste from packaging”, the an- alysts of the Slovak Institute of Environmental Policy explain in their latest study How to sort out sorted collection [40]. In the legislation of the European Community, the idea of extended producer responsibility appeared for the first time when the Directive [42] on packaging and packaging waste set Member States targets for waste sorting and recycling, as well as requirements for packaging design. However, this directive did not oblige producers to finance the collection and recycling of waste – this resulted only from the legislation of individual states. 4.3. EU Prevention of waste According to Article 9 of the Waste Framework Di- rective [3], the member states shall take measures to prevent waste generation. Those measures shall, at least: • reduce the generation of waste, in particular waste that is not suitable for preparing for re-use or recy- cling, • develop and support information campaigns to raise awareness about waste prevention and littering, • target products containing critical raw materials to prevent that those materials become waste, • encourage the re-use of products and the setting up of systems promoting repair and re-use activities, including in particular for electrical and electronic equipment, textiles and furniture, as well as pack- aging and construction materials and products, • reduce waste generation in processes related to in- dustrial production, extraction of minerals, manu- facturing, construction and demolition, taking into account best available techniques, • reduce the generation of food waste in primary pro- duction, in processing and manufacturing, in retail and other distribution of food, in restaurants and food services as well as in households as a contribu- tion to the United Nations Sustainable Development Goal [4] to reduce by 50 % the per capita global food waste at the retail and consumer levels and to reduce food losses along production and supply chains by 2030; • encourage food donation and other redistribution for human consumption, prioritising human use over animal feed and the reprocessing into non-food products. 4.4. Main aims of waste prevention in Slovak Waste Prevention program Slovak Republic implemented these goals into its Waste Prevention Programme (WPP), 2018 for years 2019–2025 [5]. The main aim is to break the link between economic growth and environmental impacts related to the generation of waste. WPP [5] states that the capacity of the currently operating waste dumps is sufficient, therefore it is not necessary to build new waste dumps. Unlike common dumps, however, landfills are more carefully designed to cordon off waste from both soci- ety and nature, maintaining their contents in a state of suspended animation. This makes it possible for landfills to one day be recovered as an invented com- mon, a source of new land upon which to build or reclaim for other purposes [43]. The Slovak Republic is a rural country which is also reflected in the method of sorted collection of biodegradable waste with predominant domestic com- posting. We analyze the main goals, financing and measures taken in the Slovak WPP [5] relating to mixed and biodegradable municipal waste. The main goal of WPP [5] as for Mixed municipal waste is to reduce the amount of mixed municipal waste by 50 % by 2025 compared to 2016 mostly by introduction of mandatory mass collection of munic- ipal waste and activities to prevent the creation of mixed municipal waste should be financed from the Environmental Fund. As for the biodegradable munic- ipal waste, the main goal of WPP [5] is to reduce the amount of biodegradable waste in mixed municipal waste by 60 % by 2025 compared to the situation in 2016. Stipulated measures in WPP [5]: • legislative, financial, and informational support for home and community composting, • creation of a unified methodology for monitoring the amount and types of biodegradable waste in mixed municipal waste. Illegal handling of biodegradable municipal waste is also a serious problem in Slovakia. A large part of it is dumped in illegal landfills or burned in the spring and autumn months on public and private lands. 4.5. Waste management Program and Slovak LAU-1 districts competencies Another important strategy that implemented the EU legislation, is the Slovak Waste Management Pro- gramme [6] for years 2021–2025. The new version of the Waste Framework Directive 2008/98/EC established new definitions (municipal waste) [3] related to the concept of waste, as well 71 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings as improving existing ones (waste management). It should be noted that the basic definition of waste remains unchanged [44]. Slovakia needs to focus strongly on separate collec- tion at source. A low level of separate collection would result in a low recycling rate of municipal waste. The separate collection of municipal waste has increased in the past period, especially for metals, paper, plastics, glass and biodegradable green waste, but there is also a need to focus on the separate collection of other components and types of waste that are currently underestimated (e.g., hazardous waste, textiles). WMP [6] states the tools to improve the separate collection of municipal waste that were included into Slovak waste legislation: • Act No. 329/2018 Coll. on fees for waste disposal – to make landfilling, which is the last in the waste management hierarchy, handicapped and to create an incentive for the sorted collection of municipal waste and to increase the recycling of municipal waste [3], • Regulation No. 330/2018 Coll. – the new rate for the disposal of mixed municipal and bulky waste, depends on the level of municipal waste sorting in the municipality, • new law on fees is the result of a number of activities at the Slovak level and the EU one. As for Slovakia, a member state of the EU since 2004, all district offices in the seat of the regions (8) shall be obliged to work out plans of regions based on the objectives and measures set in the Slovak Waste Management Program. This way there are together eight regional Waste Management Programs for years set by the Slovak program that determines the direc- tion of waste management for a set period and are binding for respective region, based on its particu- larities with specific goals and measures to support waste prevention. The new Act on Wastes imposes the duty on the producers that fulfils their duties individually to perform promotional and educational activities in the district, in which they provide for the waste collection, focusing on end users, about man- agement of selected waste streams, separate collection of municipal wastes and waste prevention [6]. 4.6. The Strategy of the Environmental Policy of the Slovak Republic until 2030 Envirostrategy (2030) that was prepared under leader- ship of Slovak Ministry of environment in 2019, defines a vision until 2030, which takes into account a pos- sible, probable, and the desired future development, identifies the fundamental systemic problems, sets the objectives until 2030 and proposes a framework for measures to improve the current situation, and it also contains basic result indicators that will enable a verification of achieved results. The basic vision of Envirostrategy 2030 is to achieve better environmen- tal quality and sustainable circulation of the economy, which is based on rigorous protection of environmental compartments and using as little non-renewable nat- ural resources and hazardous substances as possible, which will lead to an improvement in health of the population. Environmental protection and sustainable consumption will be part of the general awareness of citizens and policy makers [7]. The main aims of Envirostrategy 2030 are as follows: • to achieve better environmental quality and sustain- able circulation of the economy, • environmental protection and sustainable consump- tion will be part of the general awareness of citizens and policy makers, • the prevention and adaptation to climate change. The tools to fulfil the main objectives of Enviros- trategy 2030: • incentive-based municipal waste collection for mu- nicipalities, • increase prevention of black dumping and enforce- ment of the polluter pays, • restaurants and supermarkets will be obliged to make use of the food (charity donation of the food that fulfils food safety requirements), • if they are no longer suitable for consumption, they will be able to compost them or energetically utilize, • renewable energy production will be preferred, which by its nature does not burden the environ- ment and contributes to the long-term sustainable development of the Slovak Republic. 4.7. Results of the quantitative analysis In the opening part of this section, we focus on pre- senting selected partial indicators that reflect the per- formance of waste management. Through these indi- cators, we aim to document the progress of municipal waste management in Slovakia between 2017 and 2021, while also comparing it to the average performance of the EU-27 countries, Slovakia, and the target values set specifically for Slovakia. In Table 1 we present a comparison of total mu- nicipal waste generated. It is evident that Slovakia remains below the EU-27 average. However, economi- cally developed districts of western Slovakia, such as Trnava and Galanta, significantly surpass the EU-27 average in waste generation per capita. Waste growth index indicates a higher growth rate in Slovakia com- pared to EU-27. The assessment of a waste management system’s performance is primarily contingent on the extent to which recycling methods are utilized. In this re- gard, there have been significant and positive devel- opments. Table 2 illustrates the advancement in re- cycling, encompassing both material recycling and recycling through composting and digestion. Over the 72 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . Geopolitical entity 2017 2021 Index 2021/2017 EU-27 499 530 106 % Slovakia 378 496 131 % Best districts Sobrance: 126 Sobrance: 220 175 % Worst districts Trnava: 563 Galanta: 830 147 % Table 1. Total municipal waste generated [kg per capita]. Geopolitical entity 2017 2021 Index 2021/2017 EU-27 231 257 111.3 % Slovakia 113 242 214.2 % Best districts Nitra: 225 Galanta: 514 xxx Worst districts Sobrance: 12 Sobrance: 88 xxx Table 2. Recycling [kg per capita]. Geopolitical entity 2017 2021 Index 2021/2017 EU-27 46.3 % 48.5 % 104.7 % Slovakia 29.9 % 48.8 % 163.2 % Best districts Žiar nad Hronom: 49.6 % Žiar nad Hronom: 65.3 % 130.0 % Worst districts Medzilaborce: 6.7 % Medzilaborce: 29.8 % 428.6 % Slovakia target 2030 min 60 % Table 3. Recycling rate [%]. Geopolitical entity 2017 2021 Index 2021/2017 EU-27 127 121 95 % Slovakia 229 202 88 % Best districts Košice I-IV: 16 Košice I-IV: 7 44 % Worst districts Galanta: 429 Malacky: 360 xxx Table 4. Landfilling [kg per capita]. span of five years analyzed, recycling in Slovakia has doubled. However, as of 2021, it still falls below the average recycling rate of the EU-27 countries. Table 3 provides an overview of the share of recy- cling in relation to the total municipal waste gener- ated. Slovakia has witnessed significant progress in its recycling rate, which has increased from 29.9 % to 48.8 %, surpassing the average rate of the EU-27. This positive trend is also evident within the districts of Slovakia. The district with the lowest recycling rate has tripled its rate, showing a remarkable improve- ment, while the district with the highest rate has seen a 30 % enhancement. Although Slovakia as a whole has not yet met the recycling rate target set for year 2030, some of the best districts have already achieved this milestone. Waste landfilling is considered as a least desirable treatment method. In line with EU Landfill Directive, member states are required to reduce the amount of municipal waste sent to landfill to 10 % or less of the total amount of municipal waste generated by 2035. In Table 4 we provide per capita statistics of Slovakia and its districts. Landfilling in Slovakia remains significantly high, nearly double the average of the EU-27. Moreover, the worst-performing districts exhibit even higher values, almost triple the EU-27 average. Over the five-year period under analysis, Slovakia has managed to de- crease its landfill usage by approximately 12 %. The best practicing districts show 56 % decrease. The target rate of landfilling set in Slovakia for 2035 is 25 %. However, as of 2021, the current rate of land- filling stands at 40.7 % (Table 5). This indicates that there is still a significant gap between the current rate and the desired target, highlighting the need for fur- ther efforts and strategies to reduce landfilling and pro- mote more sustainable waste management practices. The district of Košice I-IV stands out as the best per- former, with a remarkably low landfilling rate of only 1.5 %. This achievement can be attributed to its sig- nificant reliance on incineration with energy recovery, a method that effectively reduces the amount of waste sent to landfills. Conversely, the worst-performing districts are characterized by low rates of recycling, indicating a need for improvement in their waste man- agement practices. Encouraging higher recycling rates 73 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings Geopolitical entity 2017 2021 Index 2021/2017 EU-27 25.5 % 22.8 % 89.7 % Slovakia 60.6 % 40.7 % 67.3 % Best districts Košice I-IV: 3.8 % Košice I-IV: 1.5 % 39.5 % Worst districts Medzilaborce: 93.3 % Medzilaborce: 69.7 % 74.7 % Slovakia target 2035 max 25 % Table 5. Rate of landfilling [%]. Rank Total municipal waste(D) Recycling – material(I) Recycling – composting and digestion(I) Incineration with energy recovery(I) Landfilling(D) 1 Sobrance (220) Kysucké N. Mesto (234) Galanta (369) Košice I-IV (290) Košice I-IV (7) 2 Trebišov (229) Bytča (207) Senec (324) Bratislava I-V (234) Bratislava I-V (47) 3 Medzilaborce (309) Žiar nad Hronom (192) Dunajská Streda (261) Prešov (133) Prešov (72) 4 Vranov n/Topľou (317) Zlaté Moravce (190) Malacky (257) Košice – okolie (97) Košice – okolie (91) 5 Stará Ľubovňa (322) Senec (174) Nitra (233) Rožňava (63) Sobrance (128) . . . . . . . . . . . . . . . . . . 68 Nitra (613) Zvolen (60) Detva (54) 26 districts with zero values Nitra (285) 69 Malacky (763) Stará Ľubovňa (60) Rožňava (49) Galanta (293) 70 Senec (770) Poprad (59) Gelnica (48) Komárno (309) 71 Dunajská Streda (771) Banská Štiavnica (51) Košice – okolie (36) Dunajská Streda (338) 72 Galanta (830) Medzilaborce (37) Sobrance (21) Malacky (360) (D) decreasing preference, (I) increasing preference. Table 6. Top 5 and bottom 5 districts in waste management performance by treatment-specific indicators, 2021 (districts sorted by waste in kg per capita). in these districts would contribute to reducing the re- liance on landfilling and promoting a more sustainable approach to waste management. The top 5 and bottom 5 ranks of districts based on waste management performance using treatment- specific indicators in kg per capita are presented in Table 6. The corresponding rankings based on the percentage share of quantities treated by selected methods in the total quantity of municipal waste is shown in Table 7. As evident from the previous section, partial waste treatment-specific indicators lead to different rankings of the districts, and in some cases, these rankings can be contradictory. To avoid this discrepancy, the next part of this section presents the results of the waste management performance analysis assessed us- ing composite indicators. In the analysis, a dataset comprising 72 districts was utilized, and waste man- agement performance was evaluated by considering one input variable and four output variables simulta- neously, employing the DEA model (3a). Descriptive statistics of the variables used in the analysis are provided in Table 8. The technical efficiency composite indicator of the municipal waste performance of the 72 districts of Slo- vakia for the period 2017–2021 is presented in Table 9. As evident from Table 9, the average technical effi- ciency of districts in the evaluated period increased from 0.714 in 2017 to 0.852 in 2021. If in 2017 districts, on average, achieved 71.4 % of the performance of the best-performing districts, by 2021, it had already in- creased to 85.2 %, while simultaneously reducing the variability in district efficiency. This indicates that districts are converging towards the desired state out- lined by waste management strategies. Unfortunately, it is showing that the pace of convergence is slowing down, as demonstrated in Table 10. While in 2018, the average technical efficiency of districts increased by 15 % compared to the previ- ous year, in 2019 it was a growth of 7.2 %. In 2020, there was a decline of 0.1 %, but in 2021, there was an increase of 2.4 %. The average annual growth of 74 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . Rank Recycling – material rate(I) Recycling – composting and digestion rate(I) Incineration with energy recovery rate(I) Landfilling rate(D) 1 Kysucké Nové Mesto (42) Galanta (44) Košice I-IV (65) Košice I-IV (1) 2 Košice – okolie (38) Senec (42) Bratislava I-V (41) Bratislava I-V (8) 3 Bytča (38) Topoľčany (40) Prešov (30) Prešov (16) 4 Žiar nad Hronom (37) Nitra (38) Košice – okolie (27) Košice – okolie (25) 5 Gelnica (34) Banská Štiavnica (38) Rožňava (18) Senec (31) . . . . . . . . . . . . . . . 68 Zvolen (14) Rožňava (14) 26 districts with zero values Sabinov (58) 69 Bratislava I-V (13) Detva (14) Rimavská Sobota (59) 70 Poprad (13) Revúca (12) Kežmarok (61) 71 Medzilaborce (12) Košice – okolie (10) Revúca (62) 72 Banská Štiavnica (11) Sobrance (9) Medzilaborce (70) (D) decreasing preference, (I) increasing preference. Table 7. Top 5 and bottom 5 districts in waste management performance by treatment-specific indicators, 2021 (districts sorted by % rate). Statistics 2017 2018 2019 2020 2021 I – Total municipal waste Mean 364.2 405.6 414.2 444.1 472.4 Minimum 126.3 133.4 156.5 159.4 220.2 Maximum 563.2 654.2 690.1 1017.9 830.2 Standard deviation 99.2 103.7 101.8 132.3 117.2 O1 Recycling – material (desirable treatment) Mean 52.2 89.5 91.9 85.9 108.9 Minimum 4.8 7.1 9.9 10.7 37.3 Maximum 151.5 221.4 234.1 168.6 233.7 Standard deviation 32.6 42.6 39.5 31.8 38.7 O2 Recycling – composting and digestion (desirable treatment) Mean 52.2 64.7 77.5 112.4 127.3 Minimum 5.0 5.8 11.3 22.3 20.9 Maximum 177.5 169.8 174.7 361.1 368.8 Standard deviation 31.5 34.0 35.7 62.5 63.7 O3 Incineration with energy recovery (desirable treatment) Mean 9.4 7.8 7.8 9.3 11.7 Minimum 0.0 0.0 0.0 0.0 0.0 Maximum 275.0 238.5 258.0 274.2 290.0 Standard deviation 46.2 38.2 35.8 44.6 47.2 O4 Landfilling (undesirable treatment) Mean 250.4 242.4 234.1 229.4 217.1 Minimum 15.6 23.4 17.3 6.2 6.6 Maximum 429.2 369.3 363.6 371.3 359.7 Standard deviation 69.3 65.6 63.1 60.4 58.4 Table 8. Descriptive statistics of input (I) and output (O) variables, 2017–2021, n = 72 districts [kg per capita]. 75 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings Descriptive statistics 2017 2018 2019 2020 2021 Mean 0.714 0.799 0.842 0.839 0.852 Minimum 0.443 0.467 0.499 0.533 0.612 Maximum 1.000 1.000 1.000 1.000 1.000 Standard deviation 0.159 0.151 0.118 0.118 0.099 No. of inefficient districts (TE < 1) 64 61 62 64 62 No. of efficient districts (TE = 1) 8 11 10 8 10 Table 9. Descriptive statistics of technical efficiency of districts of Slovakia, 2017–2021, n = 72. Descriptive statistics T EC index 2018/17 T EC index 2019/18 T EC index 2020/19 T EC index 2021/20 Mean annual T EC index Cumul. T EC index Mean 1.150 1.072 0.999 1.024 1.051 1.242 Minimum 0.710 0.899 0.694 0.751 0.957 0.838 Maximum 2.044 1.920 1.169 1.299 1.220 2.220 Standard deviation 0.255 0.155 0.077 0.096 0.054 0.270 No. of districts with TEC < 1 (regress) 15 21 34 20 11 11 No. of districts with TEC = 1 (stagnation) 5 9 5 7 4 3 No. of districts with TEC > 1 (progress) 52 42 33 45 57 58 T EC – technical efficiency change Table 10. Descriptive statistics of technical efficiency change indices of districts of Slovakia, 2017–2021, n = 72. technical efficiency over the entire period was 5.1 %. Overall, the average technical efficiency of districts increased by 24.2 % throughout the evaluated period. Complete results on technical efficiency change are presented in Appendix B. Table 11 shows the 5 best and the 5 worst districts. The most efficient district in Slovakia from 2017 to 2020 was district of Sobrance, while in 2021 it was Košice I-IV, which held the second position in the pre- vious period. While Sobrance are so efficient probably thanks to low waste generation, district of Košice I-IV exhibits so high efficiency score due to a high propor- tion of incineration with energy recovery. The least efficient districts in respective years of the examined period were Partizánske, Komárno, and Revúca. The detailed ranking of districts according to technical efficiency in each year is provided in Appendix A. Technical efficiency is a relative measure of perfor- mance (productivity) of districts, indicating how far the evaluated districts are from the best-performing districts that utilize the most productive waste treat- ment methods and technologies for municipal waste management. The Malmquist index allows for assess- ing changes in the overall productivity of districts in terms of the transformation of total generated mu- nicipal waste into individual components based on waste treatment methods. In Table 12 we present Malmquist indices for the period 2017–2021. Average Malmquist index, as a composite indicator of total factor productivity change of districts increased by 16.75 % in 2018 compared to the previous year, in 2019 it was a growth of 6.6 %, in 2020 there was an increase 9.6 % and in 2021 there was an increase of 2.4 %. The average annual growth of TFP measured over the en- tire period was 9.2 %. Overall, the average Malmquist index of districts increased by 45.5 % throughout the evaluated period. The district of Dunajská Streda achieved the highest average annual TFP change index of 1.3, indicating a 30 % improvement in total performance every year during the analyzed period. Furthermore, this district has the best cumulative Malmquist index of 2.852, sug- gesting that an initially inefficient district significantly improved its performance at a high pace, resulting in an overall TFP increase of 185.2 %. On the other hand, the district of Sobrance ex- hibited the lowest annual change index of 0.901 and cumulative MI index of 0.657. These values represent a regress of 9.9 % and 34.3 % respectively, likely due to uncompetitive waste management technology. The complete list of districts with Malmquist indices of TFP change can be found in Appendix C. 5. Conclusions The goal of waste strategies in the Slovak Republic is to reduce the volume of landfill waste by separat- ing its components to be used in the recycling pro- cess. By analysing the legislation and strategies of 76 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . Rank 2017 2018 2019 2020 2021 1 Sobrance Sobrance Sobrance Sobrance Košice I-IV 2 Košice I-IV Košice I-IV Košice I-IV Košice I-IV Sobrance 3 Tvrdošín Kysucké Nové Mesto Žiar nad Hronom Košice – okolie Košice – okolie 4 Žiar nad Hronom Žiar nad Hronom Kysucké Nové Mesto Galanta Kysucké Nové Mesto 5 Nitra Košice – okolie Myjava Žiar nad Hronom Prešov . . . . . . . . . . . . . . . . . . 68 Komárno Bánovce nad Bebravou Kežmarok Medzilaborce Hlohovec 69 Bánovce nad Bebravou Považská Bystrica Považská Bystrica Považská Bystrica Považská Bystrica 70 Galanta Rožňava Žarnovica Rožňava Kežmarok 71 Dunajská Streda Medzilaborce Rožňava Revúca Komárno 72 Partizánske Komárno Komárno Komárno Revúca Table 11. Top 5 and bottom 5 districts in waste management performance measured by the composite indicator of technical super-efficiency, 2017–2021. Descriptive statistics MI 2018/17 MI 2019/18 MI 2020/19 MI 2021/20 Mean annual MI Cumu- lative MI Mean 1.167 1.066 1.078 1.096 1.092 1.455 Minimum 0.673 0.851 0.827 0.774 0.901 0.657 Maximum 2.208 1.735 1.413 1.421 1.300 2.852 Standard deviation 0.266 0.146 0.113 0.116 0.069 0.388 No. of districts with MI < 1 (regress) 17 26 15 11 6 6 No. of districts with MI = 1 (stagnation) 0 1 0 0 0 0 No. of districts with MI > 1 (progress) 55 45 57 61 66 66 MI – Malmquist index of total factor productivity change Table 12. Descriptive statistics of Malmquist indices in districts of Slovakia, 2017–2021, n = 72. the Slovak Republic, as well as the EU legislation, we can formulate the conclusion that a system for waste generators should contribute to the objective of significantly reducing the overall waste generation, in particular as regards halving the amount of resid- ual, non-recycled municipal waste by 2030 and the land-filling rate should be reduced to less than 25 % by 2035. At the same time, the functionality of the extended liability system will be improved. Municipal waste management should contribute to the SDGs’ fulfilment, and we need to consider not just environmental and economic but also social factors influencing the waste generation, waste treatment and processing. The quantitative analysis of waste management performance reveals a positive trend in the average technical efficiency of the LAU-1 districts in Slovakia, increasing from 0.714 in 2017 to 0.852 in 2021, indicat- ing a significant improvement of 19.3 %. This indicates a positive convergence of district productivity towards the performance of the best-performing districts, high- lighting progress in waste management practices. We found out that among the best-performing districts belong the ones with access to waste incineration fa- cilities with energy recovery. However, it is important to note that there has been a slight deceleration in the pace of convergence. The initial improvement observed between 2017 and 2018, which amounted to a 15 % increase, decreased to a 2.4 % change between 2020 and 2021. While there is still progress, the rate of improvement has slowed down compared to the earlier period. Over the period from 2017 to 2021, the average cu- mulative municipal waste management performance, measured by Malmquist index of Total Factor Produc- tivity change, improved by a significant 45.5 %. This 77 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings improvement indicates an equiproportional growth in the quantities of municipal waste treated using desirable treatment methods, accompanied by a de- crease in quantities of waste treated using undesirable treatment methods. On average, there was an annual improvement of 9.2 % in the performance of municipal waste manage- ment in the LAU-1 districts measured by Malmquist index of TFP change. If the observed trend from 2017 to 2021 persists, it can be reasonably expected that the targets set for the recycling rate and landfilling rate for the years 2030 and 2035 will be met. However, this expectation is not statistically analysed in this paper. Examples of some districts show that the set goals in recycling and landfilling are already being met or even exceeded. On the other hand, there are districts that are far from meeting the goals, and it is not expected that they will achieve them by the specified target years. Positive advancements in waste management prac- tices demonstrate the potential for continued progress in achieving sustainable waste treatment goals. Acknowledgements This work was supported by the Slovak Research and Development Agency under the Contract No. APVV- 20-0076 entitled “Waste and Construction – Modelling the Effectiveness of Alternative Options for Cooperation between Administrative Authorities”. References [1] M. Bumbalová, A. Fehér, M. 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Rank of districts of Slovakia according to waste management performance measured by the composite indicator of technical super-efficiency, 2017–2021 Rank 2017 2018 2019 2020 2021 1 Sobrance Sobrance Sobrance Sobrance Košice I-IV 2 Košice I-IV Košice I-IV Košice I-IV Košice I-IV Sobrance 3 Tvrdošín Kysucké Nové Mesto Žiar nad Hronom Košice – okolie Košice – okolie 4 Žiar nad Hronom Žiar nad Hronom Kysucké Nové Mesto Galanta Kysucké Nové Mesto 5 Nitra Košice – okolie Myjava Žiar nad Hronom Prešov 6 Stará Ľubovňa Gelnica Košice – okolie Stropkov Žiar nad Hronom 7 Myjava Nitra Nitra Bratislava I-V Galanta 8 Brezno Myjava Svidník Zlaté Moravce Senec 9 Bratislava I-V Šaľa Galanta Žilina Bratislava I-V 10 Ilava Banská Bystrica Gelnica Ilava Myjava 11 Banská Bystrica Trebišov Veľký Krtíš Tvrdošín Tvrdošín 12 Šaľa Ilava Ilava Prešov Svidník 13 Detva Tvrdošín Šaľa Svidník Banská Štiavnica 14 Košice – okolie Stará Ľubovňa Zlaté Moravce Kysucké Nové Mesto Bytča 15 Spišská Nová Ves Skalica Bardejov Myjava Topoľčany 16 Zvolen Bratislava I-V Liptovský Mikuláš Bardejov Stará Ľubovňa 17 Žilina Nové Zámky Stropkov Nové Zámky Ilava 18 Pezinok Hlohovec Levoča Trenčín Dolný Kubín 19 Prešov Galanta Banská Bystrica Banská Bystrica Bardejov 20 Snina Veľký Krtíš Trebišov Stará Ľubovňa Humenné 21 Zlaté Moravce Lučenec Tvrdošín Veľký Krtíš Krupina 22 Bardejov Bardejov Pezinok Senec Levoča 23 Liptovský Mikuláš Stropkov Senec Banská Štiavnica Skalica 24 Turčianske Teplice Snina Bratislava I-V Šaľa Veľký Krtíš 25 Bytča Prešov Stará Ľubovňa Púchov Gelnica 26 Veľký Krtíš Námestovo Medzilaborce Nitra Piešťany 27 Gelnica Ružomberok Hlohovec Pezinok Nitra 28 Skalica Senec Spišská Nová Ves Gelnica Vranov nad Topľou 29 Lučenec Humenné Snina Dolný Kubín Snina 30 Dolný Kubín Spišská Nová Ves Ružomberok Snina Šaľa 31 Trenčín Vranov nad Topľou Prešov Skalica Námestovo 32 Humenné Liptovský Mikuláš Žilina Námestovo Trebišov 33 Martin Michalovce Dolný Kubín Michalovce Lučenec 34 Púchov Žilina Skalica Ružomberok Trenčín 35 Senica Pezinok Nové Zámky Lučenec Pezinok 36 Vranov nad Topľou Piešťany Humenné Vranov nad Topľou Ružomberok 37 Ružomberok Bytča Púchov Dunajská Streda Dunajská Streda 38 Svidník Krupina Topoľčany Hlohovec Turčianske Teplice 39 Topoľčany Topoľčany Dunajská Streda Liptovský Mikuláš Zlaté Moravce 40 Banská Štiavnica Dolný Kubín Krupina Trebišov Brezno 41 Rimavská Sobota Trnava Levice Piešťany Bánovce nad Bebravou 42 Piešťany Trenčín Michalovce Humenné Liptovský Mikuláš 43 Michalovce Martin Trenčín Krupina Stropkov 44 Nové Mesto nad Váhom Zvolen Námestovo Levice Banská Bystrica 45 Revúca Púchov Lučenec Levoča Zvolen 46 Poltár Poltár Senica Turčianske Teplice Nové Mesto nad Váhom 47 Levice Levoča Poprad Spišská Nová Ves Sabinov 48 Nové Zámky Senica Vranov nad Topľou Topoľčany Detva 49 Levoča Brezno Prievidza Bánovce nad Bebravou Poltár 50 Námestovo Rimavská Sobota Piešťany Malacky Michalovce 51 Trnava Svidník Trnava Senica Malacky 52 Žarnovica Poprad Martin Brezno Nové Zámky 53 Poprad Malacky Turčianske Teplice Bytča Martin 54 Krupina Levice Poltár Martin Žarnovica 55 Medzilaborce Kežmarok Bánovce nad Bebravou Zvolen Levice 56 Hlohovec Zlaté Moravce Banská Štiavnica Trnava Spišská Nová Ves 57 Senec Dunajská Streda Bytča Nové Mesto nad Váhom Púchov 58 Sabinov Prievidza Brezno Poltár Prievidza 59 Kysucké Nové Mesto Nové Mesto nad Váhom Zvolen Čadca Čadca 60 Prievidza Banská Štiavnica Malacky Detva Trnava 61 Kežmarok Partizánske Nové Mesto nad Váhom Prievidza Poprad 62 Čadca Revúca Detva Sabinov Partizánske 63 Považská Bystrica Detva Sabinov Rimavská Sobota Žilina 64 Malacky Sabinov Rimavská Sobota Poprad Rimavská Sobota 65 Stropkov Turčianske Teplice Čadca Partizánske Rožňava 66 Trebišov Žarnovica Revúca Kežmarok Senica 67 Rožňava Čadca Partizánske Žarnovica Medzilaborce 68 Komárno Bánovce nad Bebravou Kežmarok Medzilaborce Hlohovec 69 Bánovce nad Bebravou Považská Bystrica Považská Bystrica Považská Bystrica Považská Bystrica 70 Galanta Rožňava Žarnovica Rožňava Kežmarok 71 Dunajská Streda Medzilaborce Rožňava Revúca Komárno 72 Partizánske Komárno Komárno Komárno Revúca 80 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . Appendix B. Technical efficiency change index, 2017–2021 District 2018/ 2019/ 2020/ 2021/ Mean Cumulative 2017 2018 2019 2020 T EC T EC Bratislava I-V 1.028 0.949 1.108 1.000 1.020 1.081 Malacky 1.383 1.056 1.079 0.997 1.120 1.571 Pezinok 0.986 1.128 0.964 0.971 1.010 1.041 Senec 1.489 1.057 0.973 1.127 1.146 1.726 Dunajská Streda 1.466 1.283 1.008 0.994 1.172 1.885 Galanta 2.044 1.086 1.000 1.000 1.220 2.220 Hlohovec 1.589 0.967 0.944 0.816 1.043 1.184 Piešťany 1.196 0.976 1.063 1.060 1.071 1.315 Senica 1.004 1.127 0.986 0.916 1.006 1.022 Skalica 1.274 0.914 0.986 1.031 1.043 1.184 Trnava 1.256 0.986 0.987 1.000 1.052 1.222 Bánovce nad Bebravou 1.191 1.392 1.069 1.032 1.163 1.829 Ilava 1.071 0.965 1.043 0.932 1.001 1.005 Myjava 1.000 1.000 0.957 1.045 1.000 1.000 Nové Mesto nad Váhom 0.989 1.115 1.054 1.062 1.054 1.234 Partizánske 1.463 0.999 1.014 1.150 1.143 1.704 Považská Bystrica 1.015 1.132 1.031 1.110 1.071 1.315 Prievidza 1.183 1.208 0.907 1.081 1.088 1.401 Púchov 1.048 1.111 1.029 0.880 1.013 1.054 Trenčín 1.070 1.049 1.112 0.928 1.037 1.158 Komárno 1.006 1.069 1.068 1.197 1.083 1.375 Levice 1.064 1.202 0.995 0.938 1.045 1.194 Nitra 1.000 1.000 0.880 1.006 0.970 0.885 Nové Zámky 1.453 0.944 1.077 0.841 1.056 1.242 Šaľa 1.130 0.952 0.929 0.993 0.998 0.992 Topoľčany 1.136 1.064 0.959 1.185 1.083 1.374 Zlaté Moravce 0.858 1.405 1.054 0.839 1.016 1.066 Bytča 1.045 0.947 1.050 1.223 1.062 1.271 Čadca 1.040 1.244 1.100 1.018 1.097 1.449 Dolný Kubín 1.069 1.109 0.997 1.056 1.057 1.248 Kysucké Nové Mesto 1.795 1.000 0.964 1.037 1.157 1.794 Liptovský Mikuláš 1.070 1.137 0.896 0.988 1.019 1.077 Martin 1.069 0.985 1.003 1.012 1.017 1.069 Námestovo 1.396 0.939 1.037 1.009 1.082 1.372 Ružomberok 1.229 1.023 0.960 0.994 1.047 1.200 Turčianske Teplice 0.816 1.212 1.075 1.033 1.024 1.098 Tvrdošín 0.988 0.932 1.065 1.006 0.997 0.987 Žilina 0.973 1.092 1.130 0.751 0.975 0.902 Banská Bystrica 1.109 0.930 0.986 0.898 0.978 0.913 Banská Štiavnica 0.947 1.163 1.169 1.111 1.094 1.430 Brezno 0.710 1.059 1.056 1.055 0.957 0.838 Detva 0.743 1.134 1.041 1.075 0.985 0.943 Krupina 1.323 1.053 0.994 1.076 1.105 1.490 Lučenec 1.218 0.899 1.040 1.011 1.036 1.151 Poltár 1.130 1.025 1.010 1.051 1.053 1.229 Revúca 0.972 1.035 0.846 1.089 0.981 0.927 Rimavská Sobota 1.040 0.991 0.997 1.064 1.023 1.093 Veľký Krtíš 1.197 1.084 0.899 1.001 1.039 1.168 Zvolen 0.932 0.949 1.036 1.057 0.992 0.969 Žarnovica 0.906 1.048 1.071 1.225 1.056 1.246 Žiar nad Hronom 1.000 1.000 1.000 1.000 1.000 1.000 Bardejov 1.156 1.051 0.996 0.973 1.042 1.177 Humenné 1.156 1.012 0.969 1.078 1.051 1.222 Continued on next page. 81 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings District 2018/ 2019/ 2020/ 2021/ Mean Cumulative 2017 2018 2019 2020 T EC T EC Kežmarok 1.262 0.918 1.020 1.034 1.051 1.222 Levoča 1.129 1.295 0.885 1.087 1.089 1.406 Medzilaborce 0.796 1.920 0.694 1.114 1.042 1.182 Poprad 1.139 1.140 0.838 1.139 1.055 1.239 Prešov 1.100 1.003 1.107 1.020 1.056 1.246 Sabinov 1.120 1.136 0.981 1.156 1.096 1.443 Snina 1.120 1.000 0.983 1.006 1.026 1.108 Stará Ľubovňa 0.976 0.924 0.990 1.059 0.986 0.945 Stropkov 1.795 1.040 1.066 0.825 1.132 1.642 Svidník 1.001 1.421 0.965 1.022 1.088 1.403 Vranov nad Topľou 1.194 0.950 1.059 1.041 1.058 1.250 Gelnica 1.320 1.000 0.878 1.013 1.041 1.174 Košice I-IV 1.000 1.000 1.000 1.000 1.000 1.000 Košice – okolie 1.168 1.000 1.000 1.000 1.040 1.168 Michalovce 1.238 1.010 1.021 0.936 1.046 1.195 Rožňava 1.019 1.242 0.960 1.299 1.121 1.578 Sobrance 1.000 1.000 1.000 1.000 1.000 1.000 Spišská Nová Ves 0.996 1.048 0.912 0.954 0.976 0.908 Trebišov 2.036 0.926 0.905 1.033 1.152 1.763 82 vol. 46/2024 Municipal waste management performance: A focus on Slovakia . . . Appendix C. Malmquist index of TFP change, 2017–2021 District 2018/ 2019/ 2020/ 2021/ Mean Cumulative 2017 2018 2019 2020 MI MI Bratislava I-V 0.973 0.915 1.323 1.037 1.051 1.221 Malacky 1.491 1.034 1.152 1.119 1.187 1.987 Pezinok 1.027 1.172 1.096 1.088 1.094 1.435 Senec 1.539 1.067 1.198 1.247 1.252 2.453 Dunajská Streda 1.602 1.337 1.204 1.106 1.300 2.852 Galanta 2.208 1.075 1.079 1.079 1.289 2.763 Hlohovec 1.726 0.957 1.004 0.886 1.101 1.469 Piešťany 1.302 0.984 1.174 1.172 1.152 1.763 Senica 1.051 1.139 1.057 1.016 1.065 1.286 Skalica 1.303 0.957 1.168 1.055 1.113 1.537 Trnava 1.376 1.005 1.072 1.12 1.135 1.660 Bánovce nad Bebravou 1.223 1.394 1.269 1.035 1.223 2.239 Ilava 1.11 0.942 1.09 0.988 1.030 1.126 Myjava 0.993 1.03 1.133 1.02 1.043 1.182 Nové Mesto nad Váhom 0.982 1.154 1.128 1.182 1.109 1.511 Partizánske 1.528 0.985 1.068 1.218 1.183 1.958 Považská Bystrica 1.063 1.125 1.068 1.184 1.109 1.512 Prievidza 1.249 1.195 1.026 1.138 1.149 1.743 Púchov 1.086 1.093 0.923 1.09 1.045 1.194 Trenčín 1.12 1.1 1.287 1.004 1.123 1.592 Komárno 1.049 1.077 1.225 1.312 1.161 1.816 Levice 1.166 1.18 0.937 1.151 1.104 1.484 Nitra 0.996 1.035 1.099 1.067 1.049 1.209 Nové Zámky 1.539 0.932 0.993 1.03 1.101 1.467 Šaľa 1.083 0.973 1.178 1.046 1.068 1.298 Topoľčany 1.182 1.104 1.161 1.226 1.167 1.857 Zlaté Moravce 0.933 1.386 0.934 1.103 1.074 1.332 Bytča 1.098 0.975 1.11 1.421 1.140 1.689 Čadca 0.999 1.223 1.111 1.064 1.096 1.444 Dolný Kubín 1.102 1.15 1.086 1.078 1.104 1.484 Kysucké Nové Mesto 1.795 0.992 0.827 1.399 1.198 2.060 Liptovský Mikuláš 1.163 1.157 0.979 1.086 1.093 1.431 Martin 1.148 1.026 1.126 1.096 1.098 1.454 Námestovo 1.37 0.913 1.024 1.086 1.086 1.391 Ružomberok 1.282 1.066 1.11 1.092 1.134 1.656 Turčianske Teplice 0.869 1.25 1.227 1.084 1.096 1.445 Tvrdošín 1.024 0.945 1.132 1.066 1.039 1.168 Žilina 1.033 1.082 0.971 0.996 1.020 1.081 Banská Bystrica 1.188 0.969 1.089 0.986 1.054 1.236 Banská Štiavnica 0.933 1.156 1.413 1.105 1.139 1.684 Brezno 0.712 1.098 1.196 1.037 0.992 0.970 Detva 0.744 1.096 1.048 1.329 1.032 1.136 Krupina 1.334 1.036 1.054 1.204 1.151 1.754 Lučenec 1.253 0.879 1.027 1.188 1.077 1.344 Poltár 1.086 0.983 1.06 1.142 1.066 1.292 Revúca 0.956 1.023 0.844 1.332 1.024 1.099 Rimavská Sobota 1.015 0.964 1.129 1.042 1.036 1.151 Veľký Krtíš 1.154 1.045 0.933 1.034 1.038 1.163 Zvolen 0.984 1.006 1.174 1.054 1.052 1.225 Žarnovica 0.95 1.026 1.097 1.312 1.088 1.403 Žiar nad Hronom 1.053 1.012 1.044 1.123 1.057 1.249 Bardejov 1.099 1.001 1.052 1.053 1.051 1.219 Humenné 1.122 1.03 1.027 1.079 1.064 1.281 Continued on next page. 83 Eleonóra Marišová, Peter Fandel Acta Polytechnica CTU Proceedings District 2018/ 2019/ 2020/ 2021/ Mean Cumulative 2017 2018 2019 2020 MI MI Kežmarok 1.278 0.902 1.042 1.066 1.064 1.280 Levoča 1.088 1.272 1.085 1.009 1.109 1.515 Medzilaborce 0.673 1.735 0.843 0.929 0.978 0.914 Poprad 1.167 1.195 0.985 1.158 1.123 1.591 Prešov 1.105 0.977 1.175 1.103 1.088 1.399 Sabinov 1.065 1.105 1.05 1.131 1.087 1.398 Snina 1.11 0.971 1.07 1.005 1.038 1.159 Stará Ľubovňa 0.952 0.915 1.084 1.013 0.989 0.957 Stropkov 1.648 1.006 1.318 0.774 1.140 1.691 Svidník 1.037 1.402 0.965 1.061 1.105 1.489 Vranov nad Topľou 1.109 0.905 1.111 1.107 1.054 1.234 Gelnica 1.235 0.952 0.874 1.167 1.046 1.199 Košice I-IV 0.978 1 1.004 0.999 0.995 0.981 Košice – okolie 1.131 0.99 1.023 1.065 1.051 1.220 Michalovce 1.248 0.977 1.044 0.995 1.061 1.267 Rožňava 1.032 1.21 1.03 1.32 1.141 1.698 Sobrance 0.865 0.851 1.053 0.848 0.901 0.657 Spišská Nová Ves 1.045 1.029 0.908 0.992 0.992 0.969 Trebišov 1.919 0.894 0.98 0.988 1.135 1.661 84 Acta Polytechnica CTU Proceedings 46:65–84, 2024 1 Introduction 2 Theoretical background 3 Material and methods 4 Results 4.1 Municipal Waste – EU legislation 4.2 Implementation of EU waste legislation into Slovak one 4.3 EU Prevention of waste 4.4 Main aims of waste prevention in Slovak Waste Prevention program 4.5 Waste management Program and Slovak LAU-1 districts competencies 4.6 The Strategy of the Environmental Policy of the Slovak Republic until 2030 4.7 Results of the quantitative analysis 5 Conclusions Acknowledgements References A Rank of districts of Slovakia according to waste management performance measured by the composite indicator of technical super-efficiency, 2017–2021 B Technical efficiency change index, 2017–2021 C Malmquist index of TFP change, 2017–2021