CHEMICAL ENGINEERING TRANSACTIONS VOL. 81, 2020 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Qiuwang Wang, Min Zeng, Panos Seferlis, Ting Ma, Jiří J. Klemeš Copyright © 2020, AIDIC Servizi S.r.l. ISBN 978-88-95608-79-2; ISSN 2283-9216 Waste Production and Treatment Modelling for EU Member States Veronika Smejkalováa,*, Radovan Šompláka, Kristýna Rybováb, Vlastimír Nevrlýa, Martin Roseckýc, Boris Burcind, Tomáš Kučerad aInstitute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 616 69 Brno, Czech Republic bDepartment of Geography, Faculty of Science, Jan Evangelista Purkyně University, České mládeže 8, 400 96 Ústí nad Labem, Czech Republic. cInstitute of Mathematics, Faculty of Mechanical Engineering, Brno University of Technology, Technická 2896/2, 616 69 Brno, Czech Republic dDepartment of Demography and Geodemography, Faculty of Science, Charles University, Albertov 6, 128 43 Praha 2, Czech Republic. Veronika.Smejkalova1@vutbr.cz The modern way of life places considerable demands on energy and material resources. The limited amount of primary resources and the burden on the environment have supported the circular economy's tendencies which has EU recently incorporated into its legislation as Circular Economy Package. The aim of this package is to turn waste into material or energy source. The waste treatment methods are also regulated in EU legislation through recycling and landfilling targets. Estimation of future waste production and treatment is a fundamental prerequisite for adjustments in waste management. The aim of this paper is to model the municipal solid waste production and treatment at a national level for the EU member states. The principles of linear regression were used to reveal the relationship between waste management data (waste streams production and treatment) and 19 socio-economic, demographic and other factors. Among the EU states, the resulting models explain at least 66 % of the variability in waste streams production and 52 % in case of waste treatment methods. The most influential variables are GDP, income and educational level of inhabitants. The results of these analyses could be used for forecasting production and waste treatment on the state level and can indicate the gaps between probable development and waste management targets set by the EU. The accuracy and comparability of waste production data across the EU should be further analysed. 1. Introduction The current state of waste management in the EU is at different levels depending on the individual countries' development. Significant differences were also observed in terms of municipal solid waste (MSW) generation. Waste management in the EU is currently undergoing a transition from a linear economy to a circular economy (Morseletto, 2020). The smooth transition to the circular economy and the transformation of waste management is enshrined in the new legislation as Circular Economy Package (CEP). The waste treatment methods are regulated, and tangible recycling targets are given as well. Specifically, landfill prohibition is planned since 2035, where only 10 % at most of the total amount of MSW generated could be landfilled (Directive 2018 / 850). Further targets regulate the amount of MSW recycled, with specific percentages increasing over time in 2025, 2030, and 2035 (Directive 2018 / 851). Based on the mentioned directives, a change is needed in most countries. The motivation is to find a solution for transition, which is associated with a detailed analysis of the current state and the search for future trends. Figure 1 shows the MSW treatment in the EU member states in the year 2017 based on visualisation method presented Pomberger et al. (2017). Green area is the place of fulfilling the EU’s targets from 2035. DOI: 10.3303/CET2081116 Paper Received: 29/03/2020; Revised: 10/05/2020; Accepted: 18/05/2020 Please cite this article as: Smejkalová V., Šomplák R., Rybová K., Nevrlý V., Rosecký M., Burcin B., Kučera T., 2020, Waste Production and Treatment Modelling for EU Member States, Chemical Engineering Transactions, 81, 691-696 DOI:10.3303/CET2081116 691 Figure 1: The current situation of municipal solid waste treatment for the EU-28 member states, year 2017 (data source: Eurostat, 2019), note: B – Belgium, BG – Bulgaria, CZ – Czechia, DK – Denmark, D – Germany, EST – Estonia, IRL – Ireland, GR – Greece, E – Spain, F – France, HR – Croatia, I – Italy, CY – Cyprus, LV – Latvia, LT – Lithuania, L – Luxembourg, H – Hungary, M – Malta, N – Netherlands, A – Austria, PL – Poland, P– Portugal, RO – Romania, SLO – Slovenia, SK – Slovakia, FIN – Finland, S – Sweden, GB – United Kingdom No country is meeting its targets, except Germany. Some countries have a high energy utilization rate despite landfill restrictions. In such cases, it will be necessary to increase the separation of mixed municipal waste (MMW). Over the past few decades, most countries are moving towards material recovery and incineration. The aim is to save primary raw material resources and develop approaches to minimize emissions from waste treatment (Fan et al., 2019). The necessary step of waste management planning is to map the current situation in detail and compare it with EU requirements. It may also be key to find factors and aspects that have a significant impact on developments in waste management. Information on the amount of waste production is essential when planning waste management. It is also important to pay attention to waste treatment methods and to analyse the factors associated with it. Many authors do not deal with waste treatment modelling, most of the papers are devoted exclusively to issues of waste production (Goel et al., 2017). There are several studies from the last years addressing the EU policy, its targets and sustainability, but most of them are oriented on particular country, e.g. Croatia (Traven et al., 2018), Italy (Di Maria et al., 2020), Romania (Căilean and Teodosiu, 2016) or particular waste streams e.g. plastics (Baran, 2020), construction and demolition waste (Nunes and Mahler, 2020) or only on waste treatment without waste production aspects that are the prerequisite for the treatment (Castillo- Giménez et al., 2019). This contribution presents an analysis of both production and waste treatment in EU countries. The main links between waste management and socio-economic, demographic and other factors are included. Global plans are being developed, and the issue needs to be approached in a similar metric from the perspective of significant factors affecting waste management and treatment methods. It is important to design methods for forecasting of waste production with a link to subsequent use from the perspective of waste treatment method and EU targets. The possibility of forecasting waste management data for the future is discussed. 2. Application of intent – reviews on strategic planning Waste management in the EU is currently in the process of transition from linear to a circular economy. As stated above, many EU member states are not yet meeting given targets. A smooth transition to a circular economy requires the development of relevant plans. Strategic planning is necessary at a several levels: businesses, municipalities, regions, states. Zorpas (2020) provides a holistic approach processing how to develop, implement, monitor and improve a strategy in the framework of waste management at a local level as well as at a central level. Zotos et al. (2009) developed an approach for waste management strategy planning at both the household and companies level. Waste management involves multiple stakeholders such as government agencies, suppliers, consumers, providers of treatment and transport. An optimal waste management strategy should utilize the synergistic effect of multiple participants. Diaz-Barriga-Fernandez et al. 692 (2017) presented the multi-objective model for waste management strategy planning. The aim is to maximize the benefit of all the participating stakeholders. Strategic waste management plans are often based on operational research. The issues solved are often dealing with the waste collection routes (Hrabec et al., 2019), smart waste management technologies integration (Bong et al., 2018), the appropriate technology development (Deng et al., 2017), searching the optimal location for waste management facilities (Yadav et al., 2017) and design of waste management facilities (Becker, 1995). In terms of planning new facility capacities, information on waste production and management is essential. A review on waste generation modelling methods concluded the same statement (Beigl et al., 2008). The current situation in waste management research is not sufficient prerequisite for strategic planning, and it is also necessary to make projection of future development of waste management as well as of underlying factors influencing the waste management situation in the EU states. 3. Waste production and treatment modelling Waste generation modelling is currently a widely discussed topic, as demonstrated by the ever-increasing number of publications on this subject. Approaches to waste generation modelling have been summarized in: Beigl et al. (2008) – 45 publications and Goel et al. (2017) – 100 publications. Existing approaches have used different methods that can be divided into two basic groups – modelling relationships in data and time series analysis. Waste production is influenced by a number of socio-economic, demographic, environmental and other factors. Numerous articles show the preference of this method, which identifies the factors influencing waste production in individual states. The aggregated data can suppress variability at the municipal level. This is demonstrated by case studies from the Czech Republic. The linear regression model presented by Rybová et al. (2018) at the municipal level considered 12 factors for modelling MSW production in Czech municipalities. Eight characteristics were identified as significant but were able to explain only 5.1 % of the variability in MSW production. Compared to Kováčová et al. (2011), where it was possible to create a model for the regional level with R2 equal to 0.86. The higher accuracy compared to the work results of Rybová et al. (2018) is probably due to higher territorial details (regions). Other important information includes waste treatment methods, although it is not considered often in terms of modelling. It is key information in terms of meeting objectives and planning infrastructure. At the national level, waste generation and treatment can be modelled relatively successfully. The available data both on waste management and socio-economic for member states of EU in the year 2017 are collected from the Eurostat database (Eurostat, 2019). The influence of available socio-economic data on waste production and treatment is analysed. The linear regression model is applied in the form: 𝑦𝑖 = 𝛽0 + ∑ 𝑗∈𝐽 𝛽𝑗 𝑥𝑖,𝑗 , (1) where set 𝑖 ∈ 𝐼 is set of EU member states and 𝑗 ∈ 𝐽 is set of considered independent variables. 𝑥𝑖,𝑗 is an independent variable, 𝛽0 is an intercept and 𝛽𝑗 is searched regression coefficient. A total of 11 linear regression models in the form (1) are made for the various dependent variables, which are listed in the headers of Table 1 and Table 2. At total, 19 parameters of independent variables from the set 𝐽 are considered for each linear regression model. The estimation of regression coefficients 𝛽𝑗 is solved by the least squares method, while the assumptions are verified. The parameter test reveals independently significant independent variables. The null hypothesis 𝐻0: 𝛽𝑗 = 0 is tested, as opposed to the alternative hypothesis 𝐻𝐴: 𝛽𝑗 ≠ 0. Due to the probable high correlation between some independent variables, the effect of multicollinearity in individual models was controlled and eliminated. The significant independent variables are summarized in the Table 1 and Table 2 together with assumption testing results and quality assessment. The header of the Table 1 specifies the modelled waste production variables (total household waste production, recyclable waste, MMW and MSW) which present dependent variable. For each of these dependent variables was build an individual linear model. The first part of the Table 1 marked as “Verification of linear regression assumptions” summarizes results of linear regression assumption testing. Significance level 𝛼 was set to 0.05. For all cases, the hypothesis of residual normality (Shapiro-Wilk test), zero mean (t-test), and correlation (Durbin-Watson test) are not refused. The table includes the p-values of the tested hypothesis. The quality of the model is quantified by R2adj (adjusted coefficient of determination), which is higher than 0.82 for each dependent variable of waste production, except MSW production. The problem is that MSW is not defined everywhere in the same way. This fact limits the quality of the model. The significant variables are specified in the table and marked by plus ( ) for a positive estimated regression coefficient 𝛽𝑗 . A minus ( ) indicates a negative regression coefficient. Other variables without plus or minus symbols were found as insignificant so 693 the value of their regression coefficient 𝛽𝑗 equals zero. The nonzero intercept was found to be significant only for MSW production model. Table 1: Linear regression results for waste production Households production Recyclable waste MMW MSW Verification of linear regression assumptions Normality 0.545 0.291 0.438 0.796 Zero mean 0.547 0.948 0.990 1.000 Correlation 0.478 0.474 0.978 0.778 Model quality R2adj 0.931 0.822 0.952 0.659 Intercept 𝛽0 1,186 Significant independent variables 𝑗 GDP* Young-age dependency ratio Life expectancy at birth– males Upper secondary education Tertiary education Median equivalised net income (PPS*) Median equivalised net income (Euro) *Note: GDP – Gross domestic product PPS – Purchasing power standard Total production of household waste is influenced by education and income. The economic strength of the country naturally has an impact on recycling. An important waste stream is MMW, which is often landfilled or used for energy recovery. A significant influence on its production plays the life expectancy, education of population and income. The education can be purposefully supported at state levels. It is not possible to expect fast changes in the near future, but it is necessary to address these issues in the long term. However, MSW is more complex and depends on most of the factors listed. Countries have quite a lot of possibilities for a systematic shift such as informational campaigns, adjustment of waste fees or balance of technical solutions. The regression model (1) was then applied to the MSW treatment data. The results are summarized in Table 2, linear regression assumptions are fulfilled. The accuracy of these models is diverse based on waste treatment method. In the context of waste management planning, the model for material recovery of waste is essential, R2adj equals to 0.719. Composting is a way of material recovery and its R2adj is equal to 0.797. The significant independent variables 𝑗 are depicted in the same way as in the Table 1. The intercept is included in all waste treatment models. As with waste production, the economy influences the treatment of MSW rapidly. Material recovery, which is the fundamental identifier from the perspective of the EU's objectives, is influenced by the economic force of the country. In the case of waste composting, the gender factor also affects the model. An essential factor is also related to the way of living. In this respect, it is possible to support especially young citizens, families with children or other household features. Analysing the determinants of waste management behaviors, results reveal that waste management behaviors are different, and are likely to be driven by different motivations. (Minelgaitė and Liobikienė, 2017). Some independent variables, which were considered in the model for both the waste production and treatment, were not statistically significant. These are population, environmental protection expenditure, number of persons employed in circular economy, single person households and households with children. 694 Table 2: Linear regression results for waste treatment MSW treatm ent MSW incineratio n + energy recovery MSW landfillin g MSW incinerati on MSW energy recovery MSW material recovery MSW compost ing Verification of linear regression assumptions Normality 0.674 0.056 0.747 0.386 0.380 0.124 0.941 Zero mean 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Correlation 0.296 0.822 0.076 0.284 0880 0.948 0.854 Model quality R2adj 0.518 0.857 0.584 0.584 0.832 0.719 0.797 Intercept 𝛽0 317.2 -139.3 482.8 -278.3 -139.5 267.5 -965.4 Significant independent variables 𝑗 GDP Young-age dependency ratio Old-age dependency ratio Persons per square kilometre Life expectancy at birth– females Tertiary education Median age Masculinity index Median equalised net income (Euro) Single person households 4. Conclusion The aim of this paper was to model the municipal solid waste production and treatment at a national level for the EU member states. 19 variables possibly influencing the waste production and treatment were selected and tested. Linear regression models on the national level achieved satisfying quality based on R2adj and significant independent variables that can partly explain the current variability in waste management situation across the EU countries were identified. When planning waste management in EU countries, it is appropriate to focus on meeting EU targets. These targets set minimum recycling rates and maximum landfilling of MSW. It is necessary to analyse both waste production and in particular, waste treatment methods. Initial results mainly show the impact of the country’s economic power on waste management. But individual modelled variables from waste management are characterized by different socio-economic, demographic and other factors. This linear regression approach is unsuitable for forecasting unless significant factors can be well predicted. There exist some forecasts for socio-economic factors but their quality is often worse than the trend of data in waste management. The quality and length of the forecast for single variables is highly heterogeneous. Among economic factors, GDP and unemployment are usually predicted. However, given the dynamic development of the economy, quality long-term forecasts of economic factors are not usually available. On the other hand, demographic development can be predicted in the long term. 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