Prediction of E.U. sustainable development indicators based on fuzzy description and similarity

Prediction of E.U. sustainable development indicators based on fuzzy description and similarity David Sch€ uller a and Karel Doubravsk y b a Department of Management, University of Technology, Brno, Czech Republic; b Department of Informatics, University of Technology, Brno, Czech Republic ABSTRACT A sustainable economy is a complex issue related to economic, social and environmental areas. For European Union (E.U.) countries, it is closely linked to the issues of sustainable industry, infrastructure and innovation in R&D. Thus, the article is specifically focused on identifiers of Sustainable Development Goal 9 (S.D.G. 9) created by E.U. To meet the main targets based on sustainable development and The European Green Deal strategy, it is necessary to have an idea of the possible future development of the S.D.G. 9 indicators. The main aim of this article is to create a semi-deep prediction model using cluster analysis and fuzzy approach. The contribution of this article is the use of a fuzzy approach to create a multivariate prediction model that allows to circumvent the limitations of classical regression analysis. The E.U. countries were divided into five clusters. A semi-deep prediction model was created for each cluster using fuzzy approach. ARTICLE HISTORY Received 15 September 2022 Accepted 7 March 2023 KEYWORDS fuzzy description of time series; fuzzy similarity; sustainable industry; infrastructure and innovation; European Union (E.U.) JEL CODES C38; C53; O3 1. Introduction Sustainable development forms one of the main pillars of the European Union (E.U.). Sustainable development goals (S.D.G.s) have been anchored in European policy for a long time. The 2030 Agenda for Sustainable Development created by the E.U. includes the set of 17 S.D.G.s and their related 169 targets. The ninth S.D.G. 9 is focused on industry, innovation and infrastructure. Sustainable industrialisation is the key factor that improves standards of living of all people and reduces poverty. Innovation is a core driver for finding lasting solutions. Technical progress and innovation enhance the social, economic and ecological environment of human beings. Green innovations include ecological and environmental aspects (Franceschini et al., 2016). As innovation, the concept implies new initiatives, changes, approaches or proposals dealing with social challenges as well (S anchez-Mart ınez et al., 2020). CONTACT David Sch€ uller [email protected] ß2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 2023, VOL. 36, NO. 3, 2190399 https://doi.org/10.1080/1331677X.2023.2190399 Sustainable and digital innovation occurs in different fields such as products, processes and services. The aim of sustainable innovation is to decrease environmental impact (Schiederig et al., 2012). Ecological aspect is connected with the term ecoefficiency. Eco-efficiency is striving to add maximum value with the minimum use of resources and minimum pollution (Welford & Casagrande, 1997). Green industry will form tahe future technologies in manufacturing and will generate high added value solutions. (Stock & Seliger, 2016). These solutions will have a significant impact on economy and ecology. From an ecological point of view the allocation of resources such as materials, energy or water will be possible to realise more efficiently (Kagermann et al., 2015). Moreover, digital transformation and green industry has a great potential to improve social dimension of mankind. The core idea of green industry and digital transformation is to improve the deteriorating economic, ecological and social conditions in the world by using new industrial technologies and integrating more efficient processes (Stock & Seliger, 2016). Climate change and environmental degradation pose an existential threat to Europe. The E.U.’s response has been to create the European Green Deal, a comprehensive strategy that effectively addresses the impacts of climate change and environmental degradation (Fetting, 2020). The main objectives of the European Green Deal include no net greenhouse gas emissions by 2050 and economic growth decoupled from resource use (Gautier et al., 2022). The cooperation across E.U. countries is necessary to overcome these challenges. It will be necessary for European countries to invest massively in green innovation and infrastructure development and ensure the most efficient allocation of resources. It can be assumed that some E.U. countries are better able to meet the objectives of The European Green Deal and can transfer their experience to other European countries. Based on this assumption, the following research question was established: 1.1. Are there any differences in the development of individual S.D.G. 9 variables across E.U. countries? In order to answer this question, it is necessary to identify differences between countries and develop prediction models. Cluster analysis can be used to identify differences between countries. One way to formulate a prediction model given the uncertainty of the input data is to use probability theory and statistical methods. A widely used statistical method in these cases is regression analysis. The unilateral dependence of one variable on other variables is called multivariate regression. Then the dependence of the mean value of the dependent variable on the values of the independent variables is expressed by a regression function/model. In cases where the observed variables are represented by time series, the regression results may be biased by the presence of a trend in these time series, the dependence between the observed variables, and the volatility of the variance. This is referred to as autocorrelation, multicollinearity, and heteroskedasticity. Moreover, if multiple regression were to be used to describe the dependence of one of the observed variables on the remaining variables (as in this article), this would imply the creation of 2 D. SCHÜLLER AND K. DOUBRAVSKÝ several separate models (for each observed variable) (Fumo & Biswas, 2015;Qi& Roe, 2016). Another way to work with uncertainty is to use fuzzy set theory and fuzzy logic. Fuzzy logic is a multidimensional discipline that focuses on the problems of approximate inference and fuzzy approximation. This allows us to circumvent the limitations in the use of classical regression analysis and formulate a single model for all the observed variables. The issue of fuzzy sets and fuzzy logic, specifically fuzzy similarity, is discussed in the following part. 2. Theoretical framework 2.1. Sustainable industry Sustainable industry is connected with the fourth stage of industrialisation called Industry 4.0. This stage builds on the third industrial revolution which began in the 1970s and its principal stones were electronics, information and communication technologies and automation (Winter, 2020). Sustainable industry is seen as the production of goods or services through the implementation of integration systems that support the optimisation of the efficiency of production systems based on quality data. The main goal is to create sustainable value and economic growth (Chauhan et al., 2021). Smart factories, smart products, smart services anchored in the Internet of things form Industry 4.0 (Kagermann et al., 2015). Smart industry and digitalisation of business allows to streamline the consumption of scarce resources, reduce waste, streamline the management of production systems, maximise outputs and minimise resource utilisation, reduce overproduction and save energy (Kamble et al., 2020). These solutions will have a significant impact on the economy and ecology. According to Kamble et al. (2018) sustainable industry 4.0 framework consists of the following technologies –Internet of things, big data analytics, cloud computing, simulation and prototype, 3D printing, augmented reality and robotic systems. The framework takes into account that the integration of innovation, industrial and economic processes enable a more flexible, economical and environmentally-friendly manufacturing system (Duarte & Cruz-Machado, 2018). 2.2. Sustainable innovation In the twenty-first century there has been an increasing interest in sustainable innovation (Aghion et al., 2009). It is mainly caused by numerous long term challenges such as climate change, water scarcity, pollution, population ageing, etc. (Montalvo et al., 2007). Sustainable innovation is defined as the activity of creating new ideas, behaviour, products, processes that enable the decrease of negative impacts on the environment and ecology (Rennings, 2000). Sustainable innovation includes green, ecological and environmental aspects (Franceschini et al., 2016). Sustainable innovation occurs in different fields such as products, processes, services and business models. Sustainable innovation takes into account the economic and ecological aspects. Ecological aspect is connected with the term eco-efficiency. Norberg-Bohm (1999) regards environmental innovation as the reduction of ecological impact ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3 through waste minimisation. However, sustainable innovation has to be understood in a broader sense. For instance, new products could not only reduce the environmental burden but also improve human life factors. Sustainable innovation also contains new markets and new systems, for example, new means of transport (Bl€ attel-Mink, 1998). Sustainable innovation also contains a social aspect and tries to find the solutions for social challenges. New technologies based on sustainable innovation concepts have a high impact on the daily life of society and the standard of living of the population. Technological innovation has to be encouraged by an evolution of social and institutional structures (Freeman, 1996). In general, existing technologies are improved gradually. However, radical innovation is needed to achieve ecological, economical and social targets (Huesemann, 2003). Sustainable innovation often responds to complex challenges, which requires the development of complex solutions and therefore close relationships with a complex network of stakeholders are needed (Adams et al., 2016). 2.3. Sustainable infrastructure The world is mainly urban and more than 50% of the population lives in urban areas (Diaz-Sarachaga et al., 2016). This number has been steadily increasing and it is expected that 70% of the global population will live in cities and towns by the year 2050 (Angel et al., 2012). The significance of urban areas is also proved by the emergence of new conglomerations with more than 20 million inhabitants (Berardi, 2015). The level of infrastructure is expected to rise all over the world in the next few years and decades. The thing is that all mankind has to strive to use the limited resources efficiently and protect the environment as a whole. This idea is the cornerstone of sustainable infrastructure. Sustainable infrastructure could be defined as a system that is able to last a long time, ensuring the human-build environment to flourish and enable human society to increase its quality of life without restricting availability of natural, economic and social assets for future generations (Hendricks et al., 2018). Predominantly, building and expanding cities represent the highest consumption of limited resources. Urbanisation is the principal engine of economic progress and building infrastructure significantly increases G.D.P. and investment. The efficient use of limited resources and sustainable infrastructure development can ensure economic growth, the protection of ecology and improvement of social welfare of human beings (Hendricks et al., 2018). 3. Materials and methods Decision-making in the field of sustainable economies is linked to the study of real phenomena that require knowledge. The spectrum of knowledge about each phenomenon contains data of a diverse nature (deep and shallow). Shallow knowledge is obtained by non-numerical heuristics, qualitative interpretation of experiments, engineering intuition, etc. In an economy, time records are often used, which are traditionally used for time series analysis, and represent the most accurate information. Such a type of information is a typical example of shallow knowledge. Shallow 4 D. SCHÜLLER AND K. DOUBRAVSKÝ knowledge is rather weak and very specific, but it is obtainable relatively easily. Shallow knowledge is transferred into a semi-deep oriented model through statistical methods. If there is a strong dependence between the input variables, the use of classical statistical methods is problematic. 3.1. Cluster analysis Cluster analysis is a statistical method used for dividing a set of elements into clusters. Each group is homogeneous with respect to certain characters based on either the similarity or the dissimilarity metric. Therefore, cluster analysis is widely used as a suitable tool for marketing segmentation (Liu & Ong, 2008; Mentzer et al., 2004; van Raaij et al., 2003). Clustering differs from simple ordering in following terms: Simple ordering would be possible if the data were one-dimensional. But still, this procedure would not classify the data samples into classes. Nevertheless, more-dimensional cases cannot be simply ordered, because the decision-maker would have to choose the pivot dimension and omit the rest. If all the dimensions should be taken into account, then some kind of aggregate function would have to be employed. Such function could be arbitrary, for instance linear, i.e.: fidi1,...,din ðÞ ¼X n j¼1 ajdij, (1) where the coefficients ajwere chosen by the user or by the character of j-th dimension data range. But such function is already included in Ward’s clustering method. Hierarchical clustering algorithms were developed to avoid some disadvantages in terms of flat or partition-based clustering approaches. Partial methods in general need a user predefined parameter K to gain a clustering solution and so they are nondeterministic. Hierarchical algorithms were developed in order to create more deterministic and flexible attitude for data clustering (Jain et al., 1999). A cluster hierarchy uses the standard binary tree terminology. The roots include all the sets of data objects for clustering and thus the apex of hierarchy is formed. The entries in each cluster could be defined by traversing the tree from the current cluster to the base singleton data points. Every level in the hierarchy equals some amount of clusters. The hierarchical base contains all singleton points which create the leaves of the tree. This hierarchy of clusters is called dendrogram. The biggest advantage of the hierarchical clustering method is the fact that it is possible to cut the hierarchy at any given level and to get the number of clusters correspondingly (Aggarwal & Reddy, 2018). There are two general proposals for hierarchical clustering: Agglomerative –It is a bottom up approach where each observation begins in its own cluster, and pairs of clusters are merged on until the final maximal cluster is obtained. ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 5 Divisive –It is a top down approach where all observations begin in one cluster, and splits are done recursively as it moves down the hierarchy (Maimon et al., 2005). One of the basic marketing concepts is the concept of segmentation, i.e., classification of segmented subjects into groups according to their similarity of attributes. This concept however does not possess particular mathematical representation. On the other hand, there is a well elaborated concept of clustering, which is analogical to segmentation. When the problem of classification is tackled, having only a raw data set of n dimensions (n1), there has to be metrics (or criterion), which determine the size and the composition of classes (or clusters). The metrics decide whether various data samples within the data set are close to each other enough, in respect to n dimensions, to be classified as members of the same class. Such metrics are included in Ward’s method. The Ward’s criterion was chosen to be used within the article. The advantage of Ward’s criterion is that it produces a cluster tree that is compact and monotonic. It is caused by its incremental design in the definition of distance and it means (in contrast of non-monotonic tree) that the sections of the dendrogram do not change direction (Alikhanian et al., 2013). The Ward‘s criterion was suggested to figure out the distance between two clusters within the agglomerative hierarchy clustering method. The K-means sum of squared error criterion is used to determine the distance. Sum of squared error criterion for any two clusters C a and C b is computed by measuring the increase in the value of Ward’s criterion for the clustering gained by merging them into C a [C b (Ward, 1963). There are few implementations of Ward’s criterion which differ in distance metric d. The distance metric used in this article is defined as the squared Euclidean distance between the two centroids of the merged clusters C a and C b weighted by a proportional factor to the product of cardinalities of the merged clusters (Aggarwal & Reddy, 2018) and is defined as follows: dCa,Cb ðÞ¼ NaNb NaþNbX M v¼1 cav cbv ðÞ 2¼NaNb NaþNb dc a,cb ðÞ , (2) Naand Nbare the cardinalities of the cluster C a and C b. caand cbare elements of Caand Cbrespectively. viterates up to total number of elements in cluster union M: dis squared Euclidean distance between the two centroids. 3.2. Fuzzy theory Fuzzy set theory is based on the premise that the key elements in human thinking are not numbers but words. The most important feature of human thinking is the ability to extract from a mass of input data only such items of knowledge which are relevant to the solved task. The theory of fuzzy sets allows the existence of a type of uncertainty due to vagueness, e.g., Dubois et al. (1999,2014) and Zadeh (1965). 6 D. SCHÜLLER AND K. DOUBRAVSKÝ A linguistic value is a ‘value’that is given by words, e.g., low, medium, high. To quantify expert knowledge a set of verbal values, i.e., a dictionary, is needed. For example, a ‘verbal dictionary’could be the following set: fverylow, low, medium, high, very highg:(3) The fuzzy set A in U is a prescription (function) that assigns to each element x 2 U a single number from [0, 1]. If an element xis assigned the number ain this way, then ais called the degree of membership of element ato A and is written a¼A(x). The prescription of A is called the membership function and denoted by the symbol m. In the sense of the fuzzy set definition, the fuzzy set A is identified with its membership function. The linguistic value is transformed into the fuzzy set by the specification of membership function. Along with the definition, the interpretation of the meaning of ‘degree of membership’is very important: The closer the value of m(x) is to 1, the higher (also stronger) the membership of element xto a fuzzy set A, the closer the value of m(x) is to 0, the lower (also weaker) the affiliation of element xto a fuzzy set A. A fuzzy set can be specified in a variety of ways, but most often by a combination of verbal, graphical and mathematical descriptions. The specification must of course be correct, accurately describing the characteristics of the set of elements under consideration and the subjective idea of the user. You can specify a fuzzy set using verbal, graphical and mathematical descriptions. The specification must of course be correct, accurately describing the characteristics of the set of elements under consideration and the subjective idea of the user. Often a graphical description is just used in a computer software, e.g., Matlab, Scilab, Octave and Julia. For example, a verbal value around 5 C of the variable temp is transformed into a fuzzy set 5 C by the grade of membership function lgiven in Figure 1. The intervals a<temp <b,c<temp <drepresent such numerical values temp, which belong partially to the fuzzy set 5 C. Figure 1. Membership function. Source: own processing. ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 7 For example, the graphical description of the fuzzy set A defined by the verbal dictionary is shown in Figure 2. 3.3. Fuzzy description of time series A time behaviour of a system under study, e.g., a performance of a country’s economics, is described in a digitised form. It means that a sample period is chosen if the frequency is too high (e.g., currency exchange rates) or unemployment rates and inflation values are evaluated on monthly bases and not daily, for example. Each variable has Z different numerical values for Z sample intervals. The example given below illustrates the usage of super and subscripts: Xj ithe value of the variable Xiin j-th sample interval:(4) The observation Xj iis either not accurate or, more likely, its relevance of this observation exceeds its accuracy. The triangle shape grade of membership is often used to characterise the fuzziness of the corresponding observation. In other words each observation is fuzzyfied by a triplet of numerical values a,b¼c,d(see Figure 1). The meaning of the triplet is as follows: b¼c—a numerical value of observation, a¼b1e ðÞ , (5) d¼bð1þeÞ where the accuracy ereflects not only the actual accuracy of the information item but the fact that each observation is always partially specific/local and partially of general significance. A time window length is a number of sample intervals. If numerical values are known within L sample intervals then a correct (not bad) choice of the decision variable D can be made by the experienced human managers. Let us suppose that the system under study is (partially) controlled by an experienced controller, e.g., by a group of top experts. Their experience allows them to evaluate fairly accurately the length L of a time window. Figure 2. Fuzzy set A. Source: own processing. 8 D. SCHÜLLER AND K. DOUBRAVSKÝ The general form of the simple conditional statement is: if Athen B(6) where Ais a multidimensional set and Bis a decision or output variable(s). The time series can be used to generate many statements (Equation [6]) keeping in mind that only sequences of Lobservations are required to make a decision or to reason. if X1 1ÙX2 1Ù... ÙXL 1 ÙX1 2ÙX2 2Ù... ÙXL 2 ÙX1 nÙX2 nÙ... ÙXL n then XLþ1 nÙXLþ1 2Ù... ÙXLþ1 n (7) where an ‘layman interpretation’of the symbol Ùis fuzzy ‘and’. The length of the time window is L. The total length of time series is Z. Therefore: ZL(8) statements like (Equation [7]) can be easily created to generate the required set of conditional statements. If a set of conditional statements is available, the fuzzy reasoning can be used to answer different queries. It means that the fuzzy model represented by a set of conditional statements can be used in a similar way as a conventional mathematical model. The set of statements (Equation [7]) is a fuzzy model. There are many different fuzzy reasoning algorithms how to solve this fuzzy model. Transparency and simplicity of the reasoning algorithm is important for practical use and result interpretation. A transparent fuzzy reasoning/answering formalism: Q!fuzzy model ðEq:7Þ!R(9) is based on fuzzy similarity. A set Rof fuzzy sets and similarities is an answer to a given (chosen) n-dimensional fuzzy query Q, see (Sch€ uller & Doubravsk y, 2019). A similarity sof two n-dimensional fuzzy sets V, W is: sn,V,W ðÞ ¼min max min lVxj ðÞ ,lWxj ðÞðÞ  (10) where j¼1, 2, …,nand x j is a concrete value of a monitored variable X j . The similarity s2[0;1], s¼0 means there is no similarity of the fuzzy sets V a W, s¼1 means there is 100% similarity, i.e., the fuzzy sets V and W are identical see (Pavl akov aDo  cekalov a et al., 2017). Defuzzification is a special operation that transforms a fuzzy set R into a specific number. The most commonly used defuzzification method is the Centre of Gravity (C.O.G.) method. T¼PixilðxiÞ PilðxiÞ(11) ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 9 the cluster 5 states will invest more in research and development as well as in infrastructure. We are aware of the limits associated with prediction modelling. Variables of an extreme nature, such as the war in Ukraine, may enter the data for individual years. These kinds of variables can slow down or accelerate the development in sustainable innovation, industry and infrastructure. It is possible to combine the above-mentioned fuzzy approach with trend modelling in cases where variables that are difficult to quantify (i.e., insufficiency in data, market sentiment or political situation), need to be included in the model. Prediction of sustainable development in innovation, industry and infrastructure is also significant for macroeconomic analysis. The obtained results are an important basis for managerial decision-making in the economic field. Future research will focus on creating semi-deep predictive model with the use of the fuzzy approach for the whole of Europe and other regions of the world such as North America (N.O.R.A.M.), Latin and South America (L.A.T.A.M.), the Middle East and Africa (M.E.A.) or Asia and the Pacific (A.P.A.C.). Further research will also focus on using a semi-deep predictive model in combination with shallow modelling, which may lead to more accurate prediction results. Today’s world is characterised by a turbulent and accelerating environment (e.g., COVID-19, climate change or the War in Ukraine). These aspects need to be taken into account in prediction models, where the use of Markov chains may be a suitable for future research. Disclosure statement No potential conflict of interest was reported by the authors. Funding This article is supported by the Modelling and optimisation of processes in the corporate sphere; Registration number FP-S-22-7977 and Strategic development of the enterprise and business approaches in the context of environmental development; Register number FP-S-227924. ORCID David Sch€ uller http://orcid.org/0000-0002-4677-8665 Karel Doubravsk yhttp://orcid.org/0000-0002-6882-1046 References Adams, R., Jeanrenaud, S., Bessant, J., Denyer, D., & Overy, P. (2016). Sustainability-oriented innovation: A systematic review. International Journal of Management Reviews,18(2), 180– 205. https://doi.org/10.1111/ijmr.12068 Aggarwal, C. 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