THE INFLUENCE OF THE ECONOMIC COUNTRY CONDITIONS ON THE PREFERENCES OF SUSTAINABLE DEVELOPMENT GOALS IN METALLURGICAL AND MINING COMPANIES

Today, the sustainable development goals adopted by United Nations belong to the basic pillars of sustainable business strategies of all industrial companies, including metallurgical and mining. However, their preferences often differ significantly in different companies around the world. One of the reasons for the different preferences may be the economic country conditions. The aim of the paper is to verify the hypothesis that the preferences of individual sustainable development goals in metallurgical and mining companies strongly depend on the economic country conditions in which these companies operate. The PROMETHEE ranking method and its GAIA tool were used as the main research methodology. The obtained results led to the confirmation of the above hypothesis and the definition of the goals most preferred in companies operating in the G7 countries, developed economies, and developing economies.


INTRODUCTION
Today, sustainability is becoming an integral part of the strategy of many metallurgical and mining companies.Legislation in different parts of the world also makes a significant contribution to this.For example, the Directive 2014/95/EU of the European Parliament and of the Council as regards disclosure of non-financial and diversity information (also called as Non-Financial Reporting Directive) introduced sustainability reporting obligations for approximately 11,700 large public-interest companies and groups with more than 500 employees across the EU [1].The current state of research into the Directive discussed Korca and Costa [2].The Directive was introduced by European Union in compliance with Sustainable Development Goals (SDGs) adopted by United Nations Member States in 2015 (SDG target 12.6) [3].Therefore, SDGs are naturally integrated into sustainable reporting [4], including companies from the metallurgical and mining industry across the world.The aim of the paper is to analyse the SDGs preferences in the metallurgical and mining industry and verify the hypothesis that the preferences of individual SDGs strongly depend on the economic country conditions in which these companies operate.

LITERATURE REVIEW
United Nations Member States adopted 17 SDGs as the key part of The 2030 Agenda for Sustainable Development Business worldwide can play a crucial role in the advancement of The 2030 Agenda for Sustainable Development.With the setting of the SDGs, the role of business in economic, social and environmental development has never been more imperative [6].According to Yamane and Kaneko [7] awareness of SDGs is constantly increasing not only among companies but especially among their stakeholders.Also Van der Waal and Thijssens [8] state that the SDGs stress the necessity of businesses' active participation, appealing for their creativity and innovation to create value for the common good.However, preferences of SDGs across the business very often differ significantly.The paper builds on research conducted by Lenort, et al. [9], which analysed the SDGs preferences in the metallurgical and mining industry in terms of geographical location of individual companies.It aims to extend this analysis to an economic country conditions perspective.

METHODOLOGICAL BASE
PROMETHEE belongs to the family of multi-criteria decision-making methods based on special, so called outranking relations.In general, PROMETHEE covers several algorithms for various multi-criteria problem [10].In this paper, we will use only the graphical analysis GAIA (Graphical Analysis for Interactive Aid) to explore the relationships among the criteria and alternatives and get the performance profiles of the alternatives.Therefore, just the corresponding theoretical background is provided further.
First, let us briefly recall the ranking algorithm using a general decision-making problem with n criteria (set ) and m alternatives (set ).All PROMETHEE algorithms starts with the pairwise comparison of alternatives   ,   in terms of each individual criterion  ∈ .To do this, a preference function   , which assigns the preference degree   (  ,   ) ∈ [0,1] to the difference in performance values of the compared alternatives, must be chosen (the preference degree describes how strongly a decision-maker prefers   to   ).This choice has to be done with respect to the both data type and range of a criterion.In general, the preference function must be non-decreasing (the greater difference in performance, the stronger preference in favour of the better alternative) and with   () = 0 for  ≤ 0 (the worse-performing alternative cannot be preferred).The authors of the method recommended the linear or Gaussian shape of the preference function to handle quantitative data, see Figure 1 [10].
For the purpose of the GAIA plane, the preference degrees must be aggregated into so called single-criterion net flows, which express how much better an alternative performs in comparison with all others in terms of the given criterion: Then, each alternative is given with -dimensional vector of the flows.To display the alternatives graphically onto the plane, the PCA (Principal Component Analysis) method is applied, see Brans and De Smet [11].
Because the GAIA plane provides only the projection of the real problem, it is necessary to check the quality of projection.In line with Brans and De Smet [11] the quality better than 80 % is absolutely acceptable.

INPUT DATA
The input data sets for the study were obtained from official UN sources, namely from the UN Global Compact database (see [10]).This database gives information about sustainability strategies and operations of involved companies and non-business organizations.There are more than 17,000 members from around the world involved, from which more than 15,000 are active members.There are 7 basic parameters in the database according to which it is possible to filter the desired results [12]: • Typethe type of the member (e.g., company, NGO, city).

•
Tierthe tier of the involvement (signatory or participant).

•
Platformthe platform in which member can be involved (e.g., Peace, Justice and Strong Institutions, Water Resilience Coalition).

•
Initiative -the initiative in which member can be involved (e.g., Business Ambition for 1.5°C, GC 100, Carbon Pricing Champions).

•
Countythe country where the member HQ is located (all UN member states).

•
Statusthe status of communication with UN Global Compact (Active or Non-communicating).
For the study, the active members from the sector Basic resources and further subsector Industrial Metals & Mining where selected.This filter provided 133 members for further study.All members in that selection were either Companies (65) or Small or Medium-sized Enterprises (38).The geographical distribution can be seen in Figure 2.

Figure 2 The geographical distribution of active members in subsector Industrial Metals & Mining
Each active member should submit to the database the Communication on Progress (COP), which includes answer to the following question: "Which of the following Sustainable Development Goals (SDGs) do the activities described in your COP address?".By answering this question, members proclaim which SDGs are in their focus.In the analysed subsector 103 members provided answers to this question and binary values were obtained from their reports.The absence of answers is especially noticeable for newly involved members (17 of 20 members involved in the last 365 days) who have not yet sent the first COP report.Remaining 13 members skip this part of the report continuously.Further 3 members did not mark any SDG and were therefore removed from the analysis.
The remaining 100 members were divided according to the economy conditions (EC) of their HQ country, which was a basic parameter in further research.Four categories according to UN [13] were defined: G7, developed economies, economies in transition and developing economies.The division of members in terms of economy conditions of their HQ country is as follows: major developed economies (G7) 26, developed economies (DD) 31, economies in transition (EIT) 7, and developing economies (DNG) 36.
To get the performance values for the chosen categories, the arithmetic mean of binary values was used, i.e., the relative frequency with which the companies in the particular countries follow the given SDG (e.g., the value of 0.1 means that 10 % of companies in the countries with selected economy conditions follows the specific SDG and 90 % do not).The resulting input data are provided in Table 1.

RESULTS
To perform the analysis, Visual PROMETHEE software has been applied.Linear preference functions (see Figure 1) were adopted for all the criteria (SDGs) with the same parameters ( = 0,  = 1).This setting allows for an easy comparison of the values across the criteria (all the performance values range from 0 to 1).
Frequency analysis of individual SDGs shows that metallurgical and mining industry worldwide prefer to follow these SDGs in 2020: 8 Decent work and economic growth, 12 Responsible consumption and production, 3 Good health and well-being, 13 Climate action, and 9 Industry, innovation and infrastructure.On the other hand, the following SDGs are preferred the least often: 14 Life below water, 2 Zero hunger, 1 No poverty, 10 Reduced inequalities, and 11 Sustainable cities and communities.
Figure 3 shows the GAIA plane for the analysed input data.Quality of the GAIA projection is 93.6 % (calculated directly in the software), which guarantees reliable results.All country categories (alternatives) are situated in different quadrants.This means that the companies from individual country category tend to follow very different SDGs (criteria), which are represented by the blue vectors in the plane.Relatively large similarities in patterns of behaviour can be seen only in case of G7 and DD (developed economies) category.This result could be expected because G7 category is essentially a special subcategory of developed countries category.

Figure 1
Figure 1 Linear and Gaussian preference function's shape

Figure 3
Figure 3 GAIA plane

Figure 4
Figure4shows the GAIA action profiles.These profiles are based on the unicriterion flows   (  ) (1) and allow to compare one country category with all other ones.Also from these profiles is obvious that preferences of individual SDGs are similar only for G7 and DD category, but very different for DNG and EIT categories.

Figure 4
Figure 4 GAIA action profiles

Table 1
Input data for the analysis