Similarities and Proximity Symmetries for Decisions of Complex Valuation of Mining Resources in Anthropically Affected Areas

Abstract

After 1990, when the economic system changed in Romania, the mining industry was the most controversial field from a productive-economic point of view and subject to reforms and transformations for efficiency. Currently (2022), there are nine main mining perimeters in which the production of useful, energetic, and nonenergetic mineral substances is operational, and in others it has decreased or stopped. Still active mining areas need economic and ecological assessments to identify similarities and proximity symmetries for informed exploitation decisions and feasible complex resource utilization. The main objective of our study is to define a framework for the theoretical and practical contribution to the substantiation of decisions and expressions of interest regarding future investments in mining projects for useful and energetic and non-energetic mineral substances in Romania. Investments in the mining industry are expensive, with major risks and subunit success rates for specific geotechnological conditions. The purpose of the research is to provide the methodology for using some variables of similarities from proximity mining deposits in the stage of exploitation or post-exploitation affected by anthropogenic activity in the national geological territory through a case study of Romania. With the help of statistical scales, the research results highlight that in the exploitation and post-mining perimeters in Romania, the states of “affect” and “post-affect” anthropic, respectively, of eco-economic damage are in a maximum proportion of approximately 36% in relation to the ideal situations of no affect. For a mining investment project, knowing similar or symmetrical exploitation and post-exploitation properties and situations, and from the geological deposits in the vicinity, premises are created for optimized strategic and tactical decisions, based on reality and, above all, for the provision of expressions of interest for new investments that have a programmed, expected success rate.

1. Introduction

1.1. About Mining Resources in Romania

The extractive mining system in Romania in the last 30 years has experienced numerous critical situations through reforms and transformations, with the transition from a centralized economy (1990) to the functional market economy that exists today (2022).

Romania has more than 15 billion t of total geological reserves of various useful mineral substances. The specificity of the extraction activities determined the framework for the production of large amounts of mining waste and the appearance of negative anthropogenic effects on the environment.

In the period 1990–2022, in the mining industry in Romania, organizational and managerial restructuring actions of mining units took place, through decisions that refer to the requirements of efficiency and competitiveness in the field, in comparable terms to European and global mining performances.

Since 2019, overlapping crises have also appeared (COVID-19, the energy crisis, the effects of the war in Ukraine and Russia’s new geostrategic attitude regarding energy, inflation, the food crisis, climate disturbances caused by drought, etc.).

The requirements on the market for fuels for energy (natural gas), metals, and other useful mineral substances show new expressions of investment interest, even through projects to capitalize on deposits with poor contents, but with large volumes of reserves, or through the reprocessing of mining residues from ponds and settling ponds [1].

The gold mining project in the Apuseni Mountains, in Roșia Montană, is well known worldwide as a unique investment of exceptional interest in Europe and the world (Canada), but it is seriously controversial due to dangerous cyanidation technologies, the destruction of natural heritage, vestiges of antiquity, etc., generating serious anthropogenic effects.

On the other hand, offshore exploitation in the Black Sea is an important opportunity for Romania and the EU.

However, the prospects of large mining investments in Romania and, by extension, in Europe are not on the priority agenda of interest and action of the decision-makers in Brussels nor in Bucharest, because they require large financial funds, long design time, then prospecting, exploration, opening works on the surface and underground, high costs for machinery and equipment, additional protection of the environment, work, and deposits for dangerous situations (accidents, explosions, etc.).

1.2. Romania, a Country in the EU That Can Become Energy Independent

According to ANRE (2022) [2], the structure of energy production (18,314 MW) is, on average, the following: hydro 36.3%, coal 16.9%, wind 16.5%, hydrocarbons 14.3%, nuclear 7.7%, solar 7.6%, biomass 0.6%, biogas, waste, waste heat, and geothermal up to 100%. The proven and exploitable geological reserves are for natural gas of 100 billion m³ and another 200 billion m³ offshore in the Black Sea; crude oil: the local market has reserves of 600 million barrels; coal: known coal and lignite resources in Romania are 432 million t (1 billion t geological reserves); hydropower: 6642 MW; nuclear: 5–6% uranium can be obtained from the sorted ore (the state reserve is approximately 70 t of uranium metal) for the Cernavodă Nuclear Power Plant; wind: Romania has the largest wind potential in Southeast Europe, 14,000 MW; photovoltaic: capacity reaches 4.25 GW; solar thermal: Romania has 210 sunny days per year and an annual flow of solar energy between 1000 and 1300 kWh/m²/year; geothermal: the officially declared thermal power for geothermal wells is 480 MWth (at 25 °C), or 625 MWth (10 °C), with an annual energy of 282 thousand tons/year.

However, Romania, which is not significantly dependent on Russian natural gas, has periods of the year when it imports electricity. Mines in the Valea Jiului coal basins (hard coal) and Oltenia (lignite) are closed through the European Green Deal program. Several 3 CANDU-type groups from the Nuclear Power Plant in Cernavodă have stopped financing for over 30 years; another three new hydropower plants and two coal-fired thermal plants have stopped operation due to underfunding and environmental problems, and offshore investments in the Black Sea (which could constitute an energy hub for the EU) do not have legislative clarifications for contracts with foreign investors.

1.3. Shortage of Mining and Energy Projects

In Romania and equally in Europe, we notice that there are no mining projects, particularly energy projects with relevance and important meaning for the present economy and for the near future. This attitude of not investing in mining projects is also influenced by the investment in the Nord Stream 2 corridor (1234 km natural gas pipeline from Russia to Germany that runs through the Baltic Sea, financed by Gazprom and several European energy companies) [2] which is now in operational deadlock.

The essential question is why are there currently no proactive decisions for new mining projects for the valorization of mineral resources, especially energy, in Romania and in the EU, to overcome critical situations by securing raw materials and energy from the EU territory?

We note that in Romania and in the EU for many decades, the mining industry has played a decisive role in development, even recalling the role played by coal and steel in the birth of the European community.

Therefore, if those mining projects were successful, we note that in the complementary new mining perimeters, current mining projects can be carried out through decisions that take into account the knowledge of similarities, geotechnical, technological asymmetries, and certainties extrapolated from the proximity of geological fields with useful mineral substances and Romanian and European energy raw materials.

1.4. Similarities, Symmetries, and Proximities for New Mining and Energy Projects

Through similarities, the property of the accumulations of mineral and energy resources in Romania is sought through which the old geological deposits and the new ones (recently discovered) could be put in a current technical, technological, and partial or total efficiency correspondence.

In this way, the values of the economic and technical characteristic variables of one of the deposits, including the investment and operational risks, can be expressed through relatively simple comparative evaluations, using the similarities between the known values of the corresponding quantities characteristic of the deposits in the vicinity that have been or are in operation.

Symmetry shows the property of the spatial ensemble of old and new deposits to be composed of mutually corresponding qualitative, quantitative, and efficiency elements. On this basis, certain regularities, proportionalities, concordances, and harmonies between the parts of a whole and between their elements are retained and used in decisions.

The proximity (physical, geographical proximity) of geological deposits (old and new) of useful and energetic mineral substances in Romania shows their immediate proximity, their proximity on a geological-geographical territory of small to average size in relation to other deposits scattered throughout the country. Thus, for new mining projects, elements of knowledge from the results and older experiences of mining and energy resource exploitation in Romania, in European countries, and in the world can be capitalized on.

So, if in Romania and in the EU, there are physical, natural, geo-mining resources of useful mineral substances and energy raw materials, the second question arises: why is there a lack of commitment or interest in financing some new mining projects to contribute in the short and medium term to overcoming the overlapping mentioned crises?

The decision represents the decision taken for mining investments, and the exploitation of mineral and energy resources can be successful if the new geo-mining data is taken into account, supplemented with data and certain values of past projects and investments, in order to make a profit.

On the other hand, the classification of Exploitable Reserves was officially carried out from 1990 and validated in bodies that brought together specialists and institutions worldwide [3]. This classification brought new forms of approach to the extraction and valorization of useful mineral substances and energetic ones, both underground and from the surface (through mining pits) [4].

The referential system of classification of resources/reserves has been completed with the economic axis, which requires the reconsideration and reapproval of the physical volumes of substances of interest on an economic basis, without losses or with financial subsidies.

In other words, only “what is proven to be economic” can be the substance assimilated to the exploitable, useful, efficient deposit. This change has generated the revision of technologies (which have become eco-technologies), strategies, and tactics for the exploitation and valorization of useful mineral substances and energetic ones.

However, from an economic perspective, efficiency in Romanian mining enterprises was also influenced by “efficiency theft and cost stylization”, in the sense of reducing it often on empirical grounds. As such, the measure has over time caused the separation and neglect of the various components of environmental protection in the general productive-economic process, through anthropogenic disturbance, including with respect to CO₂ emissions, climate change, etc. For example, in the Oltenia Carboniferous Basin (lignite extraction) in the production cost per extracted ton, the costs for the restoration of mining lands following the exploitation of coal from large open pits were not included. Currently, Romania is appealing for financing through credits of hundreds of millions of USD from the World Bank for additional expenses (outside of production costs) for the return to the economic circuit of mining lands unloaded from technological burdens.

Practically, the similarities, symmetries, and proximities between the old and new deposits can now contribute to obtaining a vector result of assessing the conditions, the framework and the premises of efficiency and sustainability for the foundation of new expressions of interest, of new mining investment decisions in Romania and in the EU, respectively, of exploitation and valorization of natural/mineral resources in Romania under conditions of calculated and assumed risk.

Therefore, the explicit research requirement in the present article refers to the establishment, for the first time, of (1) the categories and groups of similarities (geological-mining, technical, economic, environmental/ anthropic), (2) accepting the adoption of similarity values from experience of exploitation and post-exploitation of older, similar deposits in the vicinity of new mining projects, (3) explaining the transfer of similarities by accepting the systematized symmetries and asymmetries from observations, (4) offering to developers of strategies, tactics, investment projects, to investors and practitioners of a methodology and a general block scheme for symmetries and asymmetries of eco-economic proximity between the anthropogenically affected mining basins in Romania, so as to increase expressions of interest for new investments in the field, especially in the energy sector.

The new economy means the ever-increasing decoupling between tangible assets (material, physical, infrastructure, material consumption) and intangible assets (increase in knowledge, computerization, digitization) [5]. This definition shows that, in fact, the expenditure curve (material consumption) has a downward slope, and the knowledge curve (knowledge, data, information) has an exponentially increasing slope, with an almost steep angle).

There are many definitions and explanations regarding the concept and the economic phenomenon called the “new economy”.

The “new economy” as defined under the OECD Glossary of Statistical terms describes aspects of an economy that are producing or intensely using innovative or new technologies. So:

“The new economy refers to the convergence of manufacturing, services and technologies to produce industries of high value added, technology-enabled and adaptable industries” [6].

“A key dynamic of the real new economy is the virtuous cycle of competition, innovation, and productivity growth” [7].

The ”new economy” is a term many economists started using in the last decade of the twentieth century. They argued that information technology, the Internet, ultra-high-tech companies and globalization had created a completely new type of economy, with considerably higher productivity and growth rates than the old one that it replaced [8].

Practically, none of the elements that make up the landscape of the new economy (actors, institutions, rules, or sectors) are necessarily new for entrepreneurs or unknown to citizens. The new economy is the result of the changes that are taking place, at the same time, in all these fields and in the way they are reinforced and strengthened. When we speak of the emergence of a new economy, we do not mean that one model of economic organization replaces another once and for all, but rather that a process marked by eventual and gradual enrichment takes place through a torrent of innovations. Simultaneous technologies develop in various fields of science, fertilizing the existing economic organization with the emergence of new elements, until it completely changes its physiognomy [9].

The principal effects of the “new economy” are more likely to be “microeconomic” than “macroeconomic”, and they will lead to profound changes in how the government should act to provide the property rights, institutional frameworks, and “rules of the game” that underpin the market economy [10].

We consider that in the new economy, seriously based on knowledge, the quasi-infinitely observed similarities (through digitalization) between old mining fields (projects) and new ones (investments), be they symmetrical or asymmetrical, and even more so those in infrastructural proximities (the old, exploited ones) or operational (the new, exploitation ones), are of real use in faster, more consistent decisions. Having “more knowledge” from similarities, symmetries, and asymmetries in proximity areas, it will not be necessary to apply “material, physical action” to reach “knowledge used in investment decisions” (for example, more geological prospecting operations, drilling, exploration galleries, geological research, etc.) Thus, it is demonstrated that this conception of ours has added value through specific know-how, for the first time in specialized literature and in mining investment practice, under conditions of efficiency and sustainability, contributing to putting mining in the general picture of the new economy.

2. Review of Specialized Literature

In the contemporary literature, combined assessments are found between: (1) situations that show anthropogenic mining effects of various processes, phenomena, objects, on geological territories (surfaces) and (2) eco-economic assessments about feasibility and sustainability. These are treated frequently on methodological bases that empirically use data and geo-eco-mining characteristics-properties, or by mathematical modeling of the respective particularities-properties, obtaining conclusions that are used to substantiate decisions for new investments.

Over the years, a significant amount of literature on similarity, similarity, and scaling has developed in mathematics, science, and technology [11].

Ming et al. (2004) [12] provides solutions for classes of distances suitable for measuring similarity relationships between sequences, and speak of “normalized information distance”, based on the non-computable notion of Kolmogorov complexity, launching the notions of “distance similarity” and “similarity metric”, and Chunlan (2005) [13] provides a similarity classification of Cowen–Douglas operators using the ordered group of commutative algebra as an invariant.

The composite conceptual domain, consisting of similarities, symmetries, and proximities, is seriously mathematized and sensitively researched from a logical-mathematical perspective in specialized literature, because the mentioned terms are operational indicators on the border between empirical and rationality in the material, physical structures, found in the case of research on our mining infrastructures. In fact, it is about the approach to scalability, the relationship between the measurable and the immeasurable regarding the identification and use of elements to contribute to the substantiation of decisions, in the present case for new mining investment projects, especially energy.

However, Bondi (2000) [14] shows that a useful and rigorous definition has not yet been found for scalability, and the basic notion is intuitive. He advanced a challenge to the technical community to come up with a definition of scalability and use it to describe technical systems (e.g., mining).

Fotis et al. (2022) [15] developed scalability and replicability plans to facilitate the adoption of new and innovative technologies and investment projects. Thanks to the enormous innovation in intelligent communication, monitoring, and management systems, intelligent networks can be developed that allow the exchange of information (similarities, symmetries, and asymmetries) “liberating flexibility potentials” and facilitating the implementation of already demonstrated solutions at a reasonable level of scalability and replicability. Thus, some new mining project demonstrators are reduced through expensive local experimental exercises.

Normativity and scalability are studied by Bertsimas and Cory-Wright (2022) [16] as well as Raynor and Lars (2022) [17] and are closely related to the determined size of a new project subject to the constraints imposed by norms and rules, which can guarantee the technical, social, economic, and environmental needs, and thus contributions are made with solutions to the problem of investment portfolio selection.

The mining industry is upstream of the development of a national economy. We agree that the classification of mining investment projects is difficult because, almost always, there is no complete information on the profitability of the future project, but the “fuzzy similarity” is often used, which helps to develop a ranking of subclassification variants with acceptable solutions.

Xian et al. (2021) [18] point out that, in this context, (1) the strength of the structural similarity of the investment project indicators, (2) the average negative effects extracted from the similarities in the old projects are important for the new projects and (3) the impact of the cohesion of positive similarities applied to new investment projects.

An interesting extension of similarities concerns the development of a way to control the consistency of preferences in group decisions about investment projects [19].

Amer and Abdalla (2020) [20] state, however, that among the similarity values, there is never a situation in which a single measure is completely effective in any new investment project. At the same time, there are still radical effects of the similarity or similarity coefficients used, because the similarity itself is difficult to measure [21].

Okada and Imaizumi (1987/, reed. 2011) [22] refer to nonmetric multidimensional scaling of asymmetric proximities, and Zielman & Heiser (1996) [23] elaborate models for asymmetric proximities.

Mesaros and Ran (2013) [24] discuss “global symmetry”, as well as the models that describe symmetry-enriched topological phases, highlighting the measurable similarities of these symmetry-enriched topological phases and generalizations for classification.

In Berlin in 2005, Okada and Imaizumi [25] published aspects of innovation in classification, advanced data science, and information systems for symmetries and asymmetries, bringing new clarifications about proximity symmetries and asymmetries.

Similarities are also seen in the processes of environmental pollution, which can be controlled by using environmentally friendly extraction operations and technologies known from the experiences of previous mining projects. For example, for coal (case studies, Pakistan’s government, and the provincial government of Sind), according to the authors Mohsin, Zhu, Naseem, Sarfraz, and Ivascu (2021) [26,27,28], it is proposed to strictly verify the adaptation of environmental standards from the perspective of costs and ecological effects, based on similarities for decisions in other new mining perimeters [29].

Bove, Okada, and Vicari (2021) [30] deal with different methods for the direct representation of asymmetry, the analysis of asymmetry groups, of symmetry proper, and advances knowledge of oblique symmetry. The authors provide a complete and up-to-date reference on methods for analyzing asymmetric proximity data through real-life examples, with a systematization of specialized statistical software available in the field.

Practical guidance is also encountered for applying multidimensional asymmetric scaling, and a set of methods and algorithms for graphing and clustering asymmetric relationships is considered.

Thus, Sanghoon and Seung-won H. (2014) [31] have concerns about techniques for reducing precision losses in eco-economic evaluations caused by each type of asymmetry in a complex domain. It is observed that the identification of similarities uses the comparison process to obtain eco-economic assessments used in decisions [32].

Martin, Fowlkes, Tal, and Malik (2001) [33] presented a database of segmented natural images on human subjects along with applications of the dataset to similarities. On this basis, a benchmark for image separation of symmetries and asymmetries is developed by which segmentation algorithms can be objectively evaluated. At the same time, models of ecological statistics based on measurements and grouping factors are defined.

In the same context, Faith, Minchin, and Belbin (1987/reed. 2016) [34] point out that many multivariate methods applied to common knowledge data operate, either explicitly or implicitly, on a matrix of compositional differences between samples. The degree of success in recovering ecological patterns in the data will depend on the nature and strength of the relationship between the values of the similarity factors and the corresponding Euclidean distances between samples in the anthropogenically affected ecological space (these are the “ecological distances”).

Asymmetric proximity data classification [35], scalable graph embedding for asymmetric proximity [36], structural-contextual similarity measurement [37], understanding the direction of action for investments by defining proximity and order in compared asymmetric pairwise data [38], and approximating high-order proximity [39] are just a few topics researched in the field, found in specialized literature.

Networks of mining perimeters with useful mineral and energy substances reduce the proximity to a number of pairs of investment projects. Obtaining proximity data is often analogous to the method of obtaining data through cluster analysis [40].

Graepel et al. (1998) [41] present results on the classification of data from investment projects represented in terms of their pairwise proximities.

The authors point out that pairwise proximities can always be calculated, but there is also an approach based on a linear threshold model for the proximity values themselves, which is optimized using Structural Risk Minimization (prior project knowledge can be incorporated by choosing distance measures and examining their various metrics).

There are also skeletonization algorithms, which break down the designs into symmetrical parts, and thus arrive at the classification of shapes. Levinshtein et al. (2013) [42] provide solutions for retrieving and grouping asymmetric parts from old and new projects. A scalable approach to large data sets provides solutions to identify the positions of investment projects with respect to mineral and energy products, environmental effects, market and technologies [43].

In various other studies, economic and investment interactions are influenced by geographical proximity [44,45,46], which fits conceptually with proximity influences between deposits in the same mining basin, where old exploitation projects have operated and new investment mining projects could be approached.

The social, economic, and environmental aspects of sustainability are typically represented by three intersecting circles, with the representation of overall sustainability in the center. Similarities in proximity areas with potential for new investment and production are considered to contribute to the formation of a more confident commitment to sustainability [47]. Asymmetries over wide geographical areas play as important a role as symmetries in proximity, because multidimensional pollution is a problem in Romania, but, for example, also in the BRICS group of countries that holds a unique position in emerging economies.

Mohshin et al. (SPR, 2022) [48] show that it is necessary to explore the relationship between environmental quality and economic growth in correspondence with the environmental Kuznets curve, as well as the decoupling index, autoregressive distributed lag—ARDL. Likewise, the cointegration approach, CO₂ emissions and environmental degradation is presented simultaneously, when economic expansion and environmental degradation are interdependent in the long term [49].

Sarfraz, Ivascu, and Cioca (2022) [50] point out that the relationship between income and pollution is often contested. Symmetric and asymmetric similarities also come from what Sarfaz, M., Mohshin, M., and Naseem, S. (2022) [51] and Azam T. et al. (2020) [52] claim which introduces new perspectives on the effect of COVID-19 on carbon emissions [53], climate change, and sustainable environment [54].

Boschma et al. (2015) [55] refer to the fact that some similarities between economic agents (in the case of the present study, between the exploitation units in the mining basins), together with the aspects of knowledge, of industrial process, taking into account the size of the organizational entity (mine, open pit) and legislative, institutional characteristics, shapes relationships and provides “framework images” for investment decisions [56].

Vellend (2010) [57] mentions that many ecologists, however, might be skeptical that a simple organizational scheme to identify proximity similarities and symmetries is fully applicable to the more complex subject of the ecology community. Waseem et al. (2008) [58] talks about the concept of information asymmetry, which has already gained attention in the field of economics and finance. Observing the quantity, quality, and content of varied data within the data set shows the existence of information asymmetries and their consequences in decision making [59]. Interaction with asymmetric information generates in the investment process different dispositions of behavior, other than those given by symmetric information [60]. In specialized literature, Think et al. (2002) [61] and Bilmes (1997) [62] emphasize that the similarities are motivating and contribute to the development of specific procedures for the eco-economic assessment of areas, by extension, including those mines in Romania where new exploitation projects should be implemented.

So, there is a broad picture of research in the field, but the essential characteristic of scientific investigations refers to mathematical deepening, digitization, widening the frontiers of knowledge with real premises of computerized modeling in the field.

3. Scientific Research Methodology

First, we proceeded to inventory the deposits of useful and energetic mineral substances on the territory of Romania. Old investment and production projects in the field, those still in operation, and those in post-operation (after exhaustion or closure) were systematized.

We find that Romania and, by extension, the EU have important natural geological deposits of useful and energetic mineral substances. In the same mining basins or in new geo-mining perimeters, there are resources that can be extracted and exploited. However, the real finding is that currently (2022) in Romania and the EU there are no mining projects for large-scale investments of particular importance, due to restrictions and obstacles related to risks regarding efficiency, environmental issues, and competitiveness.

However, the Romanian and European mining experience must be capitalized by using the indicator values from similarities, similarities, symmetries and asymmetries, from the old proximity perimeters. They contribute to better knowledge to base new investment decisions in terms of feasibility and sustainability.

We considered extensions of the interesting methodology and approach of the authors Sarfaz, Mohshin, and Naseem (2022) [51] regarding the Augmented Dickey–Fuller (ADF) test for a stationarity check of the data series, the Johansen cointegration test for determining cointegration between variables, and the Granger causality test, for directional cause and effect between exogenous and endogenous variables [63].

By comparison, accepting the verification of the stationarity of the data series, which show similarities in nearby mining areas and levels of anthropogenic eco-economic impacts, we directed the research towards obtaining an informative table for investment decisions, characterized by the cointegration between variables for new exploitation projects of useful and energetic mineral substances.

Thus, taking into account the variables from the similarities and from the anthropic, eco-economic impacts, it is possible to know the causes and the directional, bidirectional, or multidirectional effect for a mining investment.

Ecological recovery strategies for the mining sector [64], and assessment of the main technical, technological, socioeconomic, and environmental challenges in the mining industry [65], are sequences that benefit from the knowledge of similarities in the proximities for decisions in the field of exploitation and valorization of mineral and energy resources.

3.1. Quantitative, Qualitative, and Economic Efficiency Situations in the Extractive Mining Sector in Romania (1990 vs. 2021) Bearing Proximity Similarities

Mainly, in the underground of Romania there are geological reserves with expression of extensive interest for economic exploitation for substances such as: lignite—2.8 billion t, coal—900 million t, gold-silver ores—40 million t, polymetallic ores—90 million t, copper ores—900 million t, salt—4 billion t, etc. [66,67].

Currently, there are mainly 25 underground mines, 37 quarries (open pits), 9 processing plants, 50 mineral water sources, and 7 salt pans.

The main measured proven resources in Romania are: nonferrous ores (549 million t), ferrous ores (90.2 million t), nonmetallic ores (277.5 million t), salt (5.9 billion t), construction rocks (1.7 billion t), ornamental rocks (81.1 million t), marble (19.4 million t), etc.

In addition to these are added other ores, industrial minerals, including precious and rare metals [68].

On the national territory there are 33 billion t of haloid salts, 1.3 billion m3 of useful rocks, a total of 9.7 billion t of coal in 299 medium and large deposits. Romania is ranked 45th in global oil reserves (0.035% of total world reserves) with 600,000 barrels, and ranked third in Europe, after Norway and Great Britain [69].

Natural gas reserves measured at 100 billion Nm³ are found on the national territory, in Transylvania (75%), Moldova, Muntenia, and the resources of the Black Sea can competitively reposition Romania in the general energy ranking [69,70].

The areas of occurrence of mineral and energy resources in Romania are described by Anastasiu (2017) [71] (Figure 1).

The mining enterprises that inefficiently exploited deposits of useful substances (which required subsidies from the National Public Budget) after 1990 entered the process of restructuring and closure, and the mining basins entered technical, economic, and social reconversion.

Mining activity in the Romania, in exploitation perimeters, that of other countries in Europe and globally, due to its specificity, generates negative anthropogenic effects on the environment: changes in relief, degradation of the landscape, settling ponds, displacement of households and entities industrial from the exploitation areas, the occupation of large areas of land for piling, storage, large-scale industrial installations, access roads, etc., deformation of the land through subsidence; water pollution and hydrodynamic imbalance, influences on biodiversity, above ground, noise, radiation, etc. By closing 556 mining and processing units, 78 settling ponds were affected, with a volume of 341 million m³ of mining waste, located on a total area of 1770 ha. Several 675 tailings dumps (with a volume of 3100 million m³), occupy the surface of 9300 ha, and 2500 mining works (galleries, shafts, inclined planes, etc.) have been closed.

Therefore, in Romania, in the last 30 years, the extractive mining industry has recorded a deep decline.

Logically, with the reduction of industrial activities, there should also have been a reduction in anthropogenic impacts. Although there have been closures of open pits, thermal power plants that use coal, coal preparation and ore flotation plants, mechanical mining plants, etc., we find from the research carried out that: (1) even the general process of closing, liquidation of these infrastructures has proven to be largely anthropogenic, damaging to the environment, and (2) closures for many of the inefficient mining infrastructures are still not funded and implemented [72].

Abandoned tailings and tailings ponds, ecological accidents (dike break at a tailings pond in the Baia Mare mining region in the north of the country), ruined buildings and workshops in former mining premises, thousands of hectares of land affected by lignite mining in the Carboniferous Basin of Oltenia (from South-West Romania), redundancies of mining personnel, and the immigration of the inhabitants of which areas, all of these have a negative, anthropogenic influence on the environment. New activities to replace mining are subsistence and conversion tourism infrastructures are made without vision, of poor quality, and without attractiveness.

Deforestation, polluted and even radioactive mining waters, and a weak commitment to the circular economy of industrial mining wastes accentuate the ecological damage in former mining basins.

On the territory of Romania, we identify 9 major mining areas with anthropogenic effects/degradation. The central problem of the present study boils down to obtaining the answer of whether mining investments in the area (since there are real, physical deposits of useful substances and energy) are of interest to investors or not.

Through questionnaires distributed to specialists, top management from each mining area (in a representative mining unit), at one Mining Research Institute (Baia Mare, Petroșani, Craiova, Bucharest), and at the University of Petroșani/Faculty of Mines, University of Bucharest/Faculty of Geology and Geophysics, University of Petroleum and Gas in Ploiești (2019–2020) were collected through 109 response documents assessing the levels of anthropogenic impact in the nine areas with deposits of useful and energetic mineral substances. Through statistical processing (means, dispersions, and distributions) the values of the ecological impact coefficients are presented in

Table 1. Present and historical economic situation of the main mining areas in Romania.

Name of the Area	Extracted Mineral Type	Area Symbol	Economic Damage	Anthropogenic Damage (Aa)
ZM(1) Valea Jiului	hard coal	Am1	financial subsidies	0.57
ZM(2) Mehendinți	lignit	Am2	financial subsidies/closure of mining basin	0.65
ZM(3) Motru-Rovinari	lignit	Am3	financial subsidies	0.72
ZM(4) Berbești-Vâlcea	lignit	Am4	financial subsidies	0.72
ZM(5) Baia Mare	nonferrous	Am5	financial subsidies/closure of mining basin	0.29
ZM(6) Gura Humorului	ferrous	Am6	financial subsidies/closure of mining basin	0.41
ZM(7) Câmpulung-Ploiești	brown coal	Am7	financial subsidies/closure of mining basin	0.29
ZM(8) Dobrogea	nonmetallic	Am8	financial subsidies/closure of mining basin	0.15
ZM(9) Apuseni-Abrud	copper	Am9	financial subsidies	0.41

Source: Systematization and statistical assessment of anthropogenic impacts with the allocation of coefficients of weight on the scale [0,…,1]/Authors, 2022.

with the graphic representation in Figure 2.

The volumes of useful mineral substances extracted in Romania registered major quantitative decreases after 1990, but the quality of the final mining products in some categories of substances is increasing (extracted coal, lignite, salt) (

Table 2. Quantities of the main useful mineral substances extracted in Romania.

Specifications/ (Million t)	1990	1995	2000	2005	2010	2015	2020	Quality
Coal extracted (CE)	40.847	43.988	30.924	31.633	32.162	26.705	21.342	increasing
Hard Coal (H)	5.401	6.356	3.741	2.700	1.382	0.956	0.530	same
Brown Coal (CB)	0.645	0.570	0.328	0.131	0.082	0.000	0.000	same
Lignite (L)	33.700	37.062	26.465	27.200	22.600	22.405	19.986	increasing
Ferrous minerals (MF)	1.461	0.860	0.131	0.111	0.096	0.108	0.078	same
Non-ferrous minerals (MN)	0.068	0.084	0.060	0.064	0.073	0.054	0.043	same
Salt (S)	3.255	2.489	2.197	2.106	2.145	2.079	2.032	increasing

Source: National Institute of Statistics, Bucharest, 2021/Assessing the evolution of quality: Authors, 2022.

).

The consequences of the quantitative reduction in the exploitation of various useful and energetic mineral substances in Romania have causes related to: (1) inefficiency, (2) ecology (environmental effects), (3) reduction of consuming industrial infrastructures (decrease in the productive-economic capacity of the country and reduction in demand), (4) social (unemployment, redundancies), etc.

In addition, the growing importance of socially responsible investment in mining areas in minimizing climate change is recognized. In this framework, Iqbal et al. (2022) [73] estimates the asymmetric effects of time and frequency between sustainable investments [74].

Mainly, from an ecological point of view, the mining areas entered into successive restructurings, based for the most part on “closures” of production capacities, have not yet benefited from programs and financing for “restoration”, “return” in the economic circuit-productive of the respective lands.

As such, we currently encounter states that show strong anthropogenic effects, because delimitations, separations, and individualizations of post-exploitation situations have appeared that assume new specific utility for the former mining areas, now inactive. Based on this finding, we consider it useful to approach in an integrated vision the anthropic states recorded by the affected mining areas and, as such, it is useful to look for evolutionary similarities, which can serve their association in sets of characterization parameters [75].

Knowledge about symmetric and asymmetric similarities in proximity mining areas serve to support the decisions of eventual financial and entrepreneurial support of new mining projects, with the aim of restoring the respective surfaces and infrastructures back into the national and European productive-economic circuit [76]. According to calculations from different variants of strategies for the mining industry in Romania (2008, 2012, 2017) [77], 763 exploitation licenses (in force or under approval), 67 exploration licenses, and 14 prospecting permits are registered on the national territory, as well as 935 exploitation permits, and 60 licenses with approved closure.

In the period 1990–2018, the National Public Budget supported this industry with subsidies, social transfers, and capital allocations amounting to approximately USD 9.77 billion.

In a direct, simple view, it is estimated that the USD 9.77 billion represent “losses” in the national, European economy. Stopping this tendency to support unprofitable mining activities (for example, the cost of underground extraction of a ton of coal in the Valea Jiului Carboniferous Basin is EUR 73, while its selling price is approximately EUR 53–54, the difference being subsidized from the National Public Budget) represents a strategic, tactical, and operational approach.

The concept and vision of the EU regarding mineral resources on the territory of Europe is that of “development”, and Romania is a real “reservoir” of mineral and natural energy wealth. However, mining projects must also be approached from the perspective of their economic efficiency.

The objective of our study is to scientifically contribute to the elaboration of the theoretical and practical apparatus of mining investment projects by using data of symmetrical and asymmetrical similarities from the proximity mining perimeters. That is why the eco-economic assessment of post-exploitation and exploitation mining areas, for their return to the productive-economic flow, is key to reform and restructuring in the field.

3.2. Method and Methodology for Economic and Ecological Evaluations of Mining Investments through Symmetrical Similarities and Proximities

The main type of approach for the scientific investigation of the economic and ecological conditions/situations of the mining areas in Romania is that of statistical scales. In Euclidean space, the distance (similarity weight) d_ij between two mining areas z₁ and z₂ can be measured by the equation:

$$d_{ij}=\sqrt{{{\displaystyle \sum }}_{m=1}^n{\left(x_{im}-x_{jm}\right)}^2}$$

(1)

This means that the similarities of proximity have “greater power”, “relevance”, and “significance” when (on average) it is monotonically decreasing. A similarity/dissimilarity is observed when parameter (m_ij) is increasing in monotony. Proximity similarities have the potential to provide (generate) symmetries in situations where dijmin.

One proposal in the field is to use Gaussian models estimated using the Expectation-Maximization/Minimization (E-M/m) Algorithm. In fact, it is about resorting to complex multidimensional models for the distribution/distribution of similarity data in the researched areas. There are also methods of asymmetric multidimensional scaling of proximities [78], of hierarchical grouping of nonmetric proximity data based on bi-links and indiscernibility [79], when directional links between objects (firms, production, investment areas) are formed hierarchically according to their proximity [80].

A new cluster in the field of mining investments, for example, is formed when objects in two clusters are connected by bi-links, and indiscernibility is built into the establishment of those bi-links. On this basis, the level of asymmetry is controlled and decisions are made if it can be ignored when creating a pair of objects (firms, anthropogenically affected mining areas in the exploitation and post-exploitation phases, in the case of the present article).

The development of statistical scales and criteria for the economic and ecological assessment of mining areas in Romania is important for obtaining information to support decisions for the public support of infrastructure and social balance (Figure 3).

The elaborated block diagram allows for the description of the characterizations for the mining areas investigated through specific systematizations and comparisons, considering the following notation:

A_m1; A_m2; A_m3; …; A_mn = distinct mining areas;

${\mathrm{S}}_{\mathrm{m}1}^{\mathrm{ec}}$; ${\mathrm{S}}_{\mathrm{m}2}^{\mathrm{ec}}$; ${\mathrm{S}}_{\mathrm{m}3}^{\mathrm{ec}}$; …; ${\mathrm{S}}_{\mathrm{mn}}^{\mathrm{ec}}$ = economic situations in distinct mining areas;

${\mathrm{S}}_{\mathrm{m}1}^{\mathrm{e}}$; ${\mathrm{S}}_{\mathrm{m}2}^{\mathrm{e}}$; ${\mathrm{S}}_{\mathrm{m}3}^{\mathrm{e}}$; …; ${\mathrm{S}}_{\mathrm{mn}}^{\mathrm{e}}$ = ecological/anthropogenic situations in distinct mining areas;

P_m1; P_m2; P_m3; …; P_mn = eco-economic potential of each distinct mining area;

${\left(\mathrm{PF}\right)}_{\mathrm{m}1}^{\mathrm{e}/\mathrm{ec}}$; ${\left(\mathrm{PF}\right)}_{\mathrm{m}2}^{\mathrm{e}/\mathrm{ec}}$; ${\left(\mathrm{PF}\right)}_{\mathrm{m}3}^{\mathrm{e}/\mathrm{ec}}$; …; ${\left(\mathrm{PF}\right)}_{\mathrm{mn}}^{\mathrm{e}/\mathrm{ec}}$ = eco-economic performance;

(∆C) = comparisons;

[${\left(\mathrm{BD}\right)}_{\mathrm{d}}^{\mathrm{P}}\mid \mathrm{e}-\mathrm{ec}$] = the database for substantiating decisions to balance eco-economic performance

The use of a metric algorithm for the economic and ecological/anthropic assessment of mining areas is a variant of an approach that involves precision for:

(a)

knowledge of the geological deposit related to the useful substance subject to exploitation (it is impossible to outline the absolute, as the infinite geological signals characterize the deposit in terms of size and content of useful substance);

(b)

historical knowledge of the quantity and quality of the economic mining reserve extracted and processed for capitalization (mining statistical data are not absolutely finite relevant, but only the order of magnitude is conclusive on locations and stages of extraction);

(c)

knowledge of surface and underground technological infrastructure for operation of (during operation various machinery, equipment, devices, etc.). different for specific technologies are used); some equipment has been replaced with others of new types and performance;

(d)

economic knowledge of the exploitation and capitalization of useful mineral substances (in essence, indirect costs, approximation of overheads, inflation, etc., all prove oscillations and inaccuracies with a deficient character as expression);

(e)

knowledge of post-exploitation anthropogenic damage.

The latter (e) is that which may be subject to the removal of indiscretions by physical measurements in post-mining mining areas, respectively, the situational similarities between areas may be sought. The use of nonmetric algorithms is a sub-variant of the general approach for economic and ecological/anthropic assessments of mining areas in Romania.

For example, the consideration of multidimensional scales for assessments may start from the two- or three-dimensional example between the researched areas. It is certain that no two post-mining mining areas can be assimilated to objects with the same dimensions, configurations, or properties.

This finding leads to the thesis of the manifestation of proximity asymmetries, in which a dominant/dominance can be identified from one of the areas found in the examination matrix in the field [81].

On the other hand, it is clear that there are fundamental similarities, fundamental between the mining areas, starting right from the manifestation in each of them of the influences related to points a),…,e).

The use of a nonmetric algorithm to highlight the proximity asymmetries between the mining areas in Romania meant, in this paper, the choice of the case of a number of 5 exploitation/post-exploitation perimeters, found in 3 categories/types of useful mineral substances:

-

category A: Mineralogical perimeters Moldova Nouă (MN) and Baia Mare (BM), for non-ferrous ores (copper, gold-silver, lead and zinc); (underground operation);

-

category B: Valea Jiului (VJ) mining perimeter for energy and coking coal (underground mining);

-

category C: mining perimeters Oltenia (OT) (open-pit mining) and Ploiești (PL) (underground mining) for lignite-brown coal.

This arrangement allows, in a referential system, the observation of “anthropogenic damage (Aa)—economic developments (Ev)”. In fact, the levels of anthropogenic damage (x₁, x₂,…,x₅), are highlighted, respectively, the location of these perimeters in technological/productive-economic states related to post-exploitation (pEv), or the combination between exploitation and post-exploitation (p + a) Ev} (Figure 4).

These reference sites were made on the basis of nonmetric assessments of performance and anthropogenic consequences, by evaluating over time the exploitation and recovery of useful mineral substances in Romania. Our findings show that, typically, all mining and post-mining perimeters in Romania have anthropogenic ”affected” and “post-affected” situations and states: immobilized mining lands, disturbed surface relief by former open pits, land subsidence, acid water accumulations, destroyed vegetation, etc.

All these have conceptual-strategic and managerial causes that boil down to the erroneous separation (1) of the productive-economic activities of mining, with a direct EUR/t cost on the substances in question, and (2) of the anthropic problems (consequences), which generate real ecological disasters and which are included in the total cost price of those substances.

Therefore, erroneously and falsely assumed, the governmental decision-makers in Romania (for the public-owned mining operations, which are still predominant in the national economy), resort to granting subsidies per ton of extracted ore/coal, and subsequently finances from the National Public Budget restoration works of the affected mining areas. In this picture of reality, we appreciate that decision makers need elements of different substantiation of the variants of financing public budgets to support the ”anthropically affected” mining areas in Romania.

The proof of investment decisions in mining areas is complicated [82], but preliminary analysis of similarities in nearby perimeters (for example, on the larger surface of a geological plateau with deposits of useful mineral substances) provides data and useful information for investors and financiers for new mining projects.

As such, there is a need to provide economic and environmental assessments of the areas concerned to glimpse the realism of the financial allocations and their effectiveness.

In this context, at least two points of view are possible, namely: (1) to decide the individual, distinct, separate eco-economic financing of each mining area, without taking into account their evolutionary status reached in Romania, and budget allocations to be provided in absolute value, respectively; (2) to realize an integrated, complex, interrelational vision on the mining areas, perimeters, so that starting from the similarities to practice only the realistically based financial support, in which the anthropic remedies to be included in the costs total and final production.

4. Testing Cointegration and Stationarity of Data Series Using the Augmented Dickey–Fuller ADF Test. Results and Discussions

Our view is that, in this context, a recourse should be made to option (2). On this basis, it is useful to identify and determine the symmetries/asymmetries of proximity, respectively, the distances between economic and ecological similarities of basins, areas, mining perimeters d_ij(d₁₂, d₂₃, d₂₄, d₃₄, d₃₅).

Next, it is aimed to obtain elements, points (subareas) of dominance, which will provide the space and motivation for the foundation of subsidies and financial budget allocations, for the reproduction in the productive-economic circuit of the affected mining perimeters. Typically, the classification of the dominances in question can then be used.

In the case study approached, the non-metricity of the algorithm consists of indeterminacies removed with the help of weights of importance, respectively, of finding/appreciating the effects on two scales: (1) economic scale, [1,…, 10], in which 1 is a complete situation unfavorable (“catastrophic”) and 10 is the ideal, absolute, conventionally favorable situation (unattainable), and (2) the ecological scale [1,…, 10], in which 10 is a “catastrophic” situation, of calamity, completely affecting environment, and 1 represents the non-effects. The model of effective expression of impairments (A_a), formalized on the basis of combinatorial factors, is the following:

$$\left[\left\{{\mathrm{C}}_1^5\mathrm{d}\left(1\right)\ast {\mathrm{C}}_2^5\mathrm{d}\left(2\right){\ast \mathrm{C}}_3^5\mathrm{d}\left(3\right){\ast \mathrm{C}}_4^5\mathrm{d}\left(4\right)\right\}\right]⇾\left({\mathrm{A}}_{\mathrm{a}}\right)$$

(2)

Given the relational reversibility of proximity symmetries dij, at the national level, the degree of anthropogenic damage {q(A_a)[1$÷$10]} combined with the degree of economic damage [{q(A_e)[1$÷$10]} provides a image of eco-economic effects expressed by the similarities of proximity between mining areas:

$$\begin{array}{c}\hfill \left\{\mathrm{q}\right({\mathrm{A}}_{\mathrm{a}}) \ast \mathrm{q}({\mathrm{A}}_{\mathrm{e}}\left)\right\} ⇾ \hfill \\ \hfill \left({\mathrm{A}}_{\mathrm{a}}\right){\mid }_1^5\hfill \end{array}$$

(3)

The nonmetric calculation performed for the period 1990–2020, for the mining areas in Romania, according to the case study, shows the following inequality, which manifests itself as non-insertion equationally:

{(2)⇾x₅}>{(1)⇾x₄}>{(3)⇾x₃}>{(4) x2> (5) x1 (4)⇾x₂}>{(5)⇾x₁}

(4)

The factorial combination of the observed parameters, respectively, of the values of importance (weights of importance) shows that, mainly, all the researched mining areas are under the incidence of anthropogenic damages, mainly post-exploitation (

Table 3. Non-metric estimation of eco-economic impacts in anthropically affected mining areas in Romania (case study for five mining areas).

Zone	${\mathbf{C}}_{\mathbf{m}}^{\mathbf{n}}\mathbf{d}\left(\mathbf{n}\right)$				dij					xi
	${\mathbf{C}}_{\mathbf{m}}^{\mathbf{n}}\mathbf{d}\left(\mathbf{n}\right)$	${\mathbf{C}}_{\mathbf{m}}^{\mathbf{n}}\mathbf{d}\left(\mathbf{n}\right)$	${\mathbf{C}}_{\mathbf{m}}^{\mathbf{n}}\mathbf{d}\left(\mathbf{n}\right)$	${\mathbf{C}}_{\mathbf{m}}^{\mathbf{n}}\mathbf{d}\left(\mathbf{n}\right)$	[d1]	[d2]	[d3]	[d4]	[d5]	x1	x2	x3	x4	x5
(1)(GH)	(+)	-	-	-	d15	-	-	-	-	-	-	-	3.00	-
(2)(OT)	-	(+)	-	-	d12	d24	d32	-	-	-	-	-	-	3.60
(3)(VJ)	-	-	(+)	-	-	d23	d34	-	d53	-	-	2.50	-	-
(4)(PL)	-	-	-	(+)	-	-	d34	d42	-	-	-	0.20	-	-
(5)(BM)	(+)	←	→	(+)		d51	-	-	-	1.00	-	-	-	-

Source: Authors, 2022.

).

Mostly, it is observed that the most numerous proximity symmetries are registered by the Oltenia coal basins (lignite and brown coal) and the Jiu Valley (energy and coking coal), followed by the Ploiești mining perimeter. The lowest similarities in the proximity symmetries belong to the Moldova Nouă and Baia Mare basins in the category of non-ferrous ores.

The concrete eco-economic scale obtained is between (1.00–3.60), which shows that the eco-economic effects are in a maximum proportion of approximately 36% compared to the ideal nonimpact situations (

Table 4. Scalar correlations between similarity weights (xi) and anthropogenic, eco-economic impacts in mining areas in Romania (case study for five mining areas).

Zone	x1	x2	x3	x4	x5	(Aa)
(1)(GH)	-	-	-	3.00	-	0.41
(2)(OT)	-	-	-	-	3.60	0.69
(3)(VJ)	-	-	2.50	-	-	0.57
(4)(PL)	-	-	1.20	-	-	0.29
(5)(BM)	1.00	-	-	-	-	0.29

Source: Authors, 2022.

with the graphic representation in Figure 5).

This evaluation is useful to local and foreign investors, in their expressions of interest for mining projects, for the calculation of risks (investment, operational, efficiency) in the areas possessing mineral wealth on the territory of Romania. Methodologically, in the present research, we have proposed elements to improve the analysis of eco-economic impacts in anthropogenically affected mining areas in Romania and the correlations between the weights of similarities and anthropic, eco-economic impacts, such as: the impulse-response function, observing the sustainability of the data from old mining investment projects for use in new projects in mining basins, using panel data from responses to questionnaires among important, representative decision makers in the field. Estimates are obtained using data from 1990–2020. Two sets of data are shared: (1) similarities (S) and (2) anthropogenic, eco-economic impacts in five mining areas (A_a). Data series are logarithmized to remove heteroscedasticity: ln(S) = natural logarithm of similarities; ln(A_a) = natural logarithm from anthropic, eco-economic impacts. In co-integration testing, the stationarity of the data series was examined through unit root tests, and for their order of integration, through the augmented Dickey–Fuller ADF test. The cointegration condition is that the data series are integrated of the same order. If they are nonstationary, then there can be a combination of them that is stationary [83].

In the context, the Schwartz information criterion was used as it is suitable for series larger than 20 observations. The results of the enhanced Dickey–Fuller test are in

Table 5. Stationarity ADF Test for (S) and (A_a).

	(S)/t Statistic	(Aa)/t Statistic
ADF statistic test/ Critical values	−1.087572	−2.517342
1%	−3.372529	−3.37946
5%	−2.716942	−2.71341
10%	−2.544726	−2.54782

Source: processing by the authors.

.

The null hypothesis cannot be rejected as the p-value (probability) obtained for (S) is 69%, i.e., the similarities are not stationary. For the anthropogenic, eco-economic impacts in the five mining areas (Aa), the p-value (probability) obtained is 9% (greater than 5%), with these impacts being, therefore, nonstationary.

Reprocessing the ADF test with the first difference, we find that the parameters (S) and (A_a) have a p-value (probability) less than 5%, so they are stationary, and the two series are integrated of order I (

Table 6. Stationarity ADF Test for (S) and (A_a)/ first difference.

	(S)/t Statistic	(Aa)/t Statistic
ADF statistic test/ Critical values	−14.62946	−24.16392
1%	−3.649350	−3.649350
5%	−2.884193	−2.884193
10%	−2.653927	−2.653927

Source: processing by the authors.

).

The cointegration regressions between (S) (A_a) show a coefficient BETA₀ = 0.690234, which shows that an increase in similarities by 1% is accompanied by an increase in anthropogenic, eco-economic impacts by 0.69%, so the regression is real. As such, the Johansen cointegration test shows that the variables and parameters are nonstationary but integrated of the same order. In this framework, it is of interest to express the results of a causality test [84].

The definition of causality, according to the authors Unbreen et al. (2015) [85] is:

”X is a Granger cause of Y, if the present value of Y can be predicted with better precision by using past values of X rather than not doing so, other information being identical.”

To highlight causality, the Granger test, in the present situation, shows that (S) does not determine causality on the anthropogenic, eco-economic impacts (A_a). However, a bi-causality relationship is manifested as (S) affects (A_a), but (A_a) also affects (S).

So, essentially, we find that the two data series are nonstationary at primary level, but stationary at first difference. On this basis, we recommend decreasing (A_a), and cointegration, expressing a lasting relationship between variables, if it generates the reality that both (S) and (A_a) are estimated simultaneously, then, for sustainability, the increase in the set of simultaneities and the reduction of anthropic, eco-economic impacts in mining basins in Romania, thus increasing expressions of interest for investments.

5. Conclusions

The development of criteria and the formalization of statistical scales can group (reunite) some similarities from the researched areas, which provides a primary basis for typological groups of properties, situations, performance levels, etc., contributing to a vision regarding potential “classifications” or eco-economic ”hierarchies”.

Of significant importance is the establishment of some sets of variables, which show the noncontrollable (inputs) and controllable (outputs) values for investments in anthropogenically affected mining areas in Romania. In the studied areas, “batteries of tests” can be applied with the help of which answers to different types of questions are obtained, or economic or environmental “images” are separated starting from scalar proximity similarities.

Eco-economic dominance, in the sense of negative economic and ecological impacts, is manifested in the Carboniferous Basin of Oltenia, in the mining and post-mining perimeters of lignite, in large open pits with cutting technologies with bucket-rotor excavators, transport with high-capacity belts and long lengths, of coal in warehouses and at area terminals.

The manifestation of asymmetric information in anthropogenically affected mining basins leads to increased transaction costs in the mining investment process. By studying the “symmetry” of the structures and the information related to the knowledge of anthropogenic effects in the mining areas, appreciations are obtained regarding the presence of “asymmetry”. On this basis, procedures or techniques can be developed to reduce precision losses caused by asymmetry.

The next step is the creation of a countermeasure, resistance in the face of asymmetries. Therefore, the characteristics of asymmetries must be blocked, using similarities for identification from proximities that help the investment decision-making process to be resilient to risks.

It is certain that investors and practitioners need data, information, and knowledge as in-depth as possible for the decision to spend money in new mining projects. Currently, the economic and social environment is strongly disturbed by the energy crisis. It is clear that the energy comes essentially from mining sources. To the essential question of why new large mining projects for the valorization of mineral resources, especially energy resources, are not registered in Romania, including in the EU territory, in order to overcome critical situations, based on the research results in this article, we answer with the following arguments:

-

In Romania and in the EU, there are geological deposits with useful and energetic substances that meet the eco-geotechnological exploitation conditions;

-

The Green Deal program should not be restricted, but it is necessary to optimize activities by developing a road map with coherent graphs of joint action for sustainability;

-

Similarities, symmetries, and scalar or indicative proximities from the experiences of old mining projects must be exploited for new mining and energy projects, since geological deposits are immutably fixed in unchangeable natural areas.

-

Decision-makers capitalize on knowledge “from the past” and can obtain specific certainty of success for new mining projects in the face of risks and obstacles related to the exploitation of mining products;

-

In this way, the reasons for the commitments and expertise of interest for the financing of new mining projects are brought into the short and medium term to contribute to overcoming overlapping crises.

Through the results of this article, practitioners now have a conceptual framework for an integrated vision of investment risks, by knowing the symmetric and asymmetric similarities from the productive-economic events of proximity, from the mining perimeters where exploitation has been carried out or that are in the post-exploitation.

In fact, the added value is that of scientific contribution to the substantiation of decisions in the field and to the advance toward levels of high investment certainty with improved or eliminated risks.