The Importance of Green Investments in Developed Economies—MCDM Models for Achieving Adequate Green Investments

Abstract

Green investments help to create less harmful alternatives and adequate funds that contribute to economic growth, sustainable development, and social well-being. The paper aims to evaluate decision making on the choice of green investments based on multi-criteria decision making (MCDM). The applied methods are empirical and analytical based on the study of the literature, multi-criteria modeling, the determination of weights, and the ranking of criteria in deciding the green investment mapping of indicators, and mapping the indicators. The research used groups of indicators that reflect the main characteristics of green growth from the OECD database. The idea is to decide on the best green investment based on green growth criteria, which consist of grouped indicators according to the areas of the green economy rather than according to their values. The results of the Analytical Hierarchy Process (AHP method) showed that half of the investments in the green economy come from public sources (0.51) and the other half are private (0.25) and institutional investors (0.24), while the Best/Worst Method (BWM) revealed that the best criterion for the decision to invest in the green economy is the environmental and resource productivity of the economy, and the worst is the base of natural assets. This paper aims to enable decision-makers to use these results as weights for the overall assessment of green investments in ESG and to simplify the decision-making approach in future analyses.

1. Introduction

At the beginning of the third millennium, no hard importance was attached to new renewable energy sources. Energy production from fossil fuels dominated, so renewable sources were considered a small niche and were often called alternative energy. Uncertainty in the energy production from renewable sources (wind and solar energy) was the product of numerous factors such as production, technology, investor preferences, investment return, risk, existing energy system grid, etc. It was also caused by global expectations that conventional energy production has no competition. Despite these circumstances, investments were not absent even in the first decade [1]. In the period 2000–2009, the state, investors, and the public invested in the energy sector (wind, solar energy, and biofuels, excluding hydropower). Despite the financial crisis, only in 2009, about USD 147 billion were invested in new renewable energy capacities. The Green Economy Initiative, led by the United Nations Environment Program since 2008, has contributed substantially to promoting investments in green sectors.

In the next decade 2010–2019, according to the GTR [1], three times more investments (USD 2.6 trillion) were made in renewable energy capacities (solar energy USD 1.3 trillion, wind energy USD 1 trillion, and biomass and waste USD 115 billion). The largest green investments in Europe, over USD 700 billion, were achieved by Germany and Great Britain. Spain entered the “USD 1 billion plus club” (investment in capacities jumped more than five times in 2018), while Sweden, the Netherlands, and Russia have doubled their investments. The world leader in investments in 2010 was China (USD 758 billion), which is almost a third of the total global investments in sustainable energy. It is followed by the USA (14%) and Japan (8%). These investments have led to positive economic results—competition costs have enhanced, technology has improved, equipment production efficiency has increased, financing costs have decreased, and larger capacities have been installed in the energy sector. On the other hand, developing countries record a continuous negative investment gap in renewable energy every year (they need USD 1.7 trillion, and generate USD 544 billion). That is why initiatives are being strengthened to help the latter countries attract investments and sustainable funds.

Investment policies (at the national and international level) play a key role in financing the green economy. In developing and least-developed countries, conventional instruments, such as tax incentives, dominate. In contrast, developed countries are moving to financial incentives and targeting new, more complex tools (feed-in tariffs and green certificates) to promote investment and facilitate green transition. They are dominated by private investment funds, followed by public funds, while at the back are institutional investors. Therefore, governments in developing and emerging economies are including green investment measures in their national recovery plans [2]. According to UNCTAD data [3,4], the volume of introduced policies and measures from 2010 to 2022 grew annually at 13% (13.1% for G20 members and 13.7% for other economies).

The applicability of all these policies, measures, and instruments consequently influenced the creation of a greater number of indicators used in sustainable development. Due to the specificity of their countries, policymakers create derivative indicators of sustainable development, which give relatively good results and reflect acceptable outcomes for the community. However, it is often complicated to successfully compare different countries using methodologically the same indicators from the database. This makes it hard to find a unique solution to specific issues. The best example is the SDGs, where 17 unique goals apply to all the countries without exception, but with many different country-specific indicators. This inspired the authors to create a model to assist in the green investment decision-making process at a higher aggregate level than indicators as criteria. The aggregate level of the OECD Green Growth Indicators database (OECDGGI) was used in the analysis. Based on the assessment of criteria and alternatives, the MCDM model will facilitate the estimation of sustainability and decision making on green investments in countries that do not have all four groups from the database above (environmental and resource productivity of the economy, natural assets database, environmental dimension of quality of life, and economic opportunities and policy responses). The idea of this research is to examine the criteria that are crucial for investors when they decide whether to invest in the green economy or not. Based on this statement, the expected hypothesis is that the green growth criteria determine the types of investors in green investments in developed economies. We chose the group of developed countries for two reasons. First, developed countries started to deal with the green economy earlier, they have established regulations, they have a developed financial market, the business environment is favorable for investment (a wide range of financial products), there are more investors (institutional, public, and private investors), and their capacities are large enough to drive progress in the green economy. Second, these countries have large technological capacities, invest large capital in research and innovation in the green economy, create numerous tools and techniques to measure and evaluate progress in green growth and economy, and take initiatives to encourage less developed countries. The scientific contribution lies in the creation of a global coherent green growth policy from which developing countries need to reap the benefits for their ecosystems.

The rest of the paper is organized as follows: Section 2 consists of three parts. The first part presents a literature review, the second part contains explanations about the data, and the third part shows the application of the methods used. The results and discussion are presented in Section 3. The final section provides a summary of our findings.

2. Materials and Methods

2.1. Literature Review

The concept of green economy is relatively new and not yet finally defined, allowing for varying degrees of trade-offs between environmental, economic, and social benefits, which can be useful in decision making. Here, we will focus on an important and current issue from the green economy, namely green investments. Green investments represent public and private investments that, either directly or indirectly, are aimed at the sustainable use of resources, protection of natural capacities, and green growth [5,6].

In the 21st century, a need to move towards a green economy is reflected through economic growth based on environmental quality and social well-being [7,8,9,10]. The initiatives of large economies, such as China, the USA, and the EU, which create policies for sustainable financing while strengthening transparency and setting standards [4], are especially current. The Green Economy Initiative follows broader international efforts, including the Sustainable Development Goals (SDGs) and the Paris Agreement on Climate Change. It recognizes the need for a holistic and integrated approach to address environmental challenges while fostering economic growth and social well-being. The concept of a green economy has become global, and the introduction of green economy strategies, policies, and measures has become massive [11]. Different economies have different patterns in their “greening” programs [12]. According to Loiseau et al. [13], the purpose of establishing relevant concepts, approaches, partnership tools, funds, and other measures is to deal with the risks affecting economies. Carraro et al. [14] see the ideal political framework in full direct cooperation of all the countries.

Through the strategies related to the green transition, developed countries have introduced strategies and programs to improve efficiency and improve the system [15]. The green transition implies a transformation towards a green economy, achieved by finding less harmful alternatives that require huge investments. This transformation covers a series of steps that need to be taken to achieve greater investment efficiency. Governments use numerous measures and tools to encourage green investments. The Dutch government encourages investment in green funds through the Green Funds Scheme, which includes a tax credits mix (lower than market) and tax exemptions (on dividends and interest payments) to all interested investors. Multilateral banks and international development institutions also play a crucial role in technical assistance and support for capacity building [16]. The ambiguity of green investments exists in the labor market. Until recently, most literature has emphasized job losses and lost earnings in the green economy [17,18,19]. Training programs provide upskilling for the unemployed or employed, leading to recognized qualifications in green jobs [12,20]. These workers in developed countries receive a wage premium of around 4 percent [21]. The premium reflects knowledge of sustainability, renewable energy, and environmental management.

Green investment involves directing financial resources and capital towards projects that yield positive environmental and social outcomes [22]. Turbulence in the global market in recent years, caused by high inflation, rising interest rates, and the looming risk of a recession, did not slow down investment in green economies. According to UNCTAD data [4], the value of the sustainable financial market (bonds, funds, and voluntary carbon markets) reached almost USD 6 trillion in 2022. More than half of that (USD 3.3 trillion) is the value of the sustainable bond market. Green bonds continue to represent a growing source of financing for certain sectors of sustainable development (energy and water), in addition to the overall weakness of the bond market (down 11% compared to 2021). Nonetheless, financial institutions and supranational entities recorded gains, which preserved a high share of green bonds (56.2% or USD 501 billion). Among the BRICS countries, China has become the leading issuer of green bonds [23]. China is considered the most proactive country, a global front-runner, and a leading force in green finance [24].

With the growth of the role of the green economy, the number of scientific analyses and research has significantly increased. Batrancea et al. [25] show that restructuring the market economy and a green economy transition implies continuous efforts and interdependence because it cannot be achieved in isolation. Zhang et al. [26] assess the impacts on investments and public finances of the transition to a green economy using a mix of the AHP and COPRAS-G methods. Li and Gan [27] investigate the role of finance in promoting green development and highlight the significant positive effect of spatial spillovers. A broader framework of the analysis of the financial regulations’ role in the sustainable green economy in Turkey was conducted by Odugbesan et al. [28]. In the study, they showed that there is a long-term causal relationship between laws, economic freedom, inflation, and carbon productivity. Ye and Dela’s [29] study deals with the effect of green financing and investment on corporate social responsibility and the sustainable performance of a company in the chemical industry in Indonesia.

Multi-criteria decision making (MCDM) is an increasingly popular tool in energy planning, and its high flexibility enables decision making to reckon with all the criteria and objectives [30]. Various models have been developed and used effectively for the decision-making problem considering several factors/criteria [31,32]. Saaty’s Analytical Heuristic Process (AHP method) has been widely used for several decades in numerous sectors of the economy, mining [33], agro-economy [34], the IT sector [35], banking [36], sustainable development [26], etc. Rezeai’s Best/Worst Method (BWM) is a newer MCDM technique that resolves the inconsistency in pairwise comparisons. It finds application in an increasing number of areas, such as banking [37], energy sector [38], manufacturing [39], innovation [40], etc.

2.2. Data Experimentation

The conventional indicators of the economy in recent years do not reliably depict economic performance. Global changes are leading to changes in national accounts, such as the development of the System of Environmental and Economic Accounting (SEEA) by the UN Statistics Division [41]. Hence, numerous green economy indicators are created within the framework of the World Bank, OECD, IMF, and other institutions and associations. Any isolated tracking of green indicators over time and between countries will not be useful for analysis if not viewed in a broader context [42].

We started the selection of criteria for evaluating green investments through various non-financial sustainability factors related to the daily operations of companies in the green economy. These are the well-known and widespread Environmental, Social, and Governance criteria (the first column in

Table 1. Green growth criteria.

GGIs [43]	Definition
1. Environmental and Resource Productivity of the Economy (ERP)	Economic growth becomes greener with a more efficient use of natural capital and includes aspects of green sector production
2. Natural Assets Base (NAB)	The risk of green economy growth exists with a declining natural resource base
3. Environmental Dimension of Quality of Life (EDQL)	Green economy means how environmental conditions affect people’s quality of life and well-being
4. Economic Opportunities and Policy Responses (EOPR)	The effectiveness of policies in achieving green growth and socially responsible behavior that would provide opportunities for business and employment

). What is the reason that we decided to start from the basic criteria? Good practice has shown that these are indisputable criteria for evaluating the green economy. Their wide application in numerous analyses confirms the correctness of our initial assumption in the analysis. We also find confirmation in the UNEP report [41], where the green economy is defined as a system of economic activities related to the production, distribution, and consumption of goods and services “which results in improved human well-being and social equity, while significantly reducing environmental risks and ecological scarcities.” The definition of green economy reflects the above criteria through their broad coverage.

However, we considered it more convenient for the analysis to identify criteria with a clear conceptual framework. We opted for the OECD database, which consists of measurable indicators of green growth. This database is popular and contains many different green growth indicators. The authors used it as a development framework for criteria in the green investment decision-making process. We focused only on selected groups as criteria, which in the OECD database contain related indicators. These are 1. ecological and resource roductivity of the economy (ERP), 2. natural assets database (NAB), 3. ecological dimension of quality of life (EDQL) and 4. economic opportunities and policy responses (EOPR). Certain indicators within these groups are not available for all the countries or complete time series do not exist, which may be a limitation of the analysis.

In Table 1, the second column shows the four groups of criteria, which are also the main characteristics of Green Growth Indicators (GGIs) from the OECD database [43]. They enable the monitoring of progress towards green growth to make policies transparent. At the same time, this is the biggest advantage of the database. We find the confirmation of this context in the OECD report [44], where “Green growth means fostering economic growth and development while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies. To do this, it must catalyze investment and innovation which will underpin sustained growth and give rise to new economic opportunities.” In the last column of Table 1, brief definitions of each criterion from the cited database are given in the context of the green economy.

This is not a series of composite indicators but a set of internationally comparable criteria. The analysis is focused on the groups and not on the values of the individual indicators that make them up. The indicators are representative and include some metrics, such as CO₂ emissions, the adoption of green technologies, productivity and energy efficiency, etc. The crucial advantage is the flexible framework in which the data are available, as it is easily adapted to a country’s circumstances and easily improved and further developed [45]. Therefore, the use of this database is widespread. The lack of this base is objective. For some countries, data are missing for many indicators, or the time series is incomplete (e.g., for Australia, data are available for 156 indicators, but not the same time series).

The OECD [9] report states that the details for the indicators are sometimes limited and need to be placed in a valid context for analysis. An example is given where data on environmental pressure are rarely available in industrial activities, and favorable information can only be constructed at the level of the entire economy. In such cases, it is important to supplement the indicator. This led the authors to stay at the area level in the analysis and avoid the potential problem or subjective assessment of certain information due to missing data for a country-specific indicator.

The research is focused on four areas from the OECD database according to their weights. Subsequently, the MCDM approach is employed to rank these areas. The primary objective is to facilitate a comprehensive comparison of various countries at a broader scale, overcoming limitations posed by the absence of concrete official data, particularly in situations where such data are not readily available. The task of this research is to use a combination of MCDM techniques to aid in decision making. We recognize the advantage of the model in a shorter decision-making process. Namely, the problem is solved using algorithms within a hierarchical structure and a decision is made. This is an alternative procedure to models based on time series or cross-sectional data, which may sometimes be unavailable.

2.3. Multi-Criteria Decision Making (MCDM)

The application of MCDM is widespread in various research fields. It is increasingly represented in sustainable development, energy, water treatment, environmental pollution recovery, etc. MCDM has many techniques that manage the perfect design with multiple dimensions. They make available all the criteria, rank them according to priority in the presence of other goals, and help to make the appropriate decision (Figure 1). A good decision-maker can extend the model in a few steps without leaving the methodological framework and make the right decision. The MCDM techniques help the decision-maker to quantify certain criteria according to their importance within a hierarchical structure.

The goals set in the hierarchy can usually lead to different solutions at different times based on the priorities set by the decision-makers. Even a certain problem can be approached with other methods. Each method or model shows its shortcomings and limitations, and

Table 2. MCDM methods.

Methods	Steps	Strength	Weakness	References
AHP	Define a hierarchical structure Generate a pairwise comparison matrix Determine weights for each criterion Calculate the score of each alternative considering the criteria Check the consistency Show the results and create a ranking list	Adaptable No complex mathematics Based on a hierarchical structure	Moral hazard More decision-makers cause more problems in assigning weights Data based on experience The inconsistency in decision-makers	[22,36,46,47,48,49,50,51,52]
BWM	Define a set of criteria Define the best and the worst criterion Define the preferences of the best criterion over the other criteria Define the preferences of all criteria over the worst criterion Search for the optimal weights of the criteria	A clear understanding of the range of evaluation An effective strategy is mitigating the anchoring bias	The inconsistency in the provided data More than three criteria/alternatives might bring about multiple optimal solutions	[37,38,39,40,53,54]

shows the basic characteristics of the models used in this research.

The decision-making process implies a hierarchical structure in which alternatives are evaluated according to several criteria. The best of the ranked alternatives is selected, which is also the best solution to the problem. The AHP method is the most commonly used MCDM decision-making technique. This method is based on the occasion that criteria are mutually independent, and interactions between sub-criteria do not exist. The logical structure of interconnected components is shown in Figure 2. The hierarchical structure of AHP consists of a series of steps. Defining the problem is the first step. The better defined the structure of the problem, the better the hierarchical structure of the model.

All the elements at the same hierarchical level are compared with those at a higher level. Pairwise comparisons are made using Saaty’s scales of relative importance [48]. Then, the importance indicators of element i (i = 1, 2, …, n) in relation to element j (j = 1, 2, …, n) obtained by Saaty’s “a_ij” are structured into a comparison matrix (A = n × n).

In the next step, the vector of the eigenvalues of the comparison matrix is determined. First, all its elements are added in each column, and then each matrix element is divided by the sum obtained for the column in which that element is located. The normalization of the sum of rows is carried out by dividing the sum of each row by the number of rows. After that, weight values are assigned to the criteria based on the calculated eigenvalue vector.

In order to examine the consistency of the results and calculate λ_max (the maximum eigenvalue of the comparison matrix), the matrix containing the comparison results should be multiplied by the priority vector

$$\left[\begin{array}{ccc}{a}_{11}& \cdots & a_{1n}\\ ⋮& \ddots & ⋮\\ a_{1n}& \cdots & a_{nn}\end{array}\right]\left[\begin{array}{c}{w}_1\\ ⋮\\ w_n\end{array}\right]=\left[\begin{array}{c}{b}_1\\ ⋮\\ b_n\end{array}\right]$$

and then the first element of the calculated vector should be divided by the first element of the priority vector, the second element by the second, and the nth element with nth.

$$\left[\begin{array}{c}{\displaystyle \frac{b_1}{w_1}}\\ ⋮\\ {\displaystyle \frac{b_n}{w_n}}\end{array}\right]=\left[\begin{array}{c}{\lambda }_1\\ ⋮\\ {\lambda }_n\end{array}\right]$$

After determining λ_max, the consistency index (CI) is also determined.

$$Consistency Index={\displaystyle \frac{\left(\lambda max-n\right)}{\left(n-1\right)}}$$

(1)

Finally, the consistency rate (CR) is the ratio of the consistency index (CI) to the random index (RI).

$$Consistency Rate={\displaystyle \frac{Consistency Index}{Random Index}}$$

(2)

Finally, the priorities of the alternatives according to the obtained problem are defined. Consistency is checked by the degree of consistency. The threshold value is defined at the level of 0.1. When the degree of consistency is greater than 0.1, it is necessary to repeat the comparison of the rules using the eigenvalue method. This procedure further leads to a vector of weights for each level, which are then ranked. The highest values give the best solution to choose, which is also the final green investment decision.

A major drawback of this method is the inconsistency in decision-makers in pairwise comparisons (due to the large number of pairwise comparisons of criteria). To overcome this shortcoming of the model, the BWM is applied. Suppose we have n criteria and want to perform a pairwise comparison using a scale from 1/9 to 9. The resulting matrix would be

$$A=\left(\begin{array}{ccc}{a}_{11}& \cdots & a_{ij}\\ ⋮& \ddots & ⋮\\ a_{i1}& \cdots & a_{ij}\end{array}\right)$$

where a_ij shows the relative preference of criterion i to criterion j. a_ij = 1 shows that i and j are of the same importance. The importance of j to i is shown by a_ji. In order for matrix A to be reciprocal, it is required that a_ij = 1/a_ji and a_ii = 1 for all i and j.

In BWM, the first step is to define a set of criteria. In this step, we consider the criteria (c₁; c₂; …; c_n) that should be used to arrive at a decision. The best and worst criteria are determined. There is no comparison at this stage. Only the best (e.g., most desirable or most important) and worst (e.g., least desirable or least important) criteria were identified. Then, the preferences of the criteria over the others are given. The resulting Best-to-Others vector would be as follows:

A_B = (a_B1; a_B2; …; a_Bn)

where a_Bj indicates the preference of the best criterion B over criterion j, and a_Bi = 1. The resulting Others-to-Worst vector would be as follows:

A_w = (a_1W; a_2W; …; a_nW)^T

where a_jW indicates the preference of the criterion j over the worst criterion W, and a_nW = 1.

The weights of the criteria are calculated (w^*₁, w^*₂, …w^*_n) and finally, the criteria are ranked. The Best/Worst Method (BWM) is a powerful MCDM tool used to define criterion weights. It has excellent results in decision making for the criteria with the same influence on the maker. The application of the method is simple because it defines a unique best/worst criterion within the set of observed criteria.

3. Results and Discussion

From the results of the AHP model (

Table 3. Final result, AHP method.

	ERP	NAB	EDQL	EOPR	Alternative Weights
II	0.14	0.02	0.03	0.05	0.24
PU	0.22	0.06	0.08	0.15	0.51
PR	0.09	0.02	0.05	0.08	0.25
Criteria wights	0.45	0.10	0.17	0.28	1

), it can be determined that public investments dominate (PU—alternative 2) in green economy investments (0.51). Public finance focused on climate change has grown significantly over the last decade and is still accelerating further [55]. Ocolișanu et al. [56] point out that the role of public investments in the current context of sustainability in 11 CEE developing countries is even greater due to the context of globally adopted sustainable development strategies and current budget restrictions, as well as for political reasons. Investments from private sources (PR—alternative 1) and institutional investments (II—alternative 3), which have an approximate score (0.25 and 0.24), have a great advantage. Sustainable investment requires a gradual change in both public and private investment levels. Although public investment is the dominant source of financing, the potential for additional inflows of private capital is expanding [2]. In recent years, institutional investment has increased its share in total investments through project financing, the goal of which is for companies to make better investment decisions and achieve higher profits with minimal environmental impact [6].

Observing the obtained results of the analysis based on the evaluation of the criteria, it is observed that the environmental and resource productivity of the economy (ERP factors) are dominant when deciding on investments in the green economy (0.45). Taušová et al. [57] showed that the efficiency of using individual resources in EU member states varies greatly. The results showed a positive connection and correlation between the efficient use of resources in EU countries on eco-innovation, the use of materials in recycling, and the environment. The opposite conclusions are given by the research by Addai et al. [58]. They investigated the impact of energy productivity on environmental degradation in Germany between 1990 and 2019, stating that greater investment is needed to achieve an energy turnaround. The following important set of factors is determined by the economic opportunities and policy responses (EOPR criterion) with a weight of 0.28. Here, it is quite clear that the availability of resources, their efficient use, and policy responses play a key role in deciding green investments. Given the accelerated degradation of the environment, global policy efforts are being strengthened for the efficient use of resources through investments in research and technology [58]. The EDQL and NAB criteria give the lowest importance, with weights of 0.17 and 0.10, respectively. These last results may be surprising, but numerous examples confirm them. On the one hand, natural resources are the basic basis of economic activity and people’s well-being, and positive effects can only be achieved by the effective management and optimization of resource use in the context of economic development [40,56,57]. On the other hand, the ecological dimension of the quality of life is ambiguous because the commitment to a green economy is full of controversies [56,59,60].

Most public funds are state funds, and these results confirm that these are the main ones focused on sustainability issues. Public funds include a wide range of institutional measures, such as fiscal targets, rules, and measures; sectoral development panels; coordination of national bodies; project financing; and public infrastructure. A crucial role of public funds is to encourage private investment. Ocolisanu et al. [56] confirm a strong positive correlation between public and private investment (crowd-in or crowd-out effect). In a broader context, public funds can include large and small businesses, mutual funds, pension funds, individual investors, and non-profit organizations. Private sources of funds are mainly focused on profit. It is known that investing in sustainability is a risky and short-term non-profit investment, less attractive for private capital. Zhan and Santos-Paulino [2] provide a solution for private investment through a sustainable development policy framework and four principles—creating a balance between liberalization and regulation, risk and return, private and public investment, and the global scope and needs of the SDGs. Private investors include companies, banks, and insurance companies. For example, Li and Gan [27] showed in their analysis that local banks encourage green financing and that bank management is committed to strengthening the relationship between green investment and corporate sustainability. Individual investors include high-net-worth individuals, venture capitalists, and private investors. Their investments are in technology and innovation or sectors with some incentives and social benefits. Social responsibility is a crucial issue, so the range of investments in sustainable development is gradually expanding. Institutional investors, such as international financial institutions, insurance companies, and funds, provide support through various forms of global investment to encourage sustainable development. Boermans and Galema [61] analyzed how investors actively decarbonize their portfolios using the example of Dutch pension funds in 2009–2017. The results showed that pension funds that deviate from the market reference weights have a significantly smaller carbon footprint. Similar initiatives have become a part of national strategies and all significantly contribute to the processes of the implementation and financing of sustainable development [62].

According to Inderst et al. [22], when governments aim to redirect private capital to a thematic area of sustainable development (e.g., green growth), they create a business environment to make the placement of funds probable. Green investments thus focus on sustainable practices, environmentally friendly technologies, and the conservation of natural resources. With relevance to this, governments create metrics for assessing the progress of the green economy and map the zones of the realization of green investments based on UN programs [42]. Governments’ decisive role in public finance programs that encourage green investments must exist, but at the same time, they should develop an exit strategy [63]. The exit strategy does not mean public finances will be completely withdrawn from this business. On the contrary, it should free market participants to engage in greening and facilitate the transformation of the economy towards green development. Certainly, public finances will be necessary to implement green strategies and build technical capacities, including “green” prices.

A substantial amount of funds is invested in three main areas: resources, productivity factors, and energy productivity. At the same time, they are integral parts of sustainable development strategies and sustainability policies, covering the issues of CO₂ neutrality [59], resource productivity in the EU [57], and energy productivity [58]. The following important criterion in deciding to invest in the green economy or not is the policy issues of sustainable development and how these policies are implemented in the labor market, education, regulations, technology, innovation, etc. The criteria related to environmental protection mainly refer to the introduced policies of sustainable development and their implementation [59]. Such implementation requires time for investment, implementation, and outcome. The issues of natural resources, ecosystems, and biodiversity are exclusively national [9], and there is no question from which the sources of funds are financed. A large part of public finance in green investments is related to fiscal policy and taxation [12]. The states are most interested in preserving resources, and investing in resources implies large-value funds, high risk, longer repayment periods, and high investment costs [15]. Sustainable development strategies are focused on sustainable development policies, regulations, and action plans to maintain resources and resource productivity. These are mainly long-term and strategic investments.

We also tested the estimation of investment decision criteria with another model to determine if there were any deviations. The results of the BWM model showed similar results (Figure 3). That led us to the conclusion that the first-ranked criterion for the decision to invest in the green economy is ERP. The results of BWM also showed that the worst criterion is NAB. When there is a need for an alternative criterion, i.e., investment, the EOPR option can still be adopted instead of EDQL. The main reason for this outcome is the existence of a wider assortment that is financed from public funds. Also, it maintains continuity in the long term. In the IFC report, numerous green investment initiatives are mapped according to four criteria to assess their progress: investment process, public policies, direct investment, and industry inputs [64].

Due to the structured approach of BWM, inconsistencies in comparisons are minimized and are shown to be consistent with the previously obtained AHP model results. Also, the results are accurate in decision making due to secondary comparison elimination. Considering investment characteristics have been added to support the investment selection approach. This approach is considered valuable, as it helps future researchers to implement similar approaches on different types of investments.

4. Conclusions

The application of conventional economic indicators, such as GDP, gives a distorted picture of contemporary economic flows because it does not show the capacities of natural capital. In particular, it does not reflect the extent to which production and consumption activities draw on natural capital, nor does it show the economic benefits of potential natural capital. Hence, the limited capacity of similar indicators is overcome by creating numerous green and composite indicators to provide a clear picture of economic flows in sustainable energy, digitalization, biodiversity, and ecosystems.

Our research encompasses several objectives. Firstly, we aim to evaluate the significance of specific criteria in the decision-making process related to potential investments in the green economy. Additionally, our interest lies in delivering crucial insights that can enhance the governance of green policies. A key objective is to address knowledge and policy gaps within green investment, encouraging stakeholders to embrace new practices in green finance for sustainable resource management. By concentrating on developed countries, our study provides valuable insights applicable to policymaking in developing countries. Ultimately, our goal is to expand the context of the analysis globally, emphasizing the necessity for multidimensional and innovative approaches to green finance.

In the structure of green investments in developed countries, public finances dominate (alternative 2), while the private sources of financing make up the largest part of total investments (alternative 1), and institutional investors represent a small part of these flows (alternative 3). The environmental and resource productivity of the economy (ERP) is the most important of the criteria that influence the decision to invest in the green economy. Investment decisions based on the natural asset base (NAB) are the smallest as they are tied to available advantages at the national level and require long-term investments with low returns, or even in some cases, with high investment costs. Private investors are more interested in profitable businesses with minimal social responsibility in the matter of sustainable development, while institutional investors mostly have a stimulating role and provide support from a global level. The above results confirm the expected hypothesis that investors are guided by the criteria of whether they invest in the green economy.

We find the limitation of the paper in two key elements of green economy analysis. First, it is unlikely that a general definition of green investment can be derived. Second, it is not possible to quantify and represent all the measurable variables in a single number. Indicators such as the GGI can only provide a more complete picture if additional information from other indicators is included, thus providing a complementary picture of the nature of ecosystem change. Such barriers can be removed by global initiatives on opening standards on the joint inclusion and definition of composite factors in the green transition process through, for example, the inclusion of green accounting. Future research should include composite indicators that will be used for comparative analysis and to promote green investments, growth, and economy at the global level.

The advantages of a green economy for developing countries, based on the experience of developed countries, would be economic growth and the creation of new jobs, the protection and preservation of the environment, and improved quality of life. The sustainable results of the emerging economies have shown that the exciting investment opportunities available in the green economy should not be missed. For developing countries, green investments should be a challenge. On the one hand, they are in a better position than developed countries because they can install modern and efficient capacities previously unavailable. On the other hand, they are also at an advantage because their initial position will be based on efficient capacities.