Academy of Accounting and Financial Studies Journal (Print ISSN: 1096-3685; Online ISSN: 1528-2635)

Research Article: 2021 Vol: 25 Issue: 5S

Prediction of Environmental Performance Based on Countries Economic Attractiveness

V. Apalkova, Kyiv National Economic University named after Vadym Hetman

?. Nikolaienko, Oles Honchar Dnipro National University

N. Meshko, Oles Honchar Dnipro National University

Citation: Apalkova, V., Nikolaienko, A., & Meshko, N. (2021). Prediction of environmental performance based on country’s economic attractiveness. Academy of Accounting and Financial Studies Journal, 25(S5), 1-15.

Abstract

In the article, the authors investigate the relationship between the basic parameters of the environmental performance and key indicators of the country's economic development, in particular such as the Human Development Index, Global Competitiveness Index, international tourism development and others. The analysis tools include correlation model and a decision tree, which based on the application of the RapidMiner software package and a database of more than 120 countries of the world. The study revealed a significant correlation between the Environmental Performance Index (EPI) and Human Development Index (0.83), the level of competitiveness of the economy (0.79) as well as GDP per capita (0.72). Besides, the calculated decision tree showed that the key factor influencing the EPI level is the income level of the population. The calculations show that countries with GDP per capita above certain level per year belong to the cluster of either high or medium environmental efficiency and their ecosystem are significantly influenced by the human development index. In the contrary, when the level of GDP per capita in economy is very low, the dominant value is no longer the human development index, but the number of registered enterprises in the country (the more intensive entrepreneurial activity the higher probability that country will have low environmental performance). The presence of a strong relationship between the environmental index (EPI) and human development index (0.83) suggests the need to improve the quality of human capital, especially in the countries of the third cluster of low environmental efficiency.

Keywords

Environmental Performance, Circular Economy, Human Development, Global Competitiveness, Economic Attractiveness, Decision Tree.

JEL Classification

F64, O15, J2.

Introduction

Scientific publications write a lot about the need to develop a "green" and circular economy, and this topic is often number one in discussions at the world economic forum. However, as the analysis has shown, in countries such as Ukraine, Kazakhstan, Russia, businesses often perceive the raising of "environmental friendliness" standards as a whim of rich countries. In this regard, the governments of developing countries are constantly questioning the timeliness of the transition to a green or circular economy model, because this is associated with large investments, which, according to some politicians, should be attributed to a later period, when "the country can afford them."

The dynamics analysis of material productivity in the world shows Figure 1 that the cost of resources has increased slightly, but in much lower dimensions than global GDP, so from an economic point of view, there are virtually no incentives to move from extensive to intensive use of resources.

Figure 1: Material Productivity Dynamics in the World (Average by Country, USD/Kg) in Comparison with the Dynamics of GDP (Billion USD)

At the same time, climate change and environmental impacts are increasingly causing economic crises and problems.

In this article, we propose to consider a model for assessing the relationship between basic environmental parameters and key indicators of the country's economic development. Our hypothesis is that the ideas of circularity are progressive, and therefore have a positive effect on society and the economy as a whole; on the other hand, we believe that the country's economy and its subjects must achieve certain criteria in order to positively influence the environment. However, this approach requires scientific justification, as well as building a model and testing it.

Review of Literature

At the beginning of the twenty-first century, the idea of "green" economic growth reached a new level of implementation, expressed in a new UN initiative, the so-called "new global green course", which relies on combining the tasks of development and preservation of the environment through the priority development of ecological growth niches and the latest environmentally friendly technologies. The essence of this idea was the introduction of environmental standards into the world economy, focusing on the maximum reduction of the carbon and energy intensity of production (Li & Jiang, 2012; Mathews, 2017).

In 2020 World Economic Forum recognized Valuing the Environment as one of the key components of the development agenda today. Moreover, among the key directions are circular economy practices, decarbonization, and nature-based solutions that can re-orient development towards more responsible economic growth (Broeckhoven et al., 2021).

Environmental Performance, Circular Economy and Country’s Global Competitiveness

The Fourth Industrial Revolution is known to be proclaimed in 2016 as a response to society's total informatization, technology rapid development, and changing global orientations of humankind, caused by environmental problems and limited resources for human functioning. K. Schwab (2017) emphasizes that the synthesis of digital technologies and the formation of production and technological systems based on the circular economy characterize the Fourth Industrial Revolution. Boulding's work was one of the first works on the circular economy concept compared to the planet Earth's closed functioning with a spaceship (Geisendorf & Pietrulla, 2018). Meadows et al. (2019) continuing Boulding's opinion, note that the Earth's resources are not only limited and interconnected, but also considering five factors (population growth, agricultural production, limited resources, industrial production, and environmental situation), cannot maintain the economic growth rate after 2100.

McDonough & Braungart(2002) further developed the ideas, considering such factors as resources, labor and waste, and the principles of a circular economy through the prism of companies' competitiveness. The authors note: "new criterion of efficiency of enterprises creates to balance traditional economic goals with social and environmental problems". Studying the principles of the circular economy, its separate direction related to the sharing economy, in particular in the automotive industry, is often mentioned (Reshetnikova et al., 2021).

A significant stage in the theories development of the circular economy is the creation and operation of the E. McArthur Foundation, which aims to "inspire generations to rethink, redesign and build a positive future". The Foundation plans to achieve this goal due to the circular economy principles: "the circular economy provides a holistic basis for redesigning the systems' level and, as such, offers the opportunity to use innovation and creativity to build a positive, restorative economy" (MacArthur, 2013).

George et al. (2019) supported the Foundation ideas trying to build a theoretical model of a circular economy "with two types of economic resources, namely: waste and recyclables". The authors prove the irrelevance of Kuznets' economic hypothesis (1955) and ecological curve (KEC) by showing that the environment quality cannot be maintained or improved by economic growth. Logically, researchers' focus on the circular economy is the production process and its infrastructure. Sinclair et al. (2018) note: "small-scale, flexible and localized production systems reduce resource and transport emissions and extend product life".

Interesting is the bibliometric study of Rial et al. (2018), who studied the change in scientific thought in 2006-2017. Scientists have come to the following conclusion: "Works on the circular economy and the environment have significant potential, and they are open to research areas of sustainable development or industrial production". According to them, the most active countries were China, Britain, Italy, the Netherlands, and Germany. In the opinion of many authors, in the long term, it is the market economy and its competitive mechanism, close to living nature, that can stimulate the transition to the recycling of resources against the background of their rise in prices, accompanying their depletion, including without sequestering global consumption (Preston, 2012; Stahel, 2013).

Since the early 2010s, initiatives to develop a circular economy were intensified at all government levels. China and Japan were the first to develop a circular economy through the introduction of the particular law. That created the institutional basis for business development based on the digital economy. All over the world, countries started developing the circular economy concept and adapt it to the challenges they face. In particular, the European Union launched programs to conserve resources, promote recycling, and engage digital technologies to create sustainable development.

To implement the circular economy foundations, the European Commission is implementing the Action Plan "For a cleaner and more competitive Europe." The document notes that the EU cannot independently implement the European Green Agreement's ambitious goals for a climate-neutral, resource-saving, and circular economy. The reports highlight the urgent need for a more global approach: The global transformation to a circular economy involves a shift from a linear, high-emission resource system with high emissions, waste generation, and negative impacts on ecosystems and natural capital to circular, less resource-intensive systems, more efficient and better while providing opportunities for practical activities and high quality of life (European Commission, 2020). The UN Global Environment Outlook (GEO) process, which includes the Sustainable Development Goals, Multilateral Environmental Agreements, other critical environmental aspects, and, in particular, links with social and economic development, which is useful for better environment contextualization, can be considered a global initiative, and to understand the relationship between the environment, people and the economy.

Environmental Performance and Human Development

First, it must be said that consideration of the relationship between Environmental performance and human development is carried out in the context of the so-called Green Human Resource Management concept (GHRM) (Renwick et al., 2013 Ahmad, 2015). Amrutha & Geetha (2020) analyzed the publication activity on the GHRM topic in the context of issues of environmental, sustainable development and social responsibility, for the period from 1995 to 2019, which showed a sharp increase in the number of such studies since 2010. The authors identified three most actively developing areas: human resource management practices, green workplace behavior and organizational sustainability, which clearly demonstrate a transformation in the understanding of the role of people and GHRM in ensuring the environmental sustainability of an organization.

It should be noted that quite a few economists study the relationship between the state of the environment and the level of human capital development, in the context of Chinese enterprises and the economy (Paillé et al., 2014; Roscoe et al., 2019). For example, in a field study, scientists Paillé et al. (2014) studied the relationship between strategic human resource management, internal environmental concern, organizational citizenship behaviour for the environment, and environmental performance. The study was conducted in a Chinese context and showed that the organization's civic behavior towards the environment fully mediates the relationship between strategic human resource management and environmental performance, and that internal environmental concerns mitigate the impact of strategic human resource management on the organizational citizenship behavior for the environment.

In another article, scholars Roscoe et al. (2019) explore the relationship between GHRM practices, factors contributing to a green organizational culture, and a company's environmental performance. They conducted a large-scale survey of 204 employees in Chinese manufacturing companies and found that pro-ecological human resource management practices, including recruitment, training, assessment and incentives, support the development of factors that contribute to a green organizational culture.

The need for GHRM as a new approach to ensuring the environmental responsibility and sustainability of organizations is driven by two factors: the need to spread green ideas and values, and the search for new tools to improve environmental performance. Consequently, the GHRM is the focus of a large number of scientists. At the same time, we can highlight still many questions for further research, among which are the following:

  1. development of tools for integrating GHRM into the circular management system in a changing environment;
  2. formation of research methods and assessment of GHRM practices;
  3. creation of an empirical basis for assessing the possible social, economic and environmental results of greening human resources, taking into account the specifics of existing management practices, legal requirements and stakeholders.

Thus, despite the fact that many scientists are engaged in the issue of countries' competitiveness, circular economy, human potential, nevertheless, the analysis of the country's global competitiveness and its economic attractiveness in terms of ecology and innovation has not yet been fully studied. In addition, there are almost no systematic approaches to strengthening competitive positions from these aspects.

Concept Framework and Research Model

At the basis of the model, it is proposed to consider the relationship between indicators of the efficiency of the country's environment and its economic attractiveness. It should be noted that environmental performance includes many parameters. Adequate assessment of the state of the environment in a particular country is possible only with the use of a certain set of indicators (and not any separate indicator), since a universal indicator that characterizes the state of the environment in sufficient detail has not yet been found.

With the help of environmental indicators, it seems possible to quantify various parameters that describe the ecosystem in terms of the state of the environment and natural resources. This provides an information and analytical base for more efficient environmental management and development of a strategy for environmental protection in the region.

In accordance with this, it is proposed to use complex indicators characterizing the quality of air, water resources, biodiversity and habitat, the level of heavy metals, climate and energy, agriculture, forests, fisheries and others.

Environmental Performance

As an indicator that reflects the country's circular economy development state, we use the Environmental Performance Indicator (EPI). The data analysis used for determining the EPI 2018 shows that the calculation uses 24 individual environmental indicators which aggregated into a hierarchy of ten categories: 1) Air quality, 2) Water and sanitation, 3) Heavy metals, 4) Biodiversity and habitat, 5) Forests, 6) Fishing, 7) Climate and energy, 8) Air pollution, 9) Water resources, 10) Agriculture.

Those categories are further combined into two, which form targeted policies - environmental protection and ecosystem viability - and finally, a standard Indicator. To provide meaningful comparisons, the developers calculate the scores for each of the 24 indicators, placing them on a standard scale, where 0 means the worst performance and 100 - the best one. The country remoteness from achieving international sustainable development goals determines its location on such a scale. The figures are then multiplied by the weights and summed for the final EPI calculation.

Economic Attractiveness of the Country is also a complex concept and in different sources, the components that it includes vary significantly. The Global Competitiveness Index is one of the main compounded indicators that reflects a country's ability to compete with other countries in the context of the Fourth Industrial Revolution. It is determined annually by the World Economic Forum together with a network of partner organizations (leading research institutes and organizations existing in different countries of the world) according to a methodology based on a combination of publicly available statistics and the results of a global survey of company executives (Fabus, 2018).

Many researchers focus on investment and macroeconomic stability in the context of a country's economic attractiveness (Trusova et al., 2020). At the same time, an approach that includes migration and tourism components seems to be more complete. In particular, according to Lee (2016), the attractiveness of a country should be considered in terms of international business, tourism and immigration and, in a broad sense, it is defined as the degree to which a country is preferable to others in the eyes of relevant stakeholders based on certain criteria, including tangible and intangible elements.

The economic attractiveness of a country largely depends on the degree to which the investment climate is favourable, i.e., on a combination of political, economic, social, cultural, organizational, legal and geographical factors that induce or repulse investors to invest in a particular economic system (the country's economy, region, enterprise) (Galgánková, 2020).

As we can see for the relationship with big international business, foreign investments play an important role in creating beneficial conditions for economic development. Attracting foreign investment allows the recipient country to receive a number of benefits, the main of which are to improve the balance of payments; transfer of the latest technologies and know-how; integrated use of internal resources; development of export potential and reduction of the level of dependence on imports; achieving a socio-economic effect (increasing employment, building social infrastructure, etc.). Opportunities to attract investment to the country depend, first of all, on the conditions for investors, i.e. on the investment attractiveness of the country (Makarenko et al., 2019). At the same time, more and more investment projects include parameters of social and environmental responsibility.

The level of development of international tourism is an important aspect of a country's economic attractiveness. After all, the attractiveness of a region reflects the opinion of visitors about its supposed ability to satisfy their needs and encourages them to spend time there. Thus, the more a particular destination can meet the needs of tourists, the more it will be perceived as attractive and the more likely it will be chosen (Vengesayi, 2003). In other words, since tourists are attracted to a destination due to its special attributes, it is believed that a place with more attractive attributes is more likely to be selected and revisited (Lee et al., 2010).

In modern conditions, human capital, more than physical assets or financial capital, is becoming a sustainable competitive advantage. The theory of human capital has proved that the productive, intellectual, creative qualities of a person are the main force of social and economic progress. Human capital has a significant impact on the formation of effective institutions that contribute to the development of society. Therefore, the study and scientific understanding of the mutual influence of human capital and the environment in the context of the transformation of economic relations are relevant and in demand not only by science, but also by practice (Contractor & Mudambi, 2008).

Thus, in the basis of the model, it is proposed to consider the following relationships Figure 2.

Figure 2: Relationship between the Environmental Performance and the Level of Socio-Economic Development of the Country

Result and Discussion

At the initial stage, we collected static data on environmental performance, new businesses registered, foreign direct investment, GDP per capita, international tourism, Human Development Index, Global Competitiveness Index and Environmental Performance Index to calculate the model Table 1 below.

Table 1
Key Indicators for Calculating the Model
Index Type Source
Country Code (ISO)    
New businesses registered (number) 2018 Attribute X(1) New businesses registered (number) 2018
Foreign direct investment, net (BoP, current US$) 2018 Attribute X(2) Foreign direct investment, net (BoP, current US$) 2018
GDP per capita 2018 Attribute X(3) GDP per capita (current US$) - 2018. World Bank Data.
International tourism, receipts (% of total exports) 2018 Attribute X(4) International tourism, receipts (% of total exports) 2018. World Bank Data.
Human Development Index (HDI 2018) Attribute X(5) Human Development Index – 2018. UN
Global Competitiveness Index (GCI 2018) Attribute X(6) The global competitiveness report 2018. In World Economic Forum
Environmental Performance Index (EPI_2018) Y 2018 Environmental Performance Index

Further, a correlation analysis was carried out between the collected statistical data (see table below), which showed a strong relationship between the environmental efficiency index (EPI) with 1) the development of human capital (0.83), 2) the level of competitiveness of the economy (0.79) as well as 3) GDP per capita (0.72) in Figure 2.

Table 2
Correlation Matrix
Attribute EPI (2018) New businesses registered (number) 2018 Foreign direct investment, net (BoP, current US$) 2018 GDP per capita 2018 International tourism, receipts (% of total exports) 2018 HDI 2018 GCI 2018
EPI (2018) 1,00 0,16 0,36 0,72 -0,14 0,83 0,79
New businesses registered (number) 2018 0,16 1,00 -0,11 0,07 -0,20 0,16 0,27
Foreign direct investment, net (BoP, current US$) 2018 0,36 -0,11 1,00 0,41 -0,12 0,24 0,34
GDP per capita 2018 0,72 0,07 0,41 1,00 -0,31 0,72 0,81
International tourism, receipts (% of total exports) 2018 -0,14 -0,20 -0,12 -0,31 1,00 -0,28 -0,34
HDI 2018 0,83 0,16 0,24 0,72 -0,28 1,00 0,89
GCI 2018 0,79 0,27 0,34 0,81 -0,34 0,89 1,00

In the second step, we focus on clustering countries by environmental performance (EPI). According to the research purpose, there is a need to rank countries by the circular economy development level. Based on the EPI values for 2018, 3 main clusters are identified: 1) high level; 2) intermediate level; 3) low level. The maximum and minimum values were calculated to consider the change in each period's dynamics, and this difference was further divided into three groups Table 3.

Table 3
The Clustering Parameters Calculation of Countries According to Environmental Performance Indicators (EPI)
Cluster Maximum value Minimum value
High level Max(y(1),y(2),…y(n)) Max(y(1),y(2),…y(n))-A
Intermediate level Max(y(1),y(2),…y(n))-A Max(y(1),y(2),…y(n))-2*A
Low level Max(y(1),y(2),…y(n))-A Min(y(1),y(2),…y(n))

Wherein:
y(1), y(2), …y(n) –EPI of the countries in a particular year;
Max – maximum function for a number of values;
Min – minimum function for a number of values;
A – the interval between the largest and smallest values for each level is calculated as:
equation

Applying the above methodological approach to the environmental indicators analysis, we divided the countries into clusters Table 4.

Table 4
Countries Distribution by EPI Clusters
Development cluster (2018)/region high intermediate low
Europe Switzerland, France, Denmark, Malta, Sweden, United Kingdom, Luxembourg, Austria, Ireland, Finland, Iceland, Spain, Germany, Norway, Belgium, Italy, Netherlands, Greece, Cyprus, Portugal, Slovakia, Lithuania Bulgaria, Czech Republic, Slovenia, Latvia, Albania, Croatia, Hungary, Romania, Estonia, Poland, Macedonia, Serbia, Turkey, Oman Bosnia and Herzegovina
Middle East Israel Qatar, Kuwait, Jordan, Lebanon, UAE, Iran, Saudi Arabia Iraq
Asia and Oceania Japan, New Zealand, Australia, Taiwan Singapore, Brunei, South Korea, Sri Lanka, Malaysia, Philippines, Mongolia, China, Thailand Vietnam, Indonesia, Myanmar, Cambodia, Pakistan, Nepal, India, Bangladesh
America USA, Canada Costa Rica, Colombia, Dominican Republic, Uruguay, Venezuela, Cuba, Panama, Peru, Brazil, Mexico, Argentina, Jamaica, Chile, Ecuador, Bolivia, Nicaragua, Paraguay, El Salvador, Guatemala, Honduras Haiti
Africa   Trinidad and Tobago, Morocco, Tunisia, Egypt, Namibia, Algeria, Nigeria, Botswana, Sudan, Zambia, Tanzania, Ghana, Senegal Kenya, Mozambique, Gabon, Ethiopia, South Africa, Zimbabwe, Togo, Cameroon, Eritrea, Benin, Angola, Congo
Countries of the former USSR   Turkmenistan, Belarus, Russia, Azerbaijan, Armenia, Georgia, Kyrgyzstan, Kazakhstan, Ukraine, Moldova Tajikistan, Uzbekistan

Highly developed countries have the highest rates of circular economy development, leaders in innovative development: Europe, USA, Canada, Israel, and Japan. Countries with low technological development and resource-intensive economic model (countries of the former USSR and Africa), respectively, have the lowest values of environmental performance indicators. The vast majority of the world's countries belong to the central cluster growth in the environmental performance indicators.

At the third stage, using the RapidMiner software package, we built a decision tree to predict which cluster of environmental efficiency the country will belong to, depending on its indicators of economic attractiveness (the list is shown in Table 5, and the database is presented in Appendix 1). The plotting results are shown in the Figure 3 below.

Table 5
Modeling of Decision Tree for Prediction of EPI Cluster
Accuracy: 86.96% true high true intermediate true low class precision
pred. high 6 1 0 85.71%
pred. intermediate 0 12 1 92.31%
pred. low 0 1 2 66.67%
class recall 100.00% 85.71% 66.67%  

Figure 3: Plotting Results

Based on the analysis, it can be concluded that the key factor influencing the efficiency of the environment in the national ecosystem is the level of income of the population. Countries with GDP per capita above $ 2012 / year are included in the cluster of either high or medium environmental efficiency (according to the EPI level). For their ecosystem, a significant impact of the level of human capital development (HDI) has been revealed. In addition, countries with a high level of human capital development (more than 0.856) and GDP per capita (more than $ 2012 per year) in most cases (33% of total) belong to the cluster of high environmental efficiency. Where the HDI is below 0.856, but GDP per capita is above 2012 $ per year, countries belong mainly to the cluster of average environmental efficiency. However, when the level of GDP per capita is below $ 2012 / year, the dominant value is no longer the human capital development index (HDI), but the number of registered enterprises in the country. It was revealed that if this is a relatively large number - more than 4238 / year - then the country will enter a cluster of low environmental efficiency (low EPI). Otherwise, the country will be included in the cluster of average environmental efficiency. This can be explained by the fact that the level of environmental responsibility of business in countries with low per capita income is very limited. Consequently, the lack of environmental standards for business models with increasing entrepreneurial activity causes serious damage to the environment.

Conclusion

Summing up, the hypothesis about the close relationship of country’s global competitiveness and economic attractiveness with its environmental performance was tested. The results obtained prove that the ecological values increase in the ecosystem with the growth of incomes of the population, the increase in the economic attractiveness of the country and its level of global competition. The study revealed a significant correlation between the Environmental Performance Index (EPI) and Human Development Index (0.83), the level of competitiveness of the economy (0.79) as well as GDP per capita (0.72). The presence of a strong relationship between the environmental index (EPI) and human development index (0.83) suggests the need to improve the quality of human capital, especially in the countries of the third cluster of low environmental efficiency.

The use of the RapidMiner software complex allowed us to build a decision tree for predicting the cluster of environmental efficiency with a different combination of factors affecting the development of the country's economic system: new businesses registered, foreign direct investment, GDP per capita, international tourism, Human Development Index, Global Competitiveness Index and Environmental Performance Index.

The calculations show that ccountries with GDP per capita above $ 2012 per year belong to the cluster of either high or medium environmental efficiency. Their ecosystem are significantly influenced by the human development index (HDI). Moreover, countries with a high HDI (more than 0.856) and GDP per capita (more than $ 2012 per year) in most cases (33%) will be associated with the cluster of high environmental efficiency. If the country’s HDI is below 0.856, but GDP per capita is above 2012 $ per year, it will be mainly affiliated to the cluster of average environmental performance. However, when the level of GDP per capita is below $ 2012 per year, the dominant value is no longer the human development index, but the number of registered enterprises in the country. It was revealed that if this is a relatively large number - more than 4238 per year - then the country will enter a cluster of low environmental performance. Otherwise, the country will be included in the cluster of average environmental efficiency.

Thus, in countries with an intermediate and high level of environmental performance, there is a direct link between productivity and the human development, therefore, investing in human capital is strategically important for both companies and the country as a whole. For low-income countries, it is important to accelerate the process of creating an education system and ensuring access to the results of scientific and technological development in more developed countries. International investments, programs of international organizations in the field of health and the environment are important. Insufficient investment leads to the use of extensive factors in the development of national economies. The use of "dirty technologies", a high level of resource intensity of production, these and other factors in the conditions of even increasing entrepreneurial activity cause serious damage to the environment.

For middle-income countries, it is important to improve the quality of education, taking into account the orientation towards environmental principles of business and life. In the production of goods and services, it is important to form new competencies for managers. Solving the problem of circularity of business models requires the development of educational programs focused on the training of consultants, designers, designers of cyclical production of local and global scales. It is necessary to study the experience of the countries of the first cluster (high EPI) in terms of the formation of new value approaches in organizing the training of company personnel and managers of territorial development.

The formation of institutional foundations, national and regional programs and projects are vital prerequisites for business development and entrepreneurship based on resource-efficient technologies and business models of circularity. The focus on the business environmental friendliness demands a change of principles, methods and techniques of corporate management. For the production development based on a circular economy, it is essential to invest in the appropriate infrastructure development and specialists’ training in environmental management. Multinational corporations are implementing new after-sales customer service systems on a circular basis, which provides new competitive advantages in local and international markets.

Appendix

Appendix 1
Initial Data for the Calculation of Decision Tree and Correlation Model
Country Country Code Region Cluster EPI 2018 New businesses registered (number) 2018 Foreign direct investment, net (BoP, current US$ mln) 2018 GDP per capita 2018 International tourism, receipts (% of total exports) 2018 HDI 2018 GCI 2018
Switzerland CHE EU high 87,42 25 637 112 318 86 430 4,41 0,955 82,6
France FRA EU high 83,95 201 087 68 851 41 526 7,95 0,898 78
Denmark DNK EU high 81,6 36 982 -1 638 61 599 4,53 0,939 80,6
Malta MLT EU high 80,9 5 527 -11 618 30 672   0,894 68,8
Sweden SWE EU high 80,51 45 590 13 762 54 589   0,943 81,7
United Kingdom GBR EU high 79,89 664 974 -24 763 42 993   0,928 82
Luxembourg LUX EU high 79,12 7 309 73 819 116 597 4,89 0,913 76,6
Austria AUT EU high 78,97 3 830 1 971 51 453 10,03 0,921 76,3
Ireland IRL EU high 78,77 22 398 27 442 79 298 3,24 0,951 75,2
Finland FIN EU high 78,64 14 700 13 724 50 013 5,44 0,937 80,3
Iceland ISL EU high 78,57 2 283 471 74 348   0,946 74,5
Spain ESP EU high 78,39 94 676 -16 391 30 375   0,905 74,2
Germany DEU EU high 78,37 72 844 28 140 47 787 3,16 0,946 82,8
Norway NOR EU high 77,49 29 959 20 067 82 268 4,29 0,956 78,2
Belgium BEL EU high 77,38 24 677 9 073 47 555 2,29 0,93 76,6
Italy ITA EU high 76,96 114 360 -4 291 34 609 7,87 0,89 70,8
New Zealand NZL Oceania high 75,96 56 380 -1 919 43 306 18,99 0,928 77,5
Netherlands NLD EU high 75,46 71 531 63 766 53 019 3,34 0,942 82,4
Israel ISR Middle East high 75,01 17 456 -15 428 41 705 7,31 0,916 76,6
Japan JPN Asia high 74,69 29 243 134 929 39 159 4,87 0,917 82,5
Australia AUS Oceania high 74,12 235 654 -60 527 57 355 14,46 0,943 78,9
Greece GRC EU high 73,6 9 793 -3 506 19 766 26,38 0,881 62,1
Taiwan TWN Asia high 72,84   0       79,3
Cyprus CYP EU high 72,6 14 526 -5 536 29 089 18,13 0,885 65,6
Canada CAN America high 72,18 4 065 18 934 46 455   0,928 79,9
Portugal PRT EU high 71,91 43 114 -6 392 23 551 23,04 0,86 70,2
United States USA America high 71,19   -412 780 63 064 9,36 0,925 85,6
Slovak Republic SVK EU high 70,6 19 720 -1 293 19 365 3,29 0,858 66,8
Lithuania LTU EU high 69,33 6 072 -260 19 167   0,876 67,1
Bulgaria BGR EU intermediate 67,85 45 683 -886 9 428 11,61 0,813 63,6
Costa Rica CRI America intermediate 67,85 8 984 -2 183 12 469 18,83 0,808 62,1
Qatar QAT Middle East intermediate 67,8 14 824 5 709 65 908 14,86 0,845 71
Czech Republic CZE EU intermediate 67,68 30 336 -2 245 23 420 4,32 0,898 71,2
Slovenia SVN EU intermediate 67,57 4 182 -1 089 26 103 7,35 0,912 69,6
Trinidad and Tobago TTO Africa intermediate 67,36   765 17 038 4,68 0,795 57,9
Latvia LVA EU intermediate 66,12 9 864 -745 17 850   0,863 66,2
Turkmenistan TKM GUS intermediate 66,1   0 6 967   0,71  
Albania ALB EU intermediate 65,46 2 990 -1 209 5 284 48,20 0,792 58,1
Croatia HRV EU intermediate 65,45 15 585 -893 15 014 36,85 0,848 60,1
Colombia COL America intermediate 65,22 68 588 -6 409 6 730 12,03 0,764 61,6
Hungary HUN EU intermediate 65,01 24 252 -3 829 16 411 7,14 0,85 64,3
Belarus BLR GUS intermediate 64,98 8 700 -1 371 6 330 2,89 0,823  
Romania ROU EU intermediate 64,78 94 244 -5 840 12 399 3,84 0,823 63,5
Dominican Republic DOM America intermediate 64,71 10 204 -2 535 8 051 37,71 0,751 57,4
Uruguay URY America intermediate 64,65 2 796 500 18 704 14,31 0,816 62,7
Estonia EST EU intermediate 64,31 19 950 -1 426 23 159 10,25 0,889 70,8
Singapore SGP Asia intermediate 64,23 43 046 -61 076 66 679 3,07 0,936 83,5
Poland POL EU intermediate 64,11 36 879 -15 285 15 468 4,80 0,877 68,2
Venezuela VEN America intermediate 63,89   0     0,733 43,2
Russia RUS GUS intermediate 63,79 317 468 22 592 11 287 3,68 0,823 65,6
Brunei Darussalam BRN Asia intermediate 63,57 731 -516 31 628 2,70 0,836 61,4
Morocco MAR Africa intermediate 63,47 45 132 -2 764 3 227 22,01 0,68 58,5
Cuba CUB America intermediate 63,42   0 8 824   0,781  
Panama PAN America intermediate 62,71 13 068 -4 917 15 545 25,00 0,812 61
Tunisia TUN Africa intermediate 62,35 13 134 -989 3 439 11,95 0,738 55,6
Azerbaijan AZE GUS intermediate 62,33 11 611 358 4 740 11,10 0,754 60
Korea, Rep. KOR Asia intermediate 62,3   26 038 33 423 3,17 0,914 78,8
Kuwait KWT Middle East intermediate 62,28 18 535 2 993 33 399 1,08 0,807 62,1
Jordan JOR Middle East intermediate 62,2 3 289 -963 4 308 41,22 0,728 59,3
Armenia ARM GUS intermediate 62,07 6 137 -247 4 221 27,67 0,771 59,9
Peru PER America intermediate 61,92 79 346 -6 469 6 958 8,07 0,771 61,3
Egypt, Arab Rep. EGY Africa intermediate 61,21   -7 818 2 537 24,61 0,701 53,6
Lebanon LBN Middle East intermediate 61,08   -2 043 8 013 45,37 0,747 57,7
Macedonia MKD EU intermediate 61,06   0 6 087 5,08   56,6
Brazil BRA America intermediate 60,7 189 076 -76 138 9 151 2,30 0,762 59,5
Sri Lanka LKA Asia intermediate 60,61 10 510 -1 546 4 059   0,779 56
Mexico MEX America intermediate 59,69 83 903 -25 365 9 687 4,96 0,776 64,6
Argentina ARG America intermediate 59,3 5 667 -10 071 11 633 7,78 0,842 57,5
Malaysia MYS Asia intermediate 59,22 51 722 -2 539 11 378 8,86 0,805 74,4
United Arab Emirates ARE Middle East intermediate 58,9 24 716 0 43 839   0,889 73,4
Jamaica JAM America intermediate 58,58 3 159 -762 5 360   0,734 57,9
Namibia NAM Africa intermediate 58,46   -138 5 588 9,86 0,645 52,7
Iran IRN Middle East intermediate 58,16 23 689 0 3 598   0,785 54,9
Philippines PHL Asia intermediate 57,65 19 774 -5 833 3 252 10,75 0,711 62,1
Mongolia MNG Asia intermediate 57,51 11 507 -1 924 4 135 6,82 0,735 52,7
Chile CHL America intermediate 57,49 132 740 -6 450 15 888 4,59 0,849 70,3
Serbia SRB EU intermediate 57,49 8 671 -3 714 7 252 7,77 0,803 60,9
Saudi Arabia SAU Middle East intermediate 57,47 12 116 15 005 23 337 5,39 0,854 67,5
Ecuador ECU America intermediate 57,42   -1 388 6 296 8,98 0,762 55,8
Algeria DZA Africa intermediate 57,18 9 472 -586 4 154 0,44 0,746 53,8
Bolivia BOL America intermediate 55,98 3 593 -387 3 549 9,16 0,714 51,4
Georgia GEO GUS intermediate 55,69 25 241 -966 4 723 39,54 0,805 60,9
Nicaragua NIC America intermediate 55,04   -763 2 015   0,659 51,5
Kyrgyz Republic KGZ GUS intermediate 54,86   -139 1 308 18,94 0,696 53
Nigeria NGA Africa intermediate 54,76 86 309 -210 2 028 2,99 0,534 47,5
Kazakhstan KAZ GUS intermediate 54,56 23 464 -4 723 9 813 3,95 0,819 61,8
Paraguay PRY America intermediate 53,93 1 018 -458 5 783 2,74 0,727 53,4
El Salvador SLV America intermediate 53,91 2 347 -826 4 053 18,11 0,67 52,8
Turkey TUR EU intermediate 52,96 85 798 -9 235 9 453 15,47 0,817 61,6
Ukraine UKR GUS intermediate 52,87   -4 460 3 097 3,83 0,774 57
Guatemala GTM America intermediate 52,33 5 530 -778 4 478 9,25 0,657 53,4
Moldova MDA GUS intermediate 51,97 4 801 -254 4 230 14,50 0,746 55,5
Botswana BWA Africa intermediate 51,7   -204 8 280 7,76 0,73 54,5
Honduras HND America intermediate 51,51   -895 2 510 8,42 0,633 52,5
Sudan SDN Africa intermediate 51,49   -1 136 826 20,88 0,506  
Oman OMN Middle East intermediate 51,32 5 085 -5 225 16 521 6,42 0,813 64,4
Zambia ZMB Africa intermediate 50,97 10 236 -363 1 516   0,582 46,1
Tanzania TZA Africa intermediate 50,83 5 276 -972 1 043 29,14 0,524 47,2
China CHN Asia intermediate 50,74   -92 338 9 977   0,755 72,6
Thailand THA Asia intermediate 49,88 55 589 4 182 7 297 18,68 0,772 67,5
Ghana GHA Africa intermediate 49,66   -2 908 2 194 4,42 0,606 51,3
Senegal SEN Africa intermediate 49,52 4 003 -795 1 458 10,54 0,516 49
Tajikistan TJK GUS low 47,85 831 -249 853 15,31 0,661 52,2
Kenya KEN Africa low 47,25 44 259 -1 462 1 708 15,43 0,599 53,7
Vietnam VNM Asia low 46,96   -14 902 2 566 3,90 0,7 58,1
Indonesia IDN Asia low 46,92   -12 511 3 894 8,45 0,712 64,9
Mozambique MOZ Africa low 46,37   -2 692 503 5,54 0,452 39,8
Uzbekistan UZB GUS low 45,88 35 968 -623 1 529 9,30 0,717  
Myanmar MMR Asia low 45,32 14 051 -1 768 1 279 10,62 0,579  
Gabon GAB Africa low 45,05 1 106 0 7 957   0,697 45,5
Ethiopia ETH Africa low 44,78 31 198 -3 360 772 46,54 0,478 44,5
South Africa ZAF Africa low 44,73   -1 543 6 373 8,89 0,707 60,8
Zimbabwe ZWE Africa low 43,41 16 810 0 1 352   0,569 42,6
Cambodia KHM Asia low 43,23 7 007 -3 089 1 512 26,24 0,585 50,2
Iraq IRQ Middle East low 43,2   5 074 5 523 2,16 0,671  
Bosnia and Herzegovina BIH EU low 41,84 2 493 -602 6 072 13,38 0,777 54,2
Togo TGO Africa low 41,78 2 587 251 902 15,80 0,51  
Cameroon CMR Africa low 40,81   -657 1 534 8,67 0,56 45,1
Eritrea ERI Africa low 39,34   0     0,456  
Benin BEN Africa low 38,17 3 341 -184 1 241 4,55 0,541 44,4
Pakistan PAK Asia low 37,5 13 229 -1 758 1 482 2,75 0,552 51,1
Angola AGO Africa low 37,44   6 462 3 290 1,35 0,582 37,1
Haiti HTI America low 33,74   -105 1 435 34,86 0,508 36,5
Nepal NPL Asia low 31,44 24 088 -68 1 179 27,78 0,596 50,8
India IND Asia low 30,57 123 942 -30 700 1 997 5,43 0,642 62
Dem. Rep. Congo COD Africa low 30,41   -1 408 557 0,38 0,478 38,2
Bangladesh BGD Asia low 29,56 4 473 -2 402 1 698 0,81 0,625 52,1

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