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The globalization process and the war in Ukraine show us that migration is one of the strongest global trends in the modern economy. For this paper, we determined three types of migration, depending on the intention of the people involved, these being labor, educational, and refugee migration. Each type has a different influence on the macroeconomic process. However, in this paper, we investigate the influence of general migration on GDP. We analyze five factors that have major influences on GDP, namely, migration (I), interest rate (IR), active population (AP), export (E), and the consumer price index (CPI). For the purposes of this paper, vector autoregressive models (VAR models) were chosen to perform the analysis. We used the Granger causality test to investigate the lag structure and identified the exogenous variables in the VAR model, such as GDP, migration, and the active population. We investigated the cross-influence between these factors and found that migration has a negative effect on the active population and a positive effect on GDP, while GDP growth leads to a decrease in migration. The Akaike and Schwartz criteria showed the high quality of the VAR models. The impulse analysis of shock influences identifies the structure of the reaction seen in GDP and migration, depending on their shock factors. Using decomposition analysis, we found that migration and GDP influence each other by 10–14%, which can improve the forecasting of these factors and the study of structural migration by the use of these three types.

The paper proposes an insight into the relation existing between economic development and resource consumption and the associated implications and challenges for forested areas in Romania. In accordance with this purpose, the methods used are both qualitative and quantitative by using a relevant literature review, including international and national reports. In the 1950s and 1960s, Simon Kuznets introduced to modern economic theory the idea that there are certain market forces directly correlated with economic development. After almost two decades of a market economy in Romania, we are witnessing a relative decoupling process between economic development and social inequality. Moreover, the country strives to make the transition to a circular economy by involving all relevant stakeholders and by attracting the necessary financial support. Sustainable development offers the perspective of reaching an equilibrium between economic, social, and environmental aspects. Finding the right tools to assess multiple impacts has always been challenging as regards decoupling the economic development from natural resource consumption. Although there are multiple interpretations of the outcomes of a decoupling process, this paper aims at assessing the evolution of forested areas through econometric research using the Environmental curve of Simon Kuznets. As our main results, we have observed a decoupling process in the GDP growth that will not lead to a decrease in wooded areas in our country, mainly due to rural–urban migration, shifts in consumption patterns (from firewood toward natural gases, etc.) and improvement in the legislative framework in the case of forestry products. Among the weaknesses of the model used are the limited data availability and the reduced number of observations with annual frequency (data were taken only for 22 years), the use of a square pattern instead of a cubic one (it is possible for the results to differ and consequently another form of this curve, either N or N-inverted) or the use of data with a low accuracy related to the total forest areas in Romania. Among the conclusions of the papers, the decoupling process is not a single action process. Our research focused on forest area and the need to connect it with other policy actions related to mainstreaming sustainability, promoting a circular economy or bioeconomy, or developing innovative public policies and instruments.

This paper investigates the nexus between CO2 emissions (CO2E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the “vector error correction” model (VECM) and “auto regressive distributed lag” (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO2E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO2E and GDP, CO2E and ENU, CO2E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO2E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO2E. Our paper addresses some important policy implications.

There is much research about the determinants of investments, but there is a shortage of similar studies for Poland. Therefore, the overall goal of the paper is to analyze the determinants of investments made by enterprises from the energy sector as well as their delays in Poland in the years 2000–2019. In this period, a strong growth of investments in energy was observed in the country. In connection to this, the authors set four hypotheses and verified them with the following statistical methods: canonical analysis, linear and causality correlation, autocorrelation and cointegration tests. The paper found that there is a relationship between public consumption and investment spending of small enterprises in Poland. That means that only some macroeconomic parameters are connected with investments. Moreover, the changes in the value of past investments has a negative influence in current investments.

The travel and tourism industry plays a crucial role in economies around the world. The impact of the COVID-19 pandemic on the tourism industry has been very pronounced. This paper aims to study the relationship between the country’s T&T industry Share of Employment (TTEMPL) and the country’s T&T industry Share of Gross Domestic Product (TTGDP). This study is specific because we do not focus on the development of indicators over time; instead, we propose the models for 117 countries using the quantile regression (QR) while comparing models in the context of COVID-19 (between 2019 and 2021). The results of the QR determined that individual percentiles of the TTGDP are more affected by the TTEMPL than other percentiles of the TTGDP, which is then reflected in the changes in regression coefficients. In addition, we compare analyzed indicators among countries according to region and income group. The study reveals that the tourism downturn caused by COVID-19 has adverse effects on the TTEMPL and the TTGDP. In addition, the results show that the impact of COVID-19 on the tourism industry appears to be varied among countries, regions, and income groups.

India, Pakistan, and Bangladesh (IPB) are the largest South Asian countries in terms of land area, gross domestic product (GDP), and population. The growth in these countries is impacted by inadequate renewable energy policy and implementation over the years, resulting in slow progress towards human development and economic sustainability. These developing countries are blessed with huge potential for renewable energy resources; however, they still heavily rely on fossil fuels (93%). IPB is a major contributor to the total energy consumption of the world and its most energy-intensive building sector (India 47%, Pakistan 55% and Bangladesh 55%) displays inadequate energy performance. This paper comprehensively reviews the energy mix and consumption in IPB with special emphasis on current policies and its impact on economic and human development. The main performance indicators have been critically analyzed for the period 1970–2017. The strength of this paper is a broad overview on energy and development of energy integration in major South Asian countries. Furthermore, it presents a broad deepening on the main sector of energy consumption, i.e., the building sector. The paper also particularly analyzes the existing buildings energy efficiency codes and policies, with specific long-term recommendations to improve average energy consumption per person. The study also examines the technical and regulatory barriers and recommends specific measures to adapt renewable technologies, with special attention to policies affecting energy consumption. The analysis and results are general and can be applied to other developing countries of the world.

Research proposes that economic policy uncertainty (EPU) leads to exchange rate fluctuations. Given that African countries experience higher levels of uncertainty in developed/emerging markets, we examine the extent to which domestic and foreign EPU affect exchange rates for a panel of 12 ECOWAS countries covering the period 1996–2018. In order to account for non-stationarity, cross-sectional dependence, and heterogeneity, the paper employs the dynamic heterogeneous panel approach. The ECOWAS has a dual currency arrangement ranging from a common currency union (CFA) to floating exchange rates (Non-CFA). To account for this, this study splits the sample data into CFA and Non-CFA areas. In addition, this study considers the role of the global financial crisis in the exchange rate-EPU nexus. Our results show that domestic EPU has a positive effect on exchange rates in the long run for Non-CFA areas. Different from the existing literature, our results suggest that domestic EPU does not explain exchange rate fluctuations in the short run. For all countries, foreign EPU leads to appreciation in the long run and depreciation in the short run. Interestingly, foreign EPU has a more dominant effect on exchange rate fluctuations in the selected countries than domestic EPU. This may reflect the weak institutional framework in these countries, which allows external fluctuations to have a greater impact. Moreover, this could be attributed to the increase in foreign capital flows during the sample period. Thus, these countries must develop effective policies to effectively absorb these external shocks. Results are robust to different proxies of EPU.

The COVID-19 health and economic crisis has affected all areas of social life globally, including the economy. The world economy has declined due to purchasing power for individuals who have been forced to stay at home and cannot perform work. These restrictions to prevent the spread of SARS-Cov-2 have led to an increase in electronic commerce and mobile commerce as tools for procuring goods and services. In this paper, we conducted a longitudinal analysis of mobile commerce as an essential electronic commerce component, establishing the main drivers of mobile commerce and the intensity of their influences. The research focuses on mobile commerce in the United States (U.S.). It covers the period 2010–2020, the last year of this period capturing the context of the COVID-19 pandemic and its impact on electronic commerce (e-commerce) and mobile commerce (m-commerce). In the macroeconomic analysis of competitiveness, we established the main drivers of m-commerce, using artificial neural networks and the mediation effects found between the variables that describe m-commerce, e-commerce, and total sales, using structural equation modeling. The research results indicate an increase in the share of e-commerce in total sales and a predominance of the m-commerce share in e-commerce on the background of traffic restrictions and social distance rules imposed due to the COVID-19 pandemic. Stakeholders in the m-commerce area should consider the following enhancing drivers: increasing internet speed, expanding 5G and Wi-Fi networks, and increasing accessibility and trust in mobile devices and applications.

Economic activities are directly supported by the natural environment, and in this context, it has become crucial to analyse the phenomenon of decoupling economic growth from environmental degradation. The negative effects of economic activities on the environment are clearly visible, and understanding how to separate economic growth from environmental harm is of utmost importance. This paper aims to study the degree of the decoupling of economic growth, measured by GDP, from environmental degradation, quantified through CO2 and HFCs emissions, at the level of each EU27 country in the periods 2008–2012 and 2013–2020. In the analysis, graphical representations and statistical tests were utilised. In the first period, most of the EU27 countries registered negative levels for the variables considered, placing them into the negative coupling stage. In the second period, the evolution of the decoupling process was visible in all the countries, enabling them to make a significant transition to the relative or absolute stages. Overall, the Nordic countries could be observed as examples of best practices, managing to achieve the most desired stage of decoupling, i.e., the absolute one. These results are important for a wide range of stakeholders implicated in the preparation of programs, projects and policies dedicated to achieving economic growth in a sustainable manner.

This paper applies a new artificial intelligence architecture, the temporal fusion transformer (TFT), for the joint GDP forecasting of 25 OECD countries at different time horizons. This new attention-based architecture offers significant advantages over other deep learning methods. First, results are interpretable since the impact of each explanatory variable on each forecast can be calculated. Second, it allows for visualizing persistent temporal patterns and identifying significant events and different regimes. Third, it provides quantile regressions and permits training the model on multiple time series from different distributions. Results suggest that TFTs outperform regression models, especially in periods of turbulence such as the COVID-19 shock. Interesting economic interpretations are obtained depending on whether the country has domestic demand-led or export-led growth. In essence, TFT is revealed as a new tool that artificial intelligence provides to economists and policy makers, with enormous prospects for the future.

Tourism has become one of the most important sectors in many countries, significantly contributing to their economic growth and development. However, the expansion of tourism has also brought about various environmental and social challenges. The relationship between tourism, economic growth, trade openness, and the environment is diverse and complex. The objective of this paper is to investigate the relationship between the international tourism development index, GDP per capita, CO2 emissions, trade openness index as well as the energy intensity index in EU 27, over the 1995–2019 period. A composite index for international tourism was developed using the Principal Component Analysis (PCA). Panel Autoregressive distributed lag (ARDL) approach is used to reveal the long- and short-run impact of GDP per capita, CO2 emissions, trade openness index as well as the energy intensity index on the tourism development index. Panel ARDL estimates confirm some of our research hypotheses: at the level of EU countries, there is a short-run relationship between tourism and GDP per capita, but only in a few EU countries, trade openness influences tourism development index. Dumitrescu-Hurlin causality test confirms long-run feedback relationship between tourism development index and trade openness, between tourism development index and CO2 emissions, and between tourism development index and GDP and unilateral causality running from tourism development index towards energy efficiency.

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, FF, to the model, GDP = f(T, FF), gives seven months vs. five months warning time, and it gives a higher prediction skill for the recessions in the out-of-sample test set. Second, we find that including the quadratic term of the yield curve and the federal funds rate improves the prediction of the 1990 recession, but not the other recessions in the period 1977 to 2019. Third, the T caused a pronounced false peak in GDP for the test set. Restricting the learning set to periods where T and FF were leading the GDP in the learning set did not improve the forecast. In general, recessions are predicted better than the general movement in the economy. A “horse race” between GDP = f(T, FF) and the Michigan consumer sentiment index suggests that the first beats the latter by being a leading index for the observed GDP for more months (50% vs. 6%) during the first test year.

Earthquakes are one of the most costly and deadliest natural disasters. This perspective paper presents a discussion focused on the role of seismic design codes in risk perception and seismic risk reduction in Europe. The seismic design codes are a key component for both the design of new buildings, as well as for the vulnerability assessment of existing ones. The impact of seismic design codes on seismic risk reduction is discussed using as case-study countries, Italy, Turkey, Greece, and Romania, which according to the recent European seismic risk model 2020 have the largest expected mean annual losses due to earthquakes. The evaluation of the seismic exposure of the four countries shows that from the entire population of more than 170 million people, about 130 million live in buildings designed using no or low level seismic design. The mean annual expected losses due to earthquakes are of the order of 0.1–0.2% of the national GDP. Moreover, the mean annual death probability due to earthquakes is 10−6 which represents a risk level not of great concern to the average people. However, large earthquakes in Europe from the past 50 years have produced losses in excess of 10 billion Euros and several hundred thousand affected people. A solution for a better communication of seismic risk in order to increase seismic risk perception might be to provide exceedance probabilities of specific macroseismic intensity levels for time frames of 10 or 20 years, instead of annual values. Macroseismic levels from past earthquakes might be used in order to have a better understanding of the results and should complement the seismic design maps. In addition, in the case of seismic vulnerability, the use of simple terms (e.g., inhabitable or uninhabitable) along with their associated occurrence probabilities in the same time frame as in the case of the seismic hazard, might be a solution. Financial incentives for seismic strengthening, as well as a clear definition of an earthquake-prone building are also very useful for increasing seismic risk perception.

China has accelerated its banking sector reform in recent years, paying particular attention to non-performing loans (NPLs). The paper’s scope is to analyse the relationship between NPLs and macroeconomic variables in China using quarterly data from 2008/Q1 to 2021/Q1 applying wavelet analysis, which allows the study to scan both short- and long-term causal relationships and connections. The analysis produces interesting results. The GDP does not appear to be as important and as much of a driving force in the dynamics of NPLs as in other emerging countries. On the other hand, inflation shows a highly dynamic dependence on NPLs as it varies over time; however, the most interesting data is the relationship between NPLs and economic policy uncertainty. In the short term, the variables are in phase. In the long term, an increase of EPU has a reduction effect on NPLs, indicating that it affects commercial bank loan sizes by reducing enterprise demand for and bank supply of credit resources.

Nighttime lights are routinely used as a proxy for economic activity when official statistics are unavailable and are increasingly applied to study the effects of shocks or policy interventions at small geographic scales. The implicit assumption is that the ability of nighttime lights to pick up changes in GDP does not depend on local characteristics of the region under investigation or the scale of aggregation. This study uses panel data on regional GDP growth from six countries, and nighttime lights from the Defense Meteorological Satellite Program (DMSP) to investigate potential nonlinearities and measurement errors in the light production function. Our results for high statistical capacity countries (the United States and Germany) show that nightlights are significantly less responsive to changes in GDP at higher baseline level of GDP, higher population densities, and for agricultural GDP. We provide evidence that these nonlinearities are too large to be caused by differences in measurement errors across regions. We find similar but noisier relationships in other high-income countries (Italy and Spain) and emerging economies (Brazil and China). We also present results for different aggregation schemes and find that the overall relationship, including the nonlinearity, is stable across regions of different shapes and sizes but becomes noisier when regions become few and large. These findings have important implications for studies using nighttime lights to evaluate the economic effects of shocks or policy interventions. On average, nighttime lights pick up changes in GDP across many different levels of aggregation, down to relatively small geographies. However, the nonlinearity we document in this paper implies that some studies may fail to detect policy-relevant effects in places where lights react little to changes in economic activity or they may mistakenly attribute this heterogeneity to the treatment effect of their independent variable of interest.

The advent of various initiatives around the globe in shaping an energy transition towards a “greener” energy production future sparked a research interest towards the determinants that will shape their success. In this paper, we depart from the relevant literature evaluating the potential effect of geopolitical tensions on renewable energy investments, building on an explicit quantitative approach that provides clear empirical evidence. In doing so, we compile a large panel of 171 economies and measure the effect of geopolitical risk on “green” investing as measured by popular geopolitical risk indices, while controlling for all major variables proposed by literature. Our flexible Autoregressive Distributed Lag model with heterogenous effects across economies suggests that geopolitical risk has a significantly measurable effect on green investments both in the short and the long run. In fact, our results suggest that proper model specification is robust across alternate risk assessments. Overall, our study has direct policy implications suggesting that renewable energy could be an important part of our energy mix only if we take into account its linkages with geopolitical tensions.

There is huge evidence for a relationship between economic growth and environmental degradation. One of the causes of environmental degradation is CO2 emission which is added to the atmosphere through human activities and excessive industrialization. The aim of this research is to examine the relationship between CO2 emissions and measures of wealth in countries of Central and Eastern Europe between 2000 and 2019. The paper extends the research on economic affluence by taking into consideration two measures of economic growth, in addition to GDP, the HDI index is included. The basis for the investigation is the EKC concept. All analyses are based on econometric models with GDP and the HDI index as independent variables. The results are not conclusive and there is no one model which best describes the relationship between CO2 emissions and economic growth. Verification of the models indicates the better fit of models with the HDI index as the measure of affluence. Moreover, the study confirms that the key factors affecting CO2 emissions are energy consumption per capita which leads to an increase in CO2 emissions, and renewable energy consumption which reduces CO2 emissions. Therefore, technological changes and an increase in human awareness of global sustainability are required.

Logistics has become one of the most important economic sectors. It significantly affects the transport infrastructure and many other sectors that are crucial for the country’s development. It is the factor that also influences trade efficiency. However, the question arises if logistics performance is significant for the trade of critical goods which are energy raw products. The aim of the paper is primarily to investigate the EU energy trade flows in general and to estimate the effect of logistics performance on the international trade of energy raw products. The energy raw products are grouped into solid, liquid, and gaseous products, and separate estimates are made for their export and import. The analysis also differentiates between the trade flows, that is export and import within the EU and trade flows between EU member states and third countries. The empirical model is based on the theory of gravity model extended to include the six subcomponents of the Logistics Performance Index (LPI). The results present that: (1) the standard gravity model variables, such as GDPs of reporter and partner countries and contiguity, are successful in explaining the trade flows of solid and liquid raw energy but in case of gas products, are insignificant; (2) the results indicate that all logistics’ performance subcomponents are highly significant and show positive effects on the export of liquid energy products, while for the solid and gas products, it seems to be insignificant when the energy commodities are more complex and costly to transport and store, and therefore, contiguity, i.e., when countries share a common border, positively affects energy trade; (3) the EU imports most liquid energy products, but is generally very dependent on energy imports. EU policymakers should strive to either make more use of domestic resources or switch more to renewable energy sources.

COVID-19 is likely to be the 2020s’ deadliest pandemic according to the World Health Organization (WHO). There have been more than 3.7 million confirmed deaths after 15 months spread. Besides the loss of human lives, COVID-19 has other unfavorable impacts on society, education, and the economy. Due to successive lockdowns and the continuous quarantines, the demands on power resources have reduced. Therefore, there is a need to investigate the impacts of the COVID-19 pandemic on electricity prices (EP). In this paper, a set of six economic factors that are affected by COVID-19 and affect EP are considered. These factors were fed into a functional link artificial neural network (FLANN) to model the relationships between them and the EP. An empirical equation was formulated to help decision makers and strategic developers in the electricity markets come up with more appropriate plans. Italy, the Czech Republic, and China were used as case studies in this research.

Reaching climate neutrality by 2050 is one of the main long-term objectives of the European Union climate and energy policy, and renewable energy sources (RES) are integral parts of this transition. RES development results in many effects, direct and indirect, linked to each other, societal, local and individual, i.e., “multiple impacts of RES” (MI RES). These effects need to be carefully assessed and evaluated to obtain the full picture of energy field transformation and its context, and enable further development of RES. Nevertheless, the MI RES concept is often presented misleadingly and its scope varies throughout the literature. This paper provides a literature overview of the methodologies of this concept and presents a new concept of MI RES, respecting the difference between effects resulting from the implementation of RES and ultimate multiple impacts. We have summarized the effects into four groups: economic, social, environmental, and technical, which all lead to group of ultimate multiple impacts. Finally, we provide the complex overview of all MI RES and present the framework, which is used to analyze the multiple impacts and effects of RES and to show how the RES development leads and contributes to these impacts and effects. The concept is recommended to be considered in designing a robust energy policy by decision-makers.

The COVID-19 pandemic obliged the Romanian government to take drastic measures to contain the virus. More than this, they imposed the heaviest restrictions in the EU. For more than a month, during the lockdown period, everything stopped: schools and universities had only online classes, national and international flights and gatherings were forbidden, and many restrictions for travel were imposed. This paper analyzes the changes that occurred in electricity consumption linked with economic growth, during the pandemic, in Romania. For a better understanding of the correlations between gross domestic product (GDP) and electricity consumption (EC) in different economic contexts, the period 2008–2020 was divided into three series: the 2008–2012 financial crisis and the post-crisis recovery period, the 2013–2019 period of economic growth, and the Q1–Q3 2020 pandemic period. Using correlation coefficients and regression analysis, the authors found that the GDP decoupled from EC in the first period. The increase in GDP led to an increase in the consumption of electricity and the electricity produced from RESs in the second period. In Q3 2020, the real GDP is different from the calculated GDP, due to the pandemic. In Romania, the electricity consumption decreased within the first nine months of the pandemic due to the economic contraction. The electricity that comes from coal and hydropower plants suffered the biggest decrease. If the electricity that comes from NRESs can be adapted to the economic demands, the quantity of electricity that comes from RESs will be influenced by the climate conditions.

Climate changes necessitate measures to reduce greenhouse gas emissions (GHG). The European Union’s climate and energy policy places particular emphasis on the development of renewable energy, considered to be the primary mean in achieving the climate neutrality goal by 2050. Having in mind the aforementioned, the study was conducted to determine time delay in the development of renewable energy sources between the Visegrad Group (V4) countries and Germany, considered the most advanced country in developing renewable energy and having the greatest impact on shaping European Union climate and energy policy. The time delay econometric method was used in four of its variants. The research results show that, despite the existence of support mechanisms, the development of renewable energy sources (RES) in the V4 countries has a high mutual correlation. There is a hight correlation between RES penetration in energy mix and UE goals of the climate and energy policy until 2020. At the same time, the available data from the analysed period 2004–2019 indicate growing time delay between the reference facility—Germany and the surveyed group. The paper identifies the primary factors behind the identified time delay and proposes research areas that could direct the future research of those aspects.

The effects of the labor market on environmental issues are an actual problem at the global level, and recommendations are required to achieve equilibrium between labor productivity and environmental protection. Considering the ecological limits of work and the necessity of reducing the working time to mitigate GHG (greenhouse gas) emissions, this paper aims to assess the impact of the labor market on GHG emissions in the EU-28 countries. Using panel data models for 2007–2019, a positive effect of working time for employed persons on GHG emissions was detected. Labor productivity has a positive impact on emissions for most of the developed countries in the EU (old member states), while the effect is negative in the case of most of the new member states, which suggests that more efforts should be made by old member states to correlate labor productivity with a sustainable level of GHG emissions. As a novelty for research in the field, we assessed also the effect of targeted labor utilization on GHG emissions in order to describe the context of a sustainable economy that is an objective for each country in the EU. These results suggest that progress in GHG emissions mitigation might be achieved by reducing the working time for employed persons, which will also improve well-being. These recommendations could be useful also for other developed countries outside the EU that encounter the same difficulties.

The European Green Deal considers the increase in the share of renewable energy in final energy consumption (REFEC) among the main targets for achieving sustainable EU economies. In this context, the main aim of this paper is to provide an empirical evaluation of the relationship between GDP, global competitiveness index (GCI) and renewable energy consumption. According to panel data models based on the fully modified ordinary least squares method (FMOLS), there is a positive effect of renewable energy consumption progress on GDP and GCI growth, and also a positive influence of economic growth on renewable energy consumption in the period 2007–2019 in the EU countries. The energy consumption is more influenced by economic growth rather than economic competitiveness. Few scenarios were proposed for economic growth and share of renewable sources (RESs) in the final consumption using as forecasting method the proposed panel data models. The cluster analysis suggested two groups of countries according to RES share in gross final energy consumption (GFEC). The first group includes six countries (Sweden, Denmark, Finland, Latvia, Portugal and Austria) that fixed a target of 30% or more, while the second one refers to countries with lower targets. Some policy recommendations are provided for the EU countries to enhance the utilization of renewable energy.

This study looks at the global trajectory of the relationship between GDP (gross domestic product) and CO2 (carbon dioxide) emission in the time-series, comparative, and transitional point of view (from Y1992 to Y2014). It sets up a measurement framework and compares thirty-seven countries (thirty-six OECD countries and China) through the fuzzy-set ideal type analysis while focusing on the comparative and relational types. This research found that economic growth (GDP) and environmental problems (CO2 emissions) are tied together in a very solid path-dependent relationship. Particularly, the analysis of comparisons among OECD countries and China shows that the relationship between GDP and CO2 emissions is very firmly coupled, unlike the previous non-combination of one-dimensional statistics that are based on the environmental Kuznets curve (EKC) hypothesis. In short, it draws out and highlights the research implications that the existing conventions regarding the relationship between sustained economic growth and GHG (greenhouse gas) emissions reductions are ill-founded at the international comparative level. This paper reiterates the importance of relevant regulatory policies in order to reduce the harmful external effects of GHG and a need for policy measures to solve the problem in the long term.

In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

The Index of Sustainable Economic Welfare (ISEW) has been calculated in various ways for various countries and for various time spans. Based on the degree of objectivity, the Basic, Solid, and Site-specific ISEW are separated, whose sum constitutes the Total or Full ISEW. The paper proposes some guidelines for countries and smaller forms of state organizations, to apply and re-state their sustainable GDP, thus rendering it a useful figure as reported vis-à-vis the long established GDP. To demonstrate this theoretical advancement, the Turkish economy is used for an application. Turkey is a dynamic emerging economy, given its rapid GDP increase over the past two decades and the population increase on the one hand. On the other hand, it is afflicted by social inequalities and environmental problems, which if they were to be abated, they would certainly deduct from the increased income achieved so far.

This paper investigates the effects of real exchange rates and income on inbound tourism demand (tourist arrivals) from Germany, France, the UK, the Netherlands, Italy, Spain, and Sweden to the USA over the period 1996Q3–2015Q1. To achieve this aim, the Harmonized Index of Consumer Prices (HICP) for Restaurants and Hotels was used for the first time—instead of using the general Consumer Price Index (CPI)—to transform the nominal exchange rate into the real exchange rate as an independent variable in tourism demand analysis models. Panel co-integration analysis under the cross-sectional dependence (CD) test and common correlated effects (CCE) approach was applied. Empirical results show that tourists visiting the USA are more sensitive to changes in the real exchange rate than changes in GDP. While French tourists respond highly to the GDP, British tourists respond highly to the real exchange rate. It should also be noted that the UK, having the highest responsiveness to the real exchange rate, is a country outside the Eurozone and also intends to leave the European Union.