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The metabolism of T-lymphocytes has recently emerged as a pivotal area of investigation, offering insights into the supra-genic modulations that can influence the genetic mechanisms underlying lymphocyte clustering processes. Furthermore, it has become a crucial aspect in understanding lymphocyte plasticity within the immune microenvironment, both in physiological and pathological contexts. T-lymphocyte metabolism has recently emerged as a pivotal factor in both targeted therapy and the genetic signature of the T-lymphocyte, as a result of its influence on gatekeeper processes. From this perspective, the interconnections between the metabolic processes traditionally associated with energy production and the capacity to influence the genetic fate of the T lymphocyte have identified purine metabolism and nuclear/cytoplasmic signaling as pivotal elements in comprehending the intricacies of these molecular phenomena. The two aspects of purine metabolism and metabolic/molecular control of the nuclear envelope have been the subject of a number of significant studies published in recent years. However, from a certain perspective, the existing evidence remains sparse and inconclusive, hindering a comprehensive understanding of the subject matter. In this review, we endeavor to establish a connection between these aspects for the first time and to present a review of the molecular, immunological and genetic events that determine how these aspects, which have hitherto received insufficient attention, may represent a new avenue for lymphocyte reprogramming in the therapeutic field. This will be achieved by understanding the connections between nuclear control and purine flux within and outside the cell.

This study explores the relationship between income inequality, economic growth, and unemployment in Greece from 2003 to 2020, encompassing key economic phases: pre-crisis, crisis, memoranda, and post-memoranda. The aim is to analyze how economic growth (logarithm of GDP-LOGGDP) and unemployment influenced income inequality (GINI coefficient) during periods of economic turmoil and recovery. Using linear regression analysis on 18 years of annual data, this study identifies significant relationships between the variables, with diagnostic tests confirming model robustness. The findings reveal a strong positive and statistically significant relationship between LOGGDP and income inequality, indicating that economic growth, without effective redistributive mechanisms, exacerbated disparities. Unemployment had an even stronger positive effect on inequality, highlighting its role in deepening income disparities, particularly during the crisis years marked by economic contraction and austerity measures. These results underline the critical need for balanced economic policies that promote inclusive growth while addressing structural inequalities and labor market vulnerabilities. This study also employs advanced econometric methods, including Vector Autoregression (VAR), Vector Error Correction Model (VECM), and Granger Causality Test, to analyze the dynamics between GDP (LOGGDP), income inequality (GINI), and unemployment. The Granger Test reveals that unemployment Granger-causes GDP with a two-period lag, highlighting the importance of labor market conditions for economic growth, while no direct causal relationship is found between GDP and inequality. These methods provide deeper insights into the short- and long-term interactions, offering valuable guidance for balanced economic policymaking.

Our investigation, bolstered by the robust gravity trade model and panel data econometric technique, underscores the pivotal factors that influence trade interactions between Saudi Arabia and the BRICS nations—Brazil, Russia, India, China, and South Africa. The study, spanning from 1998 to 2023, delves into key economic metrics such as the gross domestic product, exchange rate fluctuations, inflationary trends, political conditions, and trade deals. We employ a range of econometric strategies, including pooled Ordinary Least Squares (OLS) and fixed effects models, to reveal that the GDP of BRICS states consistently and significantly impacts trade volumes. Specifically, a 1% increase in the GDP of partner countries correlates with a 0.37% rise in trade volume within the pooled OLS model. This effect amplifies to 1.43% when adjusting for temporal and country-specific factors in the fixed effects, underscoring the importance of accommodating unobserved heterogeneity, which refers to the unmeasured factors that can influence the relationship between GDP and trade volume. The political stability of BRICS nations mitigates transactional risks and promotes more stable trade relationships, thereby enhancing trade flows. Fluctuations in exchange rates exert positive and significant effects. This indicates that a more robust Saudi Riyal, an essential policy instrument, can enhance trade by increasing the competitiveness of Saudi exports. This study demonstrates that economic magnitude, political stability, and exchange rates affect Saudi Arabia’s trade with BRICS nations. These results bolster the Kingdom’s Vision 2030 objectives for economic diversification. This research advocates for stable political climates and strategic trade agreements to enhance trade relations. This study asserts that this approach will guarantee sustainable growth and diminish the Kingdom’s reliance on oil exports, instilling optimism in the Saudi economy.

This study investigates the correlation between the Gini index and gross domestic product (GDP) in two of the world’s largest capitalist economies: the United States and the United Kingdom. Utilizing econometric methods, including stationarity tests and linear regression, this research work aims to elucidate the relationship between economic inequality and economic growth. The results for the United States reveal a significant positive correlation between GDP and the Gini index, suggesting that economic growth is associated with rising income inequality. In contrast, the United Kingdom shows a much weaker relationship, indicating that other factors, such as redistributive policies and social welfare programs, may mitigate the impact of economic growth on income inequality. These findings highlight the importance of national policies and institutional frameworks in shaping economic outcomes and can be used in policy making. This study contributes to the existing literature by providing a comparative analysis of the correlation between GDP and the Gini index in two major capitalist economies, offering fresh empirical insights.

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development.

What are the effects of Corona Virus Disease 19 (COVID-19) on inflation, unemployment, and GDP in Africa? Using geo-coded cross-sectional data taken from the World Health Organization and International Monetary Fund, we investigate the spatial distribution of COVID-19 and its effects on inflation, unemployment, and Gross Domestic Product (GDP) in Africa by employing the Geographic Information System (GIS), multivariate analysis of covariance (MANCOVA), and spatial statistics. The entire dataset was analyzed using Stata, ArcGIS, and R software. The result shows (1) that there is evidence of a spatial pattern of COVID-19 cases and death rate clustering behavior in Africa, verifying the existence of spatial autocorrelation. The result also reveals (2) that COVID-19 has a negative effect on unemployment, inflation, and GDP in Africa. We confirmed that (3) temperature, rainfall, and humidity were statistically significantly associated with the spread of the COVID-19 pandemic in Africa. The comparison of the GDP of African countries before and after the pandemic shows (4) a large decrease in GDP, the highest in Seychelles (23 percent). The result of the study shows (5) that there has been a significant increase in inflation and unemployment rates in all countries since the outbreak of the pandemic as compared to the time before the outbreak. There is also evidence that (6) there is a significant relationship between death rate due to COVID-19 and population density; temperature with COVID-19 cases and death rate; and precipitation with death rate due to COVID-19. Therefore, respective governments and the international community need to pay attention to controlling/reducing the impact of COVID-19 on inflation, unemployment, and GDP, focusing on the indicated demographic and environmental variables.

The global population aging poses new challenges in allocating care facilities for the elderly. This demographic trend also influences economic development and the quality of urban life. However, current research focuses on the supply of elderly care facilities and primarily uses administrative divisions as a scale, resulting in low spatiotemporal sensitivity in evaluating the spatial equilibrium of elderly care facilities (SEECF). The relationship between the SEECF and economic development is not clear. In response to these problems, we proposed a spatial equilibrium model of elderly care facilities with high spatiotemporal sensitivity (SEM-HSTS) and explored the spatiotemporal associations between the SEECF and economic development. Considering the spatial accessibility rate of elderly care services (SARecs) and the spatiotemporal supply–demand ratio for elderly care services (STSDRecs), two types of supply–demand relationship factors were constructed. Then, a spatiotemporal accessibility of medical services (STAms) factor was obtained based on a modified two-step floating catchment area (M2SFCA) method. On this basis, the SEM-HSTS was constructed based on the theory of coordinated development. Further, a panel threshold model was employed to evaluate the influence relationships among population aging, SEECF, and gross domestic product (GDP) in different phases. Finally, spatial autocorrelation and Geodetector explored the spatial associations between SEECF and GDP across complex urban land use categories (ULUC). The experimental results at a 100-m grid scale showed that the SEM-HSTS exhibited higher spatiotemporal heterogeneity than the classical accessibility method, with elevated spatiotemporal sensitivity. Effectively identified various spatial imbalances, such as undersupply and resource waste. The panel model captured phased relationship changes, showing that SEECF had inhibitory and promotional effects on GDP in pre- and post-aging societies, with stronger effects as balance approached. Moreover, the combined interaction of ULUC and GDP had a more significant influence on SEECF than any individual factor, with GDP exerting a more significant influence. This study provides an empirical basis for creating resource-efficient elderly care facility systems and optimizing layouts.

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.

Most developing countries fail to provide waste collection services to all their citizens, which leads to many adverse effects. Nevertheless, research has not yet succeeded in explaining the underlying causes. We drew up a quantitative system dynamics model that can be used to diagnose the complex societal system that is leading to poor waste collection. The model describes demographic, social, economic, financial, participatory, and governance processes that may play a role. It is calibrated against real-life datasets for six Latin American countries. The calibration shows adequate performance of the model. Strong population growth appears to have a dual effect. It leads to an increase in available budgets for collection but also tends to dilute the available budget per inhabitant. Processes on the growth of GDP, government revenues, and quality of governance strongly improve access of citizens to waste collection. They do so separately but also because they reinforce each other. But, there are differences per country. Progress in Belize seems to be hampered mostly by low governance quality. For Bolivia, the hurdle seems to consist of an inability to increase public revenues and absorb new urban citizens. Ecuador and Paraguay would also benefit from increased revenues along with an ability to increase public participation. The Dominican Republic and Panama reveal an overall passivity to improve their services. This model may be useful for decision makers globally to develop effective interventions.

Energy transition is one of the main objectives of the European Union. Significant changes will mainly affect countries in which significant modifications will have to be made to their energy sources. The process will involve high investment in infrastructure and additional costs of the transformation, such as reduced production (which may affect the GDP value) in the economic sectors involved in the process. The aim of this article is to provide the energy transition community, namely the national economy in general and those involved in planning for structural change in particular, with the key lessons and challenges in researching the impact of production changes in the mining sector. This article also shows the relevance of the mining sector in the economy. Within this area, particular attention is given to the following issues: the impact of economic sectors on the country’s GDP (gross domestic product); the identification of key sectors of the economy using the input–output method; the contribution of coal mining and the mining industry to Poland’s GDP; an analysis of changes in the structure of Poland’s economy using the input–output method; and the use of the input–output method in the context of changing/reducing the supply of economic sectors.

Through the examination of the ecological consequences of human actions, policymakers are able to distinguish certain areas in which resource use can be increased and the generation of waste diminished. This study examines the effects of foreign direct investment, gross domestic product, industrialization, renewable energy consumption, and urban population on the ecological footprints in 131 countries between 1997 and 2020. The objective of this study is to establish a thorough understanding of the relationship between these variables and ecological footprints while considering temporal changes from economic and environmental aspects. The analysis of a substantial dataset encompassing many countries aims to uncover recurring patterns and trends that can provide valuable information for the formulation of policies and strategies pertaining to sustainable development on a global level. The study fills a significant gap in the knowledge on the ecological impact of different variables, providing a nuanced understanding of the interdependencies among these factors, thus guiding sustainable development strategies, and promoting global sustainability. The study utilizes quantile regression analysis, a nonparametric estimator, to estimate consistent coefficients. The statistical analysis reveals that FDI, urbanization, and GDP have statistically significant and positive effects on ecological footprints. Industrialization and renewable energy consumption show significant and negative relationships with ecological footprints. The findings of this study contribute to the understanding of the relationships among these variables and provide insight to inform policy and decision-making efforts focused on reducing ecological consequences and advancing sustainable development goals.

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.

Poland ranks among the leading European countries in terms of greenhouse gas (GHG) emissions. Many European countries have higher emissions per capita than the EU average. This research aimed to quantify the complex relationships between the consumption variables of the main fossil fuels, accounting for economic indicators such as population and gross domestic product (GDP) in relation to GHG emissions. This research attempted to find similarities in the group of 16 analyzed European countries. The hypothesis of an inverted U-shaped environmental Kuznets curve (EKC) was tested. The resulting multiple regression models showed similarities in one group of countries, namely Poland, Germany, the Czech Republic, Austria and Slovakia, in which most of the variables related to the consumption of fossil fuels, including HC and BC simultaneously, are statistically significant. The HC variable is also significant in Denmark, Estonia, the Netherlands, Finland and Bulgaria, and BC is also significant in Lithuania, Greece and Belgium. Moreover, results from Ireland, the Netherlands, and Belgium indicate a negative impact of population on GHG emissions, and in the case of Germany, the hypothesis of an environmental Kuznets curve can be accepted.

The aim of this research is to relate clean energies, CO2 emissions, and economic variables. Relationships can be generated that characterize countries that manage to relate the use of clean energy with GDP, economic openness, and economic growth. We employ a quantitative methodology that utilizes clustering techniques to identify distinct groups of countries based on their susceptibility to climate change impacts. Subsequently, we employ a generalized linear model approach to estimate the investment behaviors of these country groups in alternative energy sources in relation to CO2 emissions and macroeconomic variables. The clusters reveal that the countries grouped in each cluster exhibit significantly distinct behaviors among the clusters. This differentiation is grounded in the countries under analysis, showing the evolution of the countries in terms of the use of clean energy and the emission of CO2 in relation to macroeconomic variables. According to the conducted research, there are different groups with differentiated behavior in terms of energy consumption and CO2 emissions, which implies the implementation of policies consistent with the development characteristics of the countries and how they cope with climate risk. Moreover, as a result of this research, a recommendation for policy makers could be that sustainable and clean development countries are based in three different sustainability dimensions: environmental, economic, and social.

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.

The aim of this study is to explore the impact of economic growth (GDP), energy consumption, foreign direct investment, oil price, and exports on carbon emissions by employing yearly time series data for Italy for the period 1971–2019. For this purpose, we employed the autoregressive distributed lag (ARDL) model and wavelet coherence approach to analyze the interconnections among variables. The cointegration results confirm the long-run association between our variables. Our findings show that GDP has a positive impact on carbon emissions, while the square of GDP has a negative impact, thus confirming the presence of the EKC hypothesis. Further, oil prices have a detrimental impact on carbon emissions both in the long- and short-term; on the contrary, foreign direct investment, energy consumption, and exports promote environmental degradation. We propose some important policy recommendations based on these findings to address the environmental constraints.

The rising carbon dioxide emissions from the MENA region constitute a severe danger to the environment, public health, and the execution of the United Nations SDGs. Substantial steps are required to solve this problem and maintain the region’s sustainable future. Hence, the current study focused on distinct factors, including renewable energy, energy intensity, green innovation, GDP, and CO2 emissions from 1990 to 2021. The research determines the multifarious variables in various quantiles, including the novel Method of Moments Quantile Regression (MMQR) approach, Fully Modified Ordinary Least Square (FM-OLS), Dynamic Ordinary Least Square (D-OLS) and Driscoll-Kraay Standard Errors (DKS) applied. The findings reveal that renewable energy significantly reduces carbon emissions in all quantiles, while energy intensity, green innovation, and GDP lead to carbon emissions in lower, middle, and upper quantiles. For robust outcome confirmed by FM-OLS, D-OLS, and DKS methods. Also, Granger heterogeneous causality applied that confirmed the bidirectional causality among the variables. The study’s findings imply that authorities should emphasize the emergence of renewable energy and green innovation while adopting energy-efficient technologies to minimize carbon emissions and accomplish SDGs 7, 9, and 13 to secure the MENA region.

The summer-dominated Mediterranean island of Cyprus is often considered in the contexts of beach tourism, sunny weather, and different types of business economic activities and services. In terms of its climatic conditions, extreme heat and mild winters characterise the island; yet, recent evidence has shown that winter poses a significant threat to public health. Its excess winter mortality is amongst the highest in Europe and there is an increased risk of energy-poverty-related mortality compared to total mortality. This study is an extension of previous research, with the objective of further scrutinizing the shift observed between urban and rural energy poverty mortality in the time of a severe nationwide financial crisis. Mortality and temperature data for the period of 2008–2018, as well as macroeconomic indicators, were investigated through a linear regression analysis. The results indicated that the declining economic situation of the island severely hit rural areas, with a significant increase in energy-poverty-related mortality, while urban areas were more resilient to this. There are three existing challenges linked to energy poverty: low incomes, high energy prices, and poor building energy efficiency. In Cyprus, all three coincide and are aggravated in times of crisis, creating conditions of extreme vulnerability for populations already in a disadvantaged position. This study’s motivation was to highlight the intense vulnerability associated with crises in Cyprus, and its outcomes call for higher levels of support at such times, especially when it comes to rural populations.

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.

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.

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.

Exploring the complex relationship between ecological restoration and economic development is valuable for decision makers to formulate policy for sustainable development. The large-scale environmental restoration program—Grain for Green—was mainly implemented in the Loess Plateau of China to improve the soil retention service. However, whether this world-famous program affects local economic development has not been fully explored. In this study, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and spatializing the gross domestic product (GDP) based on the remotely sensed nightlight data, we explored the tradeoff between environment (i.e., soil retention service) and economy (i.e., GDP) for the Loess Plateau in a spatially explicit way. We found that the soil retention service increased prominently over the past 40 years, especially after implementing the Grain for Green project. Meanwhile, the GDP increased about nine-fold over the past four decades from 4.52 to 40.29 × 107 USD. A win–win situation of soil retention and economic development was achieved in the Loess Plateau of China, particularly in the loess gully and loess hilly gully regions of the Loess Plateau. The win–win situation of soil retention and economic development was as a result of the Grain for Green program, the optimization of industrial structure, and the increase in non-agriculture employment. Compared with previous studies, more spatial information was available for the Loess Plateau in this study, which is more valuable to policymakers.

We here examine the frequent claim that an increase in the tax base and a decrease in tax evasion will compensate for a loss in tax revenues caused by a lower tax level. Using a unique data set for the estimated underground economy in Italy from 1982 to 2006, we found that a loss in tax revenues equivalent to 1% of the GDP would be partly compensated by an increase in GDP of 0.55%. The compensation would come from 0.31% of the GDP increase and from 0.24% of the reductions in the underground economy. These results apply to an economy with a high tax level (>32%) and a high underground economy (≥25%). Applying a high-resolution lead–lag method to the data, we ensured that tax changes were leading the GDP and, thus, a potential cause for changes in the GDP.

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.

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.

The article deals with the issue of how negative interest rate policies, introduced in the second decade of the 21st century in some countries, affect certain macroeconomic indicators and bank performance. We concentrate specifically on Switzerland and Sweden. We use correlation analysis to reveal the relationship between interest rates and GDP, the level of foreign direct investment (FDI) and some indicators of banks’ performance. We found that negative interest rates (NIRs) are strongly correlated with the level of GDP in both Switzerland and Sweden but that they do not affect their FDI. The share of banks´ deposits in GDP is also strongly correlated with NIR. Other indicators of bank performance do not show a strong correlation for both countries. Our evidence is consistent with NIR not being associated with undesirable effects concerning economic growth and bank performance in Switzerland and Sweden. The value of FDI depends on many factors—mainly on the attractiveness of a country for foreign investors in terms of its political and economic stability and by general conditions for business operation.

Over the last few decades, climate change and global warming have intensified a serious threat that may deteriorate global sustainable development. The factors significantly contributing to global warming are greenhouse gases, mainly carbon dioxide emissions. Therefore, it is crucial to consider the variables affecting carbon emissions considerably. This study examines symmetric (linear) and asymmetric (non-linear) effects of green technology innovation (GTI), economic policy uncertainty (EPU) along with foreign direct investment (FDI), and economic development (GDP) on carbon emissions (CO2) by utilizing yearly time series data between 1970–2018 in Italy. We employed linear and non-linear autoregressive distributed lag (ARDL) approaches to examine short- and long-run estimates. The symmetric results show that GTI and EPU mitigate environmental degradation in the long run and intensify in the short run, whereas FDI increases environmental issues over the long and short run. Nevertheless, the asymmetric outcomes demonstrate that positive shocks in GTI lessen CO2 emissions, whereas negative shocks in GTI significantly escalate CO2 emissions. Furthermore, EPU and FDI positive and negative shocks significantly enhance environmental degradation. Based on these findings, important policy implications for policymakers to make strong policies to achieve carbon neutrality targets and achieve sustainable economic growth are proposed. Finally, because positive and negative changes in GTI, EPU, and FDI have different consequences on CO2 emissions, policymakers should consider asymmetry across these variables when assessing their impact.

In this work, we use a cointegration state space approach to estimate the quarterly series of the Chilean Gross Domestic Product (GDP) in the period 1965–2009. First, the equation of Engle–Granger is estimated using the data of the yearly GPD and some related variables, such as the price of copper, the exports of goods and services, and the mining production index. The estimated coefficients of this regression are then used to obtain a first estimation of the quarterly GDP series with measurement errors. A state space model is then applied to improve the preliminary estimation of the GDP with the main purpose of eliminating the maximum error of measurement such that the sum of the three-month values coincide swith the yearly GDP. The results are then compared with the traditional disaggregation methods. The resulting quarterly GDP series reflects the major events of the historical Chilean economy.

The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to calculate the different influencing factors, and employed a multiscale geographically weighted regression (MGWR) model to explore their spatial non-stationarity impact on China’s county economic development. The results showed that the multi-source big data can be useful to calculate the influencing factor of China’s county economy because they have a significant correlation with county GDP and have a good models fitting performance. Besides, the MGWR model had prominent advantages over the ordinary least squares (OLS) and geographically weighted regression (GWR) models because it could provide covariate-specific optimized bandwidths to incorporate the spatial scale effect of the independent variables. Moreover, the effects of various factors on the development of the county economy in China exhibited obvious spatial non-stationarity. In particular, the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei urban agglomerations showed different characteristics. The findings revealed in this study can furnish a scientific foundation for future regional economic planning in China.

GDP, monetary variables, corruption, and uncertainty are crucial to energy policy decisions in today’s interrelated world. The global energy crisis, aggravated by rising energy prices, has sparked a thorough analysis of its causes. We demonstrate the significance of categorizing research by influence channels while focusing on their implications for energy policy decisions. We investigate the growing number of studies that use GDP, inflation, central banks’ characteristics, corruption, and uncertainty as critical factors in determining energy policies. Energy prices fluctuate because energy policies shift the supply–demand equilibrium. We categorise the effects and show that GDP, economic policy uncertainty, and, most notably, specific economic conditions and extreme events play a significant role in determining energy prices. We observed that energy consumption, GDP growth, and energy prices have a bidirectional, causal relationship. Still, the literature has not established which causative direction is the most significant. Taxes, interest rates, and corruption also significantly determine energy prices, although the origins of corruption have not been adequately examined. Lastly, uncertainty generally increases energy costs, but this relationship requires additional research in terms of the features of countries, conditions, and, most importantly, the theoretical backgrounds used.

With climate change and rising sea levels, the residents and assets in low-elevation coastal zones (LECZ) are at increasing risk. The application of high-resolution gridded population datasets in recent years has highlighted the threats faced by people living in LECZ. However, the potential exposure of gross domestic product (GDP) within LECZ remains unknown, due to the absence of refined GDP datasets and corresponding analyzes for coastal regions. The climate-related risks faced by LECZ may still be underestimated. In this study, we estimated the potential exposure of GDP in the LECZ across China by overlying DEM with new gridded GDP datasets generated by random forest models. The results show that 24.02% and 22.7% of China’s total GDP were located in the LECZ in 2010 and 2019, respectively, while the area of the LECZ only accounted for 1.91% of China’s territory. Significant variability appears in the spatial-temporal pattern and the volume of GDP across sectors, which impedes disaster prevention and mitigation efforts within administrative regions. Interannual comparisons reveal a rapid increase in GDP within the LECZ, but a decline in its share of the country. Policy reasons may have driven the slow shift of China’s economy to regions far from the LECZ.

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.

Energy prices (EPs) play an imperative role in South Asian Country (SAC) Gross Domestic Product (GDP). This research empirically examines the influence of sustainable energy price shocks (EPSs) on macroeconomic indicators. The study is to forecast the impact of EPS on macroeconomic indicators from 1980 to 2020. The analysis is carried out by employing the Vector Auto-Regression (VAR) approach. Impulse Response Functions (IRFs) results indicate that EPS decreases Gross Domestic Product (GDP). They exist in the short run and the long run. This research study’s overall findings suggest that high EPSs have a negative impact on GDP. The study implies that policymakers should develop, adopt, and initiate some imperatives to control the unanticipated volatility and movements in EP. The study highlights that policy should be designed to prevent fluctuations in sustainable EP and plan conservative energy policies that motivate discovering alternative energy sources to meet increasing energy demand and improve economic growth.

The accurate calculation of sustainable development indicators is essential for the accurate assessment of the Sustainable Development Goals. This study develops a methodology that combines nighttime light indices, population distribution data, and statistics in order to examine changes and key drivers of SDG7 in the Aral Sea Basin from 2000–2020. In this study, the best-performing combination of four light indices and five simulation methods (two linear regression methods and three machine learning methods) was selected to simulate the spatial distribution of GDP in the Aral Sea Basin. The results showed that: (1) The prediction using the XGBoost model with TNL had better performance than other models. (2) From 2000 to 2020, the GDP of the Aral Sea Basin shows an uneven development pattern while growing rapidly (+101.73 billion, +585.5%), with the GDP of the lower Aral Sea and the Amu Darya River gradually concentrating in the middle Aral Sea and Syr Darya River basins, respectively. At the same time, the GDP of the Aral Sea Basin shows a strong negative correlation with the area of water bodies. (3) Although there is a small increase in the score (+6.57) and ranking (+9) of SDG7 for the Aral Sea Basin from 2000 to 2020, it is difficult to achieve SDG7 in 2030. Deepening inter-basin energy cooperation, enhancing investment in renewable energy, and increasing energy intensity is key to achieving SDG7.

The Rho protein, a homolog of Ras, is a member of the Ras superfamily of small GTPases. Rho family proteins are involved in cytoskeletal organization, cell mobility, and polarity, and are implicated in cancer morphogenesis. Although Rho homologs from higher-order mammalian organisms are well studied, there are few studies examining Rho proteins in lower-level single-celled organisms. Here, we report on the crystal structure of Rho1 from Schizosaccharomyces pombe (SpRho1) in complex with GDP in the presence of Mg2+ at a 2.78 Å resolution. The overall structure is similar to that of known Rho homologs, including human RhoA, human RhoC, and Aspergillus fumigatus Rho1 (AfRho1), with some exceptions. We observed subtle differences at the Switch I and II regions, in β2 and β3, and in the Rho insert domain and loop from Phe107 to Pro112. Our analysis suggests that SpRho is evolutionarily closer to HsRhoC than HsRhoA, as previously believed.

This empirical study evaluates European tourism demand in Spain from 2000 to 2020. To test the hypotheses, we have modelled tourism demand, which is measured in terms of travellers arriving in Spain. An Error Correction Model adapted to a panel structure has been utilised to work within a time series context and differentiate up to 14 European countries of origin. The findings denote that over the short and the long term, gross domestic product (GDP) and the number of beds positively relate to tourism demand. Still, the stock market indices are not significant in both terms. The price index, trade flows, and length of stay differ in the short and the long term. Results of this study call the attention of policy makers and the private sector to encourage an increase in the supply of available beds to ensure post-pandemic sustainability.

While many recent studies have used the ecological footprint as a comprehensive indicator of environmental degradation instead of CO2 emission, these were mainly focused on consumer responsibility. This study, however, aims to cover both aspects of consumption and production to elicit a more comprehensive understanding. Furthermore, this study addresses another information gap by analyzing the effect of aggregated and disaggregated economic activities on the environment. Panel data were used and sourced from 92 countries classified by income group spanning 1992 to 2015. Comprehensive financial development indicators, energy structure, energy intensity, trade openness, and urbanization were considered in examining their impacts on environmental degradation. The pooled mean group estimation was adopted in examining the long-run and short-run relationship between variables. The main findings suggest that financial development promotes green investment in high-income and upper-middle-income countries but increases degradation in lower-middle and lower-income countries. Renewable energy improves the environment in general, and energy intensity is a crucial factor in environmental modeling across all groups. Most importantly, a U-shape relationship is found on both the consumption and the production side for all income groups except for lower-income countries (inverse U-shape) on the production side. Interestingly, a U-shape relationship was found in high-income and upper-middle-income countries in the industrial sector, but a monotonic relationship in the service sector. A U-shape relationship was found for the industrial and service sectors in lower-middle-income and lower-income countries, but an inverse U-shape for agriculture outputs in lower-middle-income countries. This finding suggests the need to shift from fast-growth strategies to strategic growth planning that considers the nature of the relationship between economic sectors and the environment while diversifying the economic structure to allow for the recovery of natural capital.

Regarding the rapid development of urban agglomeration (UA) in Guangdong Province in the past two decades, this study revealed the temporal and spatial evolution of the urban pattern of the province and the current urbanization process. This study determined the geographical spatial distribution and change in the UA lighting scale in Guangdong Province, analyzed the relationship between the lighting change and development and the dynamic evolution of the gross domestic product, and explored the expansion intensity and center of gravity migration direction of UA. The results showed that from 2000 to 2020, the lighting scale of the border areas of Guangdong Province was lower than that of the inland areas, whereas the lighting growth rate of the border areas was higher than that of the inland areas. The built-up area steadily expanded from the center to the outside within the time range of the study, and the center of gravity of the ellipse tended to shift northwest. The study provides visual and scientific data for the spatiotemporal evolution of the urban pattern in Guangdong Province and has important reference significance for analyzing urbanization development and planning urban construction.

To mitigate the adverse effects of climate warming, the Paris Agreement proposed the goal to reduce global warming up to an increase of 1.5 °C above the preindustrial level. Study of the population and GDP exposure to precipitation extreme events under the 1.5 °C warming target is fundamental for disaster risk mitigation and adaptation on the Loess Plateau. This study projected the population and GDP exposure to extreme precipitation events under the 1.5 °C global warming level on the Loess Plateau using daily precipitation data from CMIP6 outputs and population and GDP data under a Shared Socioeconomic Pathway 1(SSP1) 2.6 scenario. The population and GDP exposure were evaluated by combing the frequency and the areal coverage of the extreme precipitation events. Results show that population and GDP exposure to extreme precipitation events on the Loess Plateau will increase under the 1.5 °C global warming level. The population exposure was projected from 1.32 × 106 to 2.68 × 106 person-year. The population exposure of eastern and southern Loess Plateau is significantly higher than that of the northern region. The annual exposure of GDP ranges from USD 2.9 to 12.3 billion, and the regions with the highest GDP exposure are Zhengzhou, Xi’an, Taiyuan, and Lanzhou. Our results reveal that limiting the increase of global mean temperature to 1.5 °C warming level is of great significance to reduce the social and economic exposure to extreme precipitation events on the Loess Plateau.

The conformational changes in switch domains significantly affect the activity of NRAS. Gaussian-accelerated molecular dynamics (GaMD) simulations of three separate replicas were performed to decipher the effects of G13D, Q16R, and C118S on the conformational transformation of the GDP-bound NRAS. The analyses of root-mean-square fluctuations and dynamics cross-correlation maps indicated that the structural flexibility and motion modes of the switch domains involved in the binding of NRAS to effectors are highly altered by the G13D, Q61R, and C118Smutations. The free energy landscapes (FELs) suggested that mutations induce more energetic states in NRAS than the GDP-bound WT NRAS and lead to high disorder in the switch domains. The FELs also indicated that the different numbers of sodium ions entering the GDP binding regions compensate for the changes in electrostatic environments caused by mutations, especially for G13D. The GDP–residue interactions revealed that the disorder in the switch domains was attributable to the unstable hydrogen bonds between GDP and two residues, V29 and D30. This work is expected to provide information on the energetic basis and dynamics of conformational changes in switch domains that can aid in deeply understanding the target roles of NRAS in anticancer treatment.

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.

There is no sector of the economy that is not dependent on the state of development of the energy sector. This sector produces a significant share of global CO2 emissions. Harmful CO2 emissions and greenhouse gas emissions accelerate global warming. Therefore, more and more countries are adopting a strategy for the transition to carbon-neutral energy. However, energy independence and economic competitiveness are closely linked. One cannot analyze them separately. Given these facts, we focused on conducting an econometric study of the impact of key macroeconomic indicators on the level of CO2 emissions into the air in the United States and the Asia-Pacific region as the regions with the largest CO2 emissions. The modeling was carried out using the method of a correlation–regression analysis with the subsequent construction of econometric models. The quality of the built econometric models was checked using the coefficient of determination and Fisher’s criterion. The sample of statistics was formed from all the available values of the World Bank’s annual indicators for the period 1970–2020. The findings achieved showed that: (i) The results of our study confirmed the dependence of CO2 emissions on macroeconomic factors such as GDP, exports and imports, the rate of inflation, and unemployment. It allows the governments of many countries to use research findings to diagnose, monitor, and forecast macroeconomic outcomes to reduce or maintain allowable CO2 emissions. (ii) Identifying and assessing economic losses from environmental pollution by CO2 emissions using econometric models will allow to ensure effective public environmental and economic policies aimed at reducing harmful CO2 emissions into the air. It may be regarded as the practical importance of our study.

The use of nighttime lights (NTL) data to proxy for local economic activity is well established in remote sensing and other disciplines. Validation studies comparing NTL data with traditional economic indicators, such as Gross Domestic Product (GDP), underpin this usage in applied studies. Yet the most widely cited validation studies do not use the latest NTL data products, may not distinguish between time-series and cross-sectional uses of NTL data, and usually are for aggregated units, such as nation-states or the first sub-national level, yet applied studies increasingly focus on smaller and lower-level spatial units. To provide more updated and disaggregated validation results, this study examines relationships between GDP and NTL data for 2657 county-level units in China, observed each year from 2012 to 2019. The NTL data used were from three sources: the Defense Meteorological Satellite Program (DMSP), whose time series was recently extended to 2019; and two sets of Visible Infrared Imaging Radiometer Suite (VIIRS) data products. The first set of VIIRS products is the recently released version 2 (V.2 VNL) annual composites, and the second is the NASA Black Marble annual composites. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of economic activity changes over time, and also considered different levels of spatial aggregation.

Exposure to endocrine-disrupting chemicals (EDCs) contributes to substantial disease burden worldwide. We aim to quantify the disease burden and costs of EDC exposure in Canada and to compare these results with previously published findings in the European Union (EU) and United States (US). EDC biomonitoring data from the Canadian Health Measures Survey (2007–2011) was applied to 15 exposure–response relationships, and population and cost estimates were based on the 2010 general Canadian population. EDC exposure in Canada (CAD 24.6 billion) resulted in substantially lower costs than the US (USD 340 billion) and EU (USD 217 billion). Nonetheless, our findings suggest that EDC exposure contributes to substantial and costly disease burden in Canada, amounting to 1.25% of the annual Canadian gross domestic product. As in the US, exposure to polybrominated diphenyl ethers was the greatest contributor of costs (8.8 billion for 374,395 lost IQ points and 2.6 billion for 1610 cases of intellectual disability). In the EU, organophosphate pesticides were the largest contributor to costs (USD 121 billion). While the burden of EDC exposure is greater in the US and EU, there remains a similar need for stronger EDC regulatory action in Canada beyond the current framework of the Canadian Environmental Protection Act of 1999.

COVID-19, a novel Coronavirus SARS-CoV-2, has wreaked havoc on global financial markets, economies, and societies. For example, this study looks at the impact of COVID-19 on the Chinese economy and its policy responses (fiscal, monetary, and institutional). This study also examines future issues. This study is timely and essential for policymakers and investors worldwide because of China’s size, contribution to global growth, and growing influence. The research shows that the presence of COVID-19 in China has global implications. Because of the virus threat, foreigners avoid mixing with the Chinese. Global tourists have cancelled their plans to visit China, and Chinese tourists cannot visit foreign countries. The rapid spread of the COVID-19 in China has halted normal life. The intensification of the COVID-19 may have long-term effects on China’s economy.

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.

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.

This study aimed to explore the association between the GDP of various countries and the progress of COVID-19 vaccinations; to explore how the global pattern holds in the continents, and investigate the spatial distribution pattern of COVID-19 vaccination progress for all countries. We have used consolidated data on COVID-19 vaccination and GDP from Our World in Data, an open-access data source. Data analysis and visualization were performed in R-Studio. There was a strong linear association between per capita income and the proportion of people vaccinated in countries with populations of one million or more. GDP per capita accounts for a 50% variation in the vaccination rate across the nations. Our assessments revealed that the global pattern holds in every continent. Rich European and North-American countries are most protected against COVID-19. Less developed African countries barely initiated a vaccination program. There is a significant disparity among Asian countries. The security of wealthier nations (vaccinated their citizens) cannot be guaranteed unless adequate vaccination covers the less affluent countries. Therefore, the global community should undertake initiatives to speed up the COVID-19 vaccination program in all countries of the world, irrespective of their wealth.

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.