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The significance of the transportation sector, notably in terms of the carbon emission factor, is an undeniable fact. Along with this fact, individuals' transportation preferences depend on their income levels. In this context, when the issue is considered, the income level in the USA pushes people toward cheap air travel. The main reason for this is that it is cheap, accessible, and transports one to their destinations quickly. Thus, from the perspective of road transportation, bus transportation is popular among the public. The reason why both air and road transportation modes are empirically evaluated together through income distribution is due to the preference of the US people. In this context, the effectiveness of active transportation on both air and highways in the USA from 1975 to 2023 is investigated by taking into consideration the income distribution. Empirical findings obtained through the FMOLS, DOLS, CCR, and NARDL models demonstrate that all independent variables, including GDP, energy use, air transportation, and the Gini coefficient, affect carbon dioxide emissions. In addition, wavelet analysis is performed to comprehend the form of and fluctuations in the series, which are vital to monitoring the periodical changes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The transition from coal to more sustainable energy sources represents a critical shift for economies reliant on coal production. To investigate the intricate processes involved in such a transition, the use of powerful analytical tools is essential. This study assesses the impact of the delignification process on GDP growth over a 20-year horizon (2015–2035) in the Region of Western Macedonia, Greece, using the Vensim PLE Plus 9.0.1 software, a robust tool for system dynamics modeling. By developing a dynamic model that captures the key variables and feedback loops associated with coal transition, this research examines economic, social, and investment variables, emphasizing their causal relationships. The study integrates societal, economic, and educational impacts on production transition, addressing issues such as unemployment, financial support, and investments in human resources and R&D. Additionally, it considers the influence of climate change on GDP. The model highlights population dynamics, economic development, and education as critical factors. Scenarios explore the impact of increased funding on education, research, and financial aid efficiency, providing insights into enhancing GDP in decarbonizing regions. The study reveals that increased investment in education and human capital leads to slight improvements in local GDP, though the effects are not immediate. Enhanced efficiency in government and European spending significantly boosts local GDP by creating strong value chains and local economies of scale. It is found that the increase in financial support to the regions in transition is of the utmost importance and has a multiplicative nature, something that should encourage the European Union to increase its financial support tools. The model's simulations align closely with historical GDP data, validating its accuracy. The contributions of the present work offer valuable insights to policymakers and stakeholders engaged in the transition processes. [ABSTRACT FROM AUTHOR]

This article examines the relationship between household food waste and Gross Domestic Product (GDP) in various European regions, aiming to determine how economic prosperity influences the levels of household food waste. Using comparative analysis of secondary and tertiary data, a synthetic indicator (IpFW) was developed to assess the interaction between GDP per capita and household food waste per capita. Linear correlation analysis was also applied for better interpretation of the data. Despite expectations, higher GDP is not consistently correlated with lower household food waste, challenging economic prosperity and environmental stewardship assumptions. This research highlights the complexity of the interaction between economic factors and household food waste management, revealing a lack of significant correlation even at the regional level. The findings indicate a need to re-evaluate current policies and highlight that improving food supply chains and influencing consumer behavior can promote more sustainable consumption patterns, which is in line with the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research aims to explore the fiscal and public finance viability on climate physical risk externalities cost for building social-economic-environmental sustainability. It analyzes climate physical risk impact on the real business cycle to change the macroeconomic output functions, its regressive cyclic impact alters tax revenue income and public expenditure function; This research also analyzes that the climate physical risk escalates social-economic inequality and change fiscal-financial policy functions, illustrates how the climate damage cost and adaptation cost distorts fiscal-finance cyclical and structural equilibrium function. This research uses binary and multinomial logistic regression analysis, dynamic stochastic general equilibrium method (DSGE) and Bayesian estimation model. Based on the climate disaster compensation scenarios, damage cost and adaptation cost, analyzing the increased public expenditure and reduced revenue income, demonstrates how climate physical risk externalities generate binary regression to financial fiscal equilibrium, trigger structural and cyclical public budgetary deficit and fiscal cliff. This research explores counterfactual balancing measures to compensate the fiscal deficit from climate physical risk: effectively allocating resources and conducting the financial fiscal intervention, building greening fiscal financial system for creating climate fiscal space. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Green taxes are taxes collected to protect the environment by controlling the negative effects of certain activities and products on the environment. They are also an instrument of environmental policy and can therefore contribute to several sustainable development goals. According to the studies carried out, the green economy aims to ensure sustainable development. The main objective of this paper is to identify the existing relationships between green taxes and sustainable economic development through a dynamic panel analysis. A dynamic panel analysis was therefore carried out on the existing links between environmental taxes and charges at the European level and the indicators of the circular economy. The results of the two dynamic regressions for the two dependent variables, namely total green taxes and energy taxes, show a positive and significant correlation with the variation of GDP and with primary energy consumption, confirming the hypothesis that environmental taxes and energy taxes are closely linked to these two important indicators of sustainable development. Thus, as GDP changes, the taxes on energy production and the energy products used in both transport and stationary applications increase. As a result of the analysis, we can note that the increase in primary energy consumption and the consumption of raw materials leads to an increase in environmental and energy taxes. Energy taxes are a possible solution to reduce CO2 emissions in third world countries and may even stimulate climate action. In contrast, we found no significant correlation between green taxes and the following variables: Human Development Index, net greenhouse gas emissions, private investment and gross value added related to circular economy sectors, the consumption of raw materials, waste generated, waste treatment, the supply, transformation, and consumption of renewable energy, public expenditure on environmental protection, and climate-related economic losses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of alternative energy use, urbanization, GDP, agriculture, ICT development, and FDI on carbon dioxide (CO2) emissions in the 14 leading CO2-emitting countries in Asia. This research comprises various econometric techniques, including MMQR, FMOLS, DOLS, and Driscoll–Kraay, to extend the data analysis from 1996 to 2020. The findings provide significant support for an inverted U-shaped link between economic expansion and environmental deterioration, known as the environmental Kuznets curve. Moreover, this paper verifies that the GDP square, renewable energy use, and agriculture are shown to help to decrease pollution, as indicated by the research findings. On the contrary, urbanization and the GDP are demonstrated to be variables that contribute to carbon emissions. Furthermore, the panel quantile regression models validate that the impacts of each explanatory variable on CO2 emissions vary across various quantiles. Finally, this analysis provides valuable suggestions to scholars, environmentalists, politicians, and authorities for identifying and mitigating the main cause of emissions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Climate neutrality is the main environmental goal set for the European Union Member States until 2050. EU economies can achieve this ambitious climate goal by reducing the emission intensity of economies, which has been achieved for many years by reducing pollution emitted by industry. The aim of the study is focused primarily on demonstrating the degree of relationship between the variables describing economic growth, GDP, and the level of CO2 emissions. In the first stage of the research, the potential of countries to achieve climate neutrality was assessed, which was achieved by estimating the correlation between GDP indices in relation to 2013 and the level of CO2 emissions. Research has shown that despite the countries' differences in the structure of their energy balances, they can achieve independence of economic growth from the emission level of their economies. The research also concerns Poland's special situation compared to other European Union countries according to energy balance based on coal. A model based on differential equations was used to simulate the impact of GDP, energy intensity, and the share of biofuels on temperature and CO2 concentration until 2030, using data for Poland as an example. The aim of this analysis is to answer the question of whether the energy transformation in the country will achieve the assumed emission reduction goals by 2030. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper examines the causal relationship between human capital and economic factors in BRICS countries using a panel vector autoregressive model and data from 1997 to 2020. The economic factors considered include foreign direct investment (FDI), imports, exports, and gross domestic product (GDP). The study conducts a comparative analysis of Brazil, India, China, Russia, and South Africa by adopting a vector autoregressive (VAR) model. The findings indicate a bidirectional causality between human capital and FDI in China, while a unidirectional causality from FDI to human capital is observed in Brazil. Moreover, a unidirectional causality exists from human capital to GDP in Brazil, Russia, India, and South Africa. Additionally, a unidirectional causality is found from human capital to imports and exports in South Africa. Overall, the results suggest the pivotal role of human capital in achieving sustainable economic development in BRICS countries. Policymakers should ensure sustained investment in human capital, focusing on economic growth, FDI, and international trade. [ABSTRACT FROM AUTHOR]

With the rapid advancement of global technology, high-tech industries have become key drivers for the economic growth of many nations and regions. This study delves into the spatiotemporal dynamics and determinants influencing China's high-tech sector from 2007 to 2021. The key findings include the following: (1) Nationally, the high-tech sector has been a cornerstone for China's GDP growth over the preceding 15 years. The expansion rate of the high-tech domain consistently outpaces the broader economy. In particular, since 2015, the percentage of high-tech industries' GDP has surged to approximately 42%. (2) At the provincial level, the spatial representation of the high-tech sector's GDP predominantly leans towards the east and the south, revealing pronounced spatial autocorrelation. Nevertheless, the demarcations between east and west and between north and south are progressively diminishing. (3) Regarding influential determinants, R&D internal expenditure, operating revenue, and industry agglomeration have been instrumental in spearheading innovation and bolstering growth within the high-tech realm. These insights are invaluable for comprehending the evolutional nuances of China's high-tech industry and devising pertinent policy measures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this research is to relate clean energies, CO 2 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 CO 2 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 CO 2 in relation to macroeconomic variables. According to the conducted research, there are different groups with differentiated behavior in terms of energy consumption and CO 2 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. [ABSTRACT FROM AUTHOR]

Economic growth is an important metric for the sustainable development of any region or country. Central and Eastern Europe members of the European Union are important players of the single market, which implements regional policies to mitigate socio-economic differences between its newer and established members. The present study examines the factors that shape the phenomenon of economic growth across 62 NUTS 2 regions from 11 countries in Central and Eastern Europe during the period 2011–2020. The study investigates determinants related to education level, involvement of young people in the labor market, household net income, high-speed internet facilities and overall hours spent at work during a year. Three panel data models estimated with first-differenced generalized method of moments showed that regional economic growth was significantly influenced mainly by income, the rate of young employees and educational attainment level. Relevant insights and policy implications for regions in CEE countries are addressed. [ABSTRACT FROM AUTHOR]

This article analyzes the dynamics of the changes in indicators of socio-economic development under conditions of financial and economic crises and their negative consequences. The study proves that financial crises are associated with severe and prolonged downturns in economic activity. The socio-economic development of European countries in times of crises was analyzed. The cyclical nature of the onset of crises was confirmed via the study of the dynamics of socio-economic development indicators. The main emphasis was on the financial crisis of 2008–2009 and the COVID-19 crisis (2020–2021). The main indicators characterizing the crises were identified based on an analysis of literary sources. Their classification was developed according to the following groups: leading indicators, lagging indicators, and client leading indicators of expansion. Based on the correlation analysis, indicators that have a significant impact on socio-economic development and are predictors of crisis onset were identified. The authors suggest considering such leading indicators as increases in the private credit in the GDP, budget deficit, balance of payment deficit, and real interest rate. The major lagging indicators that have strong correlations with the GDP, such as the employment rate, general government debt, stock price volatility, and investment, were identified. Client leading indicators of expansion include unemployment, an increase in the number of new enterprises, an increase in purchasing power, etc. Some indicators, such as unemployment, can be both lagging indicators and client leading indicators of expansion. The negative consequences of the crisis are caused by the crisis itself as well as by the imbalances preceding the crisis. Therefore, the study of the predictors of crisis onset is relevant for timely decision making in order to prevent the negative consequences of the crisis. Based on the identified lagging indicators, the 2008–2009 crisis and the COVID-19 crisis were studied. To study the development processes of these crises, the authors analyzed by quarters the dynamics of the development of the following macroeconomic indicators: the GDP, employment, and investment levels. The similarities and discrepancies were identified in the natures of the emergences and courses of the 2008–2009 crisis and the COVID-19 crisis using the comparison method. The case study of the Eurozone and individual EU countries (Germany, France, Italy, and Spain) was used. Considering the similar courses of the crises, the forecast of the socio-economic development was made using the analyzed indicators during the COVID-19 crisis based on the 2008–2009 crisis data. The forecast approximation indicators were calculated, and a method for constructing further forecasts was selected. Based on retrospective data, the GDP forecast was developed via the use of the extrapolation method for 2023–2024. It is necessary to consider that while forecasting crises caused by unforeseen events and external influences, it is advisable to use qualitative analysis along with quantitative analysis. This article will be useful to researchers, political elites, experts, and financial analysts when developing programs for the socio-economic development of countries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, the factors affecting economic growth in developed countries within the context of their sustainability transition are explored. By analyzing both traditional and novel factors, we aim to expand the scientific knowledge of the drivers behind sustainable economic development. To achieve this purpose, some factors that have demonstrated the potential to positively impact economic growth while simultaneously promoting environmental sustainability are included. Research results demonstrate that a 1% increase in energy consumption is associated with a 0.314% increase in real GDP, indicating a positive relationship between energy usage and economic growth. Additionally, the consumption of renewable energy boosts a positive impact on sustainable economic growth: When it grows by 1%, the real GDP increases by 0.12%. The empirical findings further reveal that scientific progress and economic freedom are significant drivers of economic growth, as a 1% increase in both factors leads to an increase in economic output by 0.349% and 0.323%, respectively. By conducting a comprehensive analysis, we provide valuable insights into the complex interplay between economic growth and sustainability in developed countries. Based on these findings, the study offers specific policy recommendations, which include the diversification of the energy mix, the promotion of education and scientific advancement, and the digitalization of public services. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of innovation on three macroeconomic indicators: GDP, self-employment, and foreign direct investment (FDI). The study analyses a sample of 120 countries using the Global Innovation Index (GII) and its constituent sub-indices and pillars, which provide a holistic evaluation of national innovation. Gross domestic product (GDP) per capita measures a country's economic output, self-employment assesses entrepreneurial activity, and FDI indicates confidence in a country's economic prospects and innovation trends. This study analyzes the data using generalized-linear and panel-corrected standard-error models. The results show that innovation positively influences GDP, domestic institutional framework, local infrastructure, local knowledge and technology, and creative outputs. In contrast, innovation negatively correlates with domestic self-employment, often associated with necessity-driven entrepreneurship. The study concludes that innovation positively affects human resources, research, and creative outputs and has no significant impact on FDI. The findings suggest that a practical regulatory framework, institutional support, domestic human capital, research and development, infrastructure, technology, and creative outputs are essential for a vibrant economy. National innovation policies supporting the GII and its constituent factors can positively affect the economy while reducing self-employment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The inter-regional freight volume is a crucial factor for transportation infrastructure planning and investment decision-making. However, existing studies on freight volume estimation have mainly focused on the total freight volume within a specific region, without taking freight flow into consideration. In this research, a gravity model was employed to estimate the inter-regional trade coefficient matrix based on the input–output tables of the 31 provinces in China in 2017. The inter-regional freight volume was then determined by converting the value flow into freight flow. To determine the model parameters, we used information from 2017 and subsequently validated the results using dates from 2012 to 2020. We also studied the impact of industrial structure change on freight volume by simulating dates from the aforementioned model in 2017. The results indicated that the model can effectively simulate inter-regional freight volume while taking into account the influence of industrial restructuring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Digital technology development caused the digital transformation of the economy and society. E-commerce, the most widespread among digital innovations, reached a significant share, particularly during the COVID-19 pandemic, impacting economic growth. The progress of digital technologies and the evolution of e-commerce can contribute to the more sustainable development of organizations and worldwide economies. This paper analyzed the influences of digital transformation and e-commerce on GDP and sustainable development. The study used the Eurostat database to gather the research variables for the EU countries. The paper used artificial neural networks and cluster analysis to reveal the significant influence of digital transformation and e-commerce on GDP and sustainable organizational development. Countries with a low level of digital transformation and e-commerce should propel these activities to increase economic performance sustainably. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China's multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China's GDP moved 128.80 km to the southwest. These results indicate China's achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article aims to uncover the asymmetric labor-market consequences of the long-run civil war in Afghanistan by employing a non-linear autoregressive distributed lags (NARDL) model and an asymmetric causality technique over the period from 2004Q3 to 2020Q4. The findings from the NARDL model reveal that the positive asymmetric shocks from the cost of war, GDP growth, final government expenditure, foreign direct investment, and the rule of law significantly decrease the unemployment rate, while their negative asymmetric shocks increase the unemployment rate in the short and long runs. Innovatively, the composite financial inclusion index has been incorporated into the model, which provides interesting results. It demonstrates that enhancing the outreach of financial services plays an important role in reducing the unemployment rate during wartime in Afghanistan, while its exclusion is found to increase the unemployment rate both in the short and long runs. Moreover, the results of the asymmetric causality test reveal that an asymmetric causality runs from both the positive and negative components of the cost of war, the composite financial inclusion index, GDP growth, foreign direct investment, inflation rate, population growth, and the rule of law to the unemployment rate, while no evidence is found to support a causality nexus between the unemployment rate, final government expenditure, and the secondary school enrollment rate. The results entail several policy implications that are discussed. [ABSTRACT FROM AUTHOR]

Nighttime light data is a mainstay method in confirming and supporting other traditional economic data indicators, which in turn influence business and policy-making decisions. Accuracy in and clear definition of economic data and its related indicators are thus of great importance not only for analysis of urban development and related policies seeking sustainable development, but also plays a key role in whether or not these policies are successful. Discovering and recognizing the applications and limitations of nighttime light and other peripheral data could prove helpful in future data analysis and sustainable development policy. One possible limitation could exist in GDP decrease, and whether or not nighttime light would decrease accordingly, as most studies show nighttime light increase confirms economic growth, thus affecting the reliability of the data's correlation with economic data. This study utilizes nighttime light data in a cross-reference with GDP data during instances of global GDP shrinkage over the years of 2007–2017, split between 2007–2012 for the DMSP dataset and 2013–2017 for the VIIRs dataset. It seeks to establish through linear regression whether or not yearly average nighttime light data products show a positive correlation even during periods of economic decline, thereby providing a clearer understanding of the strengths and limitations of NTL as an economic validation indicator. Analysis shows that both years of global GDP decrease in turn also displayed global nighttime light decrease, in addition to linear regression giving satisfactory results pointing to a positive correlation over the timespan. The VIIRS data series resulted in higher regression coefficients, which is in line with the results of previous literature. [ABSTRACT FROM AUTHOR]

(1) Background: Cardiac resynchronisation therapy (CRT) is nowadays an indispensable treatment option for heart failure. Although the indication is subject to clear cross-national guidelines by the European Society of Cardiology (ESC), there is immense variation in the number of implantations per 100,000 inhabitants in Europe, especially in German-speaking countries (Germany, Austria and Switzerland). The aim of the present study was to identify possible factors for these differences using a qualitative research approach. (2) Methods: Semi-standardized interviews were conducted with 11 experts in the field of CRT therapy (3 experts from Germany, 4 from Austria and 4 from Switzerland) using a pre-prepared interview template and analysed according to Mayring's qualitative content analysis. (3) Results: The main factors identified were the costs of purchasing the devices and the financing systems of the respective healthcare systems, although cost pressure still seems to play a subordinate role in the German-speaking countries. Moreover, "lack of implementation of ESC guidelines", "insufficient training" and "lack of medical infrastructure" could be excluded as potential reasons. (4) Conclusions: Economic factors, but not a lack of adherence to ESC guidelines, seem to have a major influence on the fluctuating implantation figures in German-speaking countries, according to the unanimous assessment of renowned experts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to promote ecological sustainability, the issue of sulphur dioxide emissions is of increasing interest to researchers. Majority of the current research, however, focuses on the relationship between sulphur dioxide (SO2) emissions, foreign direct investment (FDI), and trade, as well as the effects of trade on SO2 emissions, thus rarely takes it into account that the greater impact of the institutional environment and economic growth on SO2 emissions. Using the 2008–2017 provincial panel data, this paper uses a fixed effects model to empirically test the institutional environment and economic growth of sulphur dioxide (SO2) emissions. The results show that GDP growth and SO2 emissions had an inverted "U"-shaped relationship. The institutional environment and the higher level of government intervention in the region led to SO2 emissions decreasing significantly, and the institutional environment and the level of government intervention on economic growth and SO2 emissions form a negative regulatory role. In this paper, environmental governance research, specified by the regional environmental governance, and government environmental performance audit policy provide empirical evidence, thus promoting sustainable ecological and environmental development. [ABSTRACT FROM AUTHOR]

Economic expansion leads to higher CODe2 emissions, which puts pressure on environmental degradation. More than 30% of carbon emissions are contributed by the top0polluting countries in the world through their energy consumption. Therefore, the current study examines the association between CO2 emissions, energy consumption, GDP and industrial production, along with other control variables at the aggregated and disaggregated levels for the top emitter countries for the 1990–2019 period. The short- and long-term results indicate that CO2 emissions are positively and significantly linked with energy consumption, except carbon emissions from the gas model, by employing the PARDL model using pooled mean group (PMG) analysis. Thus, gas consumption is less polluting to the environment than other sources of energy; therefore, countries need to reduce the consumption of coal and oil, which will lead to a decrease in CO2 emissions. This refers to the composition effect, which focuses on the use of clean energy instead of dirty energy in the production and consumption processes. The shift from oil or coal to gas in the production process will help to reduce the oil demand, which ultimately controls its consumption and prices, which may help to control the prices of various other goods and services. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Utilizing natural resources wisely, reducing pollution, and taking other environmental factors into account are now critical to the prospects for long-term economic growth and, by extension, sustainable development. We investigate the impact of total natural resource rents (NRR) on India's GDP in this study. The data sample consists of NRR and GDP data from the World Bank's official website collected between 1993 and 2020. In the study, the Granger causality test and an augmented autoregressive distributed lag (ARDL) bound test were used. The NNR have a significant impact on India's GDP, according to the results of the ARDL model on the framed time series data set. Furthermore, the ARDL bound test reveals that the NRR have a significant short-term and long-term impact on the GDP of the Indian economy. This research contributes to understanding whether an exclusive policy is required for effective management of the complex interactions between various forces in the economic, political, and social environments. This is significant because there is no standard policy in India to improve the efficiency of utility extraction from natural resources. [ABSTRACT FROM AUTHOR]

Growing population and limited energy resources have impacted energy consumption. Limited fossil fuel resources and increased pollution threaten national and human societies. These elements emphasize energy sources. Renewable energy use affects growth. All new energy sources, including renewables, are crucial for global economic growth. Economic and environmental issues have led to new approaches in international environmental law, including the green economy. This study employs structural vector auto-regression (SVAR) to compare the effects and outcomes of increasing the use of renewable energy in the context of economic growth and greenhouse gas emissions in middle income countries (MICs) and high income countries (HICs). The results show that these indicators demonstrate that the production of energy from renewable sources has positive short-term and long-term economic effects with varying contributions. However, renewable energies have a greater impact on the green economy in selected MICs than in selected HICs. Therefore, the promotion of macroeconomic indicators is viewed as one of the reasons for the development of policies to increase energy production from renewable sources in selected countries. [ABSTRACT FROM AUTHOR]