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This paper introduces a meticulously organized dataset derived from the International Monetary Fund's World Economic Outlook (IMF WEO) forecasts, including GDP growth, CPI inflation, and current account balances for 196 countries from 1990 to 2024. Sourced from the WEO historical database and updated to 2024, the dataset contains forecasts of crucial economic indicators - GDP growth, CPI inflation, and current account balance - in an accessible and user-friendly Excel format. This dataset is a valuable resource for academic researchers, economists at central banks, finance ministries, and other stakeholders, enabling diverse analyses such as evaluations of IMF forecasts, research into optimism bias, and studies on equilibrium exchange rates. Additionally, it may be useful for foreign investors in making informed strategic investment decisions.

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This study examines the dynamic relationship between the share of nuclear energy, growth in CO2 emissions, and GDP growth for the wealthiest countries of the two continents, Europe and Asia, from 1965 to 2021. The results from the SVAR model show a significant positive relationship between GDP growth and the growth of CO2 emissions in all countries. However, the values of the coefficients vary in the case of different countries of both continents. Further, it has been observed that the relationship between the growth of nuclear energy and the growth in CO2 emissions is more complex and varies from country to country. The results indicate that there is a strong negative relationship between nuclear energy and CO2 emissions in France, Russia, the United Kingdom, and India. At the same time, there is no significant relationship between Germany, Italy, and China. Finally, the result shows that there is an insignificant relationship between the growth in the share of nuclear energy and the GDP growth rate. From the policy perspective, this study suggests that alternative energy sources like nuclear energy can be leveraged to obtain a clean environment.

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The foremost purpose of the study is to establish a point that an economy of G-7 countries has an abundance of resources to tackle the environmental changes that occur in the world, but these countries are still behind the line because in this modern era, environmental performance changes their shape, dimension, and nature very frequently and create a huge impact on globalization of world economy. To fill this gap, we use green investment, institutional quality, and economic growth on environmental performance for this, we use four proxies for green investment and three proxies for greenhouse gas, and we also use six proxies of institutional quality to do this using period of 1997 to 2021. Moreover, we have used the panel nonlinear autoregressive distributed lag method to evaluate the long-run and short-run asymmetric effects of green investment, institutional quality, and economic growth on greenhouse gas emissions. The findings of the study affirm that the positive change of green investment has a positive and significant relationship with environmental performance, while the negative change of green investment has a significant and positive influence with environmental performance in the long run. Furthermore, the outcomes demonstrate that the positive shock of institutional quality has a positive and significant relationship with environmental performance, while the negative shock of intuitional quality has a significant and positive association with environmental performance in the long run, whereas positive change in economic growth has a positive and significant with the environmental performance, while the negative change of economic growth has a positive effect with environmental performance in the long run. This study finds future precautions that institutional quality has to perform exceptionally and shows results very rapidly, while green investment with economic growth has also made a deadly combination to control greenhouse gas emission, so the role of G-7 countries is pretty clear and straight. Furthermore, it is suggested that governments and policymakers take a proactive stance to promote resource acquisition and investment across all industries. To reduce gas emissions, public interest might also be complementary to private ones. So, economic policymakers, specifically in G-7 countries, should consider strategies that support sustainable economic growth.

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Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007-08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015-16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid-19 shock of 2020-21.

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This study examines the impact of information and communication technologies (ICT), GDP growth, population, and globalization on the environmental quality of 31 Asian economies (i.e., categorized as lower middle-income, upper middle-income, and high-income groups Asian economies). This analysis employed the time series data from 1990 to 2018. The robust second-generation econometric technologies are used in this analysis. This study applied the Environmental Kuznets curve (EKC) premises under the extended "STIRPAT model" to add population and GDP (per capita) and information technologies (ICTs) by employing ecological footprint. To estimate, the estimators of this study used the CS-ARDL estimates, and for robustness check, this study used the augmented mean group (AMG) test. The co-integration test found the long-run association between ecological footprint and its main determinants. The results of CS-ARDL have confirmed the imperative role of information technologies in mitigating the ecological footprint in the higher, upper-middle, and lower-middle-income economies of Asian economies. The statistical findings of this study are robust to diagnostic tests and alternative estimation proxies and techniques. Moreover, policymakers need to identify the direction of the information technology-ecological footprint nexus through cooperation in combating climate change with financial assistance in the ICT sector.

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We run a 'horse race' among popular forecasting methods, including machine learning (ML) and deep learning (DL) methods, that are employed to forecast U.S. GDP growth. Given the unstable nature of GDP growth data, we implement a recursive forecasting strategy to calculate the out-of-sample performance metrics of forecasts for multiple subperiods. We use three sets of predictors: a large set of 224 predictors [of U.S. GDP growth] taken from a large quarterly macroeconomic database (namely, FRED-QD), a small set of nine strong predictors selected from the large set, and another small set including these nine strong predictors together with a high-frequency business condition index. We then obtain the following three main findings: (1) when forecasting with a large number of predictors with mixed predictive power, density-based ML methods (such as bagging, boosting, or neural networks) can somewhat outperform sparsity-based methods (such as Lasso) for short-horizon forecast, but it is not easy to distinguish the performance of these two types of methods for long-horizon forecast; (2) density-based ML methods tend to perform better with a large set of predictors than with a small subset of strong predictors, especially when it comes to shorter horizon forecast; and (3) parsimonious models using a strong high-frequency predictor can outperform other sophisticated ML and DL models using a large number of low-frequency predictors at least for long-horizon forecast, highlighting the important role of predictors in economic forecasting. We also find that ensemble ML methods (which are the special cases of density-based ML methods) can outperform popular DL methods.

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Social distancing policies have been criticized for their adverse effect on economies. However, we evidence that while they have a short-run adverse effect, they also have a long-run recovery effect on economic growth. Utilizing quarterly gross domestic product (GDP) growth rate data from OECD member states, we find that the medium-term recovery effect of stringent social distancing policies on economic growth is three times higher the short-term adverse effect. We additionally investigate social distancing measures with sub-components of GDP, as well as the conditioning roles of institutional factors.

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Among the EU countries, Greece relies heavily on coal the most, and it has lagged behind in cutting emissions. Further, following the oil crisis of the 1970s, Greece has strategically invested in lignite. Solid fossil fuels such as lignite are classified as fossil fuels that are detrimental to environmental performance. This continued burning of fossil fuels has emerged as one of the most serious concerns in Greece, even globally. The aim is to capture the effect of energy productivity on carbon dioxide emissions (CO2E) in Greece while controlling trade openness, energy consumption, and economic growth. Toward this end, we employ a nonlinear autoregressive distributed lag (NARDL) model and other econometric robust techniques. The findings of the study are as follows: (i) trade openness positively impacts carbon emissions growth; (ii) economic growth adds to increased CO2E; (iii) expanding energy productivity is beneficial to the environment as it causes CO2E to decline; and (iv) increase in energy consumptions further results in CO2 cutbacks. The recommendation of our study suggests some innovative policies to counter the detrimental effects of carbon emissions by an increase in energy efficiency for the Greek economy. The study recommends that embracing a low-carbon, resource-efficient, and circular economy is of paramount importance to Greece in order to ensure environmental protection, as well as to boost green growth, create new jobs, and combat unemployment. Greece should ensure that energy efficiency techniques are promoted, and renewable energy sources are expanded in order to increase the options for cleaner alternatives and reduce greenhouse gas emissions, thus preserving the environment.

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This study aims to analyze the asymmetric relation between renewable energy consumption and CO2 emissions in China using the STIRPAT-Kaya-EKC framework. To delve into the asymmetric effect of renewable energy consumption on the environment, the non-linear ARDL model is used. The results of this study confirm the asymmetric impact of renewable energy on the environment in the long run as well as in the short run. However, the negative shocks to renewable energy have a greater detrimental influence on the environment than the benign effect due to the positive shock to renewable energy. Population growth affects the environment in the short run, whereas technology only affects environment quality in the long run. Moreover, the study supports the EKC theory in China. This research emphasizes that the administration can improve the economy's lifespan by allocating substantial funds to establish legislation to maintain a clean environment by subsidizing renewable energy infrastructure and research and innovations for low-carbon projects.

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Many countries are trying to achieve carbon neutrality targets by using environment-friendly technology and green growth. Thus, this analysis effort to identify the key role of green growth in improving the environmental quality. This study investigates the impact of green growth, income, environmental taxes, environment-friendly technology, renewable energy, and financial development in the context of 12 Asian economies over the period of 1990 to 2018. This study used the method of cross-section - augmented autoregressive distributed lag (CS-ARDL) to find out the impact of green growth and growth (GDP) on environment quality with some plausible variables under the scheme of environmental Kuznets curve (EKC). The study employed the method of CS-ARDL and for robustness the augmented mean group (AMG) method to find out the impact of green growth and GDP growth on environment quality with some plausible variables under the scheme of EKC. The results of CS-ARDL concluded that CO2 is significantly affected by GDP growth, green growth, and technological change in the context of Asian economies. The GDP square is inversely and the GDP growth is positively related to the CO2, indicating the presence of inverted U-shaped EKC in this region. But the inverse relationship between green growth and green growth square and concave EKC is observed in Asian countries. The study used the Dumitrescu and Hurlin panel test to gauge the causality between the variables. This study suggested that policymakers should focus on transforming the country's energy system in ways that will reduce energy-related CO2 emissions faster than previously expected.

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Distinguishing the significance of business environments for technical small and medium-sized enterprises (SMEs), this study examines the connection between business environments, GDP growth, and SMEs' financing choices in Vietnam. The study considered the agency theory as a theoretical base to explain how information asymmetry between SMEs and lenders influences SMEs' financing choices and encompasses the effects on business environment and GDP growth of Vietnam. For this binary logistic regression, text is applied. Global Entrepreneurship Monitor and World Bank data were analyzed. The findings of the study are robust and showed that SME financing (e.g., formal and informal) under the financial infrastructure and tax regulation may enhance formal credit choice and reduce informal credit choice. This enhances the depth in the business environment of technical SMEs and found significant effects on GDP growth. For the first time, this research examines the impact of information asymmetry and agency theory on restaurant financing choices. The research has significance for industry practitioners and governments interested in SMEs' financial viability. On the recent topicality, study also presents policy implications for key stakeholders.

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Since export has a key role in economic growth in terms of national production quantity, export quality can be considered another important factor regarding the revenue from the export product. Hence, both export and export quality can contribute to the economic growth process positively when the countries' terms of trade have moved in a favorable direction from this point of view, it is essential to examine the relationship between the energy-growth nexus and export quality. Although available seminal studies are monitoring the energy-growth nexus, there exists a limited number of works employing the export quality. Besides, one might claim that there exists no research on how the terms of trade (export quality) alter the economic growth and energy use through regime shifts. Markov regime-shifting models estimate (a) the impact of export and terms of trade on growth, and (b) the effect of growth on the use of fossil energy and renewable energy for the USA at regime 1 and regime 2 for the period 1980:Q4-2019:Q2. After conducting the non-linear analyses, this paper (i) reveals the estimated parameters varying from one regime to another regime through transition probabilities, (ii) finds evidence that (a) export and export quality growths affect positively GDP growth, (b) GDP growth increases fossil fuel consumption growth, (c) renewable energy growth increases at decreasing rate due to GDP growth, and (iii) yields relevant energy-environmental policy proposals by underlying the prominence of terms of trade within growth-energy nexus.

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The Belt and Road Initiative (BRI) is an ambitious development project initiated by the Chinese government to foster economic progress worldwide. In this regard, this study aims to investigate the dynamics of energy, economy, and environment among 42 BRI developing countries using an annual frequency panel dataset from 1995 to 2019. The major findings from the econometric analyses revealed that higher levels of energy consumption, economic growth, population growth rate, and FDI inflows exhibit adverse environmental consequences by boosting the CO2 emission figures of the selected developing BRI member nations. However, it is interesting to observe that exploiting renewable energy sources, which are relatively cleaner compared to the traditionally-consumed fossil fuels, and fostering agricultural sector development can significantly improve environmental well-being by curbing the emission levels further. On the other hand, financial development is found to be ineffective in explaining the variations in the CO2 emission figures of the selected countries. Besides, the causality analysis shows that higher energy consumption, FDI inflows, and agricultural development cause environmental pollution by boosting CO2 emissions. However, economic growth, technology development, financial progress, and renewable energy consumption are evidenced to exhibit bidirectional causal associations with CO2 emissions. In line with these findings, several relevant policies can be recommended for the BRI to be environmentally sustainable.

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Recent research has shown a huge impact of non-renewable energy (NRE) production on environmental health. In this context, this work analyzes the effects of GDP growth and long- and short-term consumption of renewable and non-renewable energy (RE and NRE, respectively) on carbon emission in BRICS and OECD economies. The quantile autoregressive distributed lag (QARDL) model was employed on the panel data from 1980 to 2016. Findings suggest a negative GDP-carbon emission correlation and a positive NRE-carbon emission correlation in the considered economies. Furthermore, carbon emission decreases with increase in gross capital formation, whereas trade openness does not have any significant effect on carbon emission. It has been determined that the application of the error correction method (ECM) has less effect on energy consumption as compared to the past levels and changes in energy consumption. In the long-term, a positive correlation of carbon emission and energy consumption is observed, whereas limited short-term effects of energy consumption on carbon emission are observed. Therefore, an RE-based energy production approach is recommended in the selected region for the future projects.

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BACKGROUND: Accelerated globalisation has substantially contributed to the rise of emerging markets worldwide. The G7 and Emerging Markets Seven (EM7) behaved in significantly different macroeconomic ways before, during, and after the 2008 Global Crisis. Average real GDP growth rates remained substantially higher among the EM7, while unemployment rates changed their patterns after the crisis. Since 2017, however, approximately one half of the worldwide economic growth is attributable to the EM7, and only a quarter to the G7. This paper aims to analyse the association between the health spending and real GDP growth in the G7 and the EM7 countries. RESULTS: In terms of GDP growth, the EM7 exhibited a higher degree of resilience during the 2008 crisis, compared to the G7. Unemployment in the G7 nations was rising significantly, compared to pre-recession levels, but, in the EM7, it remained traditionally high. In the G7, the austerity (measured as a percentage of GDP) significantly decreased the public health expenditure, even more so than in the EM7. Out-of-pocket health expenditure grew at a far more concerning pace in the EM7 compared to the G7 during the crisis, exposing the vulnerability of households living close to the poverty line. Regression analysis demonstrated that, in the G7, real GDP growth had a positive impact on out-of-pocket expenditure, measured as a percentage of current health expenditure, expressed as a percentage of GDP (CHE). In the EM7, it negatively affected CHE, CHE per capita, and out-of-pocket expenditure per capita. CONCLUSION: The EM7 countries demonstrated stronger endurance, withstanding the consequences of the crisis as compared to the G7 economies. Evidence of this was most visible in real growth and unemployment rates, before, during and after the crisis. It influenced health spending patterns in both groups, although they tended to diverge instead of converge in several important areas.

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This study inspects the empirical association between inflation instability, GDP growth volatility, and the environmental quality in Pakistan, covering the period 1975-2018 by using an asymmetric autoregressive distributed lag (ARDL) methodological approach. The asymmetric ARDL results document that positive and negative shocks of inflation instability have different effects on environmental quality. Negative shocks of inflation instability have a positive influence on carbon dioxide emissions (CO2) and nitrous oxide emissions (N2O), while positive shocks of inflation instability have insignificant effects in the long run. Asymmetric findings also suggest that positive and negative fluctuations in GDP growth volatility affect CO2 and N2O emissions differently, while they have insignificant results on methane emissions (CH4) in the long run. Additionally, in the short run, positive and negative shocks of inflation instability and GDP growth volatility behave differently in terms of their impact on pollution emissions. Based on these findings, the study opens up innovative intuitions for policymakers to support a robust role of economic stability in attaining targets relevant to pollution reduction.

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Using annual time-series data over the period 1975-2017, the researcher applied the bootstrap autoregressive-distributed lag (ARDL) cointegration model developed by McNown et al. (1) to examine whether there is a long run relationship among health expenditure, CO2 emissions, and gross domestic product (GDP) per capita in 18 Organization for Economic Cooperation and Development (OECD) countries. We find cointegration exists in Netherlands when real GDP per capita serves as a dependent variable, in New Zealand when health expenditure is the dependent variable, and in the United States when CO2 emissions are dependent variables. The main results show evidence of a short run relationship between the three variables. The empirical results support that there is a bidirectional causality between health expenditure and GDP growth for Germany and the United States, between CO2 emissions and GDP growth for Canada, Germany, and the United States, and between health expenditure and CO2 emissions for New Zealand and Norway. The results also indicate that there are unidirectional causality in other countries.

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This study aims to investigate the relationship between carbon dioxide (CO2) emissions, macroeconomic instability, real output (GDP), the square of real output (GDP2), and financial development in Pakistan using the annual dataset over the period 1971-2016. The long-run analysis is based on the ARDL bound testing approach to cointegration, whereas the short-run dynamics are observed using error correction model. The results of the bound testing approach indicate that there exists a long-run relationship between the selected variables and macroeconomic instability increases pollution emissions. In addition, the study supports the presence of environmental Kuznets curve (EKC) hypothesis for Pakistan economy where, in low-income regime, an increase in GDP causes more emissions and, in high-income regime, the relationship between GDP and CO2 emissions becomes negative. Finally, financial development variables exert a positive impact on environmental degradation. Based on these findings, our study supports a strong role of macroeconomic stability in achieving the targets of pollution reductions.

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Political incentive is one of the most efficient methods to promote local officials to push for both economic growth and environmental progress in the context of China. Since previous scholars have never investigated the relationships between political turnover and environmental governance at the municipal level, in this work, we proxy pollution by SO2, COD, SOOT, and NHx and examine whether environmental performance affects the political turnover of the municipal party secretary by exploiting a panel conditional logit with a fixed-effect model for the period 2002-2013. The results indicate that environmental performance does not significantly impact the turnover of municipal party secretaries, offering evidence that an increase of SO2 is associated with the likelihood of promotion under the panel multinomial logit approach. However, a higher GDP growth rate plays the determinant factor affecting the promotion of China's municipal party secretaries. Specific policy recommendations are proposed in accordance with our empirical findings.

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Freetown, the capital of Sierra Leone has experienced vast land-cover changes over the past three decades. In Sierra Leone, however, availability of updated land-cover data is still a problem even for environmental managers. This study was therefore, conducted to provide up-to-date land-cover data for Freetown. Multi-temporal Landsat data at 1986, 2001, and 2015 were obtained, and a maximum likelihood supervised classification was employed. Eight land-cover classes or categories were recognized as follows: water, wetland, built-up, dense forest, sparse forest, grassland, barren, and mangrove. Land-cover changes were mapped via post-classification change detection. The persistence, gain, and loss of each land-cover class, and selected land conversions were also quantified. An overall classification accuracy of 87.3 % and a Kappa statistic of 0.85 were obtained for the 2015 map. From 1986 to 2015, water, built-up, grassland, and barren had net gains, whereas forests, wetlands, and mangrove had net loses. Conversion analyses among forests, grassland, and built-up show that built-up had targeted grassland and avoided forests. This study also revealed that, the overall land-cover change at 2001-2015 was higher (28.5 %) than that recorded at 1986-2001 (20.9 %). This is attributable to the population increase in Freetown and the high economic growth and infrastructural development recorded countrywide after the civil war. In view of the rapid land-cover change and its associated environmental impacts, this study recommends the enactment of policies that would strike a balance between urbanization and environmental sustainability in Freetown.

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