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In this paper, the authors investigate that the increasing level of fossil fuel combustion in the industrial sector has been considered the prime cause for the emissions of greenhouse gas. Meanwhile, the research focusing on the impact of fossil fuel consumption on the emission of CO₂ is limited for the developing countries containing Vietnam. This study applied the autoregressive distributed lag (ARDL) approach with structural breaks presence, and the Bayer–Hanck combined cointegration method to observe the rationality of the environmental Kuznets curve (EKC) hypothesis in the dynamic relationship between the industrialization and carbon dioxide (CO₂) emission in Vietnam, capturing the role of foreign direct investment (FDI) inflows and the fossil fuel consumption over the period of 1975–2019. The outcomes revealed the confirmation of cointegration among the variables and both short and long-run regression parameters indicated the evidence for the presence of a U-shaped association between the level of industrial growth and CO₂ emission that is further confirmed by employing the Lind and Mehlum U-test for robustness purpose. The results of Granger causality discovered a unidirectional causality from FDI and fossil fuel consumption to CO₂ emission in the short run. For the policy points, this study suggests the use of efficient and low carbon-emitting technologies.

In order to test for consistency and robustness of the cointegration analysis, this study also applied the ARDL bound testing method to find out long-run association among variables with the existence of the structural break in the dataset. The ARDL method was preferred to other traditional cointegration models; because of the smaller dataset, the results obtained from the ARDL method are efficient and consistent and equally appropriate for I(1) and I(0) variables.

The short-run and long-run causal associations among variables have been observed by employing the error correction term (ECT) augmented Granger-causality test that revealed the presence of the long-run causality among variables only when the CO₂ emission is employed as a dependent variable. The outcomes for short-run causality indicated the presence of unidirectional causality between consumption of fossil fuel and CO₂ emission, where the fossil fuel consumptions Granger-cause CO₂ emission. Industrial growth has also been found to have an impact on fossil fuel consumptions, however not the opposite. This advocates that the policies aimed at reducing the fossil fuel consumptions would not be harmful to industrial growth as other energy efficient and cleaner technology could be implemented by the firms to substitute the fossil fuel usage.

The study explored the dynamic relationship among FDI, consumption of fossil fuel, industrial growth and the CO₂ emission in Vietnam for the time period 1975–2019. The newly established Bayer–Hanck joint cointegration method and the ARDL bound testing were employed by taking into account the structural breaks in the dataset.

Introduction: The credit default swap (CDS) represents a country’s credit risk premium. CDS premium changes by being affected by several factors. These changes are followed by international investor for their investment decisions. CDS premium is important for country to determine the country default risk correctly. Purpose: In this study, the authors seek to examine the effects of macroeconomic indicators on the CDS premium, which is used as a measure of sovereign credit risk. Accordingly, in addition to the CDS premium, economic growth, the inflation rate, the interest rate, the real exchange rate, the net foreign debt rate, and the foreign trade deficit rate were employed to represent macroeconomic indicators. Methodology: The relationship between the given variables during the period spanning from 2009:I–2019:II in Turkey was analyzed with the help of the Dolado–Lütkepohl causality test and the autoregressive distributed lag method. Findings: The inflation rate, the real exchange rate, the interest rate, the net foreign debt rate, and the foreign trade deficit rate, which are among the macroeconomic variables (excluding economic growth), have a positive effect on the CDS premium in the short term as well as the long term. The effect of economic growth is negative. Additionally, from an economic standpoint, the coefficients of macroeconomic variables are in the expected direction. These findings verify the effects of macroeconomic indicators on the CDS premium.

The research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.

Regression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.

The study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.

No data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.

Previously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and analysis.

The purpose of this paper is to investigate the export‐led growth, growth‐led export, import‐led growth, growth‐led import and foreign deficit sustainability hypothesis in the case of China, using annual time series data from 1978‐2009.

For estimation evidence this study employs the Phillips Perron unit root tests to examine the level of integration and the autoregressive distributed lag (ARDL) approach is employed to determine the long run relationship, and the direction of long run and short run causal relationship is examined by using modified Granger causality test.

The results confirm the bidirectional long run relationship between the economic growth and exports, economic growth and imports, and exports and imports. These findings guided the authors to conclude that the exports‐led growth, growth‐led exports, imports‐led growth and growth‐led imports hypothesis is valid, and foreign deficit is sustainable for China. The long run elasticities are as follows: the elasticity of economic growth with respect to exports is 0.591, and elasticity of exports with respect to economic growth is 1.635. The elasticity of economic growth with respect to imports is 0.621, and elasticity of imports with respect to economic growth is 1.392. Further more the elasticity of exports with respect to imports is 1.322, and imports elasticity with respect to exports is 0.975.

This study utilizes the relative new cointegration method of ARDL approach. The empirical findings of this study are vital for policy makers of China in the formulation of trade policies.

Higher education has a vital role to play in shaping the way in which future generations learn to cope with the complexities of sustainable development. Universities and higher education institutions educate highly qualified graduates and responsible citizens able to meet the needs of all sectors of human activity; they provide opportunities for higher learning and for learning throughout life; they advance, create and disseminate knowledge through research and provide, as part of their service to the community, relevant expertise to assist societies in cultural, social and economic development; they contribute to the development and improvement of education at all levels, including through the training of teachers. The objective of this study is to examine the relationship between higher education and economic growth in Iran.

First, a baseline survey analysis of Iran supported by tables and figures was conducted. Secondly, by using multivariable time series data on the variables: annual logarithmic gross domestic product, physical capital (K), human capital, research expenditures (R) and by using an autoregressive distributed lag (ARDL) model, the long‐ and short‐run relationship between the growth and higher education variable was investigated. The following steps were followed: test of a dynamic ARDL model, CUSUM and CUSUMQ test for stability, long‐run relationship and ECM test.

The results indicated that the higher education variable had a positive effect on the economic growth of Iran in both the short and long run.

The research in this paper has implications for government policy makers responsible for investment in higher education.

The purpose of this study is to investigate the impact of foreign capital inflows and economic growth on stock market capitalization in Pakistan by using the annual time series data from the period of 1976 to 2011.

The autoregressive distributed lag bound testing cointegration approach, the error correction model and the rolling window estimation procedures have been performed to analyze the long run, short run and behavior of coefficients, respectively.

Results indicate that foreign direct investment (FDI), workers’ remittances and economic growth have significant positive relationship with the stock market capitalization in long run as well as in short run. Results of the dynamic ordinary least square and the fully modified ordinary least square suggest that the initial results of long-run coefficients are robust. Results of variance decomposition test show the bidirectional causal relationship of FDI and economic growth with stock market capitalization. However, unidirectional causal relationship is found in between workers’ remittances and stock market capitalization.

It is suggested that in Pakistan, investors can make their investment decisions through keeping an eye on the direction of the considered foreign capital inflows and economic growth.

This paper makes a unique contribution to the literature with reference to Pakistan, being a pioneering attempt to investigate the effects of foreign capital inflows and economic growth on stock market by using long time series data and applying more rigorous techniques.

The goal of this paper is twofold: to estimate the unrecorded economy (UE) of Turkey over the period 1987‐2007 using a revised version of the currency demand approach, and to analyze the relationship between the UE and recorded GDP.

The paper proposes to measure the UE using the autoregressive distributed lag (ARDL) approach to cointegration analysis. Toda‐Yamamoto causality tests are also conducted to identify the relationship between unrecorded and recorded GDP.

This research provides fresh evidence of the size of the UE relative to the recorded GDP in Turkey, which ranges from 10.7 percent to 18.9 percent over the estimation period. Moreover, empirical evidence concretely suggests that causality runs from the recorded GDP to the UE. However, there exists a mild reverse causality.

Measures of the UE, and particularly those based on monetary approaches, have been criticized on several counts, including their lack of robustness and weak theoretical foundations (e.g. the velocity of money in the recorded economy and in the UE is the same).

This analysis suggests that the UE is pro‐cyclical with respect to the recorded GDP. It suggests that the phenomenon of the UE is more dangerous when the economy is in an expensive phase. Hence, during a positive business cycle, it is clearly desirable for the government that the anti‐UE controls should be more effective.

The ARDL approach to estimating the size of the UE eliminates the criticism of the previous currency demand estimations, which were based on partial adjustment models. Therefore, the paper's econometric selected cointegration methodology and causality test is an improvement over the existing studies.

The purpose of the study is to find out the impact of gold and oil prices on the stock market.

This study uses the data on gold prices, stock exchange and oil prices for the period 1991–2016. This study applied descriptive statistics, augmented Dickey–Fuller test, correlation and autoregressive distributed lag test.

The data analysis results showed that gold and oil prices have a significant impact on the stock market.

Following empirical evidence of this study, the authors recommend that investors should invest in gold because the main reason is that hike in inflation reduces the real value of money, and people seek to invest in alternative investment avenues like gold to preserve the value of their assets and earn additional returns. This suggests that investment in gold can be used as a tool to decline inflation pressure to a sustainable level. This study was restricted to use small sample data owing to the availability of data from 1991 to 2017 and could not use structural break unit root tests with two structural break and structural break cointegration approach, as these tests require high-frequency data set.

This study provides information to the investors who want to get the benefit of diversification by investing in gold, oil and stock market. In the current era, gold prices and oil prices are fluctuating day by day, and investors think that stock returns may or may not be affected by these fluctuations. This study is unique because it focusses on current issues and takes the current data in this research to help investment institutions or portfolio managers.

While Blockchain can serve us, Bitcoin threatens our survival. If Bitcoin is assumed to be a country, it will rank 38th globally for energy consumption. With 90.2 metric million tonnes of carbon dioxide, Bitcoin mining and trading has emerged as an environmental threat. The current study investigates how the trading-specific variables, the prices of Crypto Index and Ethereum, affect bitcoin-based energy consumption. Also, the role of mining-specific variables is analyzed.

The study uses monthly data from various sources collected from December 2018 to January 2023. The authors used the Autoregressive Distributed Lag (ARDL) Model to determine the short- and long-term relationships between variables. This study uses the Theory of Green Marketing and the Theory of Cross Elasticity of Demand as a theoretical lens.

The findings show that escalating crypto market index and Ethereum prices with a one-month lag increases bitcoin-specific electricity consumption and carbon emissions. Green investors may shift to cryptocurrencies based on consensus other than of Proof-of-Work. Ethereum behaves like a substitute for Bitcoin, reflected by the long-term positive relationship between Bitcoin's energy consumption and Ethereum prices.

The study analyses how the crypto market index and Ethereum price affect bitcoin-based energy use. The relationships identified are substantiated by the literature to provide suggestions to green investors and policymakers to mitigate the harmful impact of Bitcoin's colossal energy consumption on the natural environment.