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Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

The argument whether gold is a hedge or haven is a debatable issue. Mainly, hedge is a class of asset that is negatively correlated with another asset or portfolio on average. On the other hand, a safe haven is an asset or portfolio which is negatively correlated with another asset or portfolio at the time of market turmoil. Therefore, the purpose of this research is to take Saudi Arabia as an example to examine the relationship of gold price in Saudi Arabia with key determinants such as the stock market index, oil prices, exchange rate, interest rate and consumer price index (CPI) by application of the autoregressive distributed lag model (ARDL).

The ARDL analysis was employed by using six variables based on the application of monthly time series data that were collected from 2011 to 2015.

From the present analysis, it has been discovered that gold is useful as a portfolio hedge and as a hedge against inflation because it is not affected by the CPI. External factors, for example, financial crisis, may be harmful to the CPI, thus adding a certain percentage of gold in the investment portfolio may assist in decreasing the level of risk at the time of financial turmoil.

Because gold seems to be a useful portfolio hedge, as well as an inflation hedge, government policies to curb the import of gold may be futile. The present research suggests that policies that directly address the causes of inflation and provide alternative investment opportunities for retail investors may better serve the objective of decreasing gold imports.

The climate change effects on agricultural output in different regions of the world and have been debated in the literature of emerging economies. Recently, the agriculture sector has influenced globally through climate change and also hurts all sectors of economies. This study aims to examine and explore the impact of global climate change on agricultural output in China over the period of 1982-2014.

Different unit root tests including augmented Dickey–Fuller, Phillips–Perron and Kwiatkowski, Phillips, Schmidt and Shin are used to check the order of integration among the study variables. The autoregressive distributed lag (ARDL) bounds testing approach to cointegration and the Johansen cointegration test are applied to assess the association among the study variables with the evidence of long-run and short-run analysis.

Unit root test estimations confirm that all variables are stationary at the combination of I(0) and I(1). The results show that CO₂ emissions have a significant effect on agricultural output in both long-run and short-run analyses, while temperature and rainfall have a negative effect on agricultural output in the long-run. Among other determinants, the land area under cereal crops, fertilizer consumption, and energy consumption have a positive and significant association with agricultural output in both long-run and short-run analysis. The estimated coefficient of the error correction term is also highly significant.

China’s population is multiplying, and in the coming decades, the country will face food safety and security challenges. Possible initiatives are needed to configure the Chinese Government to cope with the adverse effects of climate change on agriculture and ensure adequate food for the growing population. In concise, the analysis specifies that legislators and policy experts should spot that the climate change would transmute the total output factors, accordingly a county or regional specific and crop-specific total factor of production pattern adaptation is indorsed.

The present empirical study is the first, to the best of the authors’ knowledge, to investigate the impact of global climate change on agricultural output in China by using ARDL bounds testing approach to cointegration and Johansen cointegration test.

This paper aims to examine the effects of CO₂ emissions, energy consumption, cultivated area and the labour force on the production of cereal crops in Pakistan from the period 1971-2014.

The study used the Johansen cointegration test, the autoregressive distributed lag (ARDL) approach and Granger causality test to estimate the long-run cointegration and direction of the relationship between the dependent and independent variables.

The outcomes of the Johansen cointegration test confirmed the existence of a long-term cointegrating relationship between the production of cereal crops, CO₂ emissions, energy consumption, cultivated area and the labour force. The results of the long-run coefficients of CO₂ emissions, energy consumption, cultivated area and labour force have a positive impact on cereal crops production. The long-run relationships reveal that a 1 per cent increase in CO₂ emissions, energy consumption, cultivated area and labour force will increase cereal crops production by 0.20, 0.11, 0.56 and 0.74 per cent, respectively. Moreover, the findings show that there is a bidirectional causality running from CO₂ emissions and cultivated area to cereal crops production. Moreover, there is a unidirectional causality running from energy consumption to cereal crops production.

The present study also fills the literature gap for applying the ARDL procedure to examine this relevant issue for Pakistan.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

The purpose of this study is to empirically weigh the evidence for financial depth, liquidity and efficiency role to economic growth, and test for the existence of cointegration between financial development variables and economic growth in Tanzania.

The study used the autoregressive distributed lag model with bound testing procedures. The sample covered yearly time-series data from 1980 to 2017, i.e. 38 years.

The results suggest that financial system depth is positively related to economic growth in the short run and that financial system liquidity and efficiency is strongly negatively associated with economic growth both in the short and long run. Further, it is found that financial development is cointegrated with economic growth. Thus, financial reforms and liberalisation have not fully brought the desired positive effects on economic growth yet.

The study uses principal component analysis to capture specific dimensions within the financial system as an intuitive way to aggregate financial development effects. Unlike studies that included several countries with heterogeneous characteristics, which are sometimes difficulty to homogenise, in recognition of countries’ unique experiences, this study uses data from Tanzania as a specific case. It documents pertinent pieces of evidence for a developing economy necessary for financial policy adjustments post the financial and economic liberalisation and reforms period. It nevertheless sheds light on financial policies for other comparable developing economies during and after both financial and economic liberalisation settings.

The study explores the relationship among economic growth, population growth, gross savings and energy consumption over the period 1987– 2017.

The autoregressive distributed lag (ARDL) bounds test approach by Pesaran et al. (2001) was employed to investigate variables for the study.

In the key findings, both gross savings and population growth negatively affect economic growth. However, energy consumption has positive impact on economic growth.

These findings call for policy portfolios to address the impacts of gross savings and population growth on economic development. In particular, the financial sector needs to be revamped to be more efficient in channeling funds from the surplus units to the deficit units. It is recommended that investment be made in financial and technological innovation to provide efficient access to credits and other financial products even though individual savings may not move with economic growth.

Many studies have explored the nexus between savings and economic growth without considering population growth and energy consumption. In this study, the relationship among savings, economic growth, population growth and energy consumption provide additional knowledge in policy formulation.

The purpose of this paper is to contribute to empirical evidence by recognizing the importance of stock markets in the financial system and consequently its causality to economic growth and vice versa.

The study used the autoregressive distribute lag model (ARDL) with bound testing procedures, the sample covered quarterly time-series data from 2001q1 to 2019q2 in Tanzania.

The results suggest that stock market development have both negative and positive causality for both short-run dynamics and long-run relationship with economic growth. Economic growth is found to only cause and relate negatively to liquidity both in the short-run and in the long-run. The results show predominantly a unidirectional causality flow from stock market development to economic growth and finds partial causality flow from economic growth to stock market development, as represented by stock market turnover which proxied liquidity.

The use of quarterly data to reflect more realistically the dynamics of the variables because yearly data may sometimes cover-up specific dynamics that may be useful for prediction and policy planning. The study uses indices to capture general aspects within the stock market against economic growth as an intuitive way to aggregate the stock market development effects.

Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.

The authors utilize an autoregressive distributed lag bounds testing estimation technique.

The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.

Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.

First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.

The purpose of this study is to investigate long-run and short-run relationships between trade diversification, financial system development, capital formation and economic growth.

ARDL estimation approach is applied to analyze long-run and short-run relationships between the financial system development, capital formation, economic growth and trade diversification in case of the Sultanate of Oman over the period 39 years starting from 1979 till 2017.

The results show that financial system development and economic growth has a positive impact on trade diversification in the short-run and long-run. However, capital formation has a negative impact on trade diversification in the short run and long run. The negative relationship between trade diversification and capital formation implies that over the period of study, the investment in capital goods was made to enhance the production capacity of the oil sector to maximize revenue.

This research is limited to analyze long-run and short-run relationship between the financial system development, capital formation and economic growth and trade diversification in case of Sultanate of Oman.

To achieve the diversification goal, the policymakers need to formulate policies to strengthen the financial system and invest in infrastructure development to promote the non-oil sector. The research findings of this study will provide insights to the policymakers to formulate an effective diversification policy.

This research contributes to the existing literature by providing empirical evidence of the short-run and long-run analysis of the selected variables in the context of an oil-dependent country.