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The study aims to find the synchronization between foreign agriculture investment (FAI) and Sustainable Development Goals (SDGs) related to agriculture as classified by the Food and Agriculture Organization (FAO). The study tries to find such an association in India over 2 decades from 2001.

The Toda-Yamamoto Granger using the M-Wald test for the non-causality procedure is applied to find the synchronization. Stationarity is tested using the Augmented Dickey-Fuller, Phillips-Perron and Kwiatkowski, Phillips, Schmidt and Shin (KPSS) tests. The Johanson methodology with MacKinnon-Haug-Michelis P-value is employed for the Cointegration test.

The empirical results indicate that the FAI Granger cause SDG2 “Zero hunger” and “Overall sustainability”, but SDG13 “Climate Change”, SDG6 “Clean water and sanitation”, SDG12 “Responsible production and consumption” and SDG15 “Life on Land” granger cause global investments. Notwithstanding, SDG5 “Gender equality” and SDG14 “Life below water” found no-way causality with FAI.

Host governments should prioritize sector-level sustainable development, notably agricultural SDGs, to attract global investments. Foreign agriculture investment is influenced differently by various SDGs; thus, policymakers should concentrate on specific agricultural SDGs to enhance the flow of capital into the agriculture sector. Global investors should take sustainability into account while framing foreign investment plans, and the supra-national organization may consider global agricultural investments while addressing the problems related to global food security.

The distinguishing feature of the study is that SDGs classified by the FAO from a global investment perspective have not been studied so far.

The study aims to discover the spatial relationship between financial development, energy consumption and economic growth in 11 ASIA countries, using panel data from 1980 to 2016.

The study applies three popular spatial models, namely, (1) spatial error model (SEM), (2) spatial autoregressive model (SAR) and (3) spatial Durbin model (SDM), to explore the direct and spillover effect of financial development and energy consumption on economic growth. Furthermore, a novel test proposed by Juodis et al. (2020) is employed to check the Granger non-causality between each pair of variables.

The empirical outcomes found direct and spillover effects of financial development and energy consumption on economic growth in 11 ASIA countries. Accordingly, an expansion of the financial development in country i is beneficial for the growth of the host country and neighboring countries, and vice versa. However, an increase in energy consumption in country i leads to a decrease in the economic growth of neighboring countries. The test of Granger non-causality indicated a bidirectional causality between financial development and economic growth, and unidirectional causality running from economic growth to energy consumption.

Spillover effects of financial development and energy consumption on growth have largely been ignored in previous studies, especially in emerging countries. Thus, the study enriches the literature and provides some policy implications for ASIA countries.

Spillover effects of financial development and energy consumption on growth have largely been ignored in previous studies, especially in emerging countries. Thus, the study enriches the literature and provides some policy implications for ASIA countries.

Spillover effects of financial development and energy consumption on growth have largely been ignored in previous studies, especially in emerging countries. Thus, the study enriches the literature and provides some policy implications for ASIA countries.

This study aims to examine the dynamic bidirectional causality between oil price (OIL) and stock market indexes in net oil-exporting (Russia) and net oil-importing (China) countries.

The authors use monthly data for the period starting from October 1995 to October 2021. In this study, the bootstrap rolling-window Granger causality approach introduced by Balcilar et al. (2010) and the probit regression model are performed in order to identify the bidirectional causality.

The results show that the causal periods mainly occur during economic, financial and health crises. For oil-exporting country, the results suggest that any increase (decrease) in the OIL leads to an appreciation (depreciation) in the stock market index. The effect of the stock market on OIL is more relevant for the oil-importing country than that for the oil-exporting one. The COVID-19 consequences are demonstrated in the impact of oil on the Russian stock market. The probit regression shows that the US financial instabilities increase the probability of causality between OIL and stock market indexes in Russia and China.

The dynamic relationship between the variables must be taken into account in investment decisions. As financial instabilities in the USA drive the relationship between oil and stocks, investors should consider geopolitical, economic and financial elements when constructing their portfolios. Shareholders are required to include other assets in their portfolios since oil–stock relationship is highly risky.

This study provides further evidence of the bidirectional oil–stock causal link. Additionally, it examines the impact of financial instabilities on the probability that the OIL and the stock market index cause each other through the Granger effect.

Might the impact of the financial stress on exchange markets be asymmetric and exposed to regime changes? Departing from the existing literature, highlighting that the domestic and foreign financial stress in terms of money market have substantial effects on exchange market, this paper aims to investigate the impacts of the bond yield spreads of three emerging countries (Mexico, Russia, and South Korea) on their exchange market pressure indices using monthly observations for the period 2010:01–2019:12. Additionally, the paper analyses the impact of bond yield spread of the US on the exchange market pressure indices of the three mentioned emerging countries. The authors hypothesized whether the negative and positive changes in the bond yield spreads have varying effects on exchange market pressure indices.

To address the research question, we measure the bond yield spread of the selected countries by using the interest rate spread between 10-year and 3-month treasury bills. At the same time, the exchange market pressure index is proxied by the index introduced by Desai et al. (2017). We base the empirical analysis on nonlinear vector autoregression (VAR) models and an asymmetric quantile-based approach.

The results of the impulse response functions indicate that increases/decreases in the bond yield spreads of Mexico, Russia and South Korea raise/lower their exchange market pressure, and the effects of shocks in the bond yield spreads of the US also lead to depreciation/appreciation pressures in the local currencies of the emerging countries. The quantile connectedness analysis, which allows for the role of regimes, reveals that the weights of the domestic and foreign bond yield spread in explaining variations of exchange market pressure indices are higher when exchange market pressure indices are not in a normal regime, indicating the role of extreme development conditions in the exchange market. The quantile regression model underlines that an increase in the domestic bond yield spread leads to a rise in its exchange market pressure index during all exchange market pressure periods in Mexico, and the relevant effects are valid during periods of high exchange market pressure in Russia. Our results also show that Russia differs from Mexico and South Korea in terms of the factors influencing the demand for domestic currency, and we have demonstrated the role of domestic macroeconomic and financial conditions in surpassing the effects of US financial stress. More specifically, the impacts of the domestic and foreign financial stress vary across regimes and are asymmetric.

This study enriches the literature on factors affecting the exchange market pressure of emerging countries. The results have significant economic implications for policymakers, indicating that the exchange market pressure index may trigger a financial crisis and economic recession.

The main purpose of this study is to investigate the agriculture-induced environmental Kuznets curve (EKC) hypothesis in South Asian economies (SAE).

This study employs econometric techniques, including Westerlund cointegration tests, cross-sectional augmented distributive lag model (CS-ARDL) and Dumitrescu and Hurlin (DH) causality tests to investigate the relationship between renewable and non-renewable energy consumption, agriculture, economic growth, financial development and carbon emissions in SAE from 1990 to 2019.

The CS-ARDL test outcome supports the presence of the agriculture-induced EKC hypothesis in SAE. Additionally, through the application of the DH causality test, the study confirms a unidirectional causality running from renewable energy consumption (REC), fossil fuel consumption (FFC), economic growth (GDP) and squared economic growth (GDP²) to carbon dioxide (CO₂) emissions.

This study proposes that future research should extend comparisons to worldwide intergovernmental bodies, use advanced econometric methodologies for accurate estimates, and investigate incorporating the service or primary sector into the EKC. Such multidimensional studies can inform various methods for mitigating global climate change and ensuring ecological sustainability.

Environmental degradation has been extensively studied in different regions and countries, but SAE face significant constraints in addressing this issue, and comprehensive studies in this area are scarce. This research is pioneering as it is the first study to investigate the applicability of the agriculture-induced EKC in the South Asian region. By filling this gap in the current literature, the study provides valuable insights into major SAE and their environmental challenges.

The study examined the impact of financial development, foreign direct investment, market size and trade openness on domestic investment for 119 countries divided into four panels that are low-income countries (LIC), lower middle-income countries (LMIC), upper middle-income countries (UMIC) and high-income countries (HIC) between 1995 and 2019.

The present study bases its empirical procedure on the bases of the data mix. To this end, based on the presence of cross-sectional dependence, covariate-augmented Dickey–Fuller unit root and Westerlund cointegration second-generation tests were employed to validate the stationarity and cointegration of the variables, respectively. The novel Dynamic Common Correlation Effects estimator was employed to estimate the heterogeneous parameters while the Dumitrescu and Hurlin test was used to test for causality direction of the highlighted variables.

The empirical results show that market size and trade openness had a positive and statistically significant effect on domestic investment for all the income groups. Results also show that financial development had a positive and statically significant effect on domestic investment only for LMIC and HIC economies, while a positive and statistically insignificant effect was obtained for LIC, UMIC and the global panel. The causality results revealed a bidirectional relationship between domestic investment and the exogenous variables – financial development, foreign direct investment, market size and trade openness.

It is therefore, recommended that LIC and LMIC need to consider harmonising the financial system to lower credit limitations and adopt business-friendly policies. HIC and UMIC should seek more outward FDI policies and harmonise their trade policy, to reap more benefits from FDI and international trade.

On novelty, previous studies have been criticised for the effect on technical innovation of bank financing and institutional quality. This research tackles the deficiency using systematic institutional quality indicators and by taking other variables into account.

Unemployment is one of the major challenges facing most countries, including Africa as a continent. Seeking how to reduce unemployment, debt, inflation and increase gross domestic product (GDP), foreign direct investment (FDI) and gross capital formation in the continent has been an agenda of governments, policy makers and economists to. This study examines the relationship between economic growth, inflation, debt, FDI, gross capital formation, labor force, population and unemployment in Africa.

An updated panel dataset of 29 African countries was selected from different regions from 1991 to 2019. These countries were selected based on their unemployment, population growth and inflation rates. The Pesaran cross-sectional dependence and panel unit root test (the Dickey–Fuller cross-sectional supplemented and the Im-Pesaran-Shin cross-sectional) were applied. Further, the panel Autoregressive Distributed Lag (ARDL) model (Bounds test) and pooled mean group (PMG) estimator were utilized in this work.

This shows that economic growth, debt, labor force and population have a positive relationship with unemployment in the long run. Therefore, an increase in these variables generates an increase in the selected African countries' unemployment growth. In contrast, inflation, FDI and gross capital formation have a negative relationship with unemployment in the long run, which implies that an increase in these variables reduces unemployment in the selected African countries.

This study has potential limitations because some data from the countries are not up to date and some years are missing from the data.

This study contributes to understanding unemployment and Okun's law in the African economy. This study shows that an increase in economic growth leads to a rise in unemployment, while an increase in inflation leads to a decrease in unemployment.

This paper provides an insight into the major factors that increase and reduces unemployment for government and policy marker to take the adequate measure.

This paper aims to empirically examine the impact of economic development and energy consumption in Saudi Arabia (the leading OPEC giant and the Arab energy icon country) between 1971 and 2015, whilst incorporating globalization, financial development and capital accumulation.

This study uses econometric tools and the analytical framework based on the autoregressive distributed lag (ARDL) model.

The study found that, unlike economic development, globalization and financial development increased energy consumption. Also, capital accumulation created a boost in the country’s energy consumption. Results of variance decomposition indicate that the innovative shocks in globalization and financial development affected energy consumption at the rates of 15.28% and 28.98%, respectively, over 15 years’ period, while shocks in capital accumulation affected energy consumption at a rate of only about 1.24%. In addition, the results of impulse response function show that globalization and economic development were highly responsive to shocks in financial development, and capital accumulation greatly spurred financial development.

The findings of this study have implication for promoting an efficient and sustainable energy systems that enhance sustainable development based on the accrued benefits of globalization, financial development and capital accumulation.

Given the increasing level of globalization, financial development and energy consumption, our study uses econometric tools and the analytical framework based on the ARDL model to revisit how energy consumption is influenced by economic development in Saudi Arabia by incorporating other determinants of energy consumption such as globalization, financial development and capital accumulation. The results were validated based on the innovative accounting.

The main purpose of this paper is to analyze the role of information and communication technology (ICT) diffusion in women empowerment and in fostering the process of achieving the Sustainable Development Goals (SDGs) in South Asian Association for Regional Cooperation (SAARC) countries using panel data from 2005 to 2020.

An ICT diffusion index was constructed using principal component analysis (PCA). Further, the study uses econometric techniques robust to cross-sectional dependence (CSD) which include Pesaran's CSD tests, second-generation unit root test, Pedroni, Kao, Westerlund cointegration test, FMOLS, DCCE, Driscoll–Kraay (DK) regression, and D&H causality tests.

ICT diffusion and economic growth have a significant and favorable impact on women's empowerment. However, fertility rates and trade openness harm women's empowerment. In addition, the causality test results depict a bidirectional causal relationship between ICT and women empowerment and between growth and women empowerment. In addition, unidirectional causality is detected between education and women's empowerment. Overall, the findings indicate that expanding ICT and bridging the digital divide, particularly among women, can be effective in achieving empowerment-related SDGs.

To date, there are hardly any studies in SAARC context that empirically evaluate the link between ICT, women empowerment, and the issue of sustainability in a unified framework. Therefore, this study is unique in terms of conceptualization and methodological robustness in this context. The study will benefit policymakers and regulatory bodies to formulate appropriate policies to empower women and thereby attain the SDGs by 2030.

The present study aims to investigate the non-linear relationship between trade and income inequality to address goal 10 of sustainable development goals (SDGs) using the Kuznets Curve (KC) framework for major emerging countries during 1991–2020.

For this purpose, recent econometric techniques, such as Common Correlated Effect (CCE) and Dynamic Common Correlated Effect (DCCE) estimators have been employed to deal with the cross-section dependence (CD) that arises in panel data, while the robustness of the study is checked through Driscoll–Kraay standard errors method.

The empirical results of the study confirm the existence of inverted “U-shaped” relationship between trade and income inequality suggesting evidence for the trade-led KC in the panel of emerging countries. Along with the non-linear model, the threshold value is estimated to be between 3.5 and 4% of gross domestic product (GDP).

The authors' findings support that trade contributes significantly toward reducing income inequality and helps in achieving goal 10 of SDGs. Hence, trade policies appear to be more egalitarian. The results widen the scope for further research and provide insights for regulators and policymakers in modeling trade policies and changing the status quo trade policy framework accordingly.

The present study is a pioneering attempt to examine the non-linear relationship between trade and income inequality under the KC framework in light of the Agenda 2030 for sustainable development. The study also considers other explanatory factors that have an impact on income inequality. Furthermore, the study considers other explanatory factors that have an impact on income inequality, and the attempt to estimate the threshold value for the trade-led KC is novel and interesting.