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The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively.

First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run.

The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025.

The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.

Empirical studies regarding the impact of the real exchange rate (RER) on economic growth are extensively available. However, the literature as a whole appears to report varying results, while the causes of such differences have not been analyzed systematically. The present study aims to fill the gap in the literature.

In this paper, the authors compile 543 empirical estimates from 51 studies of the exchange rate-growth nexus in order to meta-analyze its relationship. Meta-analysis allows the authors to quantitatively synthesize previous empirical studies and explain the variation in the results. This method also enables us to investigate the possibility of publication bias, as there is a tendency in research only to report results that are both statistically significant and show the expected signs.

After addressing publication bias and heterogeneity in the estimates, the meta-regression results show that RER depreciation (or undervaluation) genuinely favors economic growth. On average, RER depreciation has a greater impact on economic growth in developing countries than the developed ones. The study’s results imply that maintaining an undervalued RER could be favorable to spur economic growth, especially in developing countries.

Initially predominant in the medical literature, meta-analysis has been on a rising edge in economics. This progress has produced many systematic quantitative review analyses with continuously improved statistical-econometric practices related to economic variables. However, to the authors’ knowledge, no comprehensive meta-regression analysis of the relationship between exchange rate and economic growth has been conducted and published in any publicly accessible academic outlet. Therefore, this study aims to fill this gap in the literature.

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

This paper aims to reexamine the relationship between financial openness and financial development in Ghana.

The study applied maximum likelihood estimation and autoregressive distributed lag approach and tested Granger causality using quarterly data from 1990:1 to 2020:4.

This study revealed a long-run equilibrium relationship between financial openness and development, indicating that financial openness is a critical factor in Ghana’s financial development. Therefore, the study recommends with caution that policies aimed at promoting financial openness could be an effective way to encourage sustainable financial development in Ghana, as financial openness alone may not bring the desired outcome.

The study contributes to the existing body of knowledge by providing empirical evidence of the link between financial openness and financial sector development in Ghana. Future research could delve deeper into the mechanisms through which financial openness affects financial development, exploring potential channels and transmission mechanisms.

The findings suggest that policymakers, particularly the Ministry of Finance and the Bank of Ghana, should prioritize policies aimed at promoting financial openness. This includes continued efforts toward financial liberalization and creating an environment conducive to domestic and international financial transactions. Moreover, policies aimed at increasing trade openness, boosting real GDP and maintaining moderate real interest rates are essential for fostering financial sector development.

Enhancing financial sector development can have significant implications for society, including increased access to financial services, improved economic opportunities and enhanced overall economic stability. By promoting financial openness and development, policymakers would contribute to poverty reduction, job creation and overall socio-economic development. The study bridges the gap between theory and practice by providing empirical evidence supporting the theoretical proposition that financial openness stimulates financial sector development.

This study fills a crucial gap in the literature on the effects of financial openness on Ghana’s financial sector development. It focuses on Ghana, which liberalized its financial sector in 1988 as part of the overall economic reforms in 1983, and this justifies the starting point of this paper in 1990, as there are no adequate data before 1990. The study uses principal component analysis to construct an index that measures financial development. The study considers the recent financial crises in Ghana in 2017 and underscores the importance of understanding the link between financial openness and financial development, which becomes useful for policymakers and researchers studying financial system development in sub-Saharan Africa which includes Ghana.

The increased capital requirements and the implementation of new liquidity standards under Basel III sparked various concerns among researchers, academics and other stakeholders. The question is whether Basel III regulation is ideal, that is, adequate to deal with a crisis, such as the 2007–2009 global financial crisis? The purpose of this paper is threefold: First, perform a stress testing exercise on the US banking sector, while examining liquidity and solvency risk indicators jointly under the Basel III regulatory framework. Second, allow the study to cover the post-crisis period, while referring to key Basel III regulatory requirements. And third, focus on the resilience of domestic systemically important banks (D-SIBs), which are supposed to support the US financial system in times of stress and therefore whose failure causes the entire financial system to fail.

The authors used a sample of the 24 largest US banks observed over the period Q1-2015 to Q1-2021 and a scenario-based vector autoregressive conditional forecasting approach.

The authors found that the model successfully produces accurate forecasts and simulates the responses of the solvency and liquidity indicators to different real and historical macroeconomic shocks. The authors also found that the US banking sector is resilient and can withstand both historical and hypothetical macroeconomic shocks because of its compliance with the Basel III capital and liquidity regulations, which consist of encouraging banks to hold high-quality liquid assets and stable funding resources and to strengthen their capital, which absorbs the losses incurred in a crisis.

The authors developed a framework for testing the resilience of the US banking sector under macroeconomic shocks, while examining liquidity and solvency risk indicators jointly under Basel III regulatory framework, a point not yet well studied elsewhere, and most studies on this subject are based on precrisis data. The authors also focused on the resilience of D-SIBs, whose failure causes the failure of the entire financial system, which previous studies have failed to examine.

This study aims to examine the effect of governance on the consumption of modern renewable energy in the East African Community (EAC), controlling for economic growth, trade openness and foreign direct investment (FDI).

The study relied on secondary data sourced from the World Development Indicators, World Governance Indicators and the International Energy Agency (IEA) for the EAC from 1996 to 2019. A panel Cross-Sectional Augmented Distributed Lag (CS-ARDL) model and second-generation panel data models were employed in the analysis.

The findings indicate that poor governance and inadequate FDI are significantly responsible for the low level of modern renewable energy consumption (MREC) in the EAC. On the other hand, trade openness significantly enhances MREC, while GDP per capita has no significant effect on MREC.

The consumption of modern renewable energy sources (excluding the traditional use of biomass) and its determinants, as most studies focus on renewable energy consumption as a whole. The study also employed the panel CS-ARDL model and second-generation panel data models.

This paper aims to examine the factors that contribute to the external debt growth in Ghana.

The study adopts the autoregressive distributed lag (ARDL) model and the error correction model (ECM) to establish the short-run and long-run relationships between the dependent variable (external debt) and the independent variables (debt service, exchange rate, gross domestic product, government expenditure, import and trade openness), using a time series data spanning from 1990 to 2019.

The results indicate that debt service, GDP, government expenditure and trade openness have a positive and significant relationship with external debt, while import and exchange rates have a negative relationship with external debt in the long run. In the short run, debt service, import, exchange rate and trade openness have a positive and significant relationship with external debt, while GDP has a negative relationship with external debt.

The study found that variables such as government expenditure, debt service and import contribute significantly to the nation’s external debt stock. These findings suggest that policymakers should focus on prioritising and cutting down expenditure in their quest to curtail the debt menace facing the nation. Since existing debt service has the tendency of influencing debt stock, it is recommended that government should reduce borrowing in order avoid debt trap. Home-grown policies to reduce imports must also be encouraged. As these drivers of external debt are tackled head-on, Ghana can be rightly positioned to record lower levels of public debt and subsequently reap the benefits of economic growth.

The study adds to the public debt literature, specifically addressing the idiosyncratic determinants of external debt within the Ghanaian context.

Modern renewable energy is crucial for environmental conservation, sustainable economic growth and energy security, especially in developing East African nations that heavily use traditional biomass. Thus, this study aims to examine urbanization and modern renewable energy consumption (MREC) in East African community (EAC) while controlling for gross domestic product (GDP), population growth, foreign direct investment (FDI), industrialization and trade openness (TOP).

This study considers a balanced panel of five EAC countries from 1996 to 2019. Long-run dynamic ordinary least squares (DOLS) and fully modified ordinary least squares estimations were used to ascertain the relationships while the vector error-correction model was used to ascertain the causal relationship.

Results show that urbanization, FDI, industrialization and TOP positively affect MREC. Whereas population growth and GDP reduce MREC, the effect for GDP is not that significant. The study also found a bidirectional causality between urbanization, FDI, TOP and MREC in the long run.

Investing in modern renewable energy facilities should be a top priority, particularly in cities with expanding populations. The governments of the EAC should endeavor to make MREC affordable among the urban population by creating income-generating activities in the urban centers and sensitizing the urban population to the benefits of using MREC. Also, the government may come up with policies that enhance the establishment of lower prices for modern renewable energy commodities so as to increase their affordability.

MREC is a new concept in the energy consumption literature. Much of the research focuses on renewable energy consumption including the use of traditional biomass which contributes to climate change negatively. Besides, the influence of factors such as urbanization has not been given significant attention. Yet urbanization is identified as a catalyst for MREC.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Examine the effects of sudden environmental disasters on the advancement of both renewable and conventional energy technologies.

Utilizing panel data from 31 Chinese provinces spanning 2011 to 2022, the SEM (Spatial Error Model) dual fixed model is utilized to examine the impact of sudden environmental disasters on energy technologies.

The findings reveal that: (1) Sudden environmental disasters exert a markedly positive influence on the Innovation of Renewable Energy Technologies (IRET), while their impact on conventional energy technologies is positively non-significant. (2) Sudden environmental disasters not only significantly enhance innovation in local renewable energy technologies but also extend this positive influence to neighboring regions, demonstrating a spatial spillover phenomenon. (3) Research and Development (R&D) funding serves as a partial mediator in the relationship between sudden environmental disasters and renewable ETI. In contrast, Foreign Direct Investment (FDI) exhibits a masking effect.

Consequently, the study advocates for intensified efforts in post-disaster reconstruction following abrupt environmental events, an elevation in the quality of foreign direct investments, and leveraging research funding to catalyze innovation in renewable energy technologies amid unforeseen environmental crises.