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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.

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.

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 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.

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.

The purpose of this study is to assess the potential benefits of digital technologies (DTs) in bolstering the circular economy (CE) transition in the construction industry, to speed up the attainment of sustainable development objectives.

A detailed literature review was undertaken to identify DTs that could influence CE transition and the benefits of these DTs in the CE transitioning efforts of the construction industry. Based on these, a survey questionnaire was formulated and administered to construction professionals using convenient sampling techniques. With a response rate of 49.42% and data reliability of over 0.800, the gathered data were analysed using frequency and percentage, mean item score, normalisation value, coefficient of variation, Kendall’s coefficient of concordance, analysis of variance and factor analysis.

This study found that the construction experts agreed that building information modelling, blockchain technology, RFID, drone technology and cloud computing are the leading DTs that have the potential to influence and speed up CE transition in construction. Also, six clusters of benefits of DTs in bolstering EC are quicken CE transition, proactive waste management, recycling and zero waste, data management and decision-making, enhance productivity and performance and resource optimisation.

Studies on the integration of DTs in CE transition are scarce and it is even lacking in the Nigerian context. To the best of the authors’ knowledge, this study is the first to assess the role of DTs in CE transitioning in the Nigerian construction industry.

The purpose of this study is to investigate barriers to the adoption of digital technologies (DTs) in the circular economy (CE) transition in the construction industry. The aim is to quantitatively investigate what the barriers to DTs-driven CE are in the Nigerian construction industry.

A review of existing literature identified 32 barriers to DTs-led CE. A well-structured quantitative research questionnaire was developed and administered to construction experts using a convenient sampling technique via hand delivery and Google form. The gathered data were analysed using arrays of both descriptive and inferential statistical methods.

The study revealed that the awareness of the digitalisation of CE is high, but the adoption is low. Five themes of the leading 10 factors responsible for the low adoption of DTs in CE transition in the Nigerian construction industry are (1) finance and demand barrier, (2) data management and information vulnerability, (3) skills shortage and infrastructure challenge, (4) poor government and management support and (5) interoperability and resistance problems.

This study could be helpful to decision-makers and policy formulators, which would provide an avenue for higher adoption of DTs in CE transition in the construction industry, better performance and environmental protection. It also provides a foundation for further research efforts in Nigeria and other developing countries of Africa and beyond.

Studies on the barriers to DT adoption in CE transition are still growing, and this is even non-existent in the Nigerian construction context. This offers a unique insight and original findings by pioneering the identification and assessment of barriers to the digitalisation of CE transition in Nigeria’s construction industry.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

The author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chib et al. (2020), and Chib et al.(2022).

Ehsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors.

The author shows that the optimal asset pricing model is an eight-factor model which contains efficient versions of the market factor, value factor (HML) and long-horizon behavioral factor (FIN). The findings show that efficient factors enhance the performance of US factor model performance. The top performing asset pricing model does not change in recent data.

The author is the only one to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in US stock returns.