This study examines the intricate dynamics between oil prices and renewable energy investments in Italy during the initial phase of the CoronaVirus Disease 2019 pandemic, a period characterized by significant economic and social upheaval. Utilizing advanced empirical techniques, such as Partial Wavelet Coherency analysis, Time-Varying Granger Causality, and Robinson Log-Periodogram tests, as well as Machine Learning (ML) regressions, this research uncovers nuanced insights into the interplay between oil prices and renewable energy series including biomass, solar, hydro, wind, and geothermal. Key findings indicate a predominant in-phase relationship with oil prices leading most renewable energy series, and unidirectional causality from renewables to oil prices in several instances, highlighting the potential influence of renewable energy on oil market dynamics. In robustness checks, ML models further elucidate the impact, with solar, hydro, and geothermal sources showing significant importance scores. These insights are critical for policymakers and stakeholders aiming to enhance energy security and transition towards sustainable energy sources amidst global crises.

Purpose: This study constructs a fully balanced panel dataset for 135 countries spanning 2013–2022 to explore the determinants of international trade. It employs classical econometric techniques – Robust Least Squares (RLS), Generalized Linear Model (GLM) and quantile regression – to capture linear effects, heterogeneity and distributional nuances. Complementing these, advanced Machine Learning (ML) methods – including Gradient Boosting Machine (GBM), bagging via Random Forest and an ensemble stacking model – uncover nonlinear relationships and complex interactions. All numeric variables are scaled, and a training/testing split is implemented, ensuring robust performance evaluation through metrics such as MAE, MSE, RMSE and R2. Design/methodology/approach: Advanced ML techniques are utilized extensively for both regression and robustness checks. For regression, ML methods such as bagging via Random Forest, boosting and stacking with a meta-learner are employed. Findings: Empirical evidence from both econometric and ML analyses reveals that a strong business environment (BE), high-tech exports (HTE), robust ICT services imports (ICTSI) and widespread ICT use (ICTU) significantly promote trade intensity across 135 countries from 2013 to 2022. Quantile regressions indicate that HTE’s positive impact intensifies at higher trade quantiles, whereas persistent underinvestment in R&D (RDC) consistently hampers trade performance. Advanced ML models, particularly GBM and ensemble stacking, further capture nonlinearities and interactions, reinforcing these findings and underscoring the critical role of digital infrastructure and innovation ecosystems in driving global trade competitiveness. Originality/value: This study uniquely bridges classical econometrics with state-of-the-art ML to examine the trade–innovation nexus. It harnesses a fully balanced panel of 135 countries (2013–2022) and employs RLS, GLM, quantile regression, alongside advanced ML techniques like gradient boosting, bagging via Random Forest and stacking ensembles. This dual approach not only captures both linear and nonlinear dynamics but also enhances predictive accuracy and model interpretability. The integration of these methods sets a novel benchmark, offering robust, data-driven insights and context-specific policy recommendations that enrich the literature on global trade patterns amid rapid technological advancement.

This study presents a micro-level indicator of farmers’ positioning in the market chain, based on the conceptual framework outlined by Antràs and Chor (2013, 2018). The indicator considers the selling location of a farming household and its crop buyers. Using panel data from the World Bank’s ‘Living Standards Measurement Study: Integrated Surveys on Agriculture’ for Ethiopia and Nigeria, this paper applies the proposed indicator empirically and showcases its superior performance in comparison to existing alternatives at the micro-level. Furthermore, by analyzing the dynamics of farmers’ food and total consumption over time and controlling for various household and production characteristics, as well as potential confounding factors, this study shows that moving towards a downstream position in the market chain has a positive impact on farmers’ food and total consumption levels. The results are validated through sensitivity analysis and robustness checks.