Data, Distribution, and Modeling Innovations in Spatiotemporal Energy Economics

The objective of the present review is to synthesize recent state-of-the-art advances in the field of energy economics. The present review aims to elucidate the interconnections among various applicable and practical methodologies that may facilitate a sustainable energy transition and therefore the novelty lies in the cross-cutting, methodological integration and forward-looking perspective that informs both academic research and practical policy development in the context of sustainable energy transitions. The contribution of this review is fourfold. First, it systematically compiles the core empirical advancements within different sectors of the energy domain, providing a structured assessment of contemporary research efforts. Second, it critically examines the challenges associated with data availability and reviews methodological innovations designed to address these limitations. Third, it consolidates developments in spatiotemporal econometric techniques, highlighting their significance in capturing dynamic spatial and temporal dimensions of energy systems. Fourth, it presents emerging machine learning-based approaches for forecasting, underscoring their potential to enhance predictive capabilities and inform policy and investment decisions. By integrating insights across these domains, the review offers a comprehensive framework for understanding the methodological evolution in energy economics and identifies pathways for future research that support the global pursuit of a sustainable energy future.