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Energy poverty presents substantial challenges for both developed and developing nations, with the latter experiencing more pronounced adverse effects due to issues related to the provision and equitable access of energy resources. This study aims to provide a deep understanding of how financial development, economic complexity and government expenditures can impact energy poverty.

This research employs generalized method of moments (GMM) estimation on panel data to investigate the economic determinants of energy poverty in 31 developing countries from 2000 to 2020. For a comprehensive analysis, the proxies for energy poverty include access to electricity, access to clean fuels and energy consumption.

The findings suggest that while financial development cannot facilitate access to clean fuels in developing countries, it contributes to an increase in energy access and consumption. Another finding is that energy poverty can be alleviated by enhancing economic complexity since economic complexity can result in increased access to electricity and increased use of clean energy sources. Furthermore, the results underscore the pivotal role of government expenditures, surpassing the influence of financial development. In other words, government expenditures have the potential to significantly improve energy poverty across all three indices.

This is a pioneering research that seeks to examine some economic dynamics including, financial development and economic complexity on energy poverty and provide valuable guidance for policymakers aiming to promote sustainable energy development with respect to economic dynamics.

The significance of this research lies in providing an understanding of how economic conditions, including financial development, informal economic activities and economic uncertainty, influence carbon emissions and tries to offer valuable insights for policymakers to promote sustainable development.

The Panel-ARDL method is employed for a group of 30 developing countries from 1990 to 2018. This study analyzes the data obtained from the World bank, International Monetary Fund and World Uncertainty databases.

Based on the empirical results of the extended model, an increase in GDP and energy intensity is associated with an 83 and 14% increase in carbon emissions, respectively. Conversely, a 1% increase in financial development and economic uncertainty is linked to significant decrease in carbon emissions (about 47 and 23%, respectively). Finally, an increase in the informal economy can lead to a negligible yet significant decrease in carbon emissions. These results reveal that financial development plays an effective role in reducing CO₂ emissions. Moreover, while economic uncertainty and informal economy are among unfavorable economic conditions, they contribute in CO₂ reduction.

Therefore, fostering financial development and addressing economic uncertainty are crucial for mitigating carbon emissions, while the impact of informal economy on emissions, though present, is relatively negligible. Accordingly, policies to control uncertainty and reduce the informal economy should be accompanied by environmental policies to avoid increase in emissions.

The originality of this paper lies in its focus on fundamental changes in the economic environment such as financial development, economic uncertainty, and informal activities as determinants of carbon emissions. This perspective opens up new avenues for understanding the intricate relationship between carbon emissions and economic factors, offering unique insights previously unexplored in the literature.

Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.

Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.

We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.

Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.