Economies

Transition to renewable energy leads to assumed economic diversification; however, the institutional risks for hydrocarbon-dependent economies remain high. This paper identifies the conditions under which transitioning economies enter a novel dependency during the renewable transition. Our analysis combines the Multi-Level Perspective with Historical Institutionalism to explore Azerbaijan’s 30-year trajectory across the oil, gas, and emerging renewable phases, serving as an illustrative case. Evidence from the literature and expert interviews illustrates that renewable investments are channelled through hydrocarbon-era institutional practices, enclave-style contracting, centralised decision-making, and reliance on foreign technology providers. These conditions constrain domestic niche formation and limit opportunities for local capability development. As a result, renewables become embedded within the existing institutional architecture rather than displacing it, serving primarily to substitute hydrocarbons as an export commodity rather than to catalyse diversification. The paper conceptualises this trajectory as a possible renewable dependence: a pathway in which renewable energy is integrated into an export-oriented, state-dominated political economy without altering its core institutional logic. The identified configurations are common across hydrocarbon economies in Central Asia and MENA, offering transferable insights into when and why renewable transitions risk reproducing, rather than transforming, established development models.

Since the Industrial Revolution, the increase in greenhouse gas emissions has led to a significant rise in global temperatures compared to the pre-industrial period. This development has heightened the importance of carbon pricing policies in combating climate change. This study aims to examine the effects of carbon pricing instruments, carbon taxes and emissions trading systems (ETS) on carbon dioxide (CO₂) emissions in OECD countries. A panel data analysis covering the period 2002–2023 was conducted, taking into account structural differences across countries as well as shared economic dynamics. The findings indicate that both carbon taxes and ETS mechanisms are effective in reducing CO₂ emissions in the long run. Moreover, while increased industrial activity contributes to higher emissions, a greater share of renewable and nuclear sources in the energy mix is found to support emission reduction. The study demonstrates that carbon pricing policies exert limited short-term effects but generate structural and lasting impacts in the long term. The findings are consistent with the existing literature and theoretical framework. Achieving permanent reductions in emissions requires a comprehensive policy approach that not only implements carbon pricing, but also strengthens energy efficiency and fuel substitution in the industrial sector while continuously increasing the share of clean sources in the energy supply. The analysis shows that carbon taxes and emissions trading systems (ETS) are effective in reducing emissions over the long run in OECD countries, and that their success varies depending on countries’ energy profiles and policy designs. These results underline that a well-designed and complementary carbon pricing framework is critical for achieving a sustainable transition.

Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies.