Escalating geopolitical factors are closely related to climate warming, but researchers have not fully considered this. Therefore, the purpose of this study is to explore the interaction between geopolitical risk (GPR) and carbon dioxide (CO₂) in China. This paper uses the recently developed GPR index and a bootstrap Granger rolling-window estimation. Empirical results spanning different subsamples reveal a two-way causal relationship between GPR and CO₂. GPR transforms energy consumption and economic activities through trade disputes, military deployments and energy issues, which have a complex impact on CO₂ emissions. Oppositely, CO₂ emissions affect GPR through changes in international cooperation and shaping of geopolitical systems. In view of these empirical results, we put forward several policy recommendations. The Chinese government can effectively consider GPR to control CO₂ emissions by increasing green investment and signing environmental contracts. Enterprises must focus on research and development (R&D) and investment in new energy innovations. In addition, international organizations can be a useful tool for monitoring decarbonization policies and resolving conflicts between countries.

Research background: Ensuring energy security (ESR) is critical for national stability and sustainable growth. However, the role of artificial intelligence technology (AIT) in enhancing ESR, particularly through global supply chain (GSC) transmission channels, remains underexplored. Purpose of the article: This study investigates the dynamic relationship between AIT and ESR in the context of the United States, examining whether AIT ensures energy security via GSC optimization and how this interplay evolves over time. Methods: A time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV) is developed to analyze the dynamic links between AIT, GSC, and ESR and capture time-varying effects and extreme-period impacts. Findings & value added: The results indicate that the impact of AIT on ESR is initially modest in the short term, but increases substantially in the medium to long term by optimizing energy production, distribution, and management. GSC stability and efficiency serve as key transmission channels, and post-2010 advancements amplify the influence of AIT. During crises, AIT ensures the continuity of energy security through intelligent GSC adjustments in the US. This study highlights the critical role of AIT-driven supply chain strategies in energy security and offers policy-makers actionable insights to leverage AIT and GSC optimization for sustainable energy systems.

Research background: In today’s digital age, traditional environmental, social, and governance (ESG) development paths are gradually facing challenges, including from digital technologies. In particular, the potential roles of artificial intelligence (AI), cloud computing (CC), and blockchain (BC) in the ESG market have not been fully explored. Purpose of the article: This study explores the deep integration of digital technology and ESG by evaluating the correlation and spillover effects among AI, CC, BC, and eight global ESG indices. Methods: This study explores the spillovers between AI, CC, BC, and eight global ESG indices by cross-quantilogram and quantile time-frequency connectedness approaches. Findings & value addition: The lower quantile of ESG returns has a weak positive (strong negative) correlation with the lower (upper) quantile of digital technology. Next, the spillover effects vary with time, frequency, and quantile levels. Meanwhile, the North America and Asia-Pacific developed ESG indices serve as the transmitter and receiver of spillover effects, respectively. Furthermore, the dependence between digital technology and ESG returns is insignificant before the COVID-19 crisis but increases after it. This quantile-dependent asymmetry fundamentally challenges linear assumptions prevalent in current ESG-technology integration theories. Overall, this study contributes by integrating AI, CC, BC, and ESG into a unified framework, and analyzing their interaction mechanisms. Furthermore, it dynamically analyzes the asymmetry over long and short-term horizons, and highlights the hedging role of digital technology in stabilizing ESG markets. Moreover, we provide novel insights about the interconnectedness between these markets, offering valuable guidance on risk management. Consequently, regulators should urgently explore the development of digital asset-based ESG derivatives as targeted risk mitigation tools. Positioned at the cutting-edge, this work sets a methodological benchmark for analyzing non-linear, frequency-sensitive interdependencies within the rapidly evolving ESG-digital nexus, transforming the theoretical framework from static linearities to dynamic non-linearities. Finally, this study proposes some reasonable suggestions, including raising risk awareness, promoting digital transformation, building integration and innovation platforms, and leveraging ESG’s diffusion role.

There is significant interest among policymakers and academics about whether green agricultural subsidy policy (GASP) has achieved its intended effect of reducing agricultural carbon emissions (ACEs) in China. Based on the panel data of 172 prefecture-level cities from 2010 to 2021, this study evaluates the impact and mechanisms of GASP on ACEs’ reduction effect by employing the DID model. The results demonstrate that GASP does significantly reduce carbon emissions. Mechanism tests illustrate that GASP promotes carbon reduction via two mechanisms: enhancing agricultural technology progress efficiency and increasing scale management efficiency. Further heterogeneity analyses reveal that the carbon reduction effects of GASP are particularly prominent in the main grain-producing regions and in cities with higher levels of carbon emissions. The empirical tests and mechanism analyses in this paper can better clarify the function of GASP, enrich and deepen the existing literature, and provide some useful references for carbon reduction.

This study sought to ascertain the existence of convergence in environmental taxation among EU countries. By employing the sequential panel selection method and the panel KSS unit root test, it is demonstrated that the environmental taxation sequences in Luxembourg, Germany, Spain, France, and Cyprus diverge, while those of the remaining 22 countries converge. Similarly, the energy tax sequences of Croatia, Spain, Germany, and France are unstable, indicating divergence, while the remaining 23 countries exhibit significant convergence. Finally, transport taxes show a convergent trend across all EU countries. The convergence of environmental taxation can be explained by the catch-up and spatial spillover effects, and the non-convergence of a few EU countries is attributed to their minimal implementation of environmental goals. This study provides recommendations for improving environmental policies.