At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy.

The global economic landscape has become increasingly volatile, as rising economic policy uncertainty (EPU) and frequent abnormal fluctuations in short-term cross-border capital flows (SCF) can easily trigger systemic financial risks (SFR). Clarifying the dynamic relationships among these three factors is therefore crucial for maintaining China’s financial stability and improving the effectiveness of policy implementation. Using monthly data on China’s financial institutions from 2000 to 2023, this study measures SFR using the DCC-GARCH-ΔCoVaR model and employs a time-varying parameter stochastic volatility vector autoregression (TVP-SV-VAR) model to analyze the dynamic linkages among EPU, SCF, and SFR. The results show that China’s SFR has generally declined, reflecting improved financial resilience; EPU consistently promotes SCF, while its impact on SFR varies significantly across different lag periods. Moreover, the medium- and long-term effects of SCF on SFR have intensified, with notable heterogeneity in shock intensity across different periods. Further analysis reveals that SCF helped mitigate SFR during the stock market crash period but significantly amplified it during the pandemic period. This study uncovers the time-varying characteristics of the EPU–SCF–SFR nexus and offers practical insights for emerging economies in seeking to balance capital flows and financial stability amid heightened policy uncertainty.

With growing global concern over climate change, achieving a balance between tourism development and carbon emission efficiency has become a critical challenge. Using panel data from nine provinces in the Yellow River Basin (YRB) from 2013 to 2022, this study employs the Super-SBM model, the coupling coordination degree (CCD) model, and the random forest model. These models are used to analyze the coordination characteristics and influencing factors of tourism carbon emission efficiency (TCE) and tourism economic resilience (TER). The results show that: (1) TCE and TER in the YRB display a fluctuating upward trend; (2) the CCD has improved from reluctant to basic coordination, with significant regional disparities, as the eastern regions outperform the central and western regions; and (3) R&D intensity, infrastructure levels, and tourism resource endowment are the main factors influencing CCD. Government intervention exerts an inhibitory effect on CCD, while industrial structure has a positive impact. The findings of this study deepen our understanding of the synergistic effects between tourism’s low-carbon transition and economic resilience. They also provide practical insights for promoting sustainable tourism development in the YRB.