With the intensification of global industrial chain shifts and the increasing uncertainty of external risks, businesses are facing unprecedented challenges. In response to the uncertainty associated with industrial chain transfer, entrepreneurs tend to exhibit risk-averse decision-making patterns, which is associated with lower stability of corporate investments. This paper, based on data from the 14th Chinese Private Enterprise Survey, empirically analyzes the association between the expected risk of industrial chain transfer and enterprise investment stability. The study finds that the expected risk of industrial chain transfer is significantly associated with lower scale of new investments in private enterprises and is linked to reduced willingness to invest in the future. Further path mechanism analysis indicates that the expected risk of industrial chain transfer is associated with lower entrepreneurial confidence, which in turn is linked to reduced investment activity. Additionally, the analysis reveals that clean government–business relations appear to buffer the negative association between industrial chain transfer risks and enterprise investments, although this moderating effect is modest and only marginally significant for future investment intention. Meanwhile, high-tech enterprises exhibit a stronger negative association with such risks. Finally, split-sample analysis reveals that the positive association between transfer-risk expectations and R D investment is concentrated among non-high-tech firms, suggesting that firms outside the high-tech sector respond to industrial chain transfer risks by increasing innovation efforts to pursue technological upgrading. The findings offer empirical implications for governments to formulate more precise and targeted industrial policies and provide theoretical insights for enterprises navigating complex external environments and uncertain risks.
Rising external debt has re-emerged as one of the most pressing macroeconomic challenges confronting Sub-Saharan Africa (SSA), raising fundamental questions about the sustainability of debt-financed development in structurally vulnerable economies. While external borrowing remains an important source of development finance, persistent debt accumulation may simultaneously weaken fiscal space, constrain productive investment, and undermine long-run economic transformation. Against this background, this study investigates the linear, nonlinear, and threshold effects of external debt on economic growth in 31 SSA countries over the period 1996–2023. The study employs a comprehensive dynamic panel framework based on System GMM and Lewbel IV-2SLS estimation, and dynamic panel threshold techniques to address endogeneity, dynamic persistence, and potential regime-dependent effects in the debt-growth relationship. The findings reveal a consistently negative relationship between external debt and economic growth, suggesting that rising debt burdens continue to generate structural constraints on long-run economic performance in SSA. In contrast, capital formation, export expansion, institutional quality, and human capital development emerge as important drivers of growth and economic resilience. The nonlinear and threshold estimations do not provide evidence of statistically significant debt thresholds, indicating that the adverse effects of debt accumulate gradually rather than intensifying only after a critical tipping point is reached. These results challenge simplified debt-threshold interpretations and instead highlight the importance of governance quality, investment efficiency, and productive economic structures in shaping debt sustainability outcomes. The findings further suggest that debt sustainability in SSA should be viewed not merely as a fiscal issue, but as a broader structural and institutional development challenge. Sustainable growth therefore requires external financing strategies that are closely aligned with productive investment, export diversification, institutional strengthening, and long-run economic transformation.
Against the backdrop of global climate change, understanding how the coordinated development of green finance and environmental regulation contributes to industrial structure decarbonization is of critical importance. Using city-level data from China spanning 2011 to 2023, this study investigates the impact of green finance–environmental regulation coordination on industrial structure decarbonization. The results show that: (1) The results show that the synergy between green finance and environmental regulation significantly promotes industrial structure decarbonization. (2)The mechanism analysis further reveals that green innovation and social environmental oversight serve as significant transmission channels, while the financing constraint alleviation mechanism receives marginal support. (3) Heterogeneity analysis indicates that the synergistic effect is more pronounced in resource-based cities, and it becomes stronger with a higher degree of marketization. (4) Spatial analysis shows that the synergy between green finance and environmental regulation significantly promotes local industrial structure decarbonization, whereas its impact is primarily localized rather than transmitted through significant spatial spillovers. These findings highlight the importance of strengthening the synergy between financial support and environmental governance to promote industrial structure decarbonization through localized green transformation.
Artificial intelligence (AI) can improve productivity in individual tasks, but firm-level gains depend on whether AI-generated information is converted into coordinated organizational action. To explain this conversion process, we develop a firm-level task-based model that distinguishes AI’s direct technological effect from a managerial channel operating through decision quality. Guided by this framework, the empirical analysis examines both AI’s overall productivity effect and the organizational pathways associated with the managerial channel. Using data on Chinese A-share listed manufacturing firms from 2010 to 2023, we find that AI software investment significantly increases firm-level total factor productivity (TFP). Mediation analysis shows that AI affects TFP through innovation-oriented action, future-oriented managerial attention, and the operational dimension of firm agility. The productivity effect is more evident in state-owned enterprises, high-technology firms, and firms whose executives lack a digital background. These findings connect task-level evidence on AI productivity to firm-level outcomes and highlight managerial decision quality as a key mechanism through which firms convert AI-generated information into productive organizational action.
This paper investigates whether uncertainty shocks weaken technology-trade performance across technology categories, through which channels they operate, and whether their effects differ across economies and time horizons. We develop a mechanism-based sectoral framework that links final goods, non-strategic components, and strategic components to distinct uncertainty-transmission channels. The panel VARX estimates are then interpreted against this framework to explain heterogeneous responses across technologies and economies. Focusing on the US, China, and European economies over 1998–2024, we estimate annual sector-specific panel VARX models for eight SITC Rev. 3 technology sectors, using two complementary trade outcomes: the export-to-import ratio (EI) and revealed comparative advantage (RCA). Operational uncertainty is proxied by Global Supply Chain Pressure Index, trade-policy uncertainty by Trade Policy Uncertainty, and geopolitical uncertainty by US-China Tensions Index. The results indicate strong own persistence and mean reversion in EI and RCA, with generally weak EI-RCA cross-transmission. Uncertainty effects are heterogeneous and mechanism-specific: operational shocks most consistently weaken trade performance in strategic component sectors, especially semiconductors, whereas trade-policy and geopolitical uncertainties primarily reallocate relative specialization rather than uniformly compress technology trade. Cross-economy patterns identify China as an exporter outlier and semiconductors as Europe’s key bottleneck, implying that resilience policies should be targeted by technology type and uncertainty dimension.
