With the growth of the new energy sector, China’s energy industry is experiencing significant transformations. This research aims to evaluate the technological progress and changes in scale efficiency of listed companies in China’s energy industry, with a particular focus on the comparison between new and traditional energy sectors. This research investigates various efficiency values, types of returns to scale, the role of patents in fostering technological progress, and the influence of financial leverage on scale efficiency changes, a comprehensive evaluation of the industry that offers a critical foundation for formulating targeted strategies. This research uses data from A-share listed energy companies spanning from 2017 to 2023, constructs input–output indicators centered on research and development (R&D) and profitability, and applies the DEA model to examine the operating efficiency of energy listed companies. A Malmquist indices is developed to analyze the dynamic evolution of technological change and scale efficiency change. In contrast to the conventional approach of using DEA efficiency scores as the dependent variable in Tobit regressions, this research uses the Malmquist indices, which more effectively captures the dynamic evolution of technological progress and scale efficiency. The study empirically assesses the impact of patent accumulation on technological progress through a Tobit panel model with random effects and the effect of financial leverage on scale efficiency changes using a Tobit four-stage incremental regression. Finally, the study draws the following conclusions: 1. In terms of industry static correlation, listed new energy companies exhibit polarization in returns to scale types; in contrast, traditional energy listed companies have a more stable and mature returns to scale structure. 2. In terms of dynamic correlation, technological progress in the new energy sector is substantial, while the traditional energy sector faces bottlenecks; efficiency changes in both industries are dependent on scale efficiency changes, rather than pure efficiency changes. 3. Regarding influencing factors for new energy listed companies, patent accumulation has a limited impact on technological progress, while financial leverage and scale efficiency change exhibit a non-linear relationship, with an inflection point effect observed in companies with high financial leverage. Finally, this study offers targeted policy recommendations for new energy and traditional energy listed companies based on the findings.

Existing research on the vulnerability of knowledge innovation networks (KINs) primarily falls into two categories: random attacks and deliberate attacks. This study introduces a novel centrality-based attack framework for the deliberate attack group and systematically examines the impact of different attack mechanisms (such as loss of connectivity, propagation delays, and structural fragmentation) on KINs. Using SCI/SSCI co-authorship data across 286 Chinese cities, this research evaluated network resilience through six key metrics, average clustering coefficient, average degree, average path length, global efficiency, isolated nodes ratio, and largest connected component ratio, thus revealing three key conclusions: First, network density and regional integration surged after 2015, peaking in localized clustering during 2021–2022; yet, post-2022, structural fragmentation and efficiency declines exposed critical vulnerabilities. Second, network disruption simulations revealed that attacks based on structural fragmentation mechanisms have the most significant impact, demonstrating the need to promote polycentric development to reduce core dependency risks and highlighting the importance of protecting high-centrality nodes such as Beijing, Shanghai, and Shenzhen. Third, the inhibitory effect of economic proximity weakened significantly by 2024, suggesting reduced economic disparity barriers to collaboration. Notably, emerging synergies between geographic and economic proximity enhanced innovation cooperation potential, emphasizing spatial–economic coordination as a crucial factor in development-stage planning. By integrating spatiotemporal dynamics with attack taxonomy, our framework advances network theory and equips policymakers with actionable strategies to optimize knowledge flows and mitigate systemic risks. Bridging structural resilience with innovation-driven development, this study offers new insights into China’s urban knowledge networks.

This study examines the structural evolution of Knowledge Innovation Networks (KINs) and Technology Innovation Networks (TINs) across Chinese cities (2015–2024). Using SCI/SSCI co-authorship and prefecture-level patent data, we construct dual-layer networks and assess their resilience through metrics such as average clustering coefficient, path length, global efficiency, and largest-component ratio. Our framework clarifies how network structure, spatial proximity, and urban hierarchy jointly shape innovation dynamics and opportunity distribution. Three main findings emerge. First, KINs have moved toward polycentricity yet remain hierarchically rigid, with persistent core–periphery gaps despite improved connectivity in tier 2–4 cities. TINs show greater cross-tier adaptability, creating new innovation gateways while intensifying intra-tier polarization. Second, under simulated disruptions, KINs are vulnerable to targeted attacks and exhibit path-dependent degradation, whereas TINs maintain efficiency until a critical threshold, then collapse abruptly. Third, MRQAP analysis reveals that economic and geographic proximity facilitate collaboration in KIN but constrain linkages in TINs, with spatial proximity exerting a stronger influence on knowledge flows. These results demonstrate how innovation networks mediate urban–rural interactions, affect spatial inequality, and shape regional resilience. We argue for differentiated policies that strengthen core–periphery connectivity while mitigating proximity-induced lock-in, fostering more inclusive, resilient, and sustainable urban innovation systems.

The impact of policy uncertainty on A-share industry returns shows significant time-varying characteristics, amplified by industry input-output relationships. Traditional TVP-VAR models overlook network structures, leading to unquantified spillover effects and imprecise systemic risk identification. To address this problem, this study embeds industry input-output tables as matrices into Time-Varying Parameter Spatial Autoregressive Model, and Bayesian methods are innovatively introduced into this model to capture the parameters. Policy uncertainty is categorized into five dimensions—economic, fiscal, monetary, exchange rate, and trade. Empirical results reveal following key findings: On average, network spillover effects explain approximately 39% of the response of A-share industry returns to policy uncertainty. Group analysis reveal that economic and fiscal policy uncertainties exhibit positive network effects, indicating synergistic effect that amplify their impact across industries. In contrast, exchange rate and trade policy uncertainties generate negative network effects, reflecting competitive or substitution effects. Systemic risk is most pronounced in fiscal and trade policy uncertainty groups. Systemic risk increases across all policy uncertainty groups except trade, which shows a declining trend. This study provides a novel framework for understanding the dual nature of spillover effect in production networks, offering valuable insights for policymakers and investors to manage systemic risks and indentify synergistic and competitive effects in interconnected industries.

The Belt and Road strategy covers a wide range of international cooperation and infrastructure construction, and it is of great significance to conduct economic evaluation. Based on the macroeconomic data and related indices of the countries along the Belt and Road from 2005 to 2023, this research evaluates the economic performance through general factor analysis and assesses the risk using Bayesian time-varying iterative model. It is found that the industrial production capacity and service level of the countries along the Belt and Road are in the middle and lower reaches, agricultural import and export capacity needs to be improved, and the risk coefficient shows a trend of increasing and then flattening, the risk uncertainty of the Maritime Silk Road index is the smallest, the risk uncertainty of the trade volume indices is higher, and the export uncertainty is higher than that of the import uncertainty. Finally, this research puts forward policy recommendations based on the empirical result.