In the context of the digital economy, corporate innovation is shifting from an internally driven, linear model to a multilateral, collaborative mechanism enabled by platform ecosystems. This study integrates persistent innovation into the analytical framework of platform ecosystem embeddedness and evaluates its impact from two dimensions: innovation input and innovation output. Using panel data from Shanghai and Shenzhen A-share listed companies between 2012 and 2023, we apply fixed effects models and mediation analysis to empirically examine the mechanisms through which platform ecosystem embeddedness affects innovation. The results show that platform ecosystem embeddedness significantly enhances companies’ persistent innovation. Specifically, it promotes innovation input by reducing supply–demand coordination costs and improving the operational efficiency of the company, and it boosts innovation output by facilitating knowledge flow and knowledge creation. Furthermore, the effects are more pronounced in industries characterized by high competition, rich regional resources, high-tech orientation, and low environmental pollution, where both input and output are strengthened. In contrast, in less competitive, resource-constrained, non-high-tech, and heavily polluting industries, the impact is mainly reflected in innovation output, with limited influence on input.

This paper constructs a digital technology multilayer convergence network model to explore the mechanisms of digital technology convergence. The analysis is based on patent data from China’s A-share listed companies from 2012 to 2021. The results show a continuous increase in network scale and structural complexity, with intensified cross-domain interactions. The company collaboration subnetwork evolved from decentralization to a more centralized structure, while the technology convergence subnetwork expanded and became increasingly complex. Core technologies maintained dominant positions, and the co-evolution between companies and technologies showed sustained development. This study reveals intricate interdependencies between technological convergence and company collaboration, providing theoretical insights and practical implications for digital technology innovation and strategic decision-making.

This paper introduces an improved slack-based game cross-efficiency measurement model that enhances the existing cross-efficiency framework and integrates it with the Data Envelopment Analysis (DEA) game cross-efficiency. The model ensures the fairness of its results through the implementation of a more stringent selection of frontier face weights. It accounts for the competitive relationships among Decision Making Units (DMUs), achieving a Nash equilibrium solution through continuous iterations. Furthermore, the model accounts for undesirable outputs and various strategic orientations, enhancing its applicability. The model’s effectiveness is validated through comparative analyses of diverse case studies. Additionally, the model’s practical utility is demonstrated through the analysis of industrial data from various Chinese provinces between 2010 and 2019. Analysis results show that the proposed model measures production efficiency with greater precision and comparability than alternative models.

Although the importance of forestry has been widely recognized, research on the impact of forestry innovation ecosystems on green economic development is still in its infancy, and existing research is limited. This paper focuses on 31 provinces in China and conducts an empirical analysis using data from 2012 to 2021. It provides empirical insights on how forestry innovation ecosystems affect green economic growth, thus addressing the gap of existing research. The findings are as follows: (1) Forestry innovation ecosystems have a significant positive effect on green total factor productivity. (2) Mechanism analysis reveals a significant positive effect of forestry innovation ecosystems on the technical efficiency index, but no significant effect on technical progress. (3) Heterogeneity analysis indicates that in eastern and central regions, forestry innovation ecosystems do not significantly impact green total factor productivity or the paths of technical efficiency and technical progress. However, in western regions, forestry innovation ecosystems have a significant effect of green total factor productivity and technical efficiency, while technical progress remains unaffected. In conclusion, the forestry innovation ecosystems play a significant role in promoting green economic development, particularly in the western regions of China. The potential contributions of this research are twofold: (1) This study addresses this gap by providing empirical insights on how forestry innovation ecosystems influence green economic growth. (2) This paper also investigates the mechanisms by which forestry innovation ecosystems impact green development through various types of innovation, offering practical references for stakeholders involved in forestry and sustainable development. Differentiated policies should be assigned to address regional disparities, with increased support for forestry innovation in the west and the encouragement of green technology investments in the east and center. Additionally, promoting technical progress through policy innovation, international cooperation, and enhanced intellectual property protection is essential to maximizing the benefits of forestry innovation ecosystems.

This paper introduces a novel decomposition method for analyzing production efficiency based on the Data Envelopment Analysis framework, addressing the limitations of traditional approaches that often fail to isolate the contributions of individual factors. The proposed method disaggregates production efficiency into capacity utilization, labor utilization efficiency, energy utilization efficiency, and technological change, providing a more granular view of how different factors contribute to overall efficiency. By incorporating both contemporaneous and intertemporal perspectives, this approach enables a comprehensive understanding of efficiency dynamics and factor interactions over time. To demonstrate the feasibility and robustness of the proposed method, we apply it to the thermal power industry using data from 30 Chinese provinces covering the period from 2011 to 2021. The empirical results validate the effectiveness of the decomposition framework, revealing distinct regional disparities in efficiency and providing insights for targeted resource optimization strategies. Based on these findings, we offer recommendations to enhance capacity utilization, improve energy efficiency, and support sustainable development within the thermal power sector. This research contributes a powerful analytical tool for disaggregating production efficiency and offers a theoretical foundation for future studies seeking to understand the nuanced relationships between comprehensive production efficiency and single-factor efficiencies, thereby supporting better policy and management decisions in complex production systems.