Cross-sectional mediation evidence with panel robustness checks of economic complexity, logistics performance, and country innovation, 2020–2024

Agricultural enterprises play a crucial role in China’s rural revitalization strategy, but persistent financing constraints hinder their innovation potential. This study examines the impact of digital inclusive finance on agricultural enterprise innovation, with a particular focus on the mediating role of financing constraints. Using panel data from agricultural enterprises listed on the Shanghai and Shenzhen Stock Exchanges (2014–2023), this study employs fixed-effect regression models and mediation analysis to explore these relationships. The empirical findings reveal that digital financial inclusion significantly enhances innovation levels in agricultural enterprises (β = 0.5127, p < 0.01). Additionally, digital financial inclusion reduces financing constraints (β = –0.0385, p < 0.01), confirming that improved access to digital financial services lowers borrowing costs and increases credit availability. Mediation analysis demonstrates a partial mediation effect, as the coefficient of digital financial inclusion on innovation decreases when financing constraints are included in the model (β = 0.5057). Subgroup analyses show that SMEs, state-owned enterprises, and non-coastal firms benefit the most from digital financial inclusion in China. These findings have important policy and managerial implications. Policymakers should strengthen digital financial infrastructure in rural areas and promote financial inclusion policies to support agribusinesses. Financial institutions should expand digital financial services, such as fintech-driven credit assessments and online lending platforms, to enhance agricultural innovation. Additionally, agricultural enterprises should leverage digital financial tools to overcome financing constraints and boost competitiveness.

Despite numerous studies in the domain of green bonds, a paucity of literature concentrates on emerging countries’ green bonds. To fill this void, this study aims to examine the factors influencing green bond yield in the Asian and Latin American contexts. The data are compiled from the Bloomberg and Fred databases between 2017 and 2022. The panel regression with the Generalized Least Square method was employed. The results reveal that Asian green bonds provide higher yields with less risk to their investors than Latin American green bonds. The regression results of Asian green bonds show negligible effects of all factors, except coupon rate with a positive effect (β = 0.844), indicating its remarkable influence on green bond yield. However, the findings of Latin American green bonds uncover that coupon (β = 0.780), maturity (β = 0.025), and bond rate (β = 2.472) surpass the green bonds yield due to their positive effects, whereas issue size (β = –1.215) causes a reduction in the green bonds yield with their negative effect. Further, Environmental, Social, and Governance disclosure shows a positive (β = 1.611) effect, indicating better yield for investors due to their potential power to vanish greenwashing in these markets. Moreover, interest rate and GDP exert significant positive (β = 0.141) and negative (β = –0.030) effects on green bond yield, respectively. This observation implies that higher lending rates increase bond yield, whereas GDP-led growth provides lower yield due to better economic prospects and high investor demand for the bonds.

Acknowledgment
The authors are grateful to Manipal Academy of Higher Education (MAHE), Manipal, for providing financial assistance in the form of a “JRF Contingency Grant” for this research article.

To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.