This study analyses the relationship between non-performing loans (NPLs) and innovation systems at a global level. The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. Since k-means is an unsupervised machine-learning algorithm, we compared the Silhouette coefficient with the Elbow method to find an optimization. The results show that the optimal number of clusters is three, as suggested using the Elbow Method. Furthermore, a panel data analysis was conducted. Results show that the level of NPLs is positively associated with cultural and creative services exports as a percentage of total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, ICT services imports as a percentage of total trade, and information and communication technologies.

The aim of this paper is to examine the connections among time-frequency dependencies associated with electrical power consumption (EPC), economic growth, and financial development (FD) in Portugal, Italy, Greece, and Spain during the period 1970–2014. Using monthly data collected from the World Bank (WB) and Federal Reserve Bank of St. Louis (FRED), the wavelet analysis is applied, which allows for assessing the co-movement between these variables. As a first step, a classical time-domain approach is used to alternatively test the connection, including unit-root tests and cointegration. To achieve a comprehensive understanding of the relationships between EPC, economic growth, and FD, we employ Wavelet Transform Coherency (WTC) and Partial Wavelet Coherency (PWC) to explore both their temporal and phase-based dynamics. The main findings show that EPC leads FD, but in the short term, and periods dominated by economic stagnations and political crises. Otherwise, FD drives EPC in the medium term, under economic expansion periods. In both cases, economic growth is crucial, being a strong binding force of the interaction between EPC and FD. The difference in the applied results provides alternative policy implications, justifying the use of the wavelet approach.

Understanding the significant role of energy in key sectors is a substantial element for energy planning and policy of an economy. In the context of global energy demand and scarcity issues, the current study is an attempt to explore the role of Energy Demand at the sectorial level of the SAARC economies by using value addition of key sectors as a starting point. To address the objectives of the study, and to explore the dynamic relationship among the variables, dynamic simultaneous panel data models are employed. In this context, the study employed Generalized Method of Moment (GMM) as an estimation technique. The findings demonstrate positive and significant impact of Energy Demand on Agriculture and Industrial Value added growth while a negative impact on Services Value Added growth has been found. Similarly, the conventional growth factors such has Human capital and Physical capital along with Population Growth contributed positively and significantly to the sectorial value addition growth. On the basis of these empirical findings the study suggests that Energy allocation towards Agriculture and Industrial sector must be focused to achieve potential economic growth of the SAARC economies.