Mehmet Akif Ersoy University

As nations race to meet increasingly urgent climate targets, understanding the institutional and behavioral conditions that enable cleaner production has become essential. This study conceptualizes entrepreneurial ecosystems (EEs) as national configurations of institutional supports and burdens, innovation and market infrastructures, and cognitive-cultural conditions, as captured by the Global Entrepreneurship Monitor (GEM). Climate performance is operationalized through four distinct domains from the Climate Change Performance Index (CCPI): renewable energy, energy efficiency, emissions, and climate policy, each analyzed separately to maintain analytical precision and identify domain-specific EE drivers. Using data from 54 countries spanning 2007 to 2023, we apply a dual-method approach that integrates Arellano-corrected fixed-effects panel regression with ensemble machine learning techniques (boosting, SHAP, PDPs) to uncover both causal relationships and predictive patterns, with the latter complementing rather than replacing the explanatory analysis. Our findings reveal that EEs are not peripheral but central to national climate outcomes. Post-school entrepreneurial education (PSEE), perceived entrepreneurial opportunities (PO), and supportive government programs (GSP) consistently predict stronger climate performance. Conversely, high tax and bureaucratic burdens (TB) and fear of failure (FFR) are associated with weaker emissions control and less ambitious climate policy. Importantly, the analysis highlights key nonlinearities: for example, the impact of entrepreneurial intentions (EI) and total early-stage entrepreneurial activity (TEA) depends heavily on institutional alignment and financial infrastructure. These insights support an integrative theoretical model that positions EEs as multi-layered infrastructures linking innovation capacity, institutional design, and behavioral readiness for climate transformation. Overall, this study provides robust empirical evidence that well-configured entrepreneurship ecosystems can act as powerful enablers of cleaner production and environmental resilience. The findings carry significant implications for policymakers, educators, and development institutions aiming to align entrepreneurship policy with sustainability goals. Entrepreneurial ecosystems do not just create startups; they can shape how well countries fight climate change. Using data from 54 countries (2007–2023), we examine how education, institutions, and entrepreneurial mindsets influence renewable energy, efficiency, emissions, and climate policy. We find that post-school entrepreneurial education, perceived opportunities, and supportive ecosystem conditions are key drivers of stronger climate performance, while high bureaucracy and fear of failure hinder progress. Importantly, these effects are not linear; ecosystem components only work when aligned with each other and broader economic conditions. Thus, the principal implication of this study is that policymakers and educators should integrate sustainability into entrepreneurship systems, by strengthening advanced education, reducing regulatory barriers, and fostering opportunity-driven mindsets, to enable entrepreneurship to actively support climate transitions and environmental resilience.

In this study, two carbazole–triazine derivatives with cyanuric acid and melamine cores, namely 2,4,6-tris(4-(9H-carbazole-9-yl)butoxy)-1,3,5-triazine (CB) and N2,N4,N⁶-tris(4-(9H-carbazole-9-yl)butyl)-1,3,5-triazine-2,4,6-triamine (MB), were synthesized and investigated as electrocatalysts for the electrooxidation of hydrazine, an environmentally benign fuel with high energy density. These organic electrocatalysts were prepared through nucleophilic substitution reactions and structurally characterized by FT-IR, NMR, XRD, SEM, and TEM analyses. Their electrochemical performance was evaluated by cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). The MB compound exhibited a current density of 16.97 mA/cm2, which is 7.68 mA/cm2 higher than that of CB. CA and EIS analyses further revealed that MB possesses higher stability and lower charge-transfer resistance than CB. These findings demonstrate that nitrogen-rich carbazole-triazine derivatives are promising metal-free anode electrocatalysts for efficient hydrazine oxidation.

This study investigated the antimicrobial efficacy of nisin, which has limited activity against Gram-negative bacteria, when enriched with pomegranate peel extract (PPE) against Salmonella Typhimurium in a frozen chicken meat model, as well as its effects on product quality. In the first stage, response surface methodology were used to optimize pomegranate peel extraction conditions, and the highest antimicrobial activity was obtained using a 10

The aim of this study is to formulate and empirically test a theoretically grounded convergence equation for financial development. Building on the Solow framework, we derive a testable convergence equation for financial development and estimate it using the System GMM methodology. We examine a panel dataset covering 160 countries, both at the global level and within three income groups, spanning the years 1990 to 2024. Key findings can be outlined as follows: (i) there is robust evidence of unconditional convergence in financial development across the global sample and within all three income groups; (ii) when major macroeconomic controls are introduced, the analysis also confirms the existence of conditional convergence; (iii) the influence of macroeconomic variables on financial development convergence differs across income categories; (iv) trade openness is a significant driver in high-income and upper-middle-income economies, whereas inflation plays a central role in lower-middle and upper-middle-income economies; (v) institutional quality has a statistically significant impact on financial development convergence, but only within upper-middle-income countries. Overall, the study provides robust evidence of financial development convergence and highlights the key macroeconomic and institutional factors shaping the convergence process across income groups.

Remote sensing techniques are increasingly utilized in agricultural research and production systems for the effective monitoring of crop growth and health. This two-year study aimed to assess the relationships between spectral reflectance characteristics, vegetation indices, and plant height estimates derived from multi-platform remote sensing approaches under split nitrogen fertilization regimes applied at different growth stages of wheat. The experiment was conducted using a randomized complete block design, with data collected during the tillering, stem elongation, booting, heading, and flowering stages. Ground-based hyperspectral measurements and unmanned aerial vehicle (UAV)-derived imagery were acquired at each developmental stage. Vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (VI), were calculated from both ground-based and UAV data. Plant height estimates were generated using a normalized Digital Surface Model (nDSM) derived from Digital Surface Model (DSM) and Digital Terrain Model (DTM) data. Statistical analyses indicated that both wheat growth stages and nitrogen application rates had significant effects on spectral responses in the blue (p < 0.05), green, red, and near-infrared (p <0.001) regions. The lowest energy absorption across all spectral bands was observed in the control (N0) treatment, whereas the highest absorption was recorded in the N3 treatment, particularly within the green, red, and NIR regions. Vegetation index values exhibited highly significant differences (p <0.001) among nitrogen treatments and developmental stages. Moreover, strong correlations were identified between UAV-derived plant height estimates and ground-measured growth parameters, confirming the reliability of UAV-based observations. Overall, the results demonstrate that multi-layer remote sensing approach provides a robust and efficient approach for monitoring the physiological and phenological responses of wheat to varying nitrogen management strategies, with particular effectiveness in capturing the dynamic growth patterns throughout the crop's phenological cycle.