Nini Johana Marín-Rodríguez

Economic entropy, as an emerging concept in econophysics, has gained increasing relevance in the analysis of complex systems characterized by uncertainty, nonlinearity, and out-of-equilibrium dynamics. However, its integration into conventional economic modeling—particularly in production functions such as the Cobb–Douglas function—remains fragmented and lacks systematic empirical validation. This study conducts a scientometric analysis of 345 Scopus-indexed documents (1973–2024) addressing the intersection between entropy, econophysics, and production functions, with the aim of mapping the intellectual structure of the field, characterizing its growth trends, identifying its core contributions, and highlighting its main research gaps. The results reveal that the field has experienced sustained growth since 2004, with a notable acceleration between 2020 and 2023, although it exhibits a fragmented authorship structure that does not conform to Lotka’s Law, suggesting that the field is still in a stage of scientific consolidation. The Cobb–Douglas function emerges as a niche topic within the econophysics literature, with limited integration between entropy-based approaches—informational, thermodynamic, and maximum entropy—and the empirical modeling of production. Furthermore, weak citation linkages between econophysics and conventional economics are observed, confirming the interdisciplinary fragmentation of the field. These findings provide a structured reference for researchers interested in advancing toward analytical frameworks that explicitly incorporate uncertainty, information, and physical constraints into economic analysis, thereby contributing to the development of econophysics as an integrative discipline.

The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impact remain limited. This study applies a scientometric approach to 135 documents indexed in Scopus and Web of Science between January 2017 and June 2025, using Bibliometrix Bibliometrix (R package version 4.5.2), VOSviewer version 1.6.20, and Voyant Tools to examine publication trends, leading contributors, collaboration patterns, thematic structures, and reported economic outcomes. The analysis shows a strong upward trajectory with an estimated 66.48% annual increase in publications, identifying Xiukang Wang and Shaowen Wang as leading contributors among 791 authors from diverse institutions. Thematic analysis reveals three interconnected clusters: (i) precision agriculture and remote sensing as the sensing backbone; (ii) prediction and soil analysis as data-driven decision-support mechanisms; and (iii) vegetation indexes and productivity as measurement tools linking spectral information to yield and input use. Economic evidence includes high disease-detection accuracy (up to 95%), notable pesticide-use reductions (around 40%), improved autonomous-navigation precision (<6 cm error), and crop-detection performance exceeding 99%. However, adoption challenges persist, including technological heterogeneity, high implementation costs, limited model transferability, and varying levels of digital readiness across regions. Overall, the findings indicate that optical sensors and deep learning are transitioning from experimental applications to technologies with measurable economic impact, offering guidance for researchers, policymakers, technology developers, and agricultural producers seeking economically viable precision-agriculture solutions.

Board gender diversity (BGD) has gained attention as a governance mechanism that may influence corporate financial decisions. However, empirical evidence from Latin America and the Caribbean (LAC) remains limited despite the region’s significant gender disparities in corporate leadership and distinct institutional characteristics. This study examines how BGD affects capital structure decisions in LAC firms, drawing on agency theory and resource dependency theory. We analyze a panel dataset of 403 firms from 2015 to 2022, sourced from the London Stock Exchange Group database, using fixed effects models with Driscoll–Kraay standard errors to control for firm heterogeneity and econometric concerns. Results show that BGD is significantly and negatively associated with leverage ratios, with a one percentage point increase in female board representation corresponding to a 0.15 to 0.25 percentage point decrease in debt-to-capital ratios. This relationship is robust across multiple specifications and exhibits threshold effects, with stronger impacts when female representation reaches 20% or higher. The negative association is more pronounced for larger firms, consistent with enhanced governance benefits in complex organizations. Our findings suggest that gender-diverse boards exercise more effective oversight of financial decisions, leading to more conservative capital structures in emerging markets where governance mechanisms are particularly important for firm credibility and stakeholder confidence.

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring.

Wavelet coherence analysis (WCA) examines the dynamic interactions between economic policy uncertainty (EPU) in Brazil, Chile, Colombia, and Mexico and key external variables, using monthly data from 2010 to 2022. The findings reveal the following: (i) medium-term co-movements (4–16 months) between EPU and global financial indicators, including the Chicago Board Options Exchange (CBOE) Market Volatility Index (RVIX), Merrill Lynch Option Volatility Estimate Index (RMOVE), and Global EPU Index (RGEPU), emphasizing the sustained influence of financial volatility on domestic policy environments, particularly during global turbulence; (ii) significant interactions between EPU and the Climate Policy Uncertainty Index (RCPU) in resource-dependent economies like Brazil and Colombia, with pronounced effects in medium- and long-term horizons; (iii) bidirectional relationships between Brent crude oil prices (RBRENT) and EPU in Brazil, Colombia, and Mexico, where oil price fluctuations shape policy uncertainty, especially during global market disruptions; and (iv) notable co-movements between EPU and the Dow Jones Sustainability World Index (RW1SGI) in Brazil, Chile, and Mexico, highlighting sensitivity to shifts in sustainability-driven markets. These results underscore the need for economic diversification, strengthened financial safeguards, and integrated climate risk management to mitigate external shocks. By exploring the time–frequency dynamics of global uncertainties and domestic policy environments, this study provides actionable insights for fostering resilience and stability in Latin America’s interconnected economies while addressing vulnerabilities to global market volatility and sustainability transitions.

This study examines the dynamic interconnectedness of economic policy uncertainty (EPU) among Latin American economies—Brazil, Chile, Colombia, and Mexico—and significant international regions, including the United States, Europe, and Japan, as well as a global EPU index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, this study reveals the evolving spillover effects and dependencies capturing how uncertainty in one market can transmit across others on both regional and global scales. The findings highlight the significant impact of external EPU, particularly from the U.S. and global EPU sources on Latin America, positioning it as a primary recipient of international uncertainty. These results underscore the need for Latin American economies to adopt resilience strategies—such as trade diversification and regional cooperation—to mitigate vulnerabilities to global shocks. This study offers valuable insights into the mechanisms of economic uncertainty transmission, guiding policymakers in developing coordinated responses to reduce the effects of external volatility and foster regional economic stability.

As investors increasingly incorporate environmental, social, and governance (ESG) factors into their decision-making, sustainable Exchange-Traded Funds (ETFs) have gained prominence in both investment portfolios and financial research. This study aims to provide a comprehensive analysis of the Sustainable ETF research landscape by utilizing scientometric and bibliometric methods with tools such as VOSviewer, Bibliometrix, and CiteSpace. Drawing from the Web of Science and Scopus databases, the study identifies key thematic areas, influential authors, and emerging trends. The findings highlight the conceptual evolution of Green ETFs, from early definitions focused on ESG-aligned investments to more complex instruments incorporating diversified screening criteria and advanced technologies like machine learning and artificial intelligence. Practical challenges such as regulatory inconsistencies, high implementation costs, and limited investor education are underscored as critical barriers to broader adoption. Future trends reveal the growing role of blockchain technology for ESG verification, crisis-specific ETF models, and the development of more inclusive screening strategies. Strategically, Green ETFs demonstrate resilience during market volatility and support sustainability-driven investment frameworks. The study provides valuable insights for investors, policymakers, and researchers, emphasizing Green ETFs’ role in driving sustainable finance and offering actionable guidance for optimizing ESG investment strategies.