Conference Agenda

– The transition to low-carbon economies has made green finance an essential element of current financial development, especially in emerging countries that are highly dependent on natural resources and vulnerable to climate change. However, the quantitative literature for Latin America has significant gaps regarding the links between green growth indicators, sustainable financial development, and aggregate macroeconomic performance. This research analyzes the relationships between these dimensions in Chile, Colombia, Costa Rica, and Mexico in the period 2010-2022, using a quantitative panel design with secondary data extracted from the OECD Green Growth Indicators Database. The analysis combines Pearson correlations, time series linear regression, and comparison of sub-periods before and after the pandemic. The results show a structural paradox: there is consistency between environmental productivity indicators, but there is a statistically significant dissociation between productive efficiency and green technological innovation. National paths are divergent, there is no regional convergence, and Latin American participation in green patents is declining. The global pandemic induced peaks in environmental productivity without altering innovation trajectories, showing that temporary disruptions are not sufficient to change structural constraints. The study provides an empirical evidence-based typology that distinguishes between green growth trajectories according to productivity, innovation, and emissions intensity arrangements, helping to explain territorial heterogeneities in sustainable finance in Latin America.

Abstract— Capital structure represents a critical strategic decision that significantly influences corporate financial performance. This research evaluates the impact of capital structure on profitability of 21 Peruvian industrial companies supervised by the Securities Market Superintendency during 2015-2024, employing panel data regression analysis with fixed effects. The findings reveal a statistically significant non-linear relationship between leverage and profitability metrics (ROE, ROA, operating margin, net margin), evidencing that moderate debt levels optimize returns through tax shields, while excessive leverage increases financial risk and reduces operational efficiency.

Keywords— capital structure, corporate profitability, financial leverage, industrial sector, emerging markets, panel data.

Mart cyber-physical systems (CPS) are consolidating as a core infrastructure of Industry 5.0, where real-time optimization must simultaneously address efficiency, resilience, cybersecurity, and sustainability across interconnected urban and industrial environments. Although AI, IoT, and digital twin technologies have accelerated CPS capabilities, prevailing approaches remain conceptually and operationally fragmented. Learning-based controllers can degrade under uncertainty and nonstationary conditions, constraint-driven optimization may become overly conservative in fast-changing regimes, and secure coordination is often implemented as an auxiliary layer rather than a design premise. This study proposes an integrated conceptual and methodological framework for real-time, cross-domain CPS orchestration that unifies (i) predictive learning through reinforcement learning, (ii) stability and constraint enforcement via model predictive control, (iii) interpretability and scenario-based evaluation through a high-fidelity digital twin environment, and (iv) trustworthy coordination through blockchain-enabled verification, traceability, and tamper resistance. Carbon-aware objectives are embedded through feedback loops that couple energy and emission signals with control decisions, enabling multi-objective optimization aligned with decarbonization requirements. Simulation-driven experimentation across mobility and multi-energy domains indicates that the hybrid RL–MPC controller yields smoother state trajectories and more consistent learning convergence than isolated baselines, while blockchain verification improves auditability and anomaly detection without compromising real-time actuation timing. The resulting architecture positions CPS as transparent, self-adaptive, and sustainability-oriented systems suitable for scalable Industry 5.0 deployments.

The district of San Jerónimo in Cusco, Peru, faces a critical environmental crisis due to emissions from artisanal brick kilns and the lack of continuous air quality monitoring systems. In this context, pollution from fine particulate matter (PM2.5 and PM10) poses a significant risk to public health and local cultural heritage. This paper presents the design, implementation, and validation of a low-cost IoT sensor network based on LoRa technology to measure PM2.5 and PM10 concentrations in areas influenced by these activities. The proposed architecture integrates autonomous solar-powered sensor nodes, a LoRa gateway, and a cloud platform deployed on Google Cloud, utilizing InfluxDB and Grafana for time-series storage and near real-time georeferenced data visualization. Field tests in a semi-urban environment evaluated radio frequency coverage, network performance, and energy autonomy, validating an effective coverage of up to 2.39 km, an average packet delivery ratio (PDR) of 76.3%, and operational autonomy between 14 and 30 hours. The results demonstrate the viability and scalability potential of the solution as a support tool for municipal environmental management and evidence-based decision-making.

The automotive industry is going through a period of constant disruptions caused by supply shortages, rising logistics costs, and new environmental requirements. These challenges have led companies in the sector to rethink the way they organize their operations, seeking more agile alternatives that allow them to maintain continuity in uncertain scenarios. Recent literature highlights advances in digitalization, automation, artificial intelligence, and collaborative systems, together with a growing interest in technologies that directly support people’s work. However, there is still a need to bring these perspectives together in a model that enables the strengthening of agility and resilience at the same time. This study presents a proposal based on the principles of Warehousing 5.0, combining technological tools and worker-centered practices to improve logistics performance. The methodology applied uses the Fuzzy AHP approach, through which the most important factors are prioritized and a ranking is obtained to support decision-making in the automotive supply chain.

Due to operational demands brought on by variables like population pressures, urban infrastructures in Latin American cities are evolving quickly, unpredictability of the climate and growing digitalization. These circumstances highlight basic flaws in the current energy, logistics, transportation, and cyber-physical systems, where the development of integrated, robust, and scalable urban infrastructure is hampered by scattered technological innovation. This paper presents a thorough conceptual framework for intelligent autonomous transportation management that is tailored to the complexities of Latin American urban networks. The model provides a unified framework that enables adaptive transport management and real-time decision-making by combining developments in machine learning, digital twins, edge computing, Internet of Things sensing, and cyber-physical systems.

Finding cross-domain interdependencies, evaluating methodological and interoperability limitations, and creating a scalable architecture that synchronizes mobility behavior with cybersecurity are some of the objectives, environmental and energy limitations. The results of a multidisciplinary synthesis show that convergence across sensing ecosystems, distributed computational management, and predictive intelligence is necessary for notable gains in transport resilience and operational efficiency. The proposed architecture demonstrates how coordinated control systems and continuous data streams may increase forecast accuracy, reduce fragmentation, and offer more equitable transportation options in quickly growing urban regions..

According to the research, Latin American surroundings now lack a completely integrated cyber-physical approach, which has led to the development of a new field of study focused on urban transport intelligence. The findings show the enormous potential of state-of-the-art transportation systems that incorporate resilience, scalability, and human-centered design for future urban growth.