Ms Lamia Enab (p)

Rapid urbanization and increasing waste generation are intensifying pressure on Solid Waste Management (SWM) systems in developing countries, where limited resources, static planning practices, and operational inefficiencies remain prevalent. Although Artificial Intelligence (AI) is frequently promoted as a transformative solution for urban services, empirical evidence of its effectiveness and institutional applicability in resource-constrained SWM systems remains scarce. This study addresses this gap by conceptualizing AI as a decision-support tool that complements, rather than replaces, conventional waste management practices. Using the Radwan neighborhood in Amman, Jordan, as a case study, the research develops and evaluates an AI-supported waste collection framework integrating Internet of Things (IoT) sensing, geospatial data, and heuristic optimization. The methodology incorporates qualitative analysis of an existing SWM system as well as quantitative simulation of AI-based routes for waste collection vehicles as well as capacities. Real-time levels of fill, spatial data from a Global Positioning System (GPS), as well as capacities of waste collection vehicles, are incorporated in order to create optimized routes. Performance of the system is evaluated through a comparative analysis of conventional and AI-optimized routes, whereby performance metrics are based on evaluation of distance travelled, route duration, and fuel consumption, as well as carbon produced. Importantly, the study demonstrates the institutional feasibility of AI adoption through a human-in-the-loop configuration that preserves municipal control and aligns with existing governance structures. The findings provide empirical evidence that AI can function as a practical lever for enhancing efficiency and sustainability in SWM systems without requiring costly infrastructure overhauls. The study offers actionable insights for Jordanian municipalities and other municipalities in comparable contexts and contributes to advancing sustainable urban planning objectives aligned with the Sustainable Development Goals (SDGs). Moreover, the study offers a foundation for future research on AI applications in urban sustainability and circular economy transitions.

Mr Josué Madama Malende (p), Dr Audrey TANGUY, Prof Valérie LAFOREST

The transition toward the circular economy is presented as a lever enabling territories to move beyond linear development models and reduce their dependence on primary resources. At the territorial scale, this transition is accompanied by a paradigm shift in the status of waste, which is now considered a strategic resource. However, in a context marked by growing demand for secondary raw materials, unequal geographical distribution of deposits, and limited availability, competitive dynamics may emerge between actors and between territories. These dynamics remain insufficiently addressed in industrial and territorial ecology, which primarily emphasizes cooperation mechanisms.

This study aims to analyze the factors driving the emergence of competitive dynamics in the implementation of circular economy strategies. The objective is to identify the conditions under which these dynamics arise in order to better understand them and to propose ways to integrate them into territorial planning approaches for waste management.

The methodology relies on a deductive approach based on a literature review. Fifty articles from environmental sciences, economics, and social sciences were analyzed. The analysis identified 124 elements, grouped into 20 emergence factors. Resource-related factors include the unequal geographical distribution of deposits, limited quantities available, quality degradation, seasonality, and the coexistence of multiple uses. Economic factors relate to the increasing value of secondary materials, rising prices of virgin materials, production and transport costs, and growing demand. Finally, additional factors are linked to the coexistence of multiple actors within the same territory and their strategies to secure resource supply.

While cooperation is a core principle of the circular economy, competitive dynamics also constitute an inherent component. Taking these dynamics into account could help avoid conflicts, territorial inequalities, and inefficiencies in resource allocation. Further research, based on interviews with territorial stakeholders, will make it possible to validate the identified factors and analyze their effects. Ultimately, this research aims to contribute to the development of indicators to characterize the level of competition at the territorial scale and to strengthen the robustness of circular economy planning processes.

Mr Atif Yassen (p), Dr. Ilona Bartuševičienė

Enablers of Green Growth in European Union: The role of Green Technology and Green Energy

Atif Yaseen1 & llona Bartuševičienė1
Institute of Business and Economics, Faculty of Public Governance and Business
Mykolas Romeris University, Lithuania
Email: atyaseen@stud.mruni.eu

Abstract
Purpose: Climate change has become a serious threat to human life, both socially and economically. If this problem continues at this pace, it will damage many lives and cause significant economic losses. The European Union is also affected by climate change. The world is moving toward green growth, which helps improve environmental quality and supports living standards. Many scholars have investigated the connection between green growth and other variables, but there is still a gap regarding the impact of green technology, green energy, and control variables (resource rents and economic growth) on green growth. This research aims to fill this gap by investigating the impact of green technology, green energy, and control variables (resource rents and economic growth) on green growth in European regions.
Research Methodology: This research uses the ARDL econometric technique to investigate the long-run and short-run relationships between green growth and other explanatory variables in the European Union from 2000 to 2025. This research applies different unit root tests along with the ARDL bounds test. Likewise, it employs different proxies for measuring green growth and uses diagnostic tests to ensure the validity of the research outcomes. The main sources of data are the WDI, the OECD, and the OWID.
Results: The ARDL model results show that green technology, green energy, and economic growth have a positive impact on green growth, while resource rents have a negative impact on green growth. Diagnostic tests ensure that there are no problems in the research model or its outcomes. These research outcomes provide value to both policymakers and the academic community. Stakeholders can use the findings to design and implement policies that strengthen environmental sustainability and foster green growth, while scholars may apply the proposed framework and indicators to examine other regions, either through replication or by adapting alternative measurement approaches.

Key words: Green Growth, Green Technology, Green Energy, Resource Rent and Economic Growth
Jel Classification: F43, F63, O32, Q42, Q32 and O44