Dynamic modelling and development of indicators for gauging Sustainable Development

Environmental and ecological sciences explore, among other things, human impacts on natural systems. Building on this foundation, this work emphasizes the importance of understanding systems as interconnected and dynamic networks, where interactions shape the behavior of components. It also highlights the need to integrate distant interactions—such as socioeconomic and environmental impacts—within a telecoupled framework, where actions in one region can have far-reaching effects (from the Greek τη˜ λε, meaning “far away,” and the Latin copula, meaning “a tie” or “binding”).

One of the core focuses in this work is Global Value Chains (GVCs), which link production, transformation, and consumption across countries. While GVCs drive economic growth, they also generate significant environmental and social spillovers that transcend national borders and continents. Addressing sustainability within this context requires tracking commodity flows and accurately quantifying their social and environmental externalities. To achieve this, we developed a new accounting framework: Throughflow-Based Accounting (TBA). This framework is designed to track externalities throughout GVCs. It enhances traditional accounting methods, offering a more integrated and detailed view to comprehensively quantify upstream externalities generated by supply chains originating from, passing through, or ending in a given country. TBA captures both direct and indirect environmental impacts, particularly those linked to trade and consumption, providing a clearer picture of global interdependencies and informing policy strategies for sustainable development.

Additionally, refining indicators to quantify social and environmental impacts along those GVCs is crucial. We improve existing industrial ecology tools, such as Life Cycle Assessment (LCA) and Multi-Regional Input-Output (MRIO) models. For environmental impacts, we focused on chemical pollutants and their related footprints. By integrating new compounds at both emission inventory and impact assessment levels, we significantly increase the completeness of toxicity footprints. Notably, we identified critical MRIO gaps, where categories like “Freshwater ecotoxicity” were vastly underrepresented, especially for organic substances, highlighting the urgent need to expand toxic emission inventories in MRIO models to enable more accurate global environmental assessments. Social impacts, particularly in low- and middle-income countries, face challenges due to data limitations. We standardize frameworks from the International Labour Organisation (ILO), World Health Organization (WHO), and Global Slavery Index (GSI) to better address issues like forced labour, modern slavery, and work-related fatalities.

While these improvements increase accuracy, many current global approaches to modelling sustainable development—particularly regarding natural capital depletion—remain rooted in traditional economic models. However, equilibrium- and optimization-based models, grounded in neoclassical welfare theory, have limited capacity to model sustainability transitions, which require a more descriptive orientation to reflect non-linear, open-ended, and non-deterministic developments. To model sustainable development dynamically, we challenge traditional equilibrium-based economic models, arguing that they fail to capture key issues such as socially embedded preferences, dynamic processes, and non-linearities in sustainability transitions. Instead, we advocate modelling sustainable development by integrating emerging models, such as Agent-Based Models (ABMs) and complexity models, which better reflect the adaptive, evolving nature of system elements. Complexity models are not a mere extension of standard economic theory; they view economies as evolving systems where time, adaptation, and emergent structures are critical. In such systems, actions and strategies constantly evolve across micro, meso, and macro layers, revealing phenomena that are invisible to traditional equilibrium analysis.

We operationalize this by developing a new complexity model through the “Forest club” concept. This concept merges a Computable General Equilibrium (CGE) approach with iterative game theory, focusing on forest depletion (and extendable to other common goods shared across national borders). Positioned at the intersection of CGE, game theory, and evolutionary economics, it offers a dynamic framework for addressing interconnected challenges, recognizing the adaptive strategies and feedback loops driving sustainability transitions. We also examine how trade agreements can play a crucial role in preserving natural resources, exploring shifts between different resource-use strategies and highlighting the critical influence of initial economic distributions and group dynamics. Overall, the “Forest club” concept and its derived framework pave the way for more accurate modeling of sustainable development, leading to more comprehensive and actionable strategies to address sustainability challenges in our interconnected world.