Project Leader: Dr. Hab. Agnieszka Szpak, Prof. UMK
The project aims to develop integrated, multidimensional research and analytical tools to diagnose, understand, and effectively implement climate policies. The initiative is based on an interdisciplinary approach. The key assumptions of the project will be developed at three spatial scales: global (integrated policy models, e.g., IPCC, COP, EU Green Deal), regional (Central and Eastern Europe as an area of particular vulnerability), and local (collaboration with cities and municipalities to test predictive tools and adaptive strategies).
The project comprises five interconnected modules:
- Analysis and Diagnosis: Policy and Security
This module aims to map existing climate policy frameworks and their links to security (energy, food, social) at various geographic scales and to analyze climate policies comparatively (UN, EU, national governments). It emphasizes studying the geopolitical aspects of energy transition to identify tensions and interdependencies between regulations and social and institutional stability. - Social Research: Resistance and Acceptance
This module focuses on understanding social reactions to climate policies and transformational technologies. It is based on qualitative and quantitative research into the attitudes of citizens and local communities. Analysis of media discourse, misinformation, and social polarization is essential to build models of acceptance and resistance, considering sociopolitical, cultural, and psychological factors. - Spatial Research: Mitigation and Adaptation
The objective of this module is to integrate spatial data (GIS, satellite imagery, local inventories) to assess the capacity and impacts of adaptive and mitigation actions. Mapping climate hazards and environmental vulnerability is necessary to analyze the effectiveness of local adaptation strategies (e.g., water retention, nature protection, urban resilience) and the costs of spatial planning on emission reduction. - Modeling: Forecasts and Scenarios
This module focuses on developing future scenarios and forecasts of the impacts of policies and socio-climatic phenomena. The goal is to build econometric and agent-based models (ABM) and develop scenarios for energy transition, mobility, agriculture, migration, and models integrating social, economic, and environmental data. This will be achieved using foresight and backcasting methods, especially multifactor computational models and big data analysis. - Evolution: Machine Learning and Implementation
In this module, models will be developed and adjusted using machine learning (ML) and artificial intelligence (AI), enabling iterative analysis and implementation of solutions. Training predictive models on multi-source data (geographical, social, climatic) will allow dynamic calibration based on changing conditions (adaptive systems). AI will be applied for public policy recommendations and participatory tools, creating prototype decision-support tools (e.g., dashboards, scenario simulators).
Collaboration Potential:
The researchers involved in the project are members of international academic networks and contribute to policy innovations for global and national institutions. At the same time, they conduct fundamental research with high regional and local applicability. The team has documented experience in developing mathematical models with diagnostic and predictive purposes, forming the basis for machine learning and neural network applications. The open structure of the team facilitates collaboration between social sciences and technical fields, creating space for interdisciplinary synergy among academic research, public policy, and innovation.