The Integrated Assessment Models (IAMs) make it possible to represent many interactions between humans and the environment, thus, in their current development, can they properly contribute to the evaluation of public policies and to trans-sectoral decision-making into a context of transitions to support sustainability? The Dubai Declaration, drafted at the conclusion of the 2018 UN World Data Forum acknowledges that the data demands for the 2030 Agenda for Sustainable Development require new solutions that leverage the power of new sources of data and technology. However, this is a difficult task for many countries. In fact, the way in which the SDG monitoring indicators are implemented depends on the availability of data, processing capacities, existing data infrastructures and institutional arrangements at the national level, which is essential to involve end-users’ communities. This research is based on the integration of top-down and bottom-up approaches to data production for the monitoring of sustainable development indicators and the development of essential variables to strengthen robustness, legitimacy, relevance and transparency of integrated climate models. The proposal has been structured around some scientific and methodological requirements, namely, (i) explore and consolidate the heterogeneity and the role of multisectoral data in the development of indicators of the sustainability locally, (ii) democratize monitoring indicators by highlighting and demonstrating the role of bottom-up and local data to complement official indicators, and, finally, (iii) generate sustainable development management boards locally and globally. This could also contribute to the improvement of existing predictive models, in particular large-scale climate models (IPCC) Taking this into account, this project therefore aims at establishing an assessment framework applicable to different spatial scales by promoting sustainable development strategies compliant with climate mitigation and adaptation needs. The specific objectives of the project are (i) the analysis of existing models, (ii) the identification of essential variables of sustainable development, and (iii) the validation of a new integrated analytical framework at the interface between predictive climate models and variables defined rather locally. On the basis of the state of the art of current practices and the development of existing integrated assessment models, initially deployed to study the complex interactions between humans and the environment with a view to mitigating climate change, a new generation of integrated models is expected resulting from conceptual and methodological advances at the frontier of data sciences and social sciences for the evaluation of public policies and decision-making support in a context of transitions. This model will be applied in two use cases through the integration of a citizen and participatory observatory to strengthen the stakeholders’ inclusiveness. Each use case will be centered around a country where development and conservation issues are numerous and complex, as social, economic, political and environmental dynamics remain intertwined and where development and environmental protection policies are conditioned on international aid: Haiti, and Madagascar. The Horizon Europe call targeted will therefore allow us to explore new scientific frontiers for a better representation of heterogeneity (more geographic and sectoral details) by using different types of models (local and global), by linking different scientific disciplines, particularly social sciences and data sciences.