Periodic Reporting for period 1 - DARETOTIP (Dynamic system strategies for climate social tipping points)
Reporting period: 2023-09-01 to 2025-08-31
This research project will result in the dissemination of minimum three publications in high-ranking scientific peer-reviewed journals in the sustainability science domain.
The project builds on the system theory framework and adopts the SD modelling as the main analytical method. It will be used in the project to model the approach of future STPs. SD refers to the temporal characteristics of a set of elements and their feedback interactions. The international System Dynamics Society defines the concept as “a computer-aided approach to policy analysis and design. It applies to dynamic problems arising in complex social, managerial, economic, or ecological systems - literally any dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality.”
I will also build on methods and tools from the Life Cycle Assessment (LCA) domain. The quantitative framework of LCA is used to assess the environmental impacts throughout all the stages of a product's life from raw material extraction to end of life.
A set of scenarios will be constructed based on selected bottom-up and top-down interventions. Top-down and bottom-up interventions can be tested and compared with one another through mixed scenarios to identify combinations that reach a social tipping point faster and with sustained impact (i.e. systemic transformation). Top-down scenarios will be based on changes to institutional frameworks, such as government policies. Bottom-up scenarios will be based on social movements (social contagion).
Quantitatively modelling the course of these interventions through to the time an STP is reached means understanding the rate of change in activities that generate environmental emissions. This also requires modelling across scales of interaction: social, economic, and ecological, as elements from these scales interact in feedback loops either reinforcing or balancing stocks and flows within the system. Thereafter, I will begin an exploration into the robustness of the model using uncertainty and sensitivity analysis methods.
I will also build on methods and tools from the Life Cycle Assessment (LCA) domain. The quantitative framework of LCA is used to assess the environmental impacts throughout all the stages of a product's life from raw material extraction to end of life.
A set of scenarios will be constructed based on selected bottom-up and top-down interventions. Top-down and bottom-up interventions can be tested and compared with one another through mixed scenarios to identify combinations that reach a social tipping point faster and with sustained impact (i.e. systemic transformation). Top-down scenarios will be based on changes to institutional frameworks, such as government policies. Bottom-up scenarios will be based on social movements (social contagion).
Quantitatively modelling the course of these interventions through to the time an STP is reached means understanding the rate of change in activities that generate environmental emissions. This also requires modelling across scales of interaction: social, economic, and ecological, as elements from these scales interact in feedback loops either reinforcing or balancing stocks and flows within the system. Thereafter, I will begin an exploration into the robustness of the model using uncertainty and sensitivity analysis methods.
This research will pioneer the quantification of STPs and estimate the impacts of global and local interventions on reaching STPs for climate change mitigation and adaptation.
The STP hypothesis has only been studied qualitatively. Its understanding and validity are thus very limited. Instead, in this project I intend to use a SD approach to study social tipping points quantitatively.
The STP hypothesis has only been studied qualitatively. Its understanding and validity are thus very limited. Instead, in this project I intend to use a SD approach to study social tipping points quantitatively.