Systems thinking-informed and data-driven urban decarbonisation framework for individual, community and urban scale...

Area of Science:

• Environmental Science
• Climate Change Mitigation
• Urban Planning

Background:

• Human activity is a primary driver of greenhouse gas (GHG) emissions, necessitating rapid reduction.
• A knowledge gap exists regarding key individual and collective drivers of emissions and harmonizing citizen action with policy.
• Effective climate action requires integrating bottom-up citizen efforts with top-down mitigation strategies.

Purpose of the Study:

• To develop an urban decarbonisation framework informed by systems thinking to support multi-level climate action.
• To demonstrate the integration of a data-driven, activity-based GHG emissions model for individuals into the framework.
• To identify key emissions drivers and their interaction with the urban decarbonisation framework.

Main Methods:

• Developed a systems thinking-informed urban decarbonisation framework.
• Integrated a data-driven, activity-based GHG emissions model for individuals.
• Collected individual activity and lifestyle data from 172 participants via a smartphone application.

Main Results:

• Identified key drivers of individual and collective GHG emissions.
• Demonstrated the interaction between individual emissions drivers and the urban decarbonisation framework.
• Modeled GHG emissions at scale using individual activity data.

Conclusions:

• The developed framework and integrated model can inform emissions mitigation efforts at individual, community, and urban scales.
• Individual data and scaled GHG emissions modeling can guide citizen-level actions and high-level policy.
• This approach accelerates the sustainability transition for societies and cities.