Using Earth Observation Informed Agent-Based Models to build a Scenario Planning Digital Twin for Local Energy Policies

Policy makers at local, national, and international levels are increasingly being required to make decisions that mitigate the effects of climate change on society and the economy. Earth Observations (EO) are already an important source of data to support such decisions, but this data represents only one aspect of the broader socio-technical systems that decision makers seek to influence. Policy effectiveness depends not only on environmental conditions, but also on household behaviour, technology adoption decisions, economic constraints, and feedbacks across scales. Capturing these dynamics requires modelling approaches that explicitly represent human decision-making alongside EO-derived inputs. This paper will present the results from the development of a scenario-planning digital twin (SPDT) designed to support decision-making processes related to local energy policies. The new SPDT will demonstrate how EO datasets can be integrated with multilevel agent-based models (MABMs) to enable specific scenarios to be used to support policy decisions.

 

The use case in this work enables policy makers to (i) model residential heating demand, (ii) test policy levers that might best encourage the uptake of low-carbon heating, and (iii) assess the implications for energy use and fuel poverty. Specifically, the MABM simulates hourly residential energy demand for space heating at the household level, accounting for building characteristics, policy levers, occupancy patterns, retail energy prices, and external ambient temperature. The MABM supports baseline demand estimation at fine spatial granularity (individual households), the assessment of new technologies (such as retrofit measures or heating controls/meters) and energy price variations, and counterfactual analysis (e.g., setpoint shifts, tariff changes, warm/cold snaps). Earth observation data is used to inform medium to long-term climate trends for the use case region. The project results presented in this paper have been developed to serve the city of Newcastle upon Tyne in the UK. We discuss results for Newcastle developed so far, and possible new future scenarios that could be developed using this type of methodology.