Leila Niamir visited ASA, IIASA

Abstract

Coupled climate-economy systems are complex adaptive systems. While changes and out-of- equilibrium dynamics are in the essence of such systems, this dynamics can be of a very different nature. Specifically, it can take a form of either gradual marginal developments along a particular trend or exhibit abrupt non-marginal shifts (Filatova, et al. 2015). Strong feedbacks between climate and economy are realized through energy: economy needs energy for development in literary any sector, while emissions need to stabilize and be even reduced to avoid catastrophic climate change (IPCC 2014). Possibilities of passing some thresholds that may drive these climate-energy-economy (CEE) systems in a completely different regime need to be explored. However, currently available models are not always suitable to study nonlinearities, paths involving critical thresholds and irreversibility (Stern 2013). To be able to formulate an appropriate energy policy for this complex adaptive CEE system, policymakers should ideally have decision support tools that are able to foresee changes in energy market over the coming decades to plan ahead accordingly. Many macro models, that assume rational representative agent with static behavior, are designed to study marginal changes only. So there is a need for models that are able to capture nonlinear changes and their emergence (Niamir & Filatova, 2015b). Agent-based Models (ABMs) are simulating human social behavior more realistically and can capture human variability and other nonlinear processes.

We present an agent-based energy market model to study Nonlinearities in the Residential lOw-carbOn economy transition (NIROO). NIROO specifically focuses on households’ energy use and potential behavioral changes, and aims to study demand-side activation and potential non-marginal changes in energy markets. NIROO disaggregates the residential energy (electricity and heating) demand side to trace cumulative impacts of behavioral change among heterogeneous households over time and space. The supply side is represented in a very simplistic way in the ABM, and the primary changes on the supply side come through the integration of NIROO with a CGE model. The market clearing occurs in the agent-based model leading to prices trends for green and grey energy emerging endogenously as micro-level changes in household demand shift the entire residential sector demand on the macro level (Niamir and Filatova, 2015a). 

Leila Niamir is a PhD student at the Department of Governance and Technology for Sustainability, University of Twente, in the Netherlands. She currently works in an EU FP7 project "Complex" focusing on design and implementation of agent-based models for energy market in the context of low-carbon strategy. Leila received her master degree in Information Science from University of Tehran, and prior to her PhD studies obtained a post- graduate diploma in advanced geo-informatics from University of Twente in the Netherlands.