The Behavioral change in ENergy Consumption of Households (BENCH) agent-based model

BENCH relies on environmental psychology that suggests that behavioral changes regarding energy use are affected by knowledge, awareness, motivation and social learning among households. We quantitatively trace an interplay of personal and social norms, with and without carbon pricing policies, to estimate the uptake of green technologies and diffusion of green behavior and corresponding reductions in residential carbon emissions. The BENCH agent-based model was developed within the PhD project of Leila Niamir.

A household’s decision-making algorithm in the BENCH-v.2 agent-based model

There is a number of energy-related actions in which individuals may pursue to influence their electricity consumption and, consequently, their carbon footprint. An individual can make an investment (A1), either large such as installing solar panels or small such as buying energy-efficient appliances. Alternatively, individuals can save energy by changing their daily routines and habits (A2) like switching off the extra lights and adjusting a thermostat/air conditioner. Finally, households can switch to a supplier that provides green electricity (A3).

The BENCH-v.2 agent-based model simulated complex and nonlinear behavior that is intractable in equilibrium models.

a Diffusion of households’ actions under behavioral and climate scenarios.
b SD and SDC comparison shows carbon price reducing 25% CO2 emissions (yellow box). FD shows that increasing social interactions alone reduces 9% CO2 emissions (green box). However, applying both carbon price and social interactions cuts down CO2 emissions by 55% (21% more than rational models could estimate)