Carbon pricing in dynamic regulation and changing economic environment - agent based model

Like many industries, the financial services sector increasingly confronts a market environment in which both consumers and regulators are anxious to see organizations develop green products and services that hold the promise of mitigating environmental degradation and encouraging sustainable use of resources. Industry players must therefore be adept at reading demand signals from each of the primary financial services sectors (retail banking, corporate and investment banking, asset management, and insurance) while also keeping a sharp eye on evolving changes in these highly regulated businesses driven by proactive government policies (Porter and Kramer, 2006). Weather derivatives, energy trading and natural resource exploration are only few of the sustainability topics vital for the economic future of our province (Alberta), and the entire world. Our paper presents an overview of complex adaptive systems and the basics of building a model of CAS. We consider CAS to be well suited to the modeling of market behavior because it is robust to micro-level behavioral influences and allows the inclusion of heterogeneous agents. CAS also offers the possibility of capturing the dynamics of agents experience through features such as learning and memory. We present an agent-based model for pricing carbon emissions and results of simulations based on Alberta's current data of demand, supply, and regulation of carbon emissions. Pricing carbon drives innovation in technologies that improve efficiency, reduce pollution and recognize the social cost of business. We analyze the results of simulations in a dynamic framework of changing parameters and input variables. Keywords: Agent Based Modeling, Carbon Market, Alberta Carbon Trading Scheme, Swarm softwar