Clean energy technologies represent a promising solution to the global warming challenge. Many clean energy technologies, however, depend on some rare materials and concerns have been raised recently. Indium is one of these materials as it is critical for two emerging energy applications, that is, Copper indium gallium selenide (CIGS) photovoltaics (PV) and light-emitting diode (LED) lighting. This study analyzes the supply and demand of indium under different energy and technology development scenarios using a dynamic material flow analysis approach. A system dynamics model is developed to capture the time-changing stocks and flows related to supply and demand of indium over a 50-year time period, while considering carrier metal (i.e. zinc) production, price elasticity of demand, and indium usage in other
Due to the wide use of incandescent lighting, residential sector has much lower energy efficiency comparing to commercial sector. However, adoption of compact fluorescent (CFL) and light-emitting diode (LED) technology in residential sector has been slow because of several obstacles such as high price tag, poor public information, and additional cost to achieve favorable lighting features. A deep understanding on consumer's behavior is needed to support policy development in order to speed up the penetration of CFL and LED in the residential sector. Agent-based modeling (ABM) has been used to capture the dynamics of complex sociotechnical systems, and represent a suitable tool. Previous work on ABM of consumer adoption of CFL and LED rely heavily on multi-criteria decision making of the agents. Since light bulbs are not a significant purchase for most households, it is highly possible that customers will not go through complex decision making mechanics. This research establishes an ABM of residential lighting purchase and usage within a hypothetical community and tries to illustrate possible adoption paths under different scenarios. Agents are divided into three groups with different simple decision heuristics when making purchase. Energy consumption and greenhouse gas (GHG) emission from each scenario are calculated and compared. Results of the simulation show that incandescent lamps will eventually fade out of the market even with no policy implemented. After 25 years, annual energy consumption can be reduced by roughly 30% compared to Year 2010. Under best case where incandescent bulbs are banned, the energy consumption reduction can be up to 70%. Among scenarios, incandescent ban and energy saving campaign yield best energy consumption and GHG emission reduction results. LED technology advancement can improve market penetration of LED lighting but has little effect on incandescent fade out. It is also shown that lighting technology retrofitting can achieve higher reduction on electricity consumption and GHG emission than electricity grid improvement.
With rapid development and deployment of clean energy technology, demand for certain minor metals has increased significantly. However, many such metals are by-products of various host metals and are economically infeasible to extract independently. Meanwhile, by-product metals present in the mined ores may not be extracted even if they are sent to smelters along with host metal concentrates if it is not economically favorable for the producers. This dependency poses potential supply risks to by-product metals. Indium is a typical by-product metal, mainly from zinc mining and refining, and is important for flat panel displays, high efficiency lighting, and emerging thin-film solar panel production. Current indium supply–demand forecast models tend to overlook the volatile and competitive nature of minor metal market and are mostly based on top-down approaches. Therefore, a bottom-up agent-based model can shed new light on the market dynamics and possible outcome of future indium supply–demand relationship. A multi-layered model would also be helpful for identifying possible bottlenecks of indium supply and finding solutions. This work takes indium as an example of minor metal market and sets up an agent-based model to predict future market situation and supply–demand balance. The market is modeled as a Cournot competition oligopolistic market by refineries with capacity restriction based on host metal production. The model maintains active Nash equilibrium each year to simulate competitions between suppliers. The model is validated and verified by historical data and sensitivity analysis. Several scenarios are also explored to illustrate possible uncertainties of the market.
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